[01/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

Repository: incubator-madlib-site
Updated Branches:
  refs/heads/asf-site 04ed412a9 -> 7258f51a7


http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/jquery.js
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diff --git a/docs/latest/jquery.js b/docs/latest/jquery.js
new file mode 100644
index 0000000..1f4d0b4
--- /dev/null
+++ b/docs/latest/jquery.js
@@ -0,0 +1,68 @@
+/*!
+ * jQuery JavaScript Library v1.7.1
+ * http://jquery.com/
+ *
+ * Copyright 2011, John Resig
+ * Dual licensed under the MIT or GPL Version 2 licenses.
+ * http://jquery.org/license
+ *
+ * Includes Sizzle.js
+ * http://sizzlejs.com/
+ * Copyright 2011, The Dojo Foundation
+ * Released under the MIT, BSD, and GPL Licenses.
+ *
+ * Date: Mon Nov 21 21:11:03 2011 -0500
+ */
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<TRUNCATED>

[07/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__profile.html
----------------------------------------------------------------------
diff --git a/docs/latest/group__grp__profile.html b/docs/latest/group__grp__profile.html
new file mode 100644
index 0000000..8218103
--- /dev/null
+++ b/docs/latest/group__grp__profile.html
@@ -0,0 +1,213 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: Profile</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
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+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="navtree_hack.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  ga('create', 'UA-45382226-1', 'auto');
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+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
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+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__profile.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Profile<div class="ingroups"><a class="el" href="group__grp__deprecated.html">Deprecated Modules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<dl class="section warning"><dt>Warning</dt><dd><em> This is an old implementation of summary function. Replacement of this function is available as the Summary module <a class="el" href="group__grp__summary.html">Summary</a></em></dd></dl>
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Implementation Notes</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module computes a "profile" of a table or view: a predefined set of aggregates to be run on each column of a table.</p>
+<p>The following aggregates are called on every integer column:</p><ul>
+<li>min(), max(), <a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4" title="Compute the average of vectors. ">avg()</a></li>
+<li>madlib.cmsketch_median()</li>
+<li>madlib.cmsketch_depth_histogram()</li>
+<li>madlib.cmsketch_width_histogram()</li>
+</ul>
+<p>The following aggregates are called on non-integer columns:</p><ul>
+<li>madlib.fmsketch_dcount()</li>
+<li>madlib.mfvsketch_quick_histogram()</li>
+<li>madlib.mfvsketch_top_histogram()</li>
+</ul>
+<p>Because the input schema of the table or view is unknown, SQL is synthesized to suit the input. This is done either via the <code>profile</code> or <code>profile_full</code> user defined function.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd></dd></dl>
+<p>Generate basic profile information (subset of predefined aggregate functions) for all columns of the input table. </p><pre class="syntax">
+profile( input_table )
+</pre><p>Generate full profile information (all predefined aggregate functions) for all columns of the input table. </p><pre class="syntax">
+profile_full( input_table,
+              buckets
+            )
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Generate basic profile information. <pre class="example">
+SELECT * FROM profile( 'pg_catalog.pg_tables');
+</pre> Result: <pre class="result">
+ schema_name | table_name | column_name |       function    | value
+&#160;------------+------------+-------------+-------------------+-------
+ pg_catalog  | pg_tables  | *           | COUNT()           | 105
+ pg_catalog  | pg_tables  | schemaname  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 6
+ pg_catalog  | pg_tables  | tablename   | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 104
+ pg_catalog  | pg_tables  | tableowner  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 2
+ pg_catalog  | pg_tables  | tablespace  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 1
+ pg_catalog  | pg_tables  | hasindexes  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 2
+ pg_catalog  | pg_tables  | hasrules    | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 1
+ pg_catalog  | pg_tables  | hastriggers | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a> | 2
+(8 rows)
+</pre></li>
+<li>Generate full profile information. <pre class="example">
+SELECT * FROM profile_full( 'pg_catalog.pg_tables',
+                             5
+                          );
+</pre> Result: <pre class="result">
+ schema_name | table_name | column_name |                        function                 |                                               value
+&#160;------------+------------+-------------+-------------------------------------------------+----------------------------------------------------------------------------------------------------
+ pg_catalog  | pg_tables  | *           | COUNT()                                         | 105
+ pg_catalog  | pg_tables  | schemaname  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 6
+ pg_catalog  | pg_tables  | schemaname  | array_collapse(mfvsketch_quick_histogram((),5)) | [0:4]={pg_catalog:68,public:19,information_schema:7,gp_toolkit:5,maddy:5}
+ pg_catalog  | pg_tables  | schemaname  | array_collapse(mfvsketch_top_histogram((),5))   | [0:4]={pg_catalog:68,public:19,information_schema:7,gp_toolkit:5,maddy:5}
+ pg_catalog  | pg_tables  | tablename   | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 104
+ pg_catalog  | pg_tables  | tablename   | array_collapse(mfvsketch_quick_histogram((),5)) | [0:4]={migrationhistory:2,pg_statistic:1,sql_features:1,sql_implementation_info:1,sql_languages:1}
+ pg_catalog  | pg_tables  | tablename   | array_collapse(mfvsketch_top_histogram((),5))   | [0:4]={migrationhistory:2,pg_statistic:1,sql_features:1,sql_implementation_info:1,sql_languages:1}
+ pg_catalog  | pg_tables  | tableowner  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 2
+ pg_catalog  | pg_tables  | tableowner  | array_collapse(mfvsketch_quick_histogram((),5)) | [0:1]={agorajek:104,alex:1}
+ pg_catalog  | pg_tables  | tableowner  | array_collapse(mfvsketch_top_histogram((),5))   | [0:1]={agorajek:104,alex:1}
+ pg_catalog  | pg_tables  | tablespace  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 1
+ pg_catalog  | pg_tables  | tablespace  | array_collapse(mfvsketch_quick_histogram((),5)) | [0:0]={pg_global:28}
+ pg_catalog  | pg_tables  | tablespace  | array_collapse(mfvsketch_top_histogram((),5))   | [0:0]={pg_global:28}
+ pg_catalog  | pg_tables  | hasindexes  | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 2
+ pg_catalog  | pg_tables  | hasindexes  | array_collapse(mfvsketch_quick_histogram((),5)) | [0:1]={t:59,f:46}
+ pg_catalog  | pg_tables  | hasindexes  | array_collapse(mfvsketch_top_histogram((),5))   | [0:1]={t:59,f:46}
+ pg_catalog  | pg_tables  | hasrules    | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 1
+ pg_catalog  | pg_tables  | hasrules    | array_collapse(mfvsketch_quick_histogram((),5)) | [0:0]={f:105}
+ pg_catalog  | pg_tables  | hasrules    | array_collapse(mfvsketch_top_histogram((),5))   | [0:0]={f:105}
+ pg_catalog  | pg_tables  | hastriggers | <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b" title="Flajolet-Martin&#39;s distinct count estimation. ">fmsketch_dcount()</a>                               | 2
+ pg_catalog  | pg_tables  | hastriggers | array_collapse(mfvsketch_quick_histogram((),5)) | [0:1]={f:102,t:3}
+ pg_catalog  | pg_tables  | hastriggers | array_collapse(mfvsketch_top_histogram((),5))   | [0:1]={f:102,t:3}
+(22 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Implementation Notes</dt><dd></dd></dl>
+<p>Because some of the aggregate functions used in profile return multi-dimensional arrays, which are not easily handled in pl/python, we are using <code>array_collapse</code> function to collaps the n-dim arrays to 1-dim arrays. All values of 2 and upper dimensions are separated with ':' character.</p>
+<p>Note that this module is not available in HAWQ. Instead, we suggest the Summary module in Descriptive Statistics as a replacement of this module.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="profile_8sql__in.html" title="SQL function for single-pass table profiles. ">profile.sql_in</a> documenting SQL functions. </dd></dl>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:46 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__quantile.html
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+<title>MADlib: Quantile</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Quantile<div class="ingroups"><a class="el" href="group__grp__deprecated.html">Deprecated Modules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<dl class="section warning"><dt>Warning</dt><dd><em> This is an old implementation of quantile. Replacement of this function is available as built-in PERCENTILE_CONT in databases</em></dd></dl>
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module computes the specified quantile value. It reads the name of the table, the specific column, and computes the quantile value based on the fraction specified as the third argument.</p>
+<p>For an implementation of quantile using sketches, check out the <a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39" title="cmsketch_centile is a scalar UDF to compute a centile value from a cmsketch. Takes the results of the...">cmsketch_centile()</a> aggregate in the <a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a> module.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd></dd></dl>
+<p>There are two implementations of quantile available depending on the size of the table.</p>
+<p><a class="el" href="quantile_8sql__in.html#a6955e44659a0817803ab7a11a9a7e8a5" title="Computes quantile. ">quantile()</a> is best used for small tables (i.e., less than 5000 rows, with 1-2 columns in total).</p>
+<pre class="syntax">
+quantile( table_name,
+          col_name,
+          quantile
+        )
+</pre><p>For larger tables, consider using <a class="el" href="quantile_8sql__in.html#a154435ddf6200110013dc05b1a6207fe" title="Computes quantile. ">quantile_big()</a> instead.</p>
+<pre class="syntax">
+quantile_big( table_name,
+              col_name,
+              quantile
+            )
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare some input. <pre class="example">
+CREATE TABLE tab1 AS SELECT generate_series(1, 1000) AS col1;
+</pre></li>
+<li>Run the <a class="el" href="quantile_8sql__in.html#a6955e44659a0817803ab7a11a9a7e8a5" title="Computes quantile. ">quantile()</a> function. <pre class="example">
+SELECT quantile( 'tab1',
+                 'col1',
+                 .3
+               );
+</pre> Result: <pre class="result">
+   quantile
+&#160;-------------
+ 301.48046875
+(1 row)
+</pre></li>
+</ol>
+<p>Note that this module is not available in HAWQ. GPDB 4.2+ and HAWQ 1.2+ support the SQL Standard percentile_cont() inverse distribution function which should be used preferentially to this implementation. It is also planned for support in Postgres 9.4. This implementation will be retired once the functionality is available in Postgres.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="quantile_8sql__in.html" title="SQL function for Quantile. ">quantile.sql_in</a> documenting the SQL function.</dd></dl>
+<p>Module <a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a> for an approximate quantile implementation. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:46 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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+  <div class="headertitle">
+<div class="title">Random Forest<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__tree.html">Tree Methods</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#get_tree">Display Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Random forests build an ensemble of classifiers, each of which is a tree model constructed using bootstrapped samples from the input data. The results of these models are then combined to yield a single prediction, which, although at the expense of some loss in interpretation, have been found to be highly accurate. Such methods of using multiple Random Forests to make predictions are called random forest methods.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>Random Forest training function has the following format: <pre class="syntax">
+forest_train(training_table_name,
+             output_table_name,
+             id_col_name,
+             dependent_variable,
+             list_of_features,
+             list_of_features_to_exclude,
+             grouping_cols,
+             num_trees,
+             num_random_features,
+             importance,
+             num_permutations,
+             max_tree_depth,
+             min_split,
+             min_bucket,
+             num_splits,
+             surrogate_params,
+             verbose,
+             sample_ratio
+             )
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>training_table_name </dt>
+<dd><p class="startdd">text. the name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table_name </dt>
+<dd><p class="startdd">text. the name of the generated table containing the model.</p>
+<p>The model table produced by the train function contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>gid </th><td>integer. group id that uniquely identifies a set of grouping column values.  </td></tr>
+<tr>
+<th>sample_id </th><td>integer. id of the bootstrap sample that this tree is a part of.  </td></tr>
+<tr>
+<th>tree </th><td>bytea8. trained tree model stored in binary format.  </td></tr>
+</table>
+<p>A summary table named <em>&lt;model_table&gt;_summary</em> is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method </th><td><p class="starttd">'forest_train' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>is_classification </th><td><p class="starttd">boolean. True if it is a classification model. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>source_table </th><td><p class="starttd">text. The data source table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>model_table </th><td><p class="starttd">text. The model table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>id_col_name </th><td><p class="starttd">text. The ID column name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">text. The dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">text. The independent variables </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>cat_features </th><td><p class="starttd">text. categorical feature names. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>con_features </th><td><p class="starttd">text. continuous feature names. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>grouping_col </th><td><p class="starttd">int. Names of grouping columns. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_trees </th><td><p class="starttd">int. Number of trees grown by the model. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_random_features </th><td><p class="starttd">int. Number of features randomly selected for each split. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>max_tree_depth </th><td><p class="starttd">int. Maximum depth of any tree in the random forest model_table. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>min_split </th><td><p class="starttd">int. Minimum number of observations in a node for it to be split. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>min_bucket </th><td><p class="starttd">int. minimum number of observations in any terminal node. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_splits </th><td><p class="starttd">int. number of buckets for continuous variables. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>verbose </th><td><p class="starttd">boolean. whether or not to display debug info. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>importance </th><td><p class="starttd">boolean. whether or not to calculate variable importance. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_permutations </th><td><p class="starttd">int. number of times feature values are permuted while calculating variable importance. The default value is 1. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">int. Number of groups during forest training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">int. Number of failed groups during forest training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_processed </th><td><p class="starttd">bigint. Total numbers of rows processed in all groups. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_skipped </th><td><p class="starttd">bigint. Total numbers of rows skipped in all groups due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_var_levels </th><td><p class="starttd">itext. For classification, the distinct levels of the dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_var_type </th><td>text. The type of dependent variable.  </td></tr>
+</table>
+<p>A group table named <em> &lt;model_table&gt;_group</em> is created, which has the following columns: </p><table  class="output">
+<tr>
+<th>gid </th><td><p class="starttd">integer. group id that uniquely identifies a set of grouping column values. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>&lt;...&gt; </th><td><p class="starttd">Same type as in the training data table. Grouping columns, if provided in input. This could be multiple columns depending on the <code>grouping_cols</code> input. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>success </th><td><p class="starttd">boolean. Indicator of the success of the group. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>cat_levels_in_text </th><td><p class="starttd">text[]. Ordered levels of categorical variables. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>cat_n_levels </th><td><p class="starttd">integer[]. Number of levels for each categorical variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>oob_error </th><td><p class="starttd">double precision. Out-of-bag error for the random forest model. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>cat_var_importance </th><td><p class="starttd">double precision[]. Variable importance for categorical features. The order corresponds to the order of the variables as found in cat_features in <em> &lt;model_table&gt;_summary</em>. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>con_var_importance </th><td><p class="starttd">double precision[]. Variable importance for continuous features. The order corresponds to the order of the variables as found in con_features in <em> &lt;model_table&gt;_summary</em>. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>id_col_name </dt>
+<dd><p class="startdd">text. Name of the column containing id information in the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_variable </dt>
+<dd><p class="startdd">text. Name of the column that contains the output for training. Boolean, integer and text are considered classification outputs, while float values are considered regression outputs.</p>
+<p class="enddd"></p>
+</dd>
+<dt>list_of_features </dt>
+<dd><p class="startdd">text. Comma-separated string of column names to use as predictors. Can also be a '*' implying all columns are to be used as predictors (except the ones included in the next argument). Boolean, integer and text columns are considered categorical columns.</p>
+<p class="enddd"></p>
+</dd>
+<dt>list_of_features_to_exclude </dt>
+<dd><p class="startdd">text. Comma-separated string of column names to exclude from the predictors list. If the <em>dependent_variable</em> argument is an expression (including cast of a column name), then this list should include the columns that are included in the <em>dependent_variable</em> expression, otherwise those columns will be included in the features (resulting in meaningless trees).</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_cols (optional) </dt>
+<dd><p class="startdd">text, default: NULL. Comma-separated list of column names to group the data by. This will lead to creating multiple random forests, one for each group.</p>
+<p class="enddd"></p>
+</dd>
+<dt>num_trees (optional) </dt>
+<dd><p class="startdd">integer, default: 100. Maximum number of trees to grow in the Random Forest model. Actual number of trees grown may be slighlty different.</p>
+<p class="enddd"></p>
+</dd>
+<dt>num_random_features (optional) </dt>
+<dd><p class="startdd">integer, default: sqrt(n) if classification tree, otherwise n/3. Number of features to randomly select at each split.</p>
+<p class="enddd"></p>
+</dd>
+<dt>importance (optional) </dt>
+<dd><p class="startdd">boolean, default: true. Whether or not to calculate variable importance.</p>
+<p class="enddd"></p>
+</dd>
+<dt>num_permutations (optional) </dt>
+<dd><p class="startdd">integer, default: 1. Number of times to permute each feature value while calculating variable importance.</p>
+<p>Variable importance for a feature is computed by permuting the variable with random values and computing the drop in predictive accuracy (using OOB samples). Setting this greater than 1 performs an average over multiple importance calculation. This increases the total run time and in most cases the default value of 1 is sufficient to compute the importance. </p>
+<p class="enddd"></p>
+</dd>
+<dt>max_depth (optional) </dt>
+<dd><p class="startdd">integer, default: 10. Maximum depth of any node of a tree, with the root node counted as depth 0.</p>
+<p class="enddd"></p>
+</dd>
+<dt>min_split (optional) </dt>
+<dd><p class="startdd">integer, default: 20. Minimum number of observations that must exist in a node for a split to be attempted.</p>
+<p class="enddd"></p>
+</dd>
+<dt>min_bucket (optional) </dt>
+<dd><p class="startdd">integer, default: min_split/3. Minimum number of observations in any terminal node. If only one of min_bucket or min_split is specified, min_split is set to min_bucket*3 or min_bucket to min_split/3, as appropriate.</p>
+<p class="enddd"></p>
+</dd>
+<dt>num_splits (optional) </dt>
+<dd><p class="startdd">integer, default: 100. Continuous-valued features are binned into discrete quantiles to compute split boundaries. This global parameter is used to compute the resolution of splits for continuous features. Higher number of bins will lead to better prediction, but will also result in higher processing time.</p>
+<p class="enddd"></p>
+</dd>
+<dt>surrogate_params (optional) </dt>
+<dd><p class="startdd">text, Comma-separated string of key-value pairs controlling the behavior of surrogate splits for each node in a tree. </p><table  class="output">
+<tr>
+<th>max_surrogates </th><td>Default: 0. Number of surrogates to store for each node.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd><p class="startdd">boolean, default: FALSE. Provides verbose output of the results of training.</p>
+<p class="enddd"></p>
+</dd>
+<dt>sample_ratio (optional) </dt>
+<dd>double precision, in the range of (0, 1], default: 1. If sample_ratio is less than 1, a bootstrap sample size smaller than the data table is expected to be used for training each tree in the forest. A ratio that is close to 0 may result in trees with only the root node. This allows users to experiment with the function in a speedy fashion. </dd>
+</dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>The prediction function is provided to estimate the conditional mean given a new predictor. It has the following syntax: <pre class="syntax">
+forest_predict(random_forest_model,
+               new_data_table,
+               output_table,
+               type)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>forest_model </dt>
+<dd><p class="startdd">text. Name of the table containing the Random Forest model.</p>
+<p class="enddd"></p>
+</dd>
+<dt>new_data_table </dt>
+<dd><p class="startdd">text. Name of the table containing prediction data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">text. Name of the table to output prediction results to.</p>
+<p class="enddd"></p>
+</dd>
+<dt>type </dt>
+<dd>text, optional, default: 'response'. For regression models, the output is always the predicted value of the dependent variable. For classification models, the <em>type</em> variable can be 'response', giving the classification prediction as output, or 'prob', giving the class probabilities as output. For each value of the dependent variable, a column with the probabilities is added to the output table.  </dd>
+</dl>
+<p><a class="anchor" id="get_tree"></a></p><dl class="section user"><dt>Display Function</dt><dd>The get_tree function is provided to output a graph representation of a single tree of the Random Forest. The output can either be in the popular 'dot' format that can be visualized using various programs including those in the GraphViz package, or in a simple text format. The details of the text format is outputted with the tree. <pre class="syntax">
+get_tree(forest_model_table,
+         gid,
+         sample_id,
+         dot_format)
+</pre></dd></dl>
+<p>An additional display function is provided to output the surrogate splits chosen for each internal node. </p><pre class="syntax">
+get_tree_surr(forest_model_table,
+              gid,
+              sample_id)
+</pre><p>The output contains the list of surrogate splits for each internal node of a tree. The nodes are sorted in ascending order by id. This is equivalent to viewing the tree in a breadth-first manner. For each surrogate, the output gives the surrogate split (variable and threshold) and also provides the number of rows that were common between the primary split and the surrogate split. Finally, the number of rows present in the majority branch of the primary split is also presented. Only surrogates that perform better than this majority branch are used. When the primary variable has a NULL value the surrogate variables are used in order to compute the split for that node. If all surrogates variables are NULL, then the majority branch is used to compute the split for a tuple.</p>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>forest_model_table </dt>
+<dd><p class="startdd">text. Name of the table containing the Random Forest model.</p>
+<p class="enddd"></p>
+</dd>
+<dt>gid </dt>
+<dd><p class="startdd">integer. Id of the group that this tree is a part of.</p>
+<p class="enddd"></p>
+</dd>
+<dt>sample_id </dt>
+<dd><p class="startdd">integer. Id of the bootstrap sample that this tree if a part of.</p>
+<p class="enddd"></p>
+</dd>
+<dt>dot_format </dt>
+<dd>boolean, default = TRUE. Output can either be in a dot format or a text format. If TRUE, the result is in the dot format, else output is in text format. </dd>
+</dl>
+<p>The output is always returned as a 'TEXT'. For the dot format, the output can be redirected to a file on the client side and then rendered using visualization programs.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><b>Note:</b> The output results may vary due the random nature of random forests.</dd></dl>
+<p><b>Random Forest Classification Example</b></p>
+<ol type="1">
+<li>Prepare input data. <pre class="example">
+DROP TABLE IF EXISTS dt_golf;
+CREATE TABLE dt_golf (
+    id integer NOT NULL,
+    "OUTLOOK" text,
+    temperature double precision,
+    humidity double precision,
+    windy text,
+    class text
+) ;
+</pre> <pre class="example">
+INSERT INTO dt_golf (id,"OUTLOOK",temperature,humidity,windy,class) VALUES
+(1, 'sunny', 85, 85, 'false', 'Don''t Play'),
+(2, 'sunny', 80, 90, 'true', 'Don''t Play'),
+(3, 'overcast', 83, 78, 'false', 'Play'),
+(4, 'rain', 70, 96, 'false', 'Play'),
+(5, 'rain', 68, 80, 'false', 'Play'),
+(6, 'rain', 65, 70, 'true', 'Don''t Play'),
+(7, 'overcast', 64, 65, 'true', 'Play'),
+(8, 'sunny', 72, 95, 'false', 'Don''t Play'),
+(9, 'sunny', 69, 70, 'false', 'Play'),
+(10, 'rain', 75, 80, 'false', 'Play'),
+(11, 'sunny', 75, 70, 'true', 'Play'),
+(12, 'overcast', 72, 90, 'true', 'Play'),
+(13, 'overcast', 81, 75, 'false', 'Play'),
+(14, 'rain', 71, 80, 'true', 'Don''t Play');
+</pre></li>
+<li>Run Random Forest train function. <pre class="example">
+DROP TABLE IF EXISTS train_output, train_output_group, train_output_summary;
+SELECT madlib.forest_train('dt_golf',         -- source table
+                           'train_output',    -- output model table
+                           'id',              -- id column
+                           'class',           -- response
+                           '"OUTLOOK", temperature, humidity, windy', -- features
+                           NULL,              -- exclude columns
+                           NULL,              -- grouping columns
+                           20::integer,       -- number of trees
+                           2::integer,        -- number of random features
+                           TRUE::boolean,     -- variable importance
+                           1::integer,        -- num_permutations
+                           8::integer,        -- max depth
+                           3::integer,        -- min split
+                           1::integer,        -- min bucket
+                           10::integer,       -- number of splits per continuous variable
+                           FALSE              -- verbose
+                           );
+\x on
+SELECT * FROM train_output_summary;
+SELECT * FROM train_output_group;
+\x off
+</pre></li>
+<li>Obtain a dot format display of a single tree within the forest. <pre class="example">
+SELECT madlib.get_tree('train_output',1,2);
+</pre> Result: <pre class="result">
+digraph "Classification tree for dt_golf" {
+"0" [label="temperature&lt;=70", shape=ellipse];
+"0" -&gt; "1"[label="yes"];
+"1" [label="\"'Play'"",shape=box];
+"0" -&gt; "2"[label="no"];
+"2" [label=""OUTLOOK"&lt;={overcast}", shape=ellipse];
+"2" -&gt; "5"[label="yes"];
+"5" [label=""'Play'"",shape=box];
+"2" -&gt; "6"[label="no"];
+"6" [label="humidity&lt;=70", shape=ellipse];
+"6" -&gt; "13"[label="yes"];
+"13" [label=""'Play'"",shape=box];
+"6" -&gt; "14"[label="no"];
+"14" [label=""'Don''t Play'"",shape=box];
+} //---end of digraph---------
+</pre></li>
+<li>Obtain a text display of the tree <pre class="example">
+SELECT madlib.get_tree('train_output',1,2,FALSE);
+</pre> Result: <pre class="result">
+&#160;-------------------------------------
+&#160;- Each node represented by 'id' inside ().
+&#160;- Leaf nodes have a * while internal nodes have the split condition at the end.
+&#160;- For each internal node (i), it's children will be at (2i+1) and (2i+2).
+&#160;- For each split the first indented child (2i+1) is the 'True' node and
+second indented child (2i+2) is the 'False' node.
+&#160;- Number of (weighted) rows for each response variable inside [].
+&#160;- Order of values = ['"Don\'t Play"', '"Play"']
+&#160;-------------------------------------
+(0)[  3 11]  temperature&lt;=70
+    (1)[ 0 7]  * --&gt; "'Play'"
+    (2)[ 3 4]  "OUTLOOK"&lt;={overcast}
+       (5)[ 0 3]  * --&gt; "'Play'"
+       (6)[ 3 1]  humidity&lt;=70
+          (13)[ 0 1]  * --&gt; "'Play'"
+          (14)[ 3 0]  * --&gt; "'Don''t Play'"
+&#160;-------------------------------------
+</pre></li>
+<li>Predict output categories for the same data as was used for input. <pre class="example">
+DROP TABLE IF EXISTS prediction_results;
+SELECT madlib.forest_predict('train_output',
+                             'dt_golf',
+                             'prediction_results',
+                             'response');
+\x off
+SELECT id, estimated_class, class
+FROM prediction_results JOIN dt_golf USING (id)
+ORDER BY id;
+</pre> Result: <pre class="result">
+ id | estimated_class |   class
+----+-----------------+------------
+  1 | Don't Play      | Don't Play
+  2 | Don't Play      | Don't Play
+  3 | Play            | Play
+  4 | Play            | Play
+  5 | Play            | Play
+  6 | Don't Play      | Don't Play
+  7 | Play            | Play
+  8 | Don't Play      | Don't Play
+  9 | Play            | Play
+ 10 | Play            | Play
+ 11 | Play            | Play
+ 12 | Play            | Play
+ 13 | Play            | Play
+ 14 | Don't Play      | Don't Play
+(14 rows)
+</pre></li>
+<li>Predict probablities of output categories for the same data. <pre class="example">
+DROP TABLE IF EXISTS prediction_prob;
+SELECT madlib.forest_predict('train_output',
+                             'dt_golf',
+                             'prediction_prob',
+                             'prob');
+\x off
+SELECT id, "estimated_prob_Play", class
+FROM prediction_prob JOIN dt_golf USING (id)
+ORDER BY id;
+</pre> Result: <pre class="result">
+ id | estimated_prob_Play |   class
+----+---------------------+------------
+  1 |                0.15 | Don't Play
+  2 |                 0.1 | Don't Play
+  3 |                0.95 | Play
+  4 |                 0.7 | Play
+  5 |                0.85 | Play
+  6 |                0.25 | Don't Play
+  7 |                0.75 | Play
+  8 |                 0.1 | Don't Play
+  9 |                0.85 | Play
+ 10 |                 0.7 | Play
+ 11 |                0.35 | Play
+ 12 |                0.75 | Play
+ 13 |                0.95 | Play
+ 14 |                0.15 | Don't Play
+(14 rows)
+</pre></li>
+</ol>
+<p><b>Random Forest Regression Example</b></p>
+<ol type="1">
+<li>Prepare input data. <pre class="example">
+DROP TABLE IF EXISTS mt_cars;
+CREATE TABLE mt_cars (
+    id integer NOT NULL,
+    mpg double precision,
+    cyl integer,
+    disp double precision,
+    hp integer,
+    drat double precision,
+    wt double precision,
+    qsec double precision,
+    vs integer,
+    am integer,
+    gear integer,
+    carb integer
+) ;
+</pre> <pre class="example">
+INSERT INTO mt_cars (id,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb) VALUES
+(1,18.7,8,360,175,3.15,3.44,17.02,0,0,3,2),
+(2,21,6,160,110,3.9,2.62,16.46,0,1,4,4),
+(3,24.4,4,146.7,62,3.69,3.19,20,1,0,4,2),
+(4,21,6,160,110,3.9,2.875,17.02,0,1,4,4),
+(5,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4),
+(6,16.4,8,275.8,180,3.078,4.07,17.4,0,0,3,3),
+(7,22.8,4,108,93,3.85,2.32,18.61,1,1,4,1),
+(8,17.3,8,275.8,180,3.078,3.73,17.6,0,0,3,3),
+(9,21.4,6,258,110,3.08,3.215,19.44,1,0,3,1),
+(10,15.2,8,275.8,180,3.078,3.78,18,0,0,3,3),
+(11,18.1,6,225,105,2.768,3.46,20.22,1,0,3,1),
+(12,32.4,4,78.7,66,4.08,2.20,19.47,1,1,4,1),
+(13,14.3,8,360,245,3.21,3.578,15.84,0,0,3,4),
+(14,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2),
+(15,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2),
+(16,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4),
+(17,33.9,4,71.14,65,4.22,1.835,19.9,1,1,4,1),
+(18,15.2,8,304,150,3.15,3.435,17.3,0,0,3,2),
+(19,10.4,8,472,205,2.93,5.25,17.98,0,0,3,4),
+(20,27.3,4,79,66,4.08,1.935,18.9,1,1,4,1),
+(21,10.4,8,460,215,3,5.424,17.82,0,0,3,4),
+(22,26,4,120.3,91,4.43,2.14,16.7,0,1,5,2),
+(23,14.7,8,440,230,3.23,5.345,17.42,0,0,3,4),
+(24,30.4,4,95.14,113,3.77,1.513,16.9,1,1,5,2),
+(25,21.5,4,120.1,97,3.70,2.465,20.01,1,0,3,1),
+(26,15.8,8,351,264,4.22,3.17,14.5,0,1,5,4),
+(27,15.5,8,318,150,2.768,3.52,16.87,0,0,3,2),
+(28,15,8,301,335,3.54,3.578,14.6,0,1,5,8),
+(29,13.3,8,350,245,3.73,3.84,15.41,0,0,3,4),
+(30,19.2,8,400,175,3.08,3.845,17.05,0,0,3,2),
+(31,19.7,6,145,175,3.62,2.77,15.5,0,1,5,6),
+(32,21.4,4,121,109,4.11,2.78,18.6,1,1,4,2);
+</pre></li>
+<li>Run Random Forest train function. <pre class="example">
+DROP TABLE IF EXISTS mt_cars_output, mt_cars_output_group, mt_cars_output_summary;
+SELECT madlib.forest_train('mt_cars',
+                           'mt_cars_output',
+                           'id',
+                           'mpg',
+                           '*',
+                           'id, hp, drat, am, gear, carb',  -- exclude columns
+                           'am',
+                           10::integer,
+                           2::integer,
+                           TRUE::boolean,
+                           1,
+                           10,
+                           8,
+                           3,
+                           10
+                           );
+\x on
+SELECT * FROM mt_cars_output_summary;
+SELECT * FROM mt_cars_output_group;
+\x off
+</pre></li>
+<li>Display a single tree of the Random Forest in dot format. <pre class="example">
+SELECT madlib.get_tree('mt_cars_output',1,1);
+</pre> Result: <pre class="result">
+digraph "Regression tree for mt_cars" {
+"0" [label="28.8444",shape=box];
+} //---end of digraph---------
+</pre></li>
+<li>Predict regression output for the same data and compare with original. <pre class="example">
+DROP TABLE IF EXISTS prediction_results;
+SELECT madlib.forest_predict('mt_cars_output',
+                             'mt_cars',
+                             'prediction_results',
+                             'response');
+SELECT am, id, estimated_mpg, mpg
+FROM prediction_results JOIN mt_cars USING (id)
+ORDER BY am, id;
+</pre> Result: <pre class="result">
+ am | id |  estimated_mpg   | mpg
+----+----+------------------+------
+  0 |  1 |  15.893525974026 | 18.7
+  0 |  3 | 21.5238492063492 | 24.4
+  0 |  5 | 20.0175396825397 | 17.8
+  0 |  6 | 14.8406818181818 | 16.4
+  0 |  8 | 14.8406818181818 | 17.3
+  0 |  9 | 20.0496825396825 | 21.4
+  0 | 10 | 14.4012272727273 | 15.2
+  0 | 11 | 20.0175396825397 | 18.1
+  0 | 13 | 15.0162878787879 | 14.3
+  0 | 14 | 21.5238492063492 | 22.8
+  0 | 16 | 20.0175396825397 | 19.2
+  0 | 18 | 15.4787532467532 | 15.2
+  0 | 19 | 14.4272987012987 | 10.4
+  0 | 21 | 14.4272987012987 | 10.4
+  0 | 23 | 14.8667532467532 | 14.7
+  0 | 25 | 21.5238492063492 | 21.5
+  0 | 27 |  15.281525974026 | 15.5
+  0 | 29 | 15.0162878787879 | 13.3
+  0 | 30 |  15.281525974026 | 19.2
+  1 |  2 | 20.6527393162393 |   21
+  1 |  4 | 20.6527393162393 |   21
+  1 |  7 | 22.7707393162393 | 22.8
+  1 | 12 | 27.0888266178266 | 32.4
+  1 | 15 | 28.2478650793651 | 30.4
+  1 | 17 | 28.2478650793651 | 33.9
+  1 | 20 | 28.2478650793651 | 27.3
+  1 | 22 | 23.8401984126984 |   26
+  1 | 24 | 26.9748650793651 | 30.4
+  1 | 26 | 20.6527393162393 | 15.8
+  1 | 28 | 20.6527393162393 |   15
+  1 | 31 | 20.6527393162393 | 19.7
+  1 | 32 | 22.7707393162393 | 21.4
+</pre></li>
+</ol>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="random__forest_8sql__in.html">random_forest.sql_in</a> documenting the training function</p>
+<p><a class="el" href="group__grp__decision__tree.html">Decision Tree</a></p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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new file mode 100644
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Tree Methods</div>  </div>
+</div><!--header-->
+<div class="contents">
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__decision__tree"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__decision__tree.html">Decision Tree</a></td></tr>
+<tr class="memdesc:group__grp__decision__tree"><td class="mdescLeft">&#160;</td><td class="mdescRight">Decision Trees. Decision trees use a tree-based predictive model to predict the value of a target variable based on several input variables. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__random__forest"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__random__forest.html">Random Forest</a></td></tr>
+<tr class="memdesc:group__grp__random__forest"><td class="mdescLeft">&#160;</td><td class="mdescRight">Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of recursive partitioning (tree) methods. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__recursive__partitioning.js
----------------------------------------------------------------------
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--- /dev/null
+++ b/docs/latest/group__grp__recursive__partitioning.js
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+var group__grp__recursive__partitioning =
+[
+    [ "Decision Tree", "group__grp__decision__tree.html", null ],
+    [ "Random Forest", "group__grp__random__forest.html", null ]
+];
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+       class="ui-resizable-handle">
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+<div id="doc-content">
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Regression Models<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods for modeling conditional expectation of a response variable. </p>
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+<tr class="memdesc:group__grp__marginal"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates marginal effects for the coefficients in regression problems. <br /></td></tr>
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+<tr class="memitem:group__grp__ordinal"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__ordinal.html">Ordinal Regression</a></td></tr>
+<tr class="memdesc:group__grp__ordinal"><td class="mdescLeft">&#160;</td><td class="mdescRight">Regression to model data with ordinal response variable. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__robust"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__robust.html">Robust Variance</a></td></tr>
+<tr class="memdesc:group__grp__robust"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates Huber-White variance estimates for linear, logistic, and multinomial regression models, and for Cox proportional hazards models. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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[19/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_l.html
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+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
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+<script type="text/javascript" src="navtreedata.js"></script>
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+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  ga('send', 'pageview');
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+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_l.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
+<li>l1norm()
+: <a class="el" href="svec__util_8sql__in.html#a9e9b64c93b29c19adfb670f0171f14bf">svec_util.sql_in</a>
+</li>
+<li>l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a1b6bb42a5176b020aed12734df23aca9">svec_util.sql_in</a>
+</li>
+<li>laplace_cdf()
+: <a class="el" href="prob_8sql__in.html#a64e197de8da3761acdeec9db7e211703">prob.sql_in</a>
+</li>
+<li>laplace_pdf()
+: <a class="el" href="prob_8sql__in.html#a750278ad29d514793f76e159b773f410">prob.sql_in</a>
+</li>
+<li>laplace_quantile()
+: <a class="el" href="prob_8sql__in.html#a77f94fc43d4777fc4f68d18e29454a81">prob.sql_in</a>
+</li>
+<li>lda_get_perplexity()
+: <a class="el" href="lda_8sql__in.html#a25c3ef12d9808d8a38c5fd2630f3b5a9">lda.sql_in</a>
+</li>
+<li>lda_get_topic_desc()
+: <a class="el" href="lda_8sql__in.html#a4ac16d87d50821aadcab0d75d65b3f1b">lda.sql_in</a>
+</li>
+<li>lda_get_topic_word_count()
+: <a class="el" href="lda_8sql__in.html#ace20b6314e5b4e98929a98a6f05d925d">lda.sql_in</a>
+</li>
+<li>lda_get_word_topic_count()
+: <a class="el" href="lda_8sql__in.html#a455699e72328d6b105c61a3c9e7ae5dc">lda.sql_in</a>
+</li>
+<li>lda_predict()
+: <a class="el" href="lda_8sql__in.html#af1fde06c39dd12bb9e5544997f815323">lda.sql_in</a>
+</li>
+<li>lda_train()
+: <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80">lda.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step()
+: <a class="el" href="crf_8sql__in.html#a590cd748d8e18055d851e7d4f4a3ca00">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_final()
+: <a class="el" href="crf_8sql__in.html#a5bf4b2738003096f4eeeccd0eb5e98f0">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_merge_states()
+: <a class="el" href="crf_8sql__in.html#a45f3eaf0c80cdfa99872cd071020d5c7">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_transition()
+: <a class="el" href="crf_8sql__in.html#a2e7104fea95ca76587e7fae9bcb0d7ed">crf.sql_in</a>
+</li>
+<li>lincrf_train()
+: <a class="el" href="crf_8sql__in.html#a336ac57ae3d07e331646d397c7651d3d">crf.sql_in</a>
+</li>
+<li>linear_solver_dense()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a885d6a36c07dc2c048948cb5287a3b92">dense_linear_systems.sql_in</a>
+</li>
+<li>linear_solver_sparse()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3b3b7eea8b91c0a16ede3e7a9c6b17ef">sparse_linear_systems.sql_in</a>
+</li>
+<li>linear_svm_igd_final()
+: <a class="el" href="lsvm_8sql__in.html#a614936d27848149eead4581db57a9236">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_merge()
+: <a class="el" href="lsvm_8sql__in.html#a241eea31c78737ecbb5da6639debec3b">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step()
+: <a class="el" href="lsvm_8sql__in.html#a8cd061b3cd2b9ef62e018c33dde27a4d">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step_serial()
+: <a class="el" href="lsvm_8sql__in.html#a027f40b148849a721001d20f098a4a0e">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_transition()
+: <a class="el" href="lsvm_8sql__in.html#a6f26e0007d7d95b4bb4b72db4f702b25">lsvm.sql_in</a>
+</li>
+<li>linregr()
+: <a class="el" href="linear_8sql__in.html#a5fcd1daa9796dccf5430929c37ea0ee9">linear.sql_in</a>
+</li>
+<li>linregr_final()
+: <a class="el" href="linear_8sql__in.html#a28b69254ba5cd513b35ba6b17efd2c35">linear.sql_in</a>
+</li>
+<li>linregr_merge_states()
+: <a class="el" href="linear_8sql__in.html#a042536820ffbad498a4e1c410f949e35">linear.sql_in</a>
+</li>
+<li>linregr_predict()
+: <a class="el" href="linear_8sql__in.html#a8da0030683d5931b93e5f2bee3fd0544">linear.sql_in</a>
+</li>
+<li>linregr_train()
+: <a class="el" href="linear_8sql__in.html#ad0175a9ecc455d9679f7d251af264848">linear.sql_in</a>
+</li>
+<li>linregr_transition()
+: <a class="el" href="linear_8sql__in.html#aa2b87a3e60272176bfa2fb859f99461a">linear.sql_in</a>
+</li>
+<li>lmf_igd_final()
+: <a class="el" href="lmf_8sql__in.html#a298f46c944cd6813b0360514bf8c9ed1">lmf.sql_in</a>
+</li>
+<li>lmf_igd_merge()
+: <a class="el" href="lmf_8sql__in.html#ae3bb16a76a927e1e59420d4a8e13012f">lmf.sql_in</a>
+</li>
+<li>lmf_igd_run()
+: <a class="el" href="lmf_8sql__in.html#ac1acb1f0e1f7008118f21c83546a4602">lmf.sql_in</a>
+</li>
+<li>lmf_igd_step()
+: <a class="el" href="lmf_8sql__in.html#a54d2ca2048d9a0c29d5593530b932a23">lmf.sql_in</a>
+</li>
+<li>lmf_igd_transition()
+: <a class="el" href="lmf_8sql__in.html#a187574cfe85888b29d6b1658554e7507">lmf.sql_in</a>
+</li>
+<li>logistic()
+: <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e">logistic.sql_in</a>
+</li>
+<li>logistic_cdf()
+: <a class="el" href="prob_8sql__in.html#a140f674876813d5e786a4d8ba8d75c87">prob.sql_in</a>
+</li>
+<li>logistic_pdf()
+: <a class="el" href="prob_8sql__in.html#afa38eb6c61d3c9825d5c172e6c17dbf7">prob.sql_in</a>
+</li>
+<li>logistic_quantile()
+: <a class="el" href="prob_8sql__in.html#a5a77a0bc5884af2a914a955174892ae2">prob.sql_in</a>
+</li>
+<li>lognormal_cdf()
+: <a class="el" href="prob_8sql__in.html#a4c05b347f8feb64e1236d21b850af61e">prob.sql_in</a>
+</li>
+<li>lognormal_pdf()
+: <a class="el" href="prob_8sql__in.html#a7370b797bf450f9aa54d4fea4d64d611">prob.sql_in</a>
+</li>
+<li>lognormal_quantile()
+: <a class="el" href="prob_8sql__in.html#aab3a6de990ae5a81834274a1cf9cad8f">prob.sql_in</a>
+</li>
+<li>logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#a2d1571ffa794176a5dfed9d35e70fed7">cross_validation.sql_in</a>
+</li>
+<li>logregr_predict()
+: <a class="el" href="logistic_8sql__in.html#acc8af7a39e7b0d604c2760d9d6bd0b5e">logistic.sql_in</a>
+</li>
+<li>logregr_predict_prob()
+: <a class="el" href="logistic_8sql__in.html#a75dba5248d75cd6c5d2fb69f944ce479">logistic.sql_in</a>
+</li>
+<li>logregr_train()
+: <a class="el" href="logistic_8sql__in.html#a3c217840f2fbe1dcd53ba15c4111b86a">logistic.sql_in</a>
+</li>
+<li>lsvm_classification()
+: <a class="el" href="lsvm_8sql__in.html#a75d126981ae4bf2e6641627501f0a2a5">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict()
+: <a class="el" href="lsvm_8sql__in.html#a0dee8d0cd54582f06de2935b25b7ecb0">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict_batch()
+: <a class="el" href="lsvm_8sql__in.html#a1c0a002f50250133c0ef1d3c43c6d338">lsvm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_m.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_m.html b/docs/latest/globals_m.html
new file mode 100644
index 0000000..d7a4344
--- /dev/null
+++ b/docs/latest/globals_m.html
@@ -0,0 +1,268 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_m.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
+<li>marginal_logregr()
+: <a class="el" href="marginal_8sql__in.html#a3286b644d2d51a8f2874896dc1b5d925">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_final()
+: <a class="el" href="marginal_8sql__in.html#ac4e06a28a76651de70768457575bb9e0">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#ae44a992f8bd650b5c1f0c2ffae7bb542">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a2115a795562bc2928c3858fc55104c4b">marginal.sql_in</a>
+</li>
+<li>marginal_mlogregr()
+: <a class="el" href="marginal_8sql__in.html#aaf0b3cc34b99d4b47334bdde2d139b81">marginal.sql_in</a>
+</li>
+<li>margins()
+: <a class="el" href="marginal_8sql__in.html#a6d002a5934d6bc614925ffcf67f24a99">marginal.sql_in</a>
+</li>
+<li>margins_logregr()
+: <a class="el" href="marginal_8sql__in.html#af520629d3624d30cdcdbce247d89ac7d">marginal.sql_in</a>
+</li>
+<li>margins_mlogregr()
+: <a class="el" href="marginal_8sql__in.html#a8311df1f591cff8d30ca8df816c0cfca">marginal.sql_in</a>
+</li>
+<li>matrix_add()
+: <a class="el" href="matrix__ops_8sql__in.html#a1cbb83dad451e7c36033c0f3df0a22e0">matrix_ops.sql_in</a>
+</li>
+<li>matrix_agg()
+: <a class="el" href="linalg_8sql__in.html#a9c439706f35d6cac89f151d553a5f111">linalg.sql_in</a>
+</li>
+<li>matrix_agg_final()
+: <a class="el" href="linalg_8sql__in.html#adb012de82ecdc71ac54f3498e88e59d0">linalg.sql_in</a>
+</li>
+<li>matrix_agg_transition()
+: <a class="el" href="linalg_8sql__in.html#ac2d98e204b4fccab518aa20edef4ec41">linalg.sql_in</a>
+</li>
+<li>matrix_block_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#a0cb243e18397fb101f1cf48d9872f805">matrix_ops.sql_in</a>
+</li>
+<li>matrix_block_square()
+: <a class="el" href="matrix__ops_8sql__in.html#af84a929ab1558a06a5f8e9e500bf9051">matrix_ops.sql_in</a>
+</li>
+<li>matrix_block_trans()
+: <a class="el" href="matrix__ops_8sql__in.html#aad2242b0b102d1d80cd2253b0be7e90a">matrix_ops.sql_in</a>
+</li>
+<li>matrix_blockize()
+: <a class="el" href="matrix__ops_8sql__in.html#aa63cfd84aa33c87ad16f52dc7f642848">matrix_ops.sql_in</a>
+</li>
+<li>matrix_column()
+: <a class="el" href="linalg_8sql__in.html#aed3a33f3d3d43a056c5ec591661d34d8">linalg.sql_in</a>
+</li>
+<li>matrix_densify()
+: <a class="el" href="matrix__ops_8sql__in.html#a9f646acba5ebc51b0aa647adeadbb96e">matrix_ops.sql_in</a>
+</li>
+<li>matrix_elem_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#aa93181583456a1f23c3665c051742173">matrix_ops.sql_in</a>
+</li>
+<li>matrix_info()
+: <a class="el" href="matrix__ops_8sql__in.html#a0535b01f414f5d61153abca8b3cd94fa">matrix_ops.sql_in</a>
+</li>
+<li>matrix_mem_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#a91383f59dcb8b7ca2fba89ea350d113b">matrix_ops.sql_in</a>
+</li>
+<li>matrix_mem_trans()
+: <a class="el" href="matrix__ops_8sql__in.html#a0731901b985a90c3aa2f686a7ce95978">matrix_ops.sql_in</a>
+</li>
+<li>matrix_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#ad89a88acce449ee27ba9d3a73950c2d7">matrix_ops.sql_in</a>
+</li>
+<li>matrix_norm()
+: <a class="el" href="matrix__ops_8sql__in.html#aa5eb2864180eb276a786d9b2172aef5f">matrix_ops.sql_in</a>
+</li>
+<li>matrix_scale_and_add()
+: <a class="el" href="matrix__ops_8sql__in.html#a68c05ce6379f28d5e30dd0144fe50565">matrix_ops.sql_in</a>
+</li>
+<li>matrix_sparsify()
+: <a class="el" href="matrix__ops_8sql__in.html#a9ed8df5fc43740c00bfdfd3f934429ef">matrix_ops.sql_in</a>
+</li>
+<li>matrix_square()
+: <a class="el" href="matrix__ops_8sql__in.html#ae4082533d1943e20660dee4c951edf22">matrix_ops.sql_in</a>
+</li>
+<li>matrix_sub()
+: <a class="el" href="matrix__ops_8sql__in.html#a66d586c0126a95e43ecad7d3efbcce69">matrix_ops.sql_in</a>
+</li>
+<li>matrix_trans()
+: <a class="el" href="matrix__ops_8sql__in.html#a058e681612aeaea330e8c72ad35ba550">matrix_ops.sql_in</a>
+</li>
+<li>matrix_unblockize()
+: <a class="el" href="matrix__ops_8sql__in.html#a0a199e7cf9e56acd6a7f3fd78e73fa22">matrix_ops.sql_in</a>
+</li>
+<li>mean()
+: <a class="el" href="svec__util_8sql__in.html#a546b947319c9f48237120a31929bdfd9">svec_util.sql_in</a>
+</li>
+<li>mfvsketch_quick_histogram()
+: <a class="el" href="sketch_8sql__in.html#aa2d4c0af50b242731f841c2dd355fc72">sketch.sql_in</a>
+</li>
+<li>mfvsketch_top_histogram()
+: <a class="el" href="sketch_8sql__in.html#ab915f092b747e3a955cbb699afc9c70c">sketch.sql_in</a>
+</li>
+<li>misclassification_avg()
+: <a class="el" href="cross__validation_8sql__in.html#ad01cd1c97ae1be0ac7cd6c42abb75bb2">cross_validation.sql_in</a>
+</li>
+<li>mlogregr()
+: <a class="el" href="multilogistic_8sql__in.html#a116c95de21b112dedf99035a9b243fd7">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_final()
+: <a class="el" href="marginal_8sql__in.html#ad80d7df29226a2529f9f80607f2a983e">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#a82ac74fd7aec70b7596bc2b4776742fa">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a7b8a7cf298ea51fc16de975e2503615a">marginal.sql_in</a>
+</li>
+<li>mlogregr_predict()
+: <a class="el" href="multilogistic_8sql__in.html#a0fdfed54d63cefe260a0b74b9c7bbad5">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_robust_step_final()
+: <a class="el" href="robust_8sql__in.html#a6bfc968b23887a0a8ddd69a5c3350ba9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#a123149abbf366743e04ecb6fc7f8b9d9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_transition()
+: <a class="el" href="robust_8sql__in.html#a3928ec1e3935e8dc4dbcb9e705805660">robust.sql_in</a>
+</li>
+<li>mlogregr_train()
+: <a class="el" href="multilogistic_8sql__in.html#ab8b5a7eb69a945435cba5a068576f2e4">multilogistic.sql_in</a>
+</li>
+<li>mode()
+: <a class="el" href="utilities_8sql__in.html#ac25ef90f8a8e2f9b2244a57d92086c91">utilities.sql_in</a>
+</li>
+<li>mse_error()
+: <a class="el" href="cross__validation_8sql__in.html#a2cd71e756f90b0c4fa46f508a1ca261e">cross_validation.sql_in</a>
+</li>
+<li>mw_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac18e666088117997da2d22236e982f5e">hypothesis_tests.sql_in</a>
+</li>
+<li>mw_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4e324c82b069ebf7b498012aa83931c5">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_n.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_n.html b/docs/latest/globals_n.html
new file mode 100644
index 0000000..b432b2a
--- /dev/null
+++ b/docs/latest/globals_n.html
@@ -0,0 +1,202 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_n.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
+<li>negative_binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#ad0a7e4474f828869fb90e62f8e6f04d7">prob.sql_in</a>
+</li>
+<li>negative_binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#ab9cbc30424eba30f2df2a32a7e45f138">prob.sql_in</a>
+</li>
+<li>negative_binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#ad9e541de8b41da2e7b7434f862db4845">prob.sql_in</a>
+</li>
+<li>non_central_beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a1361569bd86e41f796c70f8cb277010e">prob.sql_in</a>
+</li>
+<li>non_central_beta_pdf()
+: <a class="el" href="prob_8sql__in.html#ad4a12c083054f0e2d316ae76c9aaeef7">prob.sql_in</a>
+</li>
+<li>non_central_beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a3073b409eaee3faa6d43df014662c279">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#ab4b7d2cf10bb031328dcc34c6ff494ad">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#aa7a563183224593d1e0d623a3c5489d8">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad694e29187b629ae683ef1235d2b9270">prob.sql_in</a>
+</li>
+<li>non_central_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a00051df630007b530ce86b4ab44a0434">prob.sql_in</a>
+</li>
+<li>non_central_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a3d94edcf90fca1fa52671293a9ea9c2f">prob.sql_in</a>
+</li>
+<li>non_central_f_quantile()
+: <a class="el" href="prob_8sql__in.html#a92b2a978db480a6c78cfb708107ecb92">prob.sql_in</a>
+</li>
+<li>non_central_t_cdf()
+: <a class="el" href="prob_8sql__in.html#afaf4374d2720b230a54713e21ecb1955">prob.sql_in</a>
+</li>
+<li>non_central_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a4799e3bb68a496d9bc1ef1ea85265409">prob.sql_in</a>
+</li>
+<li>non_central_t_quantile()
+: <a class="el" href="prob_8sql__in.html#af50865aba2ece2e23b2af461a02f7d12">prob.sql_in</a>
+</li>
+<li>noop()
+: <a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0">utilities.sql_in</a>
+</li>
+<li>norm1()
+: <a class="el" href="linalg_8sql__in.html#a300300fe4b8576ba0b97b95d8dea3057">linalg.sql_in</a>
+</li>
+<li>norm2()
+: <a class="el" href="linalg_8sql__in.html#a50fdfe30cc0edc6888a909dbb4b4c239">linalg.sql_in</a>
+</li>
+<li>normal_cdf()
+: <a class="el" href="prob_8sql__in.html#a370e31a46781ed8832b31625a683d053">prob.sql_in</a>
+</li>
+<li>normal_pdf()
+: <a class="el" href="prob_8sql__in.html#a63f555f36385d86e229cdca223e39567">prob.sql_in</a>
+</li>
+<li>normal_quantile()
+: <a class="el" href="prob_8sql__in.html#a53d56b672fe4cd1277cb5eac5de5118f">prob.sql_in</a>
+</li>
+<li>normalize()
+: <a class="el" href="svec__util_8sql__in.html#aace16abea7c63f136520fde6c28f1f28">svec_util.sql_in</a>
+</li>
+<li>normalized_avg()
+: <a class="el" href="linalg_8sql__in.html#a0b04663ca206f03e66aed5ea2b4cc461">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#a1c22ee0672d6c550f5bb966f78ce738a">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#ad5863fed5eb4813ba0044abc268d03ce">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_o.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_o.html b/docs/latest/globals_o.html
new file mode 100644
index 0000000..20bd5c7
--- /dev/null
+++ b/docs/latest/globals_o.html
@@ -0,0 +1,139 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_o.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
+<li>one_way_anova()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd5c80afd954294de268030ee73e88cc">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5ac28bec7ff670a7da8b9eae4a8ed4cb">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ad6c05d07183c961961f171b0a925ad93">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#aa694f4ce95280210a3887773bb3f417b">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_p.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_p.html b/docs/latest/globals_p.html
new file mode 100644
index 0000000..15027c9
--- /dev/null
+++ b/docs/latest/globals_p.html
@@ -0,0 +1,169 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_p.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
+<li>pareto_cdf()
+: <a class="el" href="prob_8sql__in.html#aa1a42ebd68f20f65bc1784b427721b5d">prob.sql_in</a>
+</li>
+<li>pareto_pdf()
+: <a class="el" href="prob_8sql__in.html#a22c56a6e48bc442435b13afac2a1eb37">prob.sql_in</a>
+</li>
+<li>pareto_quantile()
+: <a class="el" href="prob_8sql__in.html#a77779e2b5fa951189ccba6806c503c4d">prob.sql_in</a>
+</li>
+<li>pca_project()
+: <a class="el" href="pca__project_8sql__in.html#a30c86aed79810df5595451481ba74265">pca_project.sql_in</a>
+</li>
+<li>pca_sparse_project()
+: <a class="el" href="pca__project_8sql__in.html#a2ffda9dc6c376df9f31e71b41b7259b3">pca_project.sql_in</a>
+</li>
+<li>pca_sparse_train()
+: <a class="el" href="pca_8sql__in.html#ace5236fb2133c297eca85ab788e6dfc2">pca.sql_in</a>
+</li>
+<li>pca_train()
+: <a class="el" href="pca_8sql__in.html#a2d1cfcf6a7c8a9c69808ec8927b900d1">pca.sql_in</a>
+</li>
+<li>pmml()
+: <a class="el" href="table__to__pmml_8sql__in.html#a3aa36483b5dd5af46f9b564397822817">table_to_pmml.sql_in</a>
+</li>
+<li>poisson_cdf()
+: <a class="el" href="prob_8sql__in.html#ae0b4313d9fe730d6efb3f7c44206f345">prob.sql_in</a>
+</li>
+<li>poisson_pmf()
+: <a class="el" href="prob_8sql__in.html#a82f1edc27261021c73cd080ff2677a9f">prob.sql_in</a>
+</li>
+<li>poisson_quantile()
+: <a class="el" href="prob_8sql__in.html#a032d26db18b2ee1034085f5521939c61">prob.sql_in</a>
+</li>
+<li>poisson_random()
+: <a class="el" href="sample_8sql__in.html#a2051a3f21bc06e3c6e3efcd7492b71c4">sample.sql_in</a>
+</li>
+<li>profile()
+: <a class="el" href="profile_8sql__in.html#a22f0136bb3d7740262b13030a2925a80">profile.sql_in</a>
+</li>
+<li>profile_full()
+: <a class="el" href="profile_8sql__in.html#a879e445d74412b093d411ed9a7180a1e">profile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_q.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_q.html b/docs/latest/globals_q.html
new file mode 100644
index 0000000..3e1f80f
--- /dev/null
+++ b/docs/latest/globals_q.html
@@ -0,0 +1,133 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_q.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_q"></a>- q -</h3><ul>
+<li>quantile()
+: <a class="el" href="quantile_8sql__in.html#a6955e44659a0817803ab7a11a9a7e8a5">quantile.sql_in</a>
+</li>
+<li>quantile_big()
+: <a class="el" href="quantile_8sql__in.html#a154435ddf6200110013dc05b1a6207fe">quantile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_r.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_r.html b/docs/latest/globals_r.html
new file mode 100644
index 0000000..dd19146
--- /dev/null
+++ b/docs/latest/globals_r.html
@@ -0,0 +1,212 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_r.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
+<li>rayleigh_cdf()
+: <a class="el" href="prob_8sql__in.html#aab0ddb8a5348cfa387d777043a3cb6d0">prob.sql_in</a>
+</li>
+<li>rayleigh_pdf()
+: <a class="el" href="prob_8sql__in.html#a798541736d9255bdd5c0bd94924d47bc">prob.sql_in</a>
+</li>
+<li>rayleigh_quantile()
+: <a class="el" href="prob_8sql__in.html#acd6757acab1683c735e2b57901494336">prob.sql_in</a>
+</li>
+<li>rb_coxph_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a504aa4de0f53044a065e6b1e8a66a285">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_coxph_step_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a0bd8046f4e0956b8dc503656d5127467">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_coxph_strata_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9826d93b250c1fb6e06d412181545754">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ad8fc6f9cca64b26bc7a12f15abfc6234">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9473298f0225d2ad1f6297f360a639d4">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a4bf2a610417ee174074b2191fb921256">robust_variance_coxph.sql_in</a>
+</li>
+<li>relative_error()
+: <a class="el" href="utilities_8sql__in.html#a4d49e00b4dd6059f61ffb9fdc0a696f7">utilities.sql_in</a>
+</li>
+<li>rf_classify()
+: <a class="el" href="rf_8sql__in.html#a116584e8a5194a4277e964900ee5383d">rf.sql_in</a>
+</li>
+<li>rf_clean()
+: <a class="el" href="rf_8sql__in.html#af33b77b75df225ee65a8acf18705256e">rf.sql_in</a>
+</li>
+<li>rf_display()
+: <a class="el" href="rf_8sql__in.html#a60b3f093409a14331b7601522ac0ac42">rf.sql_in</a>
+</li>
+<li>rf_score()
+: <a class="el" href="rf_8sql__in.html#ac6745c3d4cae3443b217f3ba66d11ac4">rf.sql_in</a>
+</li>
+<li>rf_train()
+: <a class="el" href="rf_8sql__in.html#a3cf718282802b63dc0a0d19b34f6829b">rf.sql_in</a>
+</li>
+<li>robust_linregr()
+: <a class="el" href="robust_8sql__in.html#ade1af85b04294ee404a638aa2bfbf051">robust.sql_in</a>
+</li>
+<li>robust_linregr_final()
+: <a class="el" href="robust_8sql__in.html#a458741a15dc6c2ca627f0d1c1d19b7f8">robust.sql_in</a>
+</li>
+<li>robust_linregr_merge_states()
+: <a class="el" href="robust_8sql__in.html#ad85d9925cbab4b59acd798a7b32671ff">robust.sql_in</a>
+</li>
+<li>robust_linregr_transition()
+: <a class="el" href="robust_8sql__in.html#a3f978229f9a2e229276278985d76c438">robust.sql_in</a>
+</li>
+<li>robust_logregr()
+: <a class="el" href="robust_8sql__in.html#a6a9290aafa048bcb7cdc13410d5814aa">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_final()
+: <a class="el" href="robust_8sql__in.html#af8efdd02ad3155411507bf786095a562">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#aa715a6f6885b56d919a243ec4ff07fde">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_transition()
+: <a class="el" href="robust_8sql__in.html#a840ddaa14013c4445b7a0d55e9489d8d">robust.sql_in</a>
+</li>
+<li>robust_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a209021ac52461f029353cec7fb900cd4">robust.sql_in</a>
+</li>
+<li>robust_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">clustered_variance_coxph.sql_in</a>
+, <a class="el" href="robust__variance__coxph_8sql__in.html#a9d08b7ab1efa1f58543214a311344741">robust_variance_coxph.sql_in</a>
+</li>
+<li>robust_variance_linregr()
+: <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust.sql_in</a>
+</li>
+<li>robust_variance_logregr()
+: <a class="el" href="robust_8sql__in.html#a815cadfeb54447993db9ce2bd2aa709e">robust.sql_in</a>
+</li>
+<li>robust_variance_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a93031a31e4c93d3caa22d963927e75c8">robust.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_s.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_s.html b/docs/latest/globals_s.html
new file mode 100644
index 0000000..8270dfc
--- /dev/null
+++ b/docs/latest/globals_s.html
@@ -0,0 +1,460 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_s.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
+<li>sparse_direct_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8d22e4088dedd57a931b465a73a0357a">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a37d42899e8ec22d8642aa2c943689d19">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a81014be318749e53d8c3fd58eece9600">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8175311c8e181a3ac73d9cc87814bbb0">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#ace39ff8e846754e3e0709bfbbbee9a60">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5ff2f1afd945580fc9cb9bedba84695b">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3daa8daf05cda974b4137ce07c694e2e">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5463eaef5dc5e4040acd56a1035ecc91">sparse_linear_systems.sql_in</a>
+</li>
+<li>squared_dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#a00a08e69f27524f2096032214e15b668">linalg.sql_in</a>
+</li>
+<li>students_t_cdf()
+: <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">prob.sql_in</a>
+</li>
+<li>students_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a8815c21670fff9d31946553a84b845b1">prob.sql_in</a>
+</li>
+<li>students_t_quantile()
+: <a class="el" href="prob_8sql__in.html#a7d64add02af21a95d73502b2dd466a75">prob.sql_in</a>
+</li>
+<li>sum()
+: <a class="el" href="array__ops_8sql__in.html#a26e8508a2bae10a6574cec697a270eea">array_ops.sql_in</a>
+</li>
+<li>summary()
+: <a class="el" href="summary_8sql__in.html#ae39edc1569d25b212ca5ebc06703c6d6">summary.sql_in</a>
+</li>
+<li>svd()
+: <a class="el" href="svd_8sql__in.html#a8dbab74f5bde863a63431a6ec6f66a61">svd.sql_in</a>
+</li>
+<li>svd_block()
+: <a class="el" href="svd_8sql__in.html#a96fb5620eed9c936b5bbfe24d7e37ad6">svd.sql_in</a>
+</li>
+<li>svd_sparse()
+: <a class="el" href="svd_8sql__in.html#a511f83e3eba19f6f41f8e3f7122ddb3a">svd.sql_in</a>
+</li>
+<li>svd_sparse_native()
+: <a class="el" href="svd_8sql__in.html#a0225b28fd4b71cf6e7072c034f3296c6">svd.sql_in</a>
+</li>
+<li>svdmf_run()
+: <a class="el" href="svdmf_8sql__in.html#a6cff34415cca23aa0a826cc08a6283f5">svdmf.sql_in</a>
+</li>
+<li>svec_append()
+: <a class="el" href="svec__util_8sql__in.html#a3950b17b103ceeddca520a0f0d097ddc">svec_util.sql_in</a>
+</li>
+<li>svec_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a1f1025124d5423cc1505991414f17582">svec.sql_in</a>
+</li>
+<li>svec_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af42e4b15879949032a68139c59443aac">svec.sql_in</a>
+</li>
+<li>svec_cast_float8arr()
+: <a class="el" href="svec_8sql__in.html#a96025250e64f049a053c5696b3b96e02">svec.sql_in</a>
+</li>
+<li>svec_cast_int2()
+: <a class="el" href="svec_8sql__in.html#af69458bf9b2906ca471d027966b13e6f">svec.sql_in</a>
+</li>
+<li>svec_cast_int4()
+: <a class="el" href="svec_8sql__in.html#abc01ac6b36ea9d689d338fd72abbd540">svec.sql_in</a>
+</li>
+<li>svec_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a9ea167c01fb2999dc5aaf5e8e21f5e8f">svec.sql_in</a>
+</li>
+<li>svec_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a50de0278ff7ceeff33a2ea4ca5fdf1a0">svec.sql_in</a>
+</li>
+<li>svec_cast_positions_float8arr()
+: <a class="el" href="svec_8sql__in.html#a0ee423729fbca5abd46c86fa81d51f23">svec.sql_in</a>
+</li>
+<li>svec_change()
+: <a class="el" href="svec__util_8sql__in.html#a59407764a1cbf1937da39cf39a2f447c">svec_util.sql_in</a>
+</li>
+<li>svec_cmp()
+: <a class="el" href="svec_8sql__in.html#a813762d8bd2a7fda083880e5bd8c355b">svec.sql_in</a>
+</li>
+<li>svec_concat()
+: <a class="el" href="svec_8sql__in.html#a24f184bef083fd9068c28fc94dad8054">svec.sql_in</a>
+</li>
+<li>svec_concat_replicate()
+: <a class="el" href="svec_8sql__in.html#a34cb5f83e0c010aacc719b8b078bfeb9">svec.sql_in</a>
+</li>
+<li>svec_contains()
+: <a class="el" href="svec__util_8sql__in.html#afdef1c6bf65ffc0e838cf10f33ae5557">svec_util.sql_in</a>
+</li>
+<li>svec_count()
+: <a class="el" href="svec__util_8sql__in.html#a6a21e25d3f50053c888a0bd126f831f0">svec_util.sql_in</a>
+</li>
+<li>svec_count_nonzero()
+: <a class="el" href="svec__util_8sql__in.html#abebdcbe45de346aff874db008e842e65">svec_util.sql_in</a>
+</li>
+<li>svec_dimension()
+: <a class="el" href="svec__util_8sql__in.html#ab8d1acbe4e8551755fef217b506bed8a">svec_util.sql_in</a>
+</li>
+<li>svec_div()
+: <a class="el" href="svec_8sql__in.html#a78a38512c860085e31f6153b8118cbe9">svec.sql_in</a>
+</li>
+<li>svec_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a211cf37584f54087dabc1fac14ed12dd">svec.sql_in</a>
+</li>
+<li>svec_dmax()
+: <a class="el" href="svec__util_8sql__in.html#aa05abd8cb32253c07c0f2224861256a5">svec_util.sql_in</a>
+</li>
+<li>svec_dmin()
+: <a class="el" href="svec__util_8sql__in.html#ad1011a13839994adbb1eb3e643c9074d">svec_util.sql_in</a>
+</li>
+<li>svec_dot()
+: <a class="el" href="svec_8sql__in.html#a47b111663b39ec8646891edec8ab3925">svec.sql_in</a>
+</li>
+<li>svec_elsum()
+: <a class="el" href="svec__util_8sql__in.html#a00a7b3260b9fde9b55061e6bf58a028a">svec_util.sql_in</a>
+</li>
+<li>svec_eq()
+: <a class="el" href="svec_8sql__in.html#adceeff7e2f2e43b3e17219bca5d4fea0">svec.sql_in</a>
+</li>
+<li>svec_eq_non_zero()
+: <a class="el" href="svec__util_8sql__in.html#ab3a1cccd709379973f100e5eb35559d2">svec_util.sql_in</a>
+</li>
+<li>svec_from_string()
+: <a class="el" href="svec__util_8sql__in.html#a1f5e8911574cf30cc979478040bd3262">svec_util.sql_in</a>
+</li>
+<li>svec_ge()
+: <a class="el" href="svec_8sql__in.html#ad08b0cadbcf50ff4685228f917cafd1f">svec.sql_in</a>
+</li>
+<li>svec_gt()
+: <a class="el" href="svec_8sql__in.html#a3e5d57b7a4473904e4d053884468e540">svec.sql_in</a>
+</li>
+<li>svec_hash()
+: <a class="el" href="svec__util_8sql__in.html#a0bfa8f8d083282bc9e8caf6f03bb5251">svec_util.sql_in</a>
+</li>
+<li>svec_in()
+: <a class="el" href="svec_8sql__in.html#abe896192db26c02d5c517a5ccf2144f1">svec.sql_in</a>
+</li>
+<li>svec_l1norm()
+: <a class="el" href="svec__util_8sql__in.html#ae60f7aaa5710c765d08b6c3906e0c9f0">svec_util.sql_in</a>
+</li>
+<li>svec_l2_cmp()
+: <a class="el" href="svec_8sql__in.html#a8f72c8cf6d39f08d939f23cf1338a3c6">svec.sql_in</a>
+</li>
+<li>svec_l2_eq()
+: <a class="el" href="svec_8sql__in.html#a9268d26c54c4b2905eb7938df95af8c0">svec.sql_in</a>
+</li>
+<li>svec_l2_ge()
+: <a class="el" href="svec_8sql__in.html#ac22b72fbae2a8f2b35b1c571d9f54dad">svec.sql_in</a>
+</li>
+<li>svec_l2_gt()
+: <a class="el" href="svec_8sql__in.html#ab2414d0d78286884cd199cf44c1d17b9">svec.sql_in</a>
+</li>
+<li>svec_l2_le()
+: <a class="el" href="svec_8sql__in.html#a4045437bce8b9a9bdb0eb3115c97ca99">svec.sql_in</a>
+</li>
+<li>svec_l2_lt()
+: <a class="el" href="svec_8sql__in.html#ab53b85cb3412c9172bf0ee04f6856c69">svec.sql_in</a>
+</li>
+<li>svec_l2_ne()
+: <a class="el" href="svec_8sql__in.html#af35c08aadf164fa3bbc8678765bca643">svec.sql_in</a>
+</li>
+<li>svec_l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a475f0f3e4602e8278b582e099fce2487">svec_util.sql_in</a>
+</li>
+<li>svec_lapply()
+: <a class="el" href="svec__util_8sql__in.html#a0d94c44dde95a00e3d802dee6d7c01eb">svec_util.sql_in</a>
+</li>
+<li>svec_le()
+: <a class="el" href="svec_8sql__in.html#aa6f5715df728db0783b5dcb8db82a2a2">svec.sql_in</a>
+</li>
+<li>svec_log()
+: <a class="el" href="svec__util_8sql__in.html#aab641f9db3deed5bbfa7e050c531e9c1">svec_util.sql_in</a>
+</li>
+<li>svec_lt()
+: <a class="el" href="svec_8sql__in.html#ad7f05a6ce6efc51517bab6ccdb79ee23">svec.sql_in</a>
+</li>
+<li>svec_mean_final()
+: <a class="el" href="svec__util_8sql__in.html#ac3fc45511b50b5d054bf7bbeb952b98d">svec_util.sql_in</a>
+</li>
+<li>svec_mean_prefunc()
+: <a class="el" href="svec__util_8sql__in.html#ab161647a1498c85762779966790eab71">svec_util.sql_in</a>
+</li>
+<li>svec_mean_transition()
+: <a class="el" href="svec__util_8sql__in.html#afcf2f75cd00ab0ba269caf79dcfcc62c">svec_util.sql_in</a>
+</li>
+<li>svec_median()
+: <a class="el" href="svec__util_8sql__in.html#a2284a118fe03465203ecea8c34acefa5">svec_util.sql_in</a>
+</li>
+<li>svec_median_inmemory()
+: <a class="el" href="svec__util_8sql__in.html#a93400e39ecce2f11b9ceb08403420a43">svec_util.sql_in</a>
+</li>
+<li>svec_minus()
+: <a class="el" href="svec_8sql__in.html#a170890a165bf662d343339ffc2a187cf">svec.sql_in</a>
+</li>
+<li>svec_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#adf39975fbbd8a39629569c622db33ab9">svec.sql_in</a>
+</li>
+<li>svec_mult()
+: <a class="el" href="svec_8sql__in.html#a27f0629942dba2e395b005c728d53ac3">svec.sql_in</a>
+</li>
+<li>svec_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6e6916baaf0a7ae0079424ce0565c8b4">svec.sql_in</a>
+</li>
+<li>svec_ne()
+: <a class="el" href="svec_8sql__in.html#a80d9692f67e7cb482c4bda6dbaacd82b">svec.sql_in</a>
+</li>
+<li>svec_nonbase_positions()
+: <a class="el" href="svec__util_8sql__in.html#a5bc053a080fb38601ad91045dc3ffe74">svec_util.sql_in</a>
+</li>
+<li>svec_nonbase_values()
+: <a class="el" href="svec__util_8sql__in.html#a076dcbc0a278bb3b5161b0db2ad8fa44">svec_util.sql_in</a>
+</li>
+<li>svec_out()
+: <a class="el" href="svec_8sql__in.html#a3db4af36acd318030ae57d2bfd19387a">svec.sql_in</a>
+</li>
+<li>svec_pivot()
+: <a class="el" href="svec__util_8sql__in.html#a929bde3bc91b67300d9d370b6a1495f1">svec_util.sql_in</a>
+</li>
+<li>svec_plus()
+: <a class="el" href="svec_8sql__in.html#a612d8920c61e6fdf18dd4e3e9c69c979">svec.sql_in</a>
+</li>
+<li>svec_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#aaeb54c952836c83c7f21a1c62a77955f">svec.sql_in</a>
+</li>
+<li>svec_pow()
+: <a class="el" href="svec_8sql__in.html#ab5166565f3482768db33ee677acc3d96">svec.sql_in</a>
+</li>
+<li>svec_proj()
+: <a class="el" href="svec__util_8sql__in.html#a8787222aec691f94d9808d1369aa401c">svec_util.sql_in</a>
+</li>
+<li>svec_recv()
+: <a class="el" href="svec_8sql__in.html#a585ea8bcb1d0647ef1295187b87405b2">svec.sql_in</a>
+</li>
+<li>svec_return_array()
+: <a class="el" href="svec_8sql__in.html#a7decda276d7ff1e9340dd44e0e7419c0">svec.sql_in</a>
+</li>
+<li>svec_reverse()
+: <a class="el" href="svec__util_8sql__in.html#a4a221435ca91b6fa45114e689ee96010">svec_util.sql_in</a>
+</li>
+<li>svec_send()
+: <a class="el" href="svec_8sql__in.html#a617557435170136b2781070c0a268b83">svec.sql_in</a>
+</li>
+<li>svec_sfv()
+: <a class="el" href="svec__util_8sql__in.html#a375acd521ed9cb05f63b3696dcc10bf4">svec_util.sql_in</a>
+</li>
+<li>svec_sort()
+: <a class="el" href="svec__util_8sql__in.html#a91c23a7ce58932f48f5ab3579ea0309e">svec_util.sql_in</a>
+</li>
+<li>svec_subvec()
+: <a class="el" href="svec__util_8sql__in.html#a5cb3446de5fc117befe88ccb1ebb0e4e">svec_util.sql_in</a>
+</li>
+<li>svec_sum()
+: <a class="el" href="svec__util_8sql__in.html#af1ce6cdd7244ac0832d15c65b7ccc782">svec_util.sql_in</a>
+</li>
+<li>svec_to_string()
+: <a class="el" href="svec__util_8sql__in.html#a1de746ebd23530e72ba4f031ac41d155">svec_util.sql_in</a>
+</li>
+<li>svec_unnest()
+: <a class="el" href="svec__util_8sql__in.html#a87b45a983da4066fa7f35b17ccecf699">svec_util.sql_in</a>
+</li>
+<li>svm_classification()
+: <a class="el" href="online__sv_8sql__in.html#ad90b6bf3b807f22d37b0e2b1893262f0">online_sv.sql_in</a>
+</li>
+<li>svm_cls_agg()
+: <a class="el" href="online__sv_8sql__in.html#a4fd5a4571d959a892ab28dab1fea92f3">online_sv.sql_in</a>
+</li>
+<li>svm_cls_update()
+: <a class="el" href="online__sv_8sql__in.html#a837bd8cfd5f3200e985febf0ff7ec013">online_sv.sql_in</a>
+</li>
+<li>svm_data_normalization()
+: <a class="el" href="online__sv_8sql__in.html#a78bb07d8f4aee80c3bbc8e8176e512be">online_sv.sql_in</a>
+</li>
+<li>svm_dot()
+: <a class="el" href="online__sv_8sql__in.html#ab53cac5790dafd7230359e08c72af4f1">online_sv.sql_in</a>
+</li>
+<li>svm_drop_model()
+: <a class="el" href="online__sv_8sql__in.html#ab54d33f13c0e00faa358e3e3f17c10fb">online_sv.sql_in</a>
+</li>
+<li>svm_gaussian()
+: <a class="el" href="online__sv_8sql__in.html#a9f2a96e1a241ecc66386a78b110777d3">online_sv.sql_in</a>
+</li>
+<li>svm_generate_cls_data()
+: <a class="el" href="online__sv_8sql__in.html#a1cf6d0632772caf6643212a4698d2507">online_sv.sql_in</a>
+</li>
+<li>svm_generate_nd_data()
+: <a class="el" href="online__sv_8sql__in.html#a8be25cfa6134cc12bbc6d31c04712c23">online_sv.sql_in</a>
+</li>
+<li>svm_generate_reg_data()
+: <a class="el" href="online__sv_8sql__in.html#adc9fe3f9b4a04dff13004edbaab8240f">online_sv.sql_in</a>
+</li>
+<li>svm_nd_agg()
+: <a class="el" href="online__sv_8sql__in.html#a9e0abf5bf91bd0833bf80b9d9a5ae8bf">online_sv.sql_in</a>
+</li>
+<li>svm_nd_update()
+: <a class="el" href="online__sv_8sql__in.html#ae6aef7b5654b6da487d0178adf443ee9">online_sv.sql_in</a>
+</li>
+<li>svm_novelty_detection()
+: <a class="el" href="online__sv_8sql__in.html#a75b773bcf4b5242c673d0e54128ed159">online_sv.sql_in</a>
+</li>
+<li>svm_polynomial()
+: <a class="el" href="online__sv_8sql__in.html#a1ac76fdf9623e0a4db47665f2a80be90">online_sv.sql_in</a>
+</li>
+<li>svm_predict()
+: <a class="el" href="online__sv_8sql__in.html#adc8c1c1d5e2deb20f5436790468b052f">online_sv.sql_in</a>
+</li>
+<li>svm_predict_batch()
+: <a class="el" href="online__sv_8sql__in.html#a91ac71354e9dec74e25339bf168c2e5b">online_sv.sql_in</a>
+</li>
+<li>svm_predict_sub()
+: <a class="el" href="online__sv_8sql__in.html#a5988c209ceb6d15b9139eef0c16d7b7d">online_sv.sql_in</a>
+</li>
+<li>svm_reg_agg()
+: <a class="el" href="online__sv_8sql__in.html#a225bdb74eaa687cef0ddbb35145318fc">online_sv.sql_in</a>
+</li>
+<li>svm_reg_update()
+: <a class="el" href="online__sv_8sql__in.html#a0eb68a44edff3fa53891a6a39de61e57">online_sv.sql_in</a>
+</li>
+<li>svm_regression()
+: <a class="el" href="online__sv_8sql__in.html#acaf1f4aa3eec5710de5c03e368a4b106">online_sv.sql_in</a>
+</li>
+<li>svm_store_model()
+: <a class="el" href="online__sv_8sql__in.html#ad2cd899daaa2036b475f71c28cc031ae">online_sv.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[48/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/bayes_8sql__in.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">bayes.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for naive Bayes.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a0671e6ac35a1967f41d2efb9e8417e1b"><td class="memItemLeft" align="right" valign="top">args_and_value_double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a0671e6ac35a1967f41d2efb9e8417e1b">argmax_transition</a> (args_and_value_double oldmax, integer newkey, float8 newvalue)</td></tr>
+<tr class="separator:a0671e6ac35a1967f41d2efb9e8417e1b"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ab150442ec577f4a0a3948176baf5ebd0"><td class="memItemLeft" align="right" valign="top">args_and_value_double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#ab150442ec577f4a0a3948176baf5ebd0">argmax_combine</a> (args_and_value_double max1, args_and_value_double max2)</td></tr>
+<tr class="separator:ab150442ec577f4a0a3948176baf5ebd0"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa7017323fd6fad40c9acbbee5f1fec7e"><td class="memItemLeft" align="right" valign="top">integer[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#aa7017323fd6fad40c9acbbee5f1fec7e">argmax_final</a> (args_and_value_double finalstate)</td></tr>
+<tr class="separator:aa7017323fd6fad40c9acbbee5f1fec7e"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad47c68a2b8aeedf66d509f0c32a9732d"><td class="memItemLeft" align="right" valign="top">aggregate integer[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#ad47c68a2b8aeedf66d509f0c32a9732d">argmax</a> (integer key, float8 value)</td></tr>
+<tr class="separator:ad47c68a2b8aeedf66d509f0c32a9732d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aeb4eae7843dd789cc38d5fc57f4ccfb2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2">create_nb_prepared_data_tables</a> (varchar trainingSource, varchar trainingClassColumn, varchar trainingAttrColumn, integer numAttrs, varchar featureProbsDestName, varchar classPriorsDestName)</td></tr>
+<tr class="memdesc:aeb4eae7843dd789cc38d5fc57f4ccfb2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Precompute all class priors and feature probabilities.  <a href="#aeb4eae7843dd789cc38d5fc57f4ccfb2">More...</a><br /></td></tr>
+<tr class="separator:aeb4eae7843dd789cc38d5fc57f4ccfb2"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a3ed5ddbc4cab1013db8604be58a6da3d"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a3ed5ddbc4cab1013db8604be58a6da3d">create_nb_prepared_data_tables</a> (varchar trainingSource, varchar trainingClassColumn, varchar trainingAttrColumn, varchar numericAttrsColumnIndices, integer numAttrs, varchar featureProbsDestName, varchar numericFeatureStatsDestName, varchar classPriorsDestName)</td></tr>
+<tr class="separator:a3ed5ddbc4cab1013db8604be58a6da3d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a798402280fc6db710957ae3ab58767e0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a798402280fc6db710957ae3ab58767e0">create_nb_classify_view</a> (varchar featureProbsSource, varchar classPriorsSource, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar destName)</td></tr>
+<tr class="memdesc:a798402280fc6db710957ae3ab58767e0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create a view with columns <code>(key, nb_classification)</code>  <a href="#a798402280fc6db710957ae3ab58767e0">More...</a><br /></td></tr>
+<tr class="separator:a798402280fc6db710957ae3ab58767e0"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a4e8e24120269be2b58f959ab5473b2cb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a4e8e24120269be2b58f959ab5473b2cb">create_nb_classify_view</a> (varchar featureProbsSource, varchar classPriorsSource, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar numericFeatureStatsSource, varchar destName)</td></tr>
+<tr class="separator:a4e8e24120269be2b58f959ab5473b2cb"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a6472f620099368cb8c334e0badb12133"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a76a03f1d7ff2b1891cb310668077b386"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a76a03f1d7ff2b1891cb310668077b386">create_nb_classify_view</a> (varchar trainingSource, varchar trainingClassColumn, varchar trainingAttrColumn, varchar numericAttrsColumnIndices, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar destName)</td></tr>
+<tr class="separator:a76a03f1d7ff2b1891cb310668077b386"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a163afffd0c845d325f060f74bcf02243"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a163afffd0c845d325f060f74bcf02243">create_nb_probs_view</a> (varchar featureProbsSource, varchar classPriorsSource, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar destName)</td></tr>
+<tr class="memdesc:a163afffd0c845d325f060f74bcf02243"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create view with columns <code>(key, class, nb_prob)</code>  <a href="#a163afffd0c845d325f060f74bcf02243">More...</a><br /></td></tr>
+<tr class="separator:a163afffd0c845d325f060f74bcf02243"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a2e02b0ebb0b4c2ca77b1748662bae78f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a2e02b0ebb0b4c2ca77b1748662bae78f">create_nb_probs_view</a> (varchar featureProbsSource, varchar classPriorsSource, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar numericFeatureStatsSource, varchar destName)</td></tr>
+<tr class="separator:a2e02b0ebb0b4c2ca77b1748662bae78f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aba5d695641530e55b25c8007b77142b9"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#aba5d695641530e55b25c8007b77142b9">create_nb_probs_view</a> (varchar trainingSource, varchar trainingClassColumn, varchar trainingAttrColumn, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar destName)</td></tr>
+<tr class="separator:aba5d695641530e55b25c8007b77142b9"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a6d1186aa04f4d4e3ac44035467818d3c"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="bayes_8sql__in.html#a6d1186aa04f4d4e3ac44035467818d3c">create_nb_probs_view</a> (varchar trainingSource, varchar trainingClassColumn, varchar trainingAttrColumn, varchar numericAttrsColumnIndices, varchar classifySource, varchar classifyKeyColumn, varchar classifyAttrColumn, integer numAttrs, varchar destName)</td></tr>
+<tr class="separator:a6d1186aa04f4d4e3ac44035467818d3c"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>January 2011</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to Naive Bayes Classification, see the module description <a class="el" href="group__grp__bayes.html">Naive Bayes Classification</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="ad47c68a2b8aeedf66d509f0c32a9732d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate integer [] argmax </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>key</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab150442ec577f4a0a3948176baf5ebd0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">args_and_value_double argmax_combine </td>
+          <td>(</td>
+          <td class="paramtype">args_and_value_double&#160;</td>
+          <td class="paramname"><em>max1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">args_and_value_double&#160;</td>
+          <td class="paramname"><em>max2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa7017323fd6fad40c9acbbee5f1fec7e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer [] argmax_final </td>
+          <td>(</td>
+          <td class="paramtype">args_and_value_double&#160;</td>
+          <td class="paramname"><em>finalstate</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0671e6ac35a1967f41d2efb9e8417e1b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">args_and_value_double argmax_transition </td>
+          <td>(</td>
+          <td class="paramtype">args_and_value_double&#160;</td>
+          <td class="paramname"><em>oldmax</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>newkey</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>newvalue</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a798402280fc6db710957ae3ab58767e0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_classify_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>featureProbsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classPriorsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>The created relation will be</p>
+<p><code>{TABLE|VIEW} <em>destName</em> (key, nb_classification)</code></p>
+<p>where <code>nb_classification</code> is an array containing the most likely class(es) of the record in <em>classifySource</em> identified by <code>key</code>.</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">featureProbsSource</td><td>Name of table with precomputed feature probabilities, as created with <a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2" title="Precompute all class priors and feature probabilities. ">create_nb_prepared_data_tables()</a> </td></tr>
+    <tr><td class="paramname">classPriorsSource</td><td>Name of table with precomputed class priors, as created with <a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2" title="Precompute all class priors and feature probabilities. ">create_nb_prepared_data_tables()</a> </td></tr>
+    <tr><td class="paramname">classifySource</td><td>Name of the relation that contains data to be classified </td></tr>
+    <tr><td class="paramname">classifyKeyColumn</td><td>Name of column in <em>classifySource</em> that can serve as unique identifier (the key of the source relation) </td></tr>
+    <tr><td class="paramname">classifyAttrColumn</td><td>Name of attributes-array column in <em>classifySource</em> </td></tr>
+    <tr><td class="paramname">numAttrs</td><td>Number of attributes to use for classification </td></tr>
+    <tr><td class="paramname">destName</td><td>Name of the view to create</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section note"><dt>Note</dt><dd><code>create_nb_classify_view</code> can be called in an ad-hoc fashion. See <a class="el" href="group__grp__bayes.html">Naive Bayes Classification</a> for instructions.</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ol type="1">
+<li>Create Naive Bayes classifications view: <pre>SELECT <a class="el" href="bayes_8sql__in.html#a76a03f1d7ff2b1891cb310668077b386">create_nb_classify_view</a>(
+    '<em>featureProbsName</em>', '<em>classPriorsName</em>',
+    '<em>classifySource</em>', '<em>classifyKeyColumn</em>', '<em>classifyAttrColumn</em>',
+    <em>numAttrs</em>, '<em>destName</em>'
+);</pre></li>
+<li>Show Naive Bayes classifications: <pre>SELECT * FROM <em>destName</em>;</pre> </li>
+</ol>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a4e8e24120269be2b58f959ab5473b2cb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_classify_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>featureProbsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classPriorsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>numericFeatureStatsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6472f620099368cb8c334e0badb12133"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_classify_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingClassColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a76a03f1d7ff2b1891cb310668077b386"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_classify_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingClassColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>numericAttrsColumnIndices</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aeb4eae7843dd789cc38d5fc57f4ccfb2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_prepared_data_tables </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingClassColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>featureProbsDestName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classPriorsDestName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Feature probabilities are stored in a table of format </p><pre>TABLE <em>featureProbsDestName</em> (
+    class INTEGER,
+    attr INTEGER,
+    value INTEGER,
+    cnt INTEGER,
+    attr_cnt INTEGER
+)</pre><p>Class priors are stored in a table of format </p><pre>TABLE <em>classPriorsDestName</em> (
+    class INTEGER,
+    class_cnt INTEGER,
+    all_cnt INTEGER
+)</pre><dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">trainingSource</td><td>Name of relation containing the training data </td></tr>
+    <tr><td class="paramname">trainingClassColumn</td><td>Name of class column in training data </td></tr>
+    <tr><td class="paramname">trainingAttrColumn</td><td>Name of attributes-array column in training data </td></tr>
+    <tr><td class="paramname">numAttrs</td><td>Number of attributes to use for classification </td></tr>
+    <tr><td class="paramname">featureProbsDestName</td><td>Name of feature-probabilities table to create </td></tr>
+    <tr><td class="paramname">classPriorsDestName</td><td>Name of class-priors table to create</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section user"><dt>Usage</dt><dd>Precompute feature probabilities and class priors: <pre>SELECT <a class="el" href="bayes_8sql__in.html#a3ed5ddbc4cab1013db8604be58a6da3d">create_nb_prepared_data_tables</a>(
+    '<em>trainingSource</em>', '<em>trainingClassColumn</em>', '<em>trainingAttrColumn</em>',
+    <em>numAttrs</em>, '<em>featureProbsName</em>', '<em>classPriorsName</em>'
+);</pre> </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a3ed5ddbc4cab1013db8604be58a6da3d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_prepared_data_tables </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingClassColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>numericAttrsColumnIndices</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>featureProbsDestName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>numericFeatureStatsDestName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classPriorsDestName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a163afffd0c845d325f060f74bcf02243"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_probs_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>featureProbsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classPriorsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>The created view will be of the following form:</p>
+<pre>VIEW <em>destName</em> (
+    key ANYTYPE,
+    class INTEGER,
+    nb_prob FLOAT8
+)</pre><p>where <code>nb_prob</code> is the Naive-Bayes probability that <code>class</code> is the true class of the record in <em>classifySource</em> identified by <code>key</code>.</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">featureProbsSource</td><td>Name of table with precomputed feature probabilities, as created with <a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2" title="Precompute all class priors and feature probabilities. ">create_nb_prepared_data_tables()</a> </td></tr>
+    <tr><td class="paramname">classPriorsSource</td><td>Name of table with precomputed class priors, as created with <a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2" title="Precompute all class priors and feature probabilities. ">create_nb_prepared_data_tables()</a> </td></tr>
+    <tr><td class="paramname">classifySource</td><td>Name of the relation that contains data to be classified </td></tr>
+    <tr><td class="paramname">classifyKeyColumn</td><td>Name of column in <em>classifySource</em> that can serve as unique identifier (the key of the source relation) </td></tr>
+    <tr><td class="paramname">classifyAttrColumn</td><td>Name of attributes-array column in <em>classifySource</em> </td></tr>
+    <tr><td class="paramname">numAttrs</td><td>Number of attributes to use for classification </td></tr>
+    <tr><td class="paramname">destName</td><td>Name of the view to create</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section note"><dt>Note</dt><dd><code>create_nb_probs_view</code> can be called in an ad-hoc fashion. See <a class="el" href="group__grp__bayes.html">Naive Bayes Classification</a> for instructions.</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ol type="1">
+<li>Create Naive Bayes probabilities view: <pre>SELECT <a class="el" href="bayes_8sql__in.html#a6d1186aa04f4d4e3ac44035467818d3c">create_nb_probs_view</a>(
+    '<em>featureProbsName</em>', '<em>classPriorsName</em>',
+    '<em>classifySource</em>', '<em>classifyKeyColumn</em>', '<em>classifyAttrColumn</em>',
+    <em>numAttrs</em>, '<em>destName</em>'
+);</pre></li>
+<li>Show Naive Bayes probabilities: <pre>SELECT * FROM <em>destName</em>;</pre> </li>
+</ol>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a2e02b0ebb0b4c2ca77b1748662bae78f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_probs_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>featureProbsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classPriorsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>numericFeatureStatsSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aba5d695641530e55b25c8007b77142b9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_probs_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingClassColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numAttrs</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>destName</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6d1186aa04f4d4e3ac44035467818d3c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_nb_probs_view </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingSource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingClassColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>trainingAttrColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>numericAttrsColumnIndices</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifySource</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyKeyColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>classifyAttrColumn</em>, </td>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">c45.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>C4.5 APIs and main controller written in PL/PGSQL.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a4fbee855d22101d15d195d573189eb98"><td class="memItemLeft" align="right" valign="top">c45_train_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a4fbee855d22101d15d195d573189eb98">c45_train</a> (text split_criterion, text training_table_name, text result_tree_table_name, text validation_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, float confidence_level, text how2handle_missing_value, int max_tree_depth, float node_prune_threshold, float node_split_threshold, int verbosity)</td></tr>
+<tr class="memdesc:a4fbee855d22101d15d195d573189eb98"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the long form API of training tree with all specified parameters.  <a href="#a4fbee855d22101d15d195d573189eb98">More...</a><br /></td></tr>
+<tr class="separator:a4fbee855d22101d15d195d573189eb98"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a6c039416b94686b915e2a4c1133a5d44"><td class="memItemLeft" align="right" valign="top">c45_train_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a6c039416b94686b915e2a4c1133a5d44">c45_train</a> (text split_criterion, text training_table_name, text result_tree_table_name, text validation_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, float confidence_level, text how2handle_missing_value)</td></tr>
+<tr class="memdesc:a6c039416b94686b915e2a4c1133a5d44"><td class="mdescLeft">&#160;</td><td class="mdescRight">C45 train algorithm in short form.  <a href="#a6c039416b94686b915e2a4c1133a5d44">More...</a><br /></td></tr>
+<tr class="separator:a6c039416b94686b915e2a4c1133a5d44"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a18b30ff1a063e7cd16274bf7ab2a71dc"><td class="memItemLeft" align="right" valign="top">c45_train_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a18b30ff1a063e7cd16274bf7ab2a71dc">c45_train</a> (text split_criterion, text training_table_name, text result_tree_table_name)</td></tr>
+<tr class="memdesc:a18b30ff1a063e7cd16274bf7ab2a71dc"><td class="mdescLeft">&#160;</td><td class="mdescRight">C45 train algorithm in short form.  <a href="#a18b30ff1a063e7cd16274bf7ab2a71dc">More...</a><br /></td></tr>
+<tr class="separator:a18b30ff1a063e7cd16274bf7ab2a71dc"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ac71787c47795b3b0b133cdbd37438242"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#ac71787c47795b3b0b133cdbd37438242">c45_genrule</a> (text tree_table_name, int verbosity)</td></tr>
+<tr class="memdesc:ac71787c47795b3b0b133cdbd37438242"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the trained decision tree model with rules.  <a href="#ac71787c47795b3b0b133cdbd37438242">More...</a><br /></td></tr>
+<tr class="separator:ac71787c47795b3b0b133cdbd37438242"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:acdba07d3897356a75666aa6d5999f490"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#acdba07d3897356a75666aa6d5999f490">c45_genrule</a> (text tree_table_name)</td></tr>
+<tr class="memdesc:acdba07d3897356a75666aa6d5999f490"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the trained decision tree model with rules.  <a href="#acdba07d3897356a75666aa6d5999f490">More...</a><br /></td></tr>
+<tr class="separator:acdba07d3897356a75666aa6d5999f490"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a32d2bcbc016c990991d77b6f6148306d"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a32d2bcbc016c990991d77b6f6148306d">c45_display</a> (text tree_table, int max_depth)</td></tr>
+<tr class="memdesc:a32d2bcbc016c990991d77b6f6148306d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the trained decision tree model with human readable format.  <a href="#a32d2bcbc016c990991d77b6f6148306d">More...</a><br /></td></tr>
+<tr class="separator:a32d2bcbc016c990991d77b6f6148306d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad7f190eb8e5d53f4772fac699787c0fe"><td class="memItemLeft" align="right" valign="top">set&lt; text &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#ad7f190eb8e5d53f4772fac699787c0fe">c45_display</a> (text tree_table)</td></tr>
+<tr class="memdesc:ad7f190eb8e5d53f4772fac699787c0fe"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display the whole trained decision tree model with human readable format.  <a href="#ad7f190eb8e5d53f4772fac699787c0fe">More...</a><br /></td></tr>
+<tr class="separator:ad7f190eb8e5d53f4772fac699787c0fe"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:afe136e52f498f2ff9e2b91e38e29d670"><td class="memItemLeft" align="right" valign="top">c45_classify_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#afe136e52f498f2ff9e2b91e38e29d670">c45_classify</a> (text tree_table_name, text classification_table_name, text result_table_name, int verbosity)</td></tr>
+<tr class="memdesc:afe136e52f498f2ff9e2b91e38e29d670"><td class="mdescLeft">&#160;</td><td class="mdescRight">Classify dataset using trained decision tree model. The classification result will be stored in the table which is defined as:  <a href="#afe136e52f498f2ff9e2b91e38e29d670">More...</a><br /></td></tr>
+<tr class="separator:afe136e52f498f2ff9e2b91e38e29d670"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:af5eb174eeecd11233409657221586cf1"><td class="memItemLeft" align="right" valign="top">c45_classify_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#af5eb174eeecd11233409657221586cf1">c45_classify</a> (text tree_table_name, text classification_table_name, text result_table_name)</td></tr>
+<tr class="memdesc:af5eb174eeecd11233409657221586cf1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Classify dataset using trained decision tree model. It runs in quiet mode. The classification result will be stored in the table which is defined as:  <a href="#af5eb174eeecd11233409657221586cf1">More...</a><br /></td></tr>
+<tr class="separator:af5eb174eeecd11233409657221586cf1"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a1b634db47e9006d114da0987e80b9601"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#a1b634db47e9006d114da0987e80b9601">c45_score</a> (text tree_table_name, text scoring_table_name, int verbosity)</td></tr>
+<tr class="memdesc:a1b634db47e9006d114da0987e80b9601"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check the accuracy of the decision tree model.  <a href="#a1b634db47e9006d114da0987e80b9601">More...</a><br /></td></tr>
+<tr class="separator:a1b634db47e9006d114da0987e80b9601"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:af0739749507c1097003dcf529d29fee2"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#af0739749507c1097003dcf529d29fee2">c45_score</a> (text tree_table_name, text scoring_table_name)</td></tr>
+<tr class="memdesc:af0739749507c1097003dcf529d29fee2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Check the accuracy of the decision tree model.  <a href="#af0739749507c1097003dcf529d29fee2">More...</a><br /></td></tr>
+<tr class="separator:af0739749507c1097003dcf529d29fee2"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ac25e17ecbc70149aa559018e718fc793"><td class="memItemLeft" align="right" valign="top">boolean&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793">c45_clean</a> (text result_tree_table_name)</td></tr>
+<tr class="memdesc:ac25e17ecbc70149aa559018e718fc793"><td class="mdescLeft">&#160;</td><td class="mdescRight">Cleanup the trained tree table and any relevant tables.  <a href="#ac25e17ecbc70149aa559018e718fc793">More...</a><br /></td></tr>
+<tr class="separator:ac25e17ecbc70149aa559018e718fc793"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>April 5, 2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to decision trees, see the module description <a class="el" href="group__grp__dectree.html">Decision Tree (old C4.5 implementation)</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="afe136e52f498f2ff9e2b91e38e29d670"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">c45_classify_result c45_classify </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>classification_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>CREATE TABLE classification_result ( id INT|BIGINT, class SUPPORTED_DATA_TYPE, prob FLOAT );</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table_name</td><td>The name of trained tree. </td></tr>
+    <tr><td class="paramname">classification_table_name</td><td>The name of the table/view with the source data. </td></tr>
+    <tr><td class="paramname">result_table_name</td><td>The name of result table. </td></tr>
+    <tr><td class="paramname">verbosity</td><td>&gt; 0 means this function runs in verbose mode.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A c45_classify_result object. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="af5eb174eeecd11233409657221586cf1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">c45_classify_result c45_classify </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>classification_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_table_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>CREATE TABLE classification_result ( id INT|BIGINT, class SUPPORTED_DATA_TYPE, prob FLOAT );</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table_name</td><td>The name of trained tree. </td></tr>
+    <tr><td class="paramname">classification_table_name</td><td>The name of the table/view with the source data. </td></tr>
+    <tr><td class="paramname">result_table_name</td><td>The name of result table.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A c45_classify_result object. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ac25e17ecbc70149aa559018e718fc793"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean c45_clean </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table containing the tree's information.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The status of that cleanup operation. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a32d2bcbc016c990991d77b6f6148306d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text&gt; c45_display </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_depth</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table</td><td>The name of the table containing the tree's information. </td></tr>
+    <tr><td class="paramname">max_depth</td><td>The max depth to be displayed. If null, this function will show all levels.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The text representing the tree with human readable format. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ad7f190eb8e5d53f4772fac699787c0fe"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text&gt; c45_display </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table</td><td>The name of the table containing the tree's information.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The text representing the tree with human readable format. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ac71787c47795b3b0b133cdbd37438242"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text&gt; c45_genrule </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table_name</td><td>The name of the table containing the tree's information. </td></tr>
+    <tr><td class="paramname">verbosity</td><td>If &gt;= 1 will run in verbose mode.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The rule representation text for a decision tree. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="acdba07d3897356a75666aa6d5999f490"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text&gt; c45_genrule </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table_name</td><td>The name of the table containing the tree's information.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The rule representation text for a decision tree. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a1b634db47e9006d114da0987e80b9601"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 c45_score </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>scoring_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table_name</td><td>The name of the trained tree. </td></tr>
+    <tr><td class="paramname">scoring_table_name</td><td>The name of the table/view with the source data. </td></tr>
+    <tr><td class="paramname">verbosity</td><td>&gt; 0 means this function runs in verbose mode.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The estimated accuracy information. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="af0739749507c1097003dcf529d29fee2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 c45_score </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>scoring_table_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_table_name</td><td>The name of the trained tree. </td></tr>
+    <tr><td class="paramname">scoring_table_name</td><td>The name of the table/view with the source data.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The estimated accuracy information. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a4fbee855d22101d15d195d573189eb98"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">c45_train_result c45_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>validation_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>continuous_feature_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>feature_col_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>confidence_level</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>how2handle_missing_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_tree_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>node_prune_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>node_split_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">split_criterion</td><td>The name of the split criterion that should be used for tree construction. The valid values are ‘infogain’, ‘gainratio’, and ‘gini’. It can't be NULL. Information gain(infogain) and gini index(gini) are biased toward multivalued attributes. Gain ratio(gainratio) adjusts for this bias. However, it tends to prefer unbalanced splits in which one partition is much smaller than the others. </td></tr>
+    <tr><td class="paramname">training_table_name</td><td>The name of the table/view with the source data. </td></tr>
+    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table where the resulting DT will be kept. </td></tr>
+    <tr><td class="paramname">validation_table_name</td><td>The name of the table/view that contains the validation set used for tree pruning. The default is NULL, in which case we will not do tree pruning. </td></tr>
+    <tr><td class="paramname">continuous_feature_names</td><td>A comma-separated list of the names of features whose values are continuous. The default is null, which means there are no continuous features in the training table. </td></tr>
+    <tr><td class="paramname">feature_col_names</td><td>A comma-separated list of the names of table columns, each of which defines a feature. The default value is null, which means all the columns in the training table, except columns named ‘id’ and ‘class’, will be used as features. </td></tr>
+    <tr><td class="paramname">id_col_name</td><td>The name of the column containing an ID for each record. </td></tr>
+    <tr><td class="paramname">class_col_name</td><td>The name of the column containing the labeled class. </td></tr>
+    <tr><td class="paramname">confidence_level</td><td>A statistical confidence interval of the resubstitution error. </td></tr>
+    <tr><td class="paramname">how2handle_missing_value</td><td>The way to handle missing value. The valid value is 'explicit' or 'ignore'. </td></tr>
+    <tr><td class="paramname">max_tree_depth</td><td>Specifies the maximum number of levels in the result DT to avoid overgrown DTs. </td></tr>
+    <tr><td class="paramname">node_prune_threshold</td><td>The minimum percentage of the number of records required in a child node. It can't be NULL. The range of it is in [0.0, 1.0]. This threshold only applies to the non-root nodes. Therefore, if its value is 1, then the trained tree only has one node (the root node); if its value is 0, then no nodes will be pruned by this parameter. </td></tr>
+    <tr><td class="paramname">node_split_threshold</td><td>The minimum percentage of the number of records required in a node in order for a further split to be possible. It can't be NULL. The range of it is in [0.0, 1.0]. If it's value is 1, then the trained tree only has two levels, since only the root node can grow; if its value is 0, then trees can grow extensively. </td></tr>
+    <tr><td class="paramname">verbosity</td><td>&gt; 0 means this function runs in verbose mode.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>An c45_train_result object. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a6c039416b94686b915e2a4c1133a5d44"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">c45_train_result c45_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>validation_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>continuous_feature_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>feature_col_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>confidence_level</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>how2handle_missing_value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">split_criterion</td><td>The name of the split criterion that should be used for tree construction. Possible values are ‘gain’, ‘gainratio’, and ‘gini’. </td></tr>
+    <tr><td class="paramname">training_table_name</td><td>The name of the table/view with the source data. </td></tr>
+    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table where the resulting DT will be kept. </td></tr>
+    <tr><td class="paramname">validation_table_name</td><td>The name of the table/view that contains the validation set used for tree pruning. The default is NULL, in which case we will not do tree pruning. </td></tr>
+    <tr><td class="paramname">continuous_feature_names</td><td>A comma-separated list of the names of features whose values are continuous. The default is null, which means there are no continuous features in the training table. </td></tr>
+    <tr><td class="paramname">feature_col_names</td><td>A comma-separated list of the names of table columns, each of which defines a feature. The default value is null, which means all the columns in the training table, except columns named ‘id’ and ‘class’, will be used as features. </td></tr>
+    <tr><td class="paramname">id_col_name</td><td>The name of the column containing an ID for each record. </td></tr>
+    <tr><td class="paramname">class_col_name</td><td>The name of the column containing the labeled class. </td></tr>
+    <tr><td class="paramname">confidence_level</td><td>A statistical confidence interval of the resubstitution error. </td></tr>
+    <tr><td class="paramname">how2handle_missing_value</td><td>The way to handle missing value. The valid value is 'explicit' or 'ignore'.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>An c45_train_result object.</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This calls the long form of C45 with the following default parameters:<ul>
+<li>max_tree_deapth := 10</li>
+<li>node_prune_threshold := 0.001</li>
+<li>node_split_threshold := 0.01</li>
+<li>verbosity := 0 </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a18b30ff1a063e7cd16274bf7ab2a71dc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">c45_train_result c45_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">split_criterion</td><td>The name of the split criterion that should be used for tree construction. Possible values are ‘gain’, ‘gainratio’, and ‘gini’. </td></tr>
+    <tr><td class="paramname">training_table_name</td><td>The name of the table/view with the source data. </td></tr>
+    <tr><td class="paramname">result_tree_table_name</td><td>The name of the table where the resulting DT will be kept.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>An c45_train_result object.</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This calls the above short form of C45 with the following default parameters:<ul>
+<li>validation_table_name := NULL</li>
+<li>continuous_feature_names := NULL</li>
+<li>id_column_name := 'id'</li>
+<li>class_column_name := 'class'</li>
+<li>confidence_level := 25</li>
+<li>how2handle_missing_value := 'explicit'</li>
+<li>max_tree_deapth := 10</li>
+<li>node_prune_threshold := 0.001</li>
+<li>node_split_threshold := 0.01</li>
+<li>verbosity := 0 </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_704eb8350b43e1ca74c0f90ed1ba450e.html">methods</a></li><li class="navelem"><a class="el" href="dir_fbc4f2259ae1d6f6cc75298ebbd15532.html">cart</a></li><li class="navelem"><a class="el" href="dir_42a199e31e82b0c41cf7961a25e597db.html">src</a></li><li class="navelem"><a class="el" href="dir_64bd18b6b0e4b6a9c2cd2ca1d5a05b4c.html">pg_gp</a></li><li class="navelem"><a class="el" href="c45_8sql__in.html">c45.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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[09/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+  <td style="padding-left: 0.5em;">
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+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+  <div class="headertitle">
+<div class="title">Model Evaluation</div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>Contains the cross-validation module, a collection of routines useful for <a href="http://en.wikipedia.org/wiki/Cross-validation_(statistics)">Cross-validation</a>. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__validation"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__validation.html">Cross Validation</a></td></tr>
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+<title>MADlib: MFV (Most Frequent Values)</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">MFV (Most Frequent Values)<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a> &raquo; <a class="el" href="group__grp__sketches.html">Cardinality Estimators</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>MFVSketch: Most Frequent Values variant of CountMin sketch, implemented as a UDA.</p>
+<p>Produces an n-bucket histogram for a column where each bucket counts one of the most frequent values in the column. The output is an array of doubles {value, count} in descending order of frequency; counts are approximated via CountMin sketches. Ties are handled arbitrarily.</p>
+<p><a class="anchor" id="syntax"></a> </p><pre class="syntax">
+mfvsketch_top_histogram( col_name,
+                         n
+                       )
+</pre><p>The MFV frequent-value UDA comes in two different versions:</p><ul>
+<li>a faithful implementation that preserves the approximation guarantees of Cormode/Muthukrishnan,</li>
+<li>and a "quick and dirty" version that can do parallel aggregation in Greenplum at the expense of missing some of the most frequent values.</li>
+</ul>
+<p>In PostgreSQL the two UDAs are identical. In Greenplum, the quick version should produce good results unless the number of values requested is very small, or the distribution is very flat.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Generate some data. <pre class="example">
+CREATE TABLE data(class INT, a1 INT);
+INSERT INTO data SELECT 1,1 FROM generate_series(1,10000);
+INSERT INTO data SELECT 1,2 FROM generate_series(1,15000);
+INSERT INTO data SELECT 1,3 FROM generate_series(1,10000);
+INSERT INTO data SELECT 2,5 FROM generate_series(1,1000);
+INSERT INTO data SELECT 2,6 FROM generate_series(1,1000);
+</pre></li>
+<li>Produce a histogram of 5 bins and return the most frequent value and associated count in each bin. <pre class="example">
+SELECT mfvsketch_top_histogram( a1,
+                                5
+                              )
+FROM data;
+</pre> Result: <pre class="result">
+                mfvsketch_top_histogram
+&#160;-------------------------------------------------------------
+[0:4][0:1]={{2,15000},{1,10000},{3,10000},{5,1000},{6,1000}}
+(1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd>This method is not usually called an MFV sketch in the literature; it is a natural extension of the CountMin sketch.</dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="sketch_8sql__in.html" title="SQL functions for sketch-based approximations of descriptive statistics. ">sketch.sql_in</a> documenting the SQL functions.</p>
+<p>Module <a class="el" href="group__grp__countmin.html">CountMin (Cormode-Muthukrishnan)</a>. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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+</html>

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+<title>MADlib: Multinomial Logistic Regression</title>
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+       class="ui-resizable-handle">
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+$(document).ready(function(){initNavTree('group__grp__mlogreg.html','');});
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+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Multinomial Logistic Regression<div class="ingroups"><a class="el" href="group__grp__deprecated.html">Deprecated Modules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<dl class="section warning"><dt>Warning</dt><dd><em> This is an old implementation of multinomial logistic regression. Replacement of this function is available as the Multinomial regression module <a class="el" href="group__grp__multinom.html">Multinomial Regression</a></em></dd></dl>
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Multinomial logistic regression is a widely used regression analysis tool that models the outcomes of categorical dependent random variables. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of independent variables. Multinomial logistic regression finds the vector of coefficients that maximizes the likelihood of the observations.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The multinomial logistic regression training function has the following syntax: <pre class="syntax">
+mlogregr_train(source_table,
+               output_table,
+               dependent_varname,
+               independent_varname,
+               ref_category,
+               optimizer_params
+              )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the input data.</p>
+<p></p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the generated table containing the output model. The output table produced by the multinomial logistic regression training function contains the following columns: </p><table  class="output">
+<tr>
+<th>category </th><td>INTEGER. The category. Categories are encoded as integers with values from {0, 1, 2,..., <em>numCategories</em> &ndash; 1}  </td></tr>
+<tr>
+<th>ref_category </th><td>INTEGER. The reference category. Categories are encoded as integers with values from {0, 1, 2,..., <em>numCategories</em> &ndash; 1}  </td></tr>
+<tr>
+<th>coef </th><td>FLOAT8[]. An array of coefficients, \( \boldsymbol c \).   </td></tr>
+<tr>
+<th>log_likelihood </th><td>FLOAT8. The log-likelihood, \( l(\boldsymbol c) \).  </td></tr>
+<tr>
+<th>std_err </th><td>FLOAT8[]. An array of the standard errors.  </td></tr>
+<tr>
+<th>z_stats </th><td>FLOAT8[]. An array of the Wald z-statistics.  </td></tr>
+<tr>
+<th>p_values </th><td>FLOAT8[]. An array of the Wald p-values.  </td></tr>
+<tr>
+<th>odds_ratios </th><td>FLOAT8[]. An array of the odds ratios.  </td></tr>
+<tr>
+<th>condition_no </th><td>FLOAT8. The condition number of the matrix, computed using the coefficients of the iteration immediately preceding convergence.  </td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The number of iterations executed before the algorithm completed.  </td></tr>
+</table>
+<p>A summary table named &lt;out_table&gt;_summary is also created at the same time, and it contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>source_table </th><td>The data source table name.  </td></tr>
+<tr>
+<th>out_table </th><td>The output table name.  </td></tr>
+<tr>
+<th>dependent_varname </th><td>The dependent variable.  </td></tr>
+<tr>
+<th>independent_varname </th><td>The independent variables.  </td></tr>
+<tr>
+<th>optimizer_params </th><td>The optimizer parameters. It is a copy of the optimizer_params in the training function's arguments.  </td></tr>
+<tr>
+<th>ref_category </th><td>An integer, the value of reference category used.  </td></tr>
+<tr>
+<th>num_rows_processed </th><td>INTEGER. The number of rows actually processed, which is equal to the total number of rows in the source table minus the number of skipped rows.  </td></tr>
+<tr>
+<th>num_missing_rows_skipped </th><td>INTEGER. The number of rows skipped during the training. A row will be skipped if the ind_col is NULL or contains NULL values.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">TEXT. The name of the column containing the dependent variable.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">TEXT. Expression list to evaluate for the independent variables. An intercept variable is not assumed. The number of independent variables cannot exceed 65535.</p>
+<p class="enddd"></p>
+</dd>
+<dt>ref_category (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 0. The reference category ranges from [0, <em>numCategories</em> &ndash; 1].</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer_params (optional) </dt>
+<dd>VARCHAR, default: NULL, which uses the default values of optimizer parameters. It should be a string that contains pairs of 'key=value' separated by commas. Supported parameters with their default values: max_iter=20, optimizer='irls', precision=1e-4. Currently, only 'irls' and 'newton' are allowed for 'optimizer'.  </dd>
+</dl>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>Table names can be optionally schema qualified and table and column names should follow the same case-sensitivity and quoting rules as in the database.</dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>The prediction function is provided to estimate the conditional mean given a new predictor. It has the following syntax: <pre class="syntax">
+mlogregr_predict(
+    model_table,
+    new_data_table,
+    id_col_name,
+    output_table,
+    type)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. Name of the table containing the multilogistic model. This should be the output table returned from <em>mlogregr_train</em>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>new_data_table </dt>
+<dd><p class="startdd">TEXT. Name of the table containing prediction data. This table is expected to contain the same features that were used during training. The table should also contain <em>id_col_name</em> used for identifying each row.</p>
+<p class="enddd"></p>
+</dd>
+<dt>id_col_name </dt>
+<dd><p class="startdd">TEXT. Name of the column containing id information in the source data. This is a mandatory argument and is used for correlating prediction table rows with the source. The values of this column are expected to be unique for each tuple. </p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. Name of the table to output prediction results to. If this table already exists then an error is returned. This output table contains the <em>id_col_name</em> column giving the 'id' for each prediction.</p>
+<p>If <em>type</em> = 'response', then the table has a single additional column with the prediction value of the response. The type of this column depends on the type of the response variable used during training.</p>
+<p>If <em>type</em> = 'prob', then the table has multiple additional columns, one for each possible category. The columns are labeled as 'estimated_prob_<em>category_value</em>', where <em>category_value</em> represents the values of categories (0 to K-1).</p>
+<p class="enddd"></p>
+</dd>
+<dt>type </dt>
+<dd><p class="startdd">TEXT, optional, default: 'response'.</p>
+<p>When <em>type</em> = 'prob', the probabilities of each category (including the reference category) is given.</p>
+<p class="enddd">When <em>type</em> = 'response', a single output is provided which represents the prediction category for each tuple. This represents the category with the highest probability.  </p>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Create the training data table. <pre class="example">
+DROP TABLE IF EXISTS test3;
+CREATE TABLE test3 (
+    feat1 INTEGER,
+    feat2 INTEGER,
+    cat INTEGER
+);
+INSERT INTO test3(feat1, feat2, cat) VALUES
+(1,35,1),
+(2,33,0),
+(3,39,1),
+(1,37,1),
+(2,31,1),
+(3,36,0),
+(2,36,1),
+(2,31,1),
+(2,41,1),
+(2,37,1),
+(1,44,1),
+(3,33,2),
+(1,31,1),
+(2,44,1),
+(1,35,1),
+(1,44,0),
+(1,46,0),
+(2,46,1),
+(2,46,2),
+(3,49,1),
+(2,39,0),
+(2,44,1),
+(1,47,1),
+(1,44,1),
+(1,37,2),
+(3,38,2),
+(1,49,0),
+(2,44,0),
+(3,61,2),
+(1,65,2),
+(3,67,1),
+(3,65,2),
+(1,65,2),
+(2,67,2),
+(1,65,2),
+(1,62,2),
+(3,52,2),
+(3,63,2),
+(2,59,2),
+(3,65,2),
+(2,59,0),
+(3,67,2),
+(3,67,2),
+(3,60,2),
+(3,67,2),
+(3,62,2),
+(2,54,2),
+(3,65,2),
+(3,62,2),
+(2,59,2),
+(3,60,2),
+(3,63,2),
+(3,65,2),
+(2,63,1),
+(2,67,2),
+(2,65,2),
+(2,62,2);
+</pre></li>
+<li>Run the multilogistic regression function. <pre class="example">
+DROP TABLE IF EXISTS test3_output;
+DROP TABLE IF EXISTS test3_output_summary;
+SELECT madlib.mlogregr_train('test3',
+                             'test3_output',
+                             'cat',
+                             'ARRAY[1, feat1, feat2]',
+                             0,
+                             'max_iter=20, optimizer=irls, precision=0.0001'
+                             );
+</pre></li>
+<li>View the result: <pre class="example">
+-- Set extended display on for easier reading of output
+\x on
+SELECT * FROM test3_output;
+</pre> Results: <pre class="result">
+-[ RECORD 1 ]--+------------------------------------------------------------
+category       | 1
+ref_category   | 0
+coef           | {1.45474045211601,0.0849956182104023,-0.0172383499601956}
+loglikelihood  | -39.14759930999
+std_err        | {2.13085072854143,0.585021661344715,0.0431487356292144}
+z_stats        | {0.682704063982831,0.145286275409074,-0.39950996729842}
+p_values       | {0.494793861210936,0.884484850387893,0.689517480964129}
+odd_ratios     | {4.28337158128448,1.08871229617973,0.982909380301134}
+condition_no   | 280069.034217586
+num_iterations | 5
+-[ RECORD 2 ]--+------------------------------------------------------------
+category       | 2
+ref_category   | 0
+coef           | {-7.12908167688326,0.87648787696783,0.127886153027713}
+loglikelihood  | -39.14759930999
+std_err        | {2.52104008297868,0.639575886323862,0.0445757462972303}
+z_stats        | {-2.82783352990566,1.37042045472615,2.86896269049475}
+p_values       | {0.00468641692252239,0.170555690550421,0.00411820373218956}
+odd_ratios     | {0.000801455044349486,2.40244718187161,1.13642361694409}
+condition_no   | 280069.034217586
+num_iterations | 5
+</pre></li>
+<li>View all parameters used during the training <pre class="example">
+\x on
+SELECT * FROM test3_output_summary;
+</pre> Results: <pre class="result">
+-[ RECORD 1 ]------------+--------------------------------------------------
+method                   | mlogregr
+source_table             | test3
+out_table                | test3_output
+dependent_varname        | cat
+independent_varname      | ARRAY[1, feat1, feat2]
+optimizer_params         | max_iter=20, optimizer=irls, precision=0.0001
+ref_category             | 0
+num_categories           | 3
+num_rows_processed       | 57
+num_missing_rows_skipped | 0
+variance_covariance      | {{4.54052482732554,3.01080140927409,-0.551901021610841,-0.380754019900586,-0.0784151362989211,-0.0510014701718268},{3.01080140927409,6.35564309998514,-0.351902272617974,-0.766730342510818,-0.051877550252329,-0.0954432017695571},{-0.551901021610841,-0.351902272617974,0.34225034424253,0.231740815080827,-0.00117521831508331,-0.00114043921343171},{-0.380754019900586,-0.766730342510818,0.231740815080827,0.409057314366954,-0.000556498286025567,-0.000404735750986327},{-0.0784151362989211,-0.051877550252329,-0.00117521831508331,-0.000556498286025569,0.00186181338639984,0.00121080293928445},{-0.0510014701718268,-0.0954432017695571,-0.00114043921343171,-0.000404735750986325,0.00121080293928446,0.00198699715795504}}
+coef                     | {{1.45474045211601,0.0849956182104023,-0.0172383499601956},{-7.12908167688326,0.87648787696783,0.127886153027713}}
+</pre></li>
+</ol>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd>Multinomial logistic regression models the outcomes of categorical dependent random variables (denoted \( Y \in \{ 0,1,2 \ldots k \} \)). The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of independent variables (usually denoted \( \boldsymbol x \)). That is, <p class="formulaDsp">
+\[ E[Y \mid \boldsymbol x] = \sigma(\boldsymbol c^T \boldsymbol x) \]
+</p>
+ for some unknown vector of coefficients \( \boldsymbol c \) and where \( \sigma(x) = \frac{1}{1 + \exp(-x)} \) is the logistic function. Multinomial logistic regression finds the vector of coefficients \( \boldsymbol c \) that maximizes the likelihood of the observations.</dd></dl>
+<p>Let</p><ul>
+<li>\( \boldsymbol y \in \{ 0,1 \}^{n \times k} \) denote the vector of observed dependent variables, with \( n \) rows and \( k \) columns, containing the observed values of the dependent variable,</li>
+<li>\( X \in \mathbf R^{n \times k} \) denote the design matrix with \( k \) columns and \( n \) rows, containing all observed vectors of independent variables \( \boldsymbol x_i \) as rows.</li>
+</ul>
+<p>By definition, </p><p class="formulaDsp">
+\[ P[Y = y_i | \boldsymbol x_i] = \sigma((-1)^{y_i} \cdot \boldsymbol c^T \boldsymbol x_i) \,. \]
+</p>
+<p> Maximizing the likelihood \( \prod_{i=1}^n \Pr(Y = y_i \mid \boldsymbol x_i) \) is equivalent to maximizing the log-likelihood \( \sum_{i=1}^n \log \Pr(Y = y_i \mid \boldsymbol x_i) \), which simplifies to </p><p class="formulaDsp">
+\[ l(\boldsymbol c) = -\sum_{i=1}^n \log(1 + \exp((-1)^{y_i} \cdot \boldsymbol c^T \boldsymbol x_i)) \,. \]
+</p>
+<p> The Hessian of this objective is \( H = -X^T A X \) where \( A = \text{diag}(a_1, \dots, a_n) \) is the diagonal matrix with \( a_i = \sigma(\boldsymbol c^T \boldsymbol x) \cdot \sigma(-\boldsymbol c^T \boldsymbol x) \,. \) Since \( H \) is non-positive definite, \( l(\boldsymbol c) \) is convex. There are many techniques for solving convex optimization problems. Currently, logistic regression in MADlib can use:</p><ul>
+<li>Iteratively Reweighted Least Squares</li>
+</ul>
+<p>We estimate the standard error for coefficient \( i \) as </p><p class="formulaDsp">
+\[ \mathit{se}(c_i) = \left( (X^T A X)^{-1} \right)_{ii} \,. \]
+</p>
+<p> The Wald z-statistic is </p><p class="formulaDsp">
+\[ z_i = \frac{c_i}{\mathit{se}(c_i)} \,. \]
+</p>
+<p>The Wald \( p \)-value for coefficient \( i \) gives the probability (under the assumptions inherent in the Wald test) of seeing a value at least as extreme as the one observed, provided that the null hypothesis ( \( c_i = 0 \)) is true. Letting \( F \) denote the cumulative density function of a standard normal distribution, the Wald \( p \)-value for coefficient \( i \) is therefore </p><p class="formulaDsp">
+\[ p_i = \Pr(|Z| \geq |z_i|) = 2 \cdot (1 - F( |z_i| )) \]
+</p>
+<p> where \( Z \) is a standard normally distributed random variable.</p>
+<p>The odds ratio for coefficient \( i \) is estimated as \( \exp(c_i) \).</p>
+<p>The condition number is computed as \( \kappa(X^T A X) \) during the iteration immediately <em>preceding</em> convergence (i.e., \( A \) is computed using the coefficients of the previous iteration). A large condition number (say, more than 1000) indicates the presence of significant multicollinearity.</p>
+<p>The multinomial logistic regression uses a default reference category of zero, and the regression coefficients in the output are in the order described below. For a problem with \( K \) dependent variables \( (1, ..., K) \) and \( J \) categories \( (0, ..., J-1) \), let \( {m_{k,j}} \) denote the coefficient for dependent variable \( k \) and category \( j \). The output is \( {m_{k_1, j_0}, m_{k_1, j_1} \ldots m_{k_1, j_{J-1}}, m_{k_2, j_0}, m_{k_2, j_1}, \ldots m_{k_2, j_{J-1}} \ldots m_{k_K, j_{J-1}}} \). The order is NOT CONSISTENT with the multinomial regression marginal effect calculation with function <em>marginal_mlogregr</em>. This is deliberate because the interfaces of all multinomial regressions (robust, clustered, ...) will be moved to match that used in marginal.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>A collection of nice write-ups, with valuable pointers into further literature:</p>
+<p>[1] Annette J. Dobson: An Introduction to Generalized Linear Models, Second Edition. Nov 2001</p>
+<p>[2] Cosma Shalizi: Statistics 36-350: Data Mining, Lecture Notes, 18 November 2009, <a href="http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf">http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf</a></p>
+<p>[3] Scott A. Czepiel: Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation, Retrieved Jul 12 2012, <a href="http://czep.net/stat/mlelr.pdf">http://czep.net/stat/mlelr.pdf</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="multilogistic_8sql__in.html" title="SQL functions for multinomial logistic regression. ">multilogistic.sql_in</a> documenting the multinomial logistic regression functions</p>
+<p><a class="el" href="group__grp__logreg.html">Logistic Regression</a></p>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Multinomial Regression<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>In statistics, multinomial regression is a classification method that generalizes binomial regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The multinomial regression training function has the following syntax: <pre class="syntax">
+multinom(source_table,
+         model_table,
+         dependent_varname,
+         independent_varname,
+         ref_category,
+         link_func,
+         grouping_col,
+         optim_params,
+         verbose
+        )
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>model_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the generated table containing the model.</p>
+<p>The model table produced by multinom() contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td><p class="starttd">Grouping columns, if provided in input. This could be multiple columns depending on the <code>grouping_col</code> input. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>category </th><td><p class="starttd">VARCHAR. String representation of category value. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>coef </th><td><p class="starttd">FLOAT8[]. Vector of the coefficients in linear predictor. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>log_likelihood </th><td><p class="starttd">FLOAT8. The log-likelihood \( l(\boldsymbol \beta) \). The value will be the same across categories within the same group. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>std_err </th><td><p class="starttd">FLOAT8[]. Vector of the standard errors of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>z_stats </th><td><p class="starttd">FLOAT8[]. Vector of the z-statistics of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>p_values </th><td><p class="starttd">FLOAT8[]. Vector of the p-values of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_processed </th><td><p class="starttd">BIGINT. Number of rows processed. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_skipped </th><td><p class="starttd">BIGINT. Number of rows skipped due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. Number of iterations actually completed. This would be different from the <code>nIterations</code> argument if a <code>tolerance</code> parameter is provided and the algorithm converges before all iterations are completed.  </td></tr>
+</table>
+<p>A summary table named &lt;model_table&gt;_summary is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method </th><td><p class="starttd">VARCHAR. String describes the model: 'multinom'. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>source_table </th><td><p class="starttd">VARCHAR. Data source table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>model_table </th><td><p class="starttd">VARCHAR. Model table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">VARCHAR. Expression for dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">VARCHAR. Expression for independent variables. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>ref_category </th><td><p class="starttd">VARCHAR. String representation of reference category. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>link_func </th><td><p class="starttd">VARCHAR. String that contains link function parameters: only 'logit' is implemented now </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>grouping_col </th><td><p class="starttd">VARCHAR. String representation of grouping columns. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>optimizer_params </th><td><p class="starttd">VARCHAR. String that contains optimizer parameters, and has the form of 'optimizer=..., max_iter=..., tolerance=...'. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">INTEGER. Number of groups in glm training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">INTEGER. Number of failed groups in glm training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_processed </th><td><p class="starttd">BIGINT. Total number of rows processed in all groups. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_skipped </th><td><p class="starttd">BIGINT. Total number of rows skipped in all groups due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">VARCHAR. Name of the dependent variable column.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant <code>1</code> term in the independent variable list.</p>
+<p class="enddd"></p>
+</dd>
+<dt>link_function (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: 'logit'. Parameters for link function. Currently, we support logit. </p>
+<p class="enddd"></p>
+</dd>
+<dt>ref_category (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: '0'. Parameters to specify the reference category. </p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optim_params (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: 'max_iter=100,optimizer=irls,tolerance=1e-6'. Parameters for optimizer. Currently, we support tolerance=[tolerance for relative error between log-likelihoods], max_iter=[maximum iterations to run], optimizer=irls.</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Provides verbose output of the results of training. </dd>
+</dl>
+<dl class="section note"><dt>Note</dt><dd>For p-values, we just return the computation result directly. Other statistical packages, like 'R', produce the same result, but on printing the result to screen, another format function is used and any p-value that is smaller than the machine epsilon (the smallest positive floating-point number 'x' such that '1 + x != 1') will be printed on screen as "&lt; xxx" (xxx is the value of the machine epsilon). Although the result may look different, they are in fact the same. </dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>Multinomial regression prediction function has the following format: <pre class="syntax">
+multinom_predict(model_table,
+                 predict_table_input,
+                 output_table,
+                 predict_type,
+                 verbose,
+                 id_column
+                )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the model, which is the output table from multinom().</p>
+<p class="enddd"></p>
+</dd>
+<dt>predict_table_input </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the data to predict on. The table must contain id column as the primary key.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the predicted values.</p>
+<p>The model table produced by multinom_predict contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>id </th><td><p class="starttd">SERIAL. Column to identify the predicted value. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>category </th><td><p class="starttd">TEXT. Available if the predicted type = 'response'. Column contains the predicted categories </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>category_value </th><td>FLOAT8. The predicted probability for the specific category_value.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>predict_type </dt>
+<dd>TEXT. Either 'response' or 'probability'. Using 'response' will give the predicted category with the largest probability. Using probability will give the predicted probabilities for all categories </dd>
+<dt>verbose </dt>
+<dd><p class="startdd">BOOLEAN. Control whether verbose is displayed. The default is FALSE. </p>
+<p class="enddd"></p>
+</dd>
+<dt>id_column </dt>
+<dd>TEXT. The name of the column in the input table. </dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Create the training data table. <pre class="example">
+DROP TABLE IF EXISTS test3;
+CREATE TABLE test3 (
+    feat1 INTEGER,
+    feat2 INTEGER,
+    cat INTEGER
+);
+INSERT INTO test3(feat1, feat2, cat) VALUES
+(1,35,1),
+(2,33,0),
+(3,39,1),
+(1,37,1),
+(2,31,1),
+(3,36,0),
+(2,36,1),
+(2,31,1),
+(2,41,1),
+(2,37,1),
+(1,44,1),
+(3,33,2),
+(1,31,1),
+(2,44,1),
+(1,35,1),
+(1,44,0),
+(1,46,0),
+(2,46,1),
+(2,46,2),
+(3,49,1),
+(2,39,0),
+(2,44,1),
+(1,47,1),
+(1,44,1),
+(1,37,2),
+(3,38,2),
+(1,49,0),
+(2,44,0),
+(3,61,2),
+(1,65,2),
+(3,67,1),
+(3,65,2),
+(1,65,2),
+(2,67,2),
+(1,65,2),
+(1,62,2),
+(3,52,2),
+(3,63,2),
+(2,59,2),
+(3,65,2),
+(2,59,0),
+(3,67,2),
+(3,67,2),
+(3,60,2),
+(3,67,2),
+(3,62,2),
+(2,54,2),
+(3,65,2),
+(3,62,2),
+(2,59,2),
+(3,60,2),
+(3,63,2),
+(3,65,2),
+(2,63,1),
+(2,67,2),
+(2,65,2),
+(2,62,2);
+</pre></li>
+<li>Run the multilogistic regression function. <pre class="example">
+DROP TABLE IF EXISTS test3_output;
+DROP TABLE IF EXISTS test3_output_summary;
+SELECT madlib.multinom('test3',
+                       'test3_output',
+                       'cat',
+                       'ARRAY[1, feat1, feat2]',
+                       '0',
+                       'logit'
+                       );
+</pre></li>
+<li>View the regression results. <pre class="example">
+-- Set extended display on for easier reading of output
+\x on
+SELECT * FROM test3_output;
+</pre></li>
+</ol>
+<p>Result: </p><pre class="result">
+-[ RECORD 1 ]------+------------------------------------------------------------
+category           | 1
+coef               | {1.45474045165731,0.084995618282504,-0.0172383499512136}
+log_likelihood     | -39.1475993094045
+std_err            | {2.13085878785549,0.585023211942952,0.0431489262260687}
+z_stats            | {0.682701481650677,0.145285890452484,-0.399508202380224}
+p_values           | {0.494795493298706,0.884485154314181,0.689518781152604}
+num_rows_processed | 57
+num_rows_skipped   | 0
+iteration          | 6
+-[ RECORD 2 ]------+------------------------------------------------------------
+category           | 2
+coef               | {-7.1290816775109,0.876487877074751,0.127886153038661}
+log_likelihood     | -39.1475993094045
+std_err            | {2.52105418324135,0.639578886139654,0.0445760103748678}
+z_stats            | {-2.82781771407425,1.37041402721253,2.86894569440347}
+p_values           | {0.00468664844488755,0.170557695812408,0.00411842502754068}
+num_rows_processed | 57
+num_rows_skipped   | 0
+iteration          | 6
+</pre><ol type="1">
+<li>Predicting dependent variable using multinomial model. (This example uses the original data table to perform the prediction. Typically a different test dataset with the same features as the original training dataset would be used for prediction.)</li>
+</ol>
+<pre class="example">
+\x off
+-- Add the id column for prediction function
+ALTER TABLE test3 ADD COLUMN id SERIAL;
+-- Predict probabilities for all categories using the original data
+SELECT madlib.multinom_predict('test3_out','test3', 'test3_prd_prob', 'probability');
+-- Display the predicted value
+SELECT * FROM test3_prd_prob;
+</pre><p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd>When link = 'logit', multinomial logistic regression models the outcomes of categorical dependent random variables (denoted \( Y \in \{ 0,1,2 \ldots k \} \)). The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of independent variables (usually denoted \( \boldsymbol x \)). That is, <p class="formulaDsp">
+\[ E[Y \mid \boldsymbol x] = \sigma(\boldsymbol c^T \boldsymbol x) \]
+</p>
+ for some unknown vector of coefficients \( \boldsymbol c \) and where \( \sigma(x) = \frac{1}{1 + \exp(-x)} \) is the logistic function. Multinomial logistic regression finds the vector of coefficients \( \boldsymbol c \) that maximizes the likelihood of the observations.</dd></dl>
+<p>Let</p><ul>
+<li>\( \boldsymbol y \in \{ 0,1 \}^{n \times k} \) denote the vector of observed dependent variables, with \( n \) rows and \( k \) columns, containing the observed values of the dependent variable,</li>
+<li>\( X \in \mathbf R^{n \times k} \) denote the design matrix with \( k \) columns and \( n \) rows, containing all observed vectors of independent variables \( \boldsymbol x_i \) as rows.</li>
+</ul>
+<p>By definition, </p><p class="formulaDsp">
+\[ P[Y = y_i | \boldsymbol x_i] = \sigma((-1)^{y_i} \cdot \boldsymbol c^T \boldsymbol x_i) \,. \]
+</p>
+<p> Maximizing the likelihood \( \prod_{i=1}^n \Pr(Y = y_i \mid \boldsymbol x_i) \) is equivalent to maximizing the log-likelihood \( \sum_{i=1}^n \log \Pr(Y = y_i \mid \boldsymbol x_i) \), which simplifies to </p><p class="formulaDsp">
+\[ l(\boldsymbol c) = -\sum_{i=1}^n \log(1 + \exp((-1)^{y_i} \cdot \boldsymbol c^T \boldsymbol x_i)) \,. \]
+</p>
+<p> The Hessian of this objective is \( H = -X^T A X \) where \( A = \text{diag}(a_1, \dots, a_n) \) is the diagonal matrix with \( a_i = \sigma(\boldsymbol c^T \boldsymbol x) \cdot \sigma(-\boldsymbol c^T \boldsymbol x) \,. \) Since \( H \) is non-positive definite, \( l(\boldsymbol c) \) is convex. There are many techniques for solving convex optimization problems. Currently, logistic regression in MADlib can use:</p><ul>
+<li>Iteratively Reweighted Least Squares</li>
+</ul>
+<p>We estimate the standard error for coefficient \( i \) as </p><p class="formulaDsp">
+\[ \mathit{se}(c_i) = \left( (X^T A X)^{-1} \right)_{ii} \,. \]
+</p>
+<p> The Wald z-statistic is </p><p class="formulaDsp">
+\[ z_i = \frac{c_i}{\mathit{se}(c_i)} \,. \]
+</p>
+<p>The Wald \( p \)-value for coefficient \( i \) gives the probability (under the assumptions inherent in the Wald test) of seeing a value at least as extreme as the one observed, provided that the null hypothesis ( \( c_i = 0 \)) is true. Letting \( F \) denote the cumulative density function of a standard normal distribution, the Wald \( p \)-value for coefficient \( i \) is therefore </p><p class="formulaDsp">
+\[ p_i = \Pr(|Z| \geq |z_i|) = 2 \cdot (1 - F( |z_i| )) \]
+</p>
+<p> where \( Z \) is a standard normally distributed random variable.</p>
+<p>The odds ratio for coefficient \( i \) is estimated as \( \exp(c_i) \).</p>
+<p>The condition number is computed as \( \kappa(X^T A X) \) during the iteration immediately <em>preceding</em> convergence (i.e., \( A \) is computed using the coefficients of the previous iteration). A large condition number (say, more than 1000) indicates the presence of significant multicollinearity.</p>
+<p>The multinomial logistic regression uses a default reference category of zero, and the regression coefficients in the output are in the order described below. For a problem with \( K \) dependent variables \( (1, ..., K) \) and \( J \) categories \( (0, ..., J-1) \), let \( {m_{k,j}} \) denote the coefficient for dependent variable \( k \) and category \( j \). The output is \( {m_{k_1, j_0}, m_{k_1, j_1} \ldots m_{k_1, j_{J-1}}, m_{k_2, j_0}, m_{k_2, j_1}, \ldots m_{k_2, j_{J-1}} \ldots m_{k_K, j_{J-1}}} \). The order is NOT CONSISTENT with the multinomial regression marginal effect calculation with function <em>marginal_mlogregr</em>. This is deliberate because the interfaces of all multinomial regressions (robust, clustered, ...) will be moved to match that used in marginal.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>A collection of nice write-ups, with valuable pointers into further literature:</p>
+<p>[1] Annette J. Dobson: An Introduction to Generalized Linear Models, Second Edition. Nov 2001</p>
+<p>[2] Cosma Shalizi: Statistics 36-350: Data Mining, Lecture Notes, 18 November 2009, <a href="http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf">http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf</a></p>
+<p>[3] Scott A. Czepiel: Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation, Retrieved Jul 12 2012, <a href="http://czep.net/stat/mlelr.pdf">http://czep.net/stat/mlelr.pdf</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="multiresponseglm_8sql__in.html" title="SQL functions for multinomial regression. ">multiresponseglm.sql_in</a> documenting the multinomial regression functions</p>
+<p><a class="el" href="group__grp__logreg.html">Logistic Regression</a></p>
+<p><a class="el" href="group__grp__ordinal.html">Ordinal Regression</a></p>
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+<div class="title">Ordinal Regression<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Model Details</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>In statistics, ordinal regression is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. The two most common types of ordinal regression models are ordered logit, which applies to data that meet the proportional odds assumption, and ordered probit.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The ordinal regression training function has the following syntax: <pre class="syntax">
+ordinal(source_table,
+         model_table,
+         dependent_varname,
+         independent_varname,
+         cat_order,
+         link_func,
+         grouping_col,
+         optim_params,
+         verbose
+        )
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>model_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the generated table containing the model.</p>
+<p>The model table produced by ordinal() contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td><p class="starttd">Grouping columns, if provided in input. This could be multiple columns depending on the <code>grouping_col</code> input. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>coef_threshold </th><td><p class="starttd">FLOAT8[]. Vector of the threshold coefficients in linear predictor. The threshold coefficients are the intercepts specific to each categorical levels </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>std_err_threshold </th><td><p class="starttd">FLOAT8[]. Vector of the threshold standard errors of the threshold coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>z_stats_threshold </th><td><p class="starttd">FLOAT8[]. Vector of the threshold z-statistics of the thresholdcoefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>p_values_threshold </th><td><p class="starttd">FLOAT8[]. Vector of the threshold p-values of the threshold coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>log_likelihood </th><td><p class="starttd">FLOAT8. The log-likelihood \( l(\boldsymbol \beta) \). The value will be the same across categories within the same group. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>coef_feature </th><td><p class="starttd">FLOAT8[]. Vector of the feature coefficients in linear predictor. The feature coefficients are the coefficients for the independent variables. They are the same across categories. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>std_err_feature </th><td><p class="starttd">FLOAT8[]. Vector of the feature standard errors of the feature coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>z_stats_feature </th><td><p class="starttd">FLOAT8[]. Vector of the feature z-statistics of the feature coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>p_values_feature </th><td><p class="starttd">FLOAT8[]. Vector of the feature p-values of the feature coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_processed </th><td><p class="starttd">BIGINT. Number of rows processed. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_skipped </th><td><p class="starttd">BIGINT. Number of rows skipped due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. Number of iterations actually completed. This would be different from the <code>nIterations</code> argument if a <code>tolerance</code> parameter is provided and the algorithm converges before all iterations are completed.  </td></tr>
+</table>
+<p>A summary table named &lt;model_table&gt;_summary is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method </th><td><p class="starttd">VARCHAR. String describes the model: 'ordinal'. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>source_table </th><td><p class="starttd">VARCHAR. Data source table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>model_table </th><td><p class="starttd">VARCHAR. Model table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">VARCHAR. Expression for dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">VARCHAR. Expression for independent variables. The independent variables should not include intercept term. Otherwise there will be an error message indicating Hessian matrix is not finite. In that case, the user should drop the intercept and rerun the function agian. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>cat_order </th><td><p class="starttd">VARCHAR. String representation of category order. Default is the sorted categories in data using python sort </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>link_func </th><td><p class="starttd">VARCHAR. String that contains link function parameters: 'logit' and 'probit' links are implemented now </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>grouping_col </th><td><p class="starttd">VARCHAR. String representation of grouping columns. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>optimizer_params </th><td><p class="starttd">VARCHAR. String that contains optimizer parameters, and has the form of 'optimizer=..., max_iter=..., tolerance=...'. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">INTEGER. Number of groups in ordinal regression training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">INTEGER. Number of failed groups in ordinal regression training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_processed </th><td><p class="starttd">BIGINT. Total number of rows processed in all groups. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_skipped </th><td><p class="starttd">BIGINT. Total number of rows skipped in all groups due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">VARCHAR. Name of the dependent variable column.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">VARCHAR. Expression list to evaluate for the independent variables. The intercept should not be included here since the cumulative probability force to have intercepts for each category level.</p>
+<p class="enddd"></p>
+</dd>
+<dt>cat_order </dt>
+<dd><p class="startdd">VARCHAR, String that represents the order of category. The order is specified by charactor '&lt;'. </p>
+<p class="enddd"></p>
+</dd>
+<dt>link_function (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: 'logit'. Parameters for link function. Currently, we support logit and probit. </p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optim_params (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: 'max_iter=100,optimizer=irls,tolerance=1e-6'. Parameters for optimizer. Currently, we support tolerance=[tolerance for relative error between log-likelihoods], max_iter=[maximum iterations to run], optimizer=irls.</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Provides verbose output of the results of training. </dd>
+</dl>
+<dl class="section note"><dt>Note</dt><dd>To calculate the standard error the coefficient, we are using the square root of the diagnal elements of the expected Fisher information matrix, which is a by-product of iteratively reweighted least square. This method is used in the original ordinal regression paper by McCullagh(1980). In some software like Stata, the standard error is calculated by the observed information matrix, which is supported by Efron and Hinkley (1978). In R, polr() uses the approximated observed information matrix while the optimization is achieved by first order optimization method. Therefore, there will be some difference on standard error, z-stats and p-value from other software.</dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>Ordinal regression prediction function has the following format: <pre class="syntax">
+ordinal_predict(
+                    model_table,
+                    predict_table_input,
+                    output_table,
+                    predict_type,
+                    verbose
+               )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the model, which is the output table from ordinal().</p>
+<p class="enddd"></p>
+</dd>
+<dt>predict_table_input </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the data to predict on. The table must contain id column as the primary key.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the predicted values.</p>
+<p>The model table produced by ordinal_predict contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>id </th><td><p class="starttd">SERIAL. Column to identify the predicted value. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>category </th><td><p class="starttd">TEXT. Available if the predicted type = 'response'. Column contains the predicted categories </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>category_value </th><td>FLOAT8. The predicted probability for the specific category_value.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>predict_type </dt>
+<dd><p class="startdd">TEXT. Either 'response' or 'probability'. Using 'response' will give the predicted category with the largest probability. Using probability will give the predicted probabilities for all categories</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose </dt>
+<dd>BOOLEAN. Whether verbose is displayed </dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Create the training data table. <pre class="example">
+DROP TABLE IF EXISTS test3;
+CREATE TABLE test3 (
+    feat1 INTEGER,
+    feat2 INTEGER,
+    cat INTEGER
+);
+INSERT INTO test3(feat1, feat2, cat) VALUES
+(1,35,1),
+(2,33,0),
+(3,39,1),
+(1,37,1),
+(2,31,1),
+(3,36,0),
+(2,36,1),
+(2,31,1),
+(2,41,1),
+(2,37,1),
+(1,44,1),
+(3,33,2),
+(1,31,1),
+(2,44,1),
+(1,35,1),
+(1,44,0),
+(1,46,0),
+(2,46,1),
+(2,46,2),
+(3,49,1),
+(2,39,0),
+(2,44,1),
+(1,47,1),
+(1,44,1),
+(1,37,2),
+(3,38,2),
+(1,49,0),
+(2,44,0),
+(3,61,2),
+(1,65,2),
+(3,67,1),
+(3,65,2),
+(1,65,2),
+(2,67,2),
+(1,65,2),
+(1,62,2),
+(3,52,2),
+(3,63,2),
+(2,59,2),
+(3,65,2),
+(2,59,0),
+(3,67,2),
+(3,67,2),
+(3,60,2),
+(3,67,2),
+(3,62,2),
+(2,54,2),
+(3,65,2),
+(3,62,2),
+(2,59,2),
+(3,60,2),
+(3,63,2),
+(3,65,2),
+(2,63,1),
+(2,67,2),
+(2,65,2),
+(2,62,2);
+</pre></li>
+<li>Run the multilogistic regression function. <pre class="example">
+DROP TABLE IF EXISTS test3_output;
+DROP TABLE IF EXISTS test3_output_summary;
+SELECT madlib.ordinal('test3',
+                       'test3_output',
+                       'cat',
+                       'ARRAY[feat1, feat2]',
+                       '0&lt;1&lt;2',
+                       'logit'
+                       );
+</pre></li>
+<li>View the regression results. <pre class="example">
+-- Set extended display on for easier reading of output
+\x on
+SELECT * FROM test3_output;
+</pre></li>
+</ol>
+<p>Result: </p><pre class="result">
+-[ RECORD 1 ]------+-------------------------------------------
+coef_threshold     | {4.12831944358935,6.55999442887089}
+std_err_threshold  | {1.3603408170882,1.54843501580999}
+z_stats_threshold  | {3.03476848722806,4.23653195768075}
+p_values_threshold | {0.00240720390579325,2.26998625331282e-05}
+log_likelihood     | -42.1390192418541
+coef_feature       | {0.574822563129293,0.108115645059558}
+std_err_feature    | {0.394064908788145,0.0276025960683975}
+z_stats_feature    | {1.45870020473791,3.91686509456046}
+p_values_feature   | {0.144647639733733,8.9707915817562e-05}
+num_rows_processed | 57
+num_rows_skipped   | 0
+iteration          | 7
+</pre><ol type="1">
+<li>Predicting dependent variable using ordinal model. (This example uses the original data table to perform the prediction. Typically a different test dataset with the same features as the original training dataset would be used for prediction.)</li>
+</ol>
+<pre class="example">
+\x off
+-- Add the id column for prediction function
+ALTER TABLE test3 ADD COLUMN id SERIAL;
+-- Predict probabilities for all categories using the original data 
+SELECT ordinal_predict('test3_output','test3', 'test3_prd_prob', 'probability');
+-- Display the predicted value
+SELECT * FROM test3_prd_prob;
+</pre><p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Model Details</dt><dd></dd></dl>
+<p>The function ordinal() fit the ordinal response model using a cumulative link model. The ordinal reponse variable, denoted by \( Y_i \), can fall in \( j = 1,.. , J\) categories. Then \( Y_i \) follows a multinomial distribution with parameter \(\pi\) where \(\pi_{ij}\) denote the probability that the \(i\)th observation falls in response category \(j\). We define the cumulative probabilities as </p><p class="formulaDsp">
+\[ \gamma_{ij} = \Pr(Y_i \le j)= \pi_{i1} +...+ \pi_{ij} . \]
+</p>
+<p> Next we will consider the logit link for illustration purpose. The logit function is defined as \( \mbox{logit}(\pi) = \log[\pi/(1-\pi)] \) and cumulative logits are defined as: </p><p class="formulaDsp">
+\[ \mbox{logit}(\gamma_{ij})=\mbox{logit}(\Pr(Y_i \le j))=\log \frac{\Pr(Y_i \le j)}{1-\Pr(Y_i\le j)}, j=1,...,J−1 \]
+</p>
+<p> so that the cumulative logits are defined for all but the last category.</p>
+<p>A cumulative link model with a logit link, or simply cumulative logit model is a regression model for cumulative logits: </p><p class="formulaDsp">
+\[ \mbox{logit}(\gamma_{ij}) = \theta_j - x^T_i \beta \]
+</p>
+<p> where \(x_i\) is a vector of explanatory variables for the \(i\)th observation and \(\beta\) is the corresponding set of regression parameters. The \(\{\theta_j\}\) parameters provide each cumulative logit (for each \(j\)) with its own intercept. A key point is that the regression part \(x^T_i\beta\) is independent of \(j\), so \(\beta\) has the same effect for each of the J − 1 cumulative logits. Note that \(x^T_i\beta\) does not contain an intercept, since the \(\{\theta_j\}\) act as intercepts. For small values of \(x^T_i\beta\) the response is likely to fall in the first category and for large values of \(x^T_i\beta\) the response is likely to fall in the last category. The horizontal displacements of the curves are given by the values of \(\{\theta_j\}\).</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>A collection of nice write-ups, with valuable pointers into further literature:</p>
+<p>[1] Peter McCullagh: Regression Models for Ordinal Data, Journal of the Royal Statistical Society. Series B (Methodological), Volume 42, Issue 2 (1980), 109-142</p>
+<p>[2] Rune Haubo B Christensen: Analysis of ordinal data with cumulative link models &ndash; estimation with the R-package ordinal. cran.r-project.org/web/packages/ordinal/vignettes/clm_intro.pdf</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="ordinal_8sql__in.html" title="SQL functions for ordinal regression. ">ordinal.sql_in</a> documenting the ordinal regression functions</p>
+<p><a class="el" href="group__grp__multinom.html">Multinomial Regression</a></p>
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+<p>the common functions written in PL/PGSQL shared by C4.5 and RF  
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+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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+<tr class="separator:aeaebc7786b60192fdf1e619630f1b3a9"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:af3f4e9330f24da89ed4f060a82110104"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a6962f1f0e776eccb6aa508116114758a"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt_8sql__in.html#a6962f1f0e776eccb6aa508116114758a">__treemodel_score</a> (text tree_table_name, text scoring_table_name, int verbosity)</td></tr>
+<tr class="separator:a6962f1f0e776eccb6aa508116114758a"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a927bd6160ce8a3ac6d1c124c4a864ada"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt_8sql__in.html#a927bd6160ce8a3ac6d1c124c4a864ada">__check_dt_common_params</a> (text split_criterion, text training_table_name, text result_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, text how2handle_missing_value, int max_tree_depth, float node_prune_threshold, float node_split_threshold, int verbosity, text error_msg)</td></tr>
+<tr class="separator:a927bd6160ce8a3ac6d1c124c4a864ada"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aba2c3021282a3b55d3a6ec1f8dd3df29"><td class="memItemLeft" align="right" valign="top">text[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt_8sql__in.html#aba2c3021282a3b55d3a6ec1f8dd3df29">__gen_enc_meta_names</a> (text result_table_name, text error_msg)</td></tr>
+<tr class="separator:aba2c3021282a3b55d3a6ec1f8dd3df29"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a4bf09c8561bcfe6ea90ae739198f174f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt_8sql__in.html#a4bf09c8561bcfe6ea90ae739198f174f">__check_training_table</a> (text training_table_name, text[] continuous_feature_names, text[] feature_col_names, text id_col_name, text class_col_name, int features_per_node)</td></tr>
+<tr class="separator:a4bf09c8561bcfe6ea90ae739198f174f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:adb5272b611e0ff27ae5c5aac84c18880"><td class="memItemLeft" align="right" valign="top">record&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt_8sql__in.html#adb5272b611e0ff27ae5c5aac84c18880">__encode_and_train</a> (text dt_algo_name, text split_criterion, int num_trees, int features_per_node, text training_table_name, text validation_table_name, text tree_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, float8 confidence_level, text how2handle_missing_value, int max_tree_depth, float8 sampling_percentage, bool sampling_needed, float8 node_prune_threshold, float8 node_split_threshold, text error_msg, int verbosity)</td></tr>
+<tr class="separator:adb5272b611e0ff27ae5c5aac84c18880"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>April 5, 2012 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="aa8642bda8d691e9a96de16c22528a21a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __array_indexed_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adb30a86c68851825eaea7ab5177535d9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_indexed_agg_ffunc </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5b11d5994aa54ec795bcd5ad1a4d48db"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_indexed_agg_prefunc </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab68e6854d41c1096d864fdac1f144227"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_indexed_agg_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>elem</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int8&#160;</td>
+          <td class="paramname"><em>elem_cnt</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int8&#160;</td>
+          <td class="paramname"><em>elem_idx</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0788025ac1212c14d1e7caa0d0fbaf26"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __best_scv_aggr </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab27e49b2e2fd5d0ab7cfeb1e0fe99434"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __best_scv_prefunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>sfunc1_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>sfunc2_result</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="accc1e10002e877e0d8f3797fceff593e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __best_scv_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>scv</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>fid</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>split_value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af6c95704957abbd43777161ddd0dbedf"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bigint [] __bigint_array_add </td>
+          <td>(</td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0080a73793982aa08420843ede8a8d19"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bigint [] __bigint_array_sum </td>
+          <td>(</td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a927bd6160ce8a3ac6d1c124c4a864ada"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __check_dt_common_params </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>continuous_feature_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>feature_col_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>how2handle_missing_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_tree_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>node_prune_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>node_split_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>error_msg</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4bf09c8561bcfe6ea90ae739198f174f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __check_training_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>continuous_feature_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>feature_col_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>features_per_node</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a004b8355d238bf16a2050029f2df53da"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __create_tree_tables </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a14ae0d984fcf7f2fbecdabbd11913dc7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __display_node_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>is_cont</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>feat_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>curr_val</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>split_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>max_prob</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>max_class</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_of_samples</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa223a8fdd6ad726d9afd4a23a7f37a48"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __display_tree_no_ordered_aggr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>feature_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>is_cont</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>split_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>metatable_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>tree_id</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a96a4d6fa81ee64b1ad6bae4c537edbcc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bigint [] __dt_acc_count_aggr </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a85520b55ce3fb5c62797ac12fe9dc6e7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bigint [] __dt_acc_count_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">bigint[]&#160;</td>
+          <td class="paramname"><em>count_array</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_of_class</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>count</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>class</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a67f26bae272c28b01e7847dcb83b5e07"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">int [] __dt_get_node_split_fids </td>
+          <td>(</td>
+          <td class="paramtype">int4&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int4&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int4&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int4&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad765843601c38db24e3b546367cf5d17"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __ebp_calc_errors </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>total</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>prob</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>confidence_level</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3d6fe43980a8b0c2b5f17083f89bfd84"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __ebp_prune_tree </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adb5272b611e0ff27ae5c5aac84c18880"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">record __encode_and_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dt_algo_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_trees</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>features_per_node</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>validation_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>continuous_feature_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>feature_col_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>confidence_level</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>how2handle_missing_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_tree_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>sampling_percentage</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bool&#160;</td>
+          <td class="paramname"><em>sampling_needed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>node_prune_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>node_split_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>error_msg</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9c910bacdc581826629aa115065b6561"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __find_best_split </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>confidence_level</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>feature_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>continue_grow</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>h2hmv_routine_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_classes</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a36a6fd5dab72e0245334b86486318d9e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__gen_acc_time __gen_acc </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>encoded_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>metatable_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tr_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>sf_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_featrue_try</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_classes</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>sampling_needed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aba2c3021282a3b55d3a6ec1f8dd3df29"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text [] __gen_enc_meta_names </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>error_msg</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0367356a8a2a00c53ada0e24c48f6f0a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __generate_final_tree </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a8923c9984c28c620b106670ef5ebcfa6"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_features_of_nodes </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>nid_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_chosen_fids</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>total_num_fids</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5e652d8c63a3607016ddd200061a8adb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bool __is_valid_enc_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>enc_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4c36d32b7439fd613710d61e55adea4f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bigint [] __rep_aggr_class_count </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a017cb331505144c8dfbb5034f65938eb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bigint [] __rep_aggr_class_count_ffunc </td>
+          <td>(</td>
+          <td class="paramtype">bigint[]&#160;</td>
+          <td class="paramname"><em>class_count_array</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6a075a120d8018fbed9eb38b85a75e6a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bigint [] __rep_aggr_class_count_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">bigint[]&#160;</td>
+          <td class="paramname"><em>class_count_array</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>classified_class</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>original_class</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_num_of_classes</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af63e5fdb5719dbeda6c4e530b8df1eee"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __rep_prune_tree </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>validation_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_num_classes</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a13e75a310d16b0de9009958e319ddaff"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __sample_with_replacement </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_of_tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>size_per_tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>src_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>target_table</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aaaed339f96774a9e08862e7f8090ac30"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;bigint&gt; __sample_within_range </td>
+          <td>(</td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>num_of_samples</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>low</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>high</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a646a6050ff41857276d9672c334208de"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __scv_aggr </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a75fffc6ac8d5d8468d1507b054e76b3e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __scv_aggr_ffunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>internal_result</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a516db8863709a21b932aca3c7761dbc7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __scv_aggr_prefunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>sfunc1_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>sfunc2_result</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af99d316d7dc10720aa2a5752c4b39a2b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __scv_aggr_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>sc_code</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>is_cont</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_class</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>le_array</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>total_array</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>true_total</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a27b8e062312d85fc1c60cddc6ecef833"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__train_result __train_tree </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_trees</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>features_per_node</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_meta</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>result_tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>validation_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>confidence_level</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_tree_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>sampling_percentage</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>node_prune_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>node_split_threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>sampling_needed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>h2hmv_routine_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aeaebc7786b60192fdf1e619630f1b3a9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text [] __treemodel_classify_internal </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>classification_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af3f4e9330f24da89ed4f060a82110104"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text [] __treemodel_classify_internal_serial </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>classification_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae706328e3d641ad2c14776957525dd74"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text&gt; __treemodel_display_no_ordered_aggr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int[]&#160;</td>
+          <td class="paramname"><em>tree_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_depth</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a8826ae3075d1324c244499f3bd88e0d9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text&gt; __treemodel_display_with_ordered_aggr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int[]&#160;</td>
+          <td class="paramname"><em>tree_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>max_depth</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a50e90916081531e404878cb7b320cd64"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __treemodel_get_vote_result </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>src_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dst_table</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6962f1f0e776eccb6aa508116114758a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float __treemodel_score </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tree_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>scoring_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_704eb8350b43e1ca74c0f90ed1ba450e.html">methods</a></li><li class="navelem"><a class="el" href="dir_fbc4f2259ae1d6f6cc75298ebbd15532.html">cart</a></li><li class="navelem"><a class="el" href="dir_42a199e31e82b0c41cf7961a25e597db.html">src</a></li><li class="navelem"><a class="el" href="dir_64bd18b6b0e4b6a9c2cd2ca1d5a05b4c.html">pg_gp</a></li><li class="navelem"><a class="el" href="dt_8sql__in.html">dt.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[27/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x70.html
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diff --git a/docs/latest/globals_0x70.html b/docs/latest/globals_0x70.html
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@@ -0,0 +1,167 @@
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+<title>MADlib: File Members</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x70.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
+<li>pareto_cdf()
+: <a class="el" href="prob_8sql__in.html#aa1a42ebd68f20f65bc1784b427721b5d">prob.sql_in</a>
+</li>
+<li>pareto_pdf()
+: <a class="el" href="prob_8sql__in.html#a22c56a6e48bc442435b13afac2a1eb37">prob.sql_in</a>
+</li>
+<li>pareto_quantile()
+: <a class="el" href="prob_8sql__in.html#a77779e2b5fa951189ccba6806c503c4d">prob.sql_in</a>
+</li>
+<li>pca_project()
+: <a class="el" href="pca__project_8sql__in.html#a30c86aed79810df5595451481ba74265">pca_project.sql_in</a>
+</li>
+<li>pca_sparse_project()
+: <a class="el" href="pca__project_8sql__in.html#a2ffda9dc6c376df9f31e71b41b7259b3">pca_project.sql_in</a>
+</li>
+<li>pca_sparse_train()
+: <a class="el" href="pca_8sql__in.html#ace5236fb2133c297eca85ab788e6dfc2">pca.sql_in</a>
+</li>
+<li>pca_train()
+: <a class="el" href="pca_8sql__in.html#a2d1cfcf6a7c8a9c69808ec8927b900d1">pca.sql_in</a>
+</li>
+<li>pmml()
+: <a class="el" href="table__to__pmml_8sql__in.html#a3aa36483b5dd5af46f9b564397822817">table_to_pmml.sql_in</a>
+</li>
+<li>poisson_cdf()
+: <a class="el" href="prob_8sql__in.html#ae0b4313d9fe730d6efb3f7c44206f345">prob.sql_in</a>
+</li>
+<li>poisson_pmf()
+: <a class="el" href="prob_8sql__in.html#a82f1edc27261021c73cd080ff2677a9f">prob.sql_in</a>
+</li>
+<li>poisson_quantile()
+: <a class="el" href="prob_8sql__in.html#a032d26db18b2ee1034085f5521939c61">prob.sql_in</a>
+</li>
+<li>poisson_random()
+: <a class="el" href="sample_8sql__in.html#a2051a3f21bc06e3c6e3efcd7492b71c4">sample.sql_in</a>
+</li>
+<li>profile()
+: <a class="el" href="profile_8sql__in.html#a22f0136bb3d7740262b13030a2925a80">profile.sql_in</a>
+</li>
+<li>profile_full()
+: <a class="el" href="profile_8sql__in.html#a879e445d74412b093d411ed9a7180a1e">profile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x71.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x71.html b/docs/latest/globals_0x71.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+   <div id="projectbrief">User Documentation</div>
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
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+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x71.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_q"></a>- q -</h3><ul>
+<li>quantile()
+: <a class="el" href="quantile_8sql__in.html#a6955e44659a0817803ab7a11a9a7e8a5">quantile.sql_in</a>
+</li>
+<li>quantile_big()
+: <a class="el" href="quantile_8sql__in.html#a154435ddf6200110013dc05b1a6207fe">quantile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x72.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x72.html b/docs/latest/globals_0x72.html
new file mode 100644
index 0000000..eb16bb6
--- /dev/null
+++ b/docs/latest/globals_0x72.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
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+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
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+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x72.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
+<li>rayleigh_cdf()
+: <a class="el" href="prob_8sql__in.html#aab0ddb8a5348cfa387d777043a3cb6d0">prob.sql_in</a>
+</li>
+<li>rayleigh_pdf()
+: <a class="el" href="prob_8sql__in.html#a798541736d9255bdd5c0bd94924d47bc">prob.sql_in</a>
+</li>
+<li>rayleigh_quantile()
+: <a class="el" href="prob_8sql__in.html#acd6757acab1683c735e2b57901494336">prob.sql_in</a>
+</li>
+<li>rb_coxph_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a504aa4de0f53044a065e6b1e8a66a285">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_coxph_step_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a0bd8046f4e0956b8dc503656d5127467">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_coxph_strata_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9826d93b250c1fb6e06d412181545754">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ad8fc6f9cca64b26bc7a12f15abfc6234">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9473298f0225d2ad1f6297f360a639d4">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a4bf2a610417ee174074b2191fb921256">robust_variance_coxph.sql_in</a>
+</li>
+<li>relative_error()
+: <a class="el" href="utilities_8sql__in.html#a4d49e00b4dd6059f61ffb9fdc0a696f7">utilities.sql_in</a>
+</li>
+<li>rf_classify()
+: <a class="el" href="rf_8sql__in.html#a116584e8a5194a4277e964900ee5383d">rf.sql_in</a>
+</li>
+<li>rf_clean()
+: <a class="el" href="rf_8sql__in.html#af33b77b75df225ee65a8acf18705256e">rf.sql_in</a>
+</li>
+<li>rf_display()
+: <a class="el" href="rf_8sql__in.html#a60b3f093409a14331b7601522ac0ac42">rf.sql_in</a>
+</li>
+<li>rf_score()
+: <a class="el" href="rf_8sql__in.html#ac6745c3d4cae3443b217f3ba66d11ac4">rf.sql_in</a>
+</li>
+<li>rf_train()
+: <a class="el" href="rf_8sql__in.html#a3cf718282802b63dc0a0d19b34f6829b">rf.sql_in</a>
+</li>
+<li>robust_linregr()
+: <a class="el" href="robust_8sql__in.html#ade1af85b04294ee404a638aa2bfbf051">robust.sql_in</a>
+</li>
+<li>robust_linregr_final()
+: <a class="el" href="robust_8sql__in.html#a458741a15dc6c2ca627f0d1c1d19b7f8">robust.sql_in</a>
+</li>
+<li>robust_linregr_merge_states()
+: <a class="el" href="robust_8sql__in.html#ad85d9925cbab4b59acd798a7b32671ff">robust.sql_in</a>
+</li>
+<li>robust_linregr_transition()
+: <a class="el" href="robust_8sql__in.html#a3f978229f9a2e229276278985d76c438">robust.sql_in</a>
+</li>
+<li>robust_logregr()
+: <a class="el" href="robust_8sql__in.html#a6a9290aafa048bcb7cdc13410d5814aa">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_final()
+: <a class="el" href="robust_8sql__in.html#af8efdd02ad3155411507bf786095a562">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#aa715a6f6885b56d919a243ec4ff07fde">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_transition()
+: <a class="el" href="robust_8sql__in.html#a840ddaa14013c4445b7a0d55e9489d8d">robust.sql_in</a>
+</li>
+<li>robust_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a209021ac52461f029353cec7fb900cd4">robust.sql_in</a>
+</li>
+<li>robust_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">clustered_variance_coxph.sql_in</a>
+, <a class="el" href="robust__variance__coxph_8sql__in.html#a9d08b7ab1efa1f58543214a311344741">robust_variance_coxph.sql_in</a>
+</li>
+<li>robust_variance_linregr()
+: <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust.sql_in</a>
+</li>
+<li>robust_variance_logregr()
+: <a class="el" href="robust_8sql__in.html#a815cadfeb54447993db9ce2bd2aa709e">robust.sql_in</a>
+</li>
+<li>robust_variance_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a93031a31e4c93d3caa22d963927e75c8">robust.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
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+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
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+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x73.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+   <div id="projectbrief">User Documentation</div>
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+$(document).ready(function(){initNavTree('globals_0x73.html','');});
+</script>
+<div id="doc-content">
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+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
+<li>sparse_direct_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8d22e4088dedd57a931b465a73a0357a">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a37d42899e8ec22d8642aa2c943689d19">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a81014be318749e53d8c3fd58eece9600">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8175311c8e181a3ac73d9cc87814bbb0">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#ace39ff8e846754e3e0709bfbbbee9a60">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5ff2f1afd945580fc9cb9bedba84695b">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3daa8daf05cda974b4137ce07c694e2e">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5463eaef5dc5e4040acd56a1035ecc91">sparse_linear_systems.sql_in</a>
+</li>
+<li>squared_dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#a00a08e69f27524f2096032214e15b668">linalg.sql_in</a>
+</li>
+<li>students_t_cdf()
+: <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">prob.sql_in</a>
+</li>
+<li>students_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a8815c21670fff9d31946553a84b845b1">prob.sql_in</a>
+</li>
+<li>students_t_quantile()
+: <a class="el" href="prob_8sql__in.html#a7d64add02af21a95d73502b2dd466a75">prob.sql_in</a>
+</li>
+<li>sum()
+: <a class="el" href="array__ops_8sql__in.html#a26e8508a2bae10a6574cec697a270eea">array_ops.sql_in</a>
+</li>
+<li>summary()
+: <a class="el" href="summary_8sql__in.html#ae39edc1569d25b212ca5ebc06703c6d6">summary.sql_in</a>
+</li>
+<li>svd()
+: <a class="el" href="svd_8sql__in.html#a8dbab74f5bde863a63431a6ec6f66a61">svd.sql_in</a>
+</li>
+<li>svd_block()
+: <a class="el" href="svd_8sql__in.html#a96fb5620eed9c936b5bbfe24d7e37ad6">svd.sql_in</a>
+</li>
+<li>svd_sparse()
+: <a class="el" href="svd_8sql__in.html#a511f83e3eba19f6f41f8e3f7122ddb3a">svd.sql_in</a>
+</li>
+<li>svd_sparse_native()
+: <a class="el" href="svd_8sql__in.html#a0225b28fd4b71cf6e7072c034f3296c6">svd.sql_in</a>
+</li>
+<li>svdmf_run()
+: <a class="el" href="svdmf_8sql__in.html#a6cff34415cca23aa0a826cc08a6283f5">svdmf.sql_in</a>
+</li>
+<li>svec_append()
+: <a class="el" href="svec__util_8sql__in.html#a3950b17b103ceeddca520a0f0d097ddc">svec_util.sql_in</a>
+</li>
+<li>svec_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a1f1025124d5423cc1505991414f17582">svec.sql_in</a>
+</li>
+<li>svec_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af42e4b15879949032a68139c59443aac">svec.sql_in</a>
+</li>
+<li>svec_cast_float8arr()
+: <a class="el" href="svec_8sql__in.html#a96025250e64f049a053c5696b3b96e02">svec.sql_in</a>
+</li>
+<li>svec_cast_int2()
+: <a class="el" href="svec_8sql__in.html#af69458bf9b2906ca471d027966b13e6f">svec.sql_in</a>
+</li>
+<li>svec_cast_int4()
+: <a class="el" href="svec_8sql__in.html#abc01ac6b36ea9d689d338fd72abbd540">svec.sql_in</a>
+</li>
+<li>svec_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a9ea167c01fb2999dc5aaf5e8e21f5e8f">svec.sql_in</a>
+</li>
+<li>svec_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a50de0278ff7ceeff33a2ea4ca5fdf1a0">svec.sql_in</a>
+</li>
+<li>svec_cast_positions_float8arr()
+: <a class="el" href="svec_8sql__in.html#a0ee423729fbca5abd46c86fa81d51f23">svec.sql_in</a>
+</li>
+<li>svec_change()
+: <a class="el" href="svec__util_8sql__in.html#a59407764a1cbf1937da39cf39a2f447c">svec_util.sql_in</a>
+</li>
+<li>svec_cmp()
+: <a class="el" href="svec_8sql__in.html#a813762d8bd2a7fda083880e5bd8c355b">svec.sql_in</a>
+</li>
+<li>svec_concat()
+: <a class="el" href="svec_8sql__in.html#a24f184bef083fd9068c28fc94dad8054">svec.sql_in</a>
+</li>
+<li>svec_concat_replicate()
+: <a class="el" href="svec_8sql__in.html#a34cb5f83e0c010aacc719b8b078bfeb9">svec.sql_in</a>
+</li>
+<li>svec_contains()
+: <a class="el" href="svec__util_8sql__in.html#afdef1c6bf65ffc0e838cf10f33ae5557">svec_util.sql_in</a>
+</li>
+<li>svec_count()
+: <a class="el" href="svec__util_8sql__in.html#a6a21e25d3f50053c888a0bd126f831f0">svec_util.sql_in</a>
+</li>
+<li>svec_count_nonzero()
+: <a class="el" href="svec__util_8sql__in.html#abebdcbe45de346aff874db008e842e65">svec_util.sql_in</a>
+</li>
+<li>svec_dimension()
+: <a class="el" href="svec__util_8sql__in.html#ab8d1acbe4e8551755fef217b506bed8a">svec_util.sql_in</a>
+</li>
+<li>svec_div()
+: <a class="el" href="svec_8sql__in.html#a78a38512c860085e31f6153b8118cbe9">svec.sql_in</a>
+</li>
+<li>svec_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a211cf37584f54087dabc1fac14ed12dd">svec.sql_in</a>
+</li>
+<li>svec_dmax()
+: <a class="el" href="svec__util_8sql__in.html#aa05abd8cb32253c07c0f2224861256a5">svec_util.sql_in</a>
+</li>
+<li>svec_dmin()
+: <a class="el" href="svec__util_8sql__in.html#ad1011a13839994adbb1eb3e643c9074d">svec_util.sql_in</a>
+</li>
+<li>svec_dot()
+: <a class="el" href="svec_8sql__in.html#a47b111663b39ec8646891edec8ab3925">svec.sql_in</a>
+</li>
+<li>svec_elsum()
+: <a class="el" href="svec__util_8sql__in.html#a00a7b3260b9fde9b55061e6bf58a028a">svec_util.sql_in</a>
+</li>
+<li>svec_eq()
+: <a class="el" href="svec_8sql__in.html#adceeff7e2f2e43b3e17219bca5d4fea0">svec.sql_in</a>
+</li>
+<li>svec_eq_non_zero()
+: <a class="el" href="svec__util_8sql__in.html#ab3a1cccd709379973f100e5eb35559d2">svec_util.sql_in</a>
+</li>
+<li>svec_from_string()
+: <a class="el" href="svec__util_8sql__in.html#a1f5e8911574cf30cc979478040bd3262">svec_util.sql_in</a>
+</li>
+<li>svec_ge()
+: <a class="el" href="svec_8sql__in.html#ad08b0cadbcf50ff4685228f917cafd1f">svec.sql_in</a>
+</li>
+<li>svec_gt()
+: <a class="el" href="svec_8sql__in.html#a3e5d57b7a4473904e4d053884468e540">svec.sql_in</a>
+</li>
+<li>svec_hash()
+: <a class="el" href="svec__util_8sql__in.html#a0bfa8f8d083282bc9e8caf6f03bb5251">svec_util.sql_in</a>
+</li>
+<li>svec_in()
+: <a class="el" href="svec_8sql__in.html#abe896192db26c02d5c517a5ccf2144f1">svec.sql_in</a>
+</li>
+<li>svec_l1norm()
+: <a class="el" href="svec__util_8sql__in.html#ae60f7aaa5710c765d08b6c3906e0c9f0">svec_util.sql_in</a>
+</li>
+<li>svec_l2_cmp()
+: <a class="el" href="svec_8sql__in.html#a8f72c8cf6d39f08d939f23cf1338a3c6">svec.sql_in</a>
+</li>
+<li>svec_l2_eq()
+: <a class="el" href="svec_8sql__in.html#a9268d26c54c4b2905eb7938df95af8c0">svec.sql_in</a>
+</li>
+<li>svec_l2_ge()
+: <a class="el" href="svec_8sql__in.html#ac22b72fbae2a8f2b35b1c571d9f54dad">svec.sql_in</a>
+</li>
+<li>svec_l2_gt()
+: <a class="el" href="svec_8sql__in.html#ab2414d0d78286884cd199cf44c1d17b9">svec.sql_in</a>
+</li>
+<li>svec_l2_le()
+: <a class="el" href="svec_8sql__in.html#a4045437bce8b9a9bdb0eb3115c97ca99">svec.sql_in</a>
+</li>
+<li>svec_l2_lt()
+: <a class="el" href="svec_8sql__in.html#ab53b85cb3412c9172bf0ee04f6856c69">svec.sql_in</a>
+</li>
+<li>svec_l2_ne()
+: <a class="el" href="svec_8sql__in.html#af35c08aadf164fa3bbc8678765bca643">svec.sql_in</a>
+</li>
+<li>svec_l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a475f0f3e4602e8278b582e099fce2487">svec_util.sql_in</a>
+</li>
+<li>svec_lapply()
+: <a class="el" href="svec__util_8sql__in.html#a0d94c44dde95a00e3d802dee6d7c01eb">svec_util.sql_in</a>
+</li>
+<li>svec_le()
+: <a class="el" href="svec_8sql__in.html#aa6f5715df728db0783b5dcb8db82a2a2">svec.sql_in</a>
+</li>
+<li>svec_log()
+: <a class="el" href="svec__util_8sql__in.html#aab641f9db3deed5bbfa7e050c531e9c1">svec_util.sql_in</a>
+</li>
+<li>svec_lt()
+: <a class="el" href="svec_8sql__in.html#ad7f05a6ce6efc51517bab6ccdb79ee23">svec.sql_in</a>
+</li>
+<li>svec_mean_final()
+: <a class="el" href="svec__util_8sql__in.html#ac3fc45511b50b5d054bf7bbeb952b98d">svec_util.sql_in</a>
+</li>
+<li>svec_mean_prefunc()
+: <a class="el" href="svec__util_8sql__in.html#ab161647a1498c85762779966790eab71">svec_util.sql_in</a>
+</li>
+<li>svec_mean_transition()
+: <a class="el" href="svec__util_8sql__in.html#afcf2f75cd00ab0ba269caf79dcfcc62c">svec_util.sql_in</a>
+</li>
+<li>svec_median()
+: <a class="el" href="svec__util_8sql__in.html#a2284a118fe03465203ecea8c34acefa5">svec_util.sql_in</a>
+</li>
+<li>svec_median_inmemory()
+: <a class="el" href="svec__util_8sql__in.html#a93400e39ecce2f11b9ceb08403420a43">svec_util.sql_in</a>
+</li>
+<li>svec_minus()
+: <a class="el" href="svec_8sql__in.html#a170890a165bf662d343339ffc2a187cf">svec.sql_in</a>
+</li>
+<li>svec_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#adf39975fbbd8a39629569c622db33ab9">svec.sql_in</a>
+</li>
+<li>svec_mult()
+: <a class="el" href="svec_8sql__in.html#a27f0629942dba2e395b005c728d53ac3">svec.sql_in</a>
+</li>
+<li>svec_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6e6916baaf0a7ae0079424ce0565c8b4">svec.sql_in</a>
+</li>
+<li>svec_ne()
+: <a class="el" href="svec_8sql__in.html#a80d9692f67e7cb482c4bda6dbaacd82b">svec.sql_in</a>
+</li>
+<li>svec_nonbase_positions()
+: <a class="el" href="svec__util_8sql__in.html#a5bc053a080fb38601ad91045dc3ffe74">svec_util.sql_in</a>
+</li>
+<li>svec_nonbase_values()
+: <a class="el" href="svec__util_8sql__in.html#a076dcbc0a278bb3b5161b0db2ad8fa44">svec_util.sql_in</a>
+</li>
+<li>svec_out()
+: <a class="el" href="svec_8sql__in.html#a3db4af36acd318030ae57d2bfd19387a">svec.sql_in</a>
+</li>
+<li>svec_pivot()
+: <a class="el" href="svec__util_8sql__in.html#a929bde3bc91b67300d9d370b6a1495f1">svec_util.sql_in</a>
+</li>
+<li>svec_plus()
+: <a class="el" href="svec_8sql__in.html#a612d8920c61e6fdf18dd4e3e9c69c979">svec.sql_in</a>
+</li>
+<li>svec_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#aaeb54c952836c83c7f21a1c62a77955f">svec.sql_in</a>
+</li>
+<li>svec_pow()
+: <a class="el" href="svec_8sql__in.html#ab5166565f3482768db33ee677acc3d96">svec.sql_in</a>
+</li>
+<li>svec_proj()
+: <a class="el" href="svec__util_8sql__in.html#a8787222aec691f94d9808d1369aa401c">svec_util.sql_in</a>
+</li>
+<li>svec_recv()
+: <a class="el" href="svec_8sql__in.html#a585ea8bcb1d0647ef1295187b87405b2">svec.sql_in</a>
+</li>
+<li>svec_return_array()
+: <a class="el" href="svec_8sql__in.html#a7decda276d7ff1e9340dd44e0e7419c0">svec.sql_in</a>
+</li>
+<li>svec_reverse()
+: <a class="el" href="svec__util_8sql__in.html#a4a221435ca91b6fa45114e689ee96010">svec_util.sql_in</a>
+</li>
+<li>svec_send()
+: <a class="el" href="svec_8sql__in.html#a617557435170136b2781070c0a268b83">svec.sql_in</a>
+</li>
+<li>svec_sfv()
+: <a class="el" href="svec__util_8sql__in.html#a375acd521ed9cb05f63b3696dcc10bf4">svec_util.sql_in</a>
+</li>
+<li>svec_sort()
+: <a class="el" href="svec__util_8sql__in.html#a91c23a7ce58932f48f5ab3579ea0309e">svec_util.sql_in</a>
+</li>
+<li>svec_subvec()
+: <a class="el" href="svec__util_8sql__in.html#a5cb3446de5fc117befe88ccb1ebb0e4e">svec_util.sql_in</a>
+</li>
+<li>svec_sum()
+: <a class="el" href="svec__util_8sql__in.html#af1ce6cdd7244ac0832d15c65b7ccc782">svec_util.sql_in</a>
+</li>
+<li>svec_to_string()
+: <a class="el" href="svec__util_8sql__in.html#a1de746ebd23530e72ba4f031ac41d155">svec_util.sql_in</a>
+</li>
+<li>svec_unnest()
+: <a class="el" href="svec__util_8sql__in.html#a87b45a983da4066fa7f35b17ccecf699">svec_util.sql_in</a>
+</li>
+<li>svm_classification()
+: <a class="el" href="online__sv_8sql__in.html#ad90b6bf3b807f22d37b0e2b1893262f0">online_sv.sql_in</a>
+</li>
+<li>svm_cls_agg()
+: <a class="el" href="online__sv_8sql__in.html#a4fd5a4571d959a892ab28dab1fea92f3">online_sv.sql_in</a>
+</li>
+<li>svm_cls_update()
+: <a class="el" href="online__sv_8sql__in.html#a837bd8cfd5f3200e985febf0ff7ec013">online_sv.sql_in</a>
+</li>
+<li>svm_data_normalization()
+: <a class="el" href="online__sv_8sql__in.html#a78bb07d8f4aee80c3bbc8e8176e512be">online_sv.sql_in</a>
+</li>
+<li>svm_dot()
+: <a class="el" href="online__sv_8sql__in.html#ab53cac5790dafd7230359e08c72af4f1">online_sv.sql_in</a>
+</li>
+<li>svm_drop_model()
+: <a class="el" href="online__sv_8sql__in.html#ab54d33f13c0e00faa358e3e3f17c10fb">online_sv.sql_in</a>
+</li>
+<li>svm_gaussian()
+: <a class="el" href="online__sv_8sql__in.html#a9f2a96e1a241ecc66386a78b110777d3">online_sv.sql_in</a>
+</li>
+<li>svm_generate_cls_data()
+: <a class="el" href="online__sv_8sql__in.html#a1cf6d0632772caf6643212a4698d2507">online_sv.sql_in</a>
+</li>
+<li>svm_generate_nd_data()
+: <a class="el" href="online__sv_8sql__in.html#a8be25cfa6134cc12bbc6d31c04712c23">online_sv.sql_in</a>
+</li>
+<li>svm_generate_reg_data()
+: <a class="el" href="online__sv_8sql__in.html#adc9fe3f9b4a04dff13004edbaab8240f">online_sv.sql_in</a>
+</li>
+<li>svm_nd_agg()
+: <a class="el" href="online__sv_8sql__in.html#a9e0abf5bf91bd0833bf80b9d9a5ae8bf">online_sv.sql_in</a>
+</li>
+<li>svm_nd_update()
+: <a class="el" href="online__sv_8sql__in.html#ae6aef7b5654b6da487d0178adf443ee9">online_sv.sql_in</a>
+</li>
+<li>svm_novelty_detection()
+: <a class="el" href="online__sv_8sql__in.html#a75b773bcf4b5242c673d0e54128ed159">online_sv.sql_in</a>
+</li>
+<li>svm_polynomial()
+: <a class="el" href="online__sv_8sql__in.html#a1ac76fdf9623e0a4db47665f2a80be90">online_sv.sql_in</a>
+</li>
+<li>svm_predict()
+: <a class="el" href="online__sv_8sql__in.html#adc8c1c1d5e2deb20f5436790468b052f">online_sv.sql_in</a>
+</li>
+<li>svm_predict_batch()
+: <a class="el" href="online__sv_8sql__in.html#a91ac71354e9dec74e25339bf168c2e5b">online_sv.sql_in</a>
+</li>
+<li>svm_predict_sub()
+: <a class="el" href="online__sv_8sql__in.html#a5988c209ceb6d15b9139eef0c16d7b7d">online_sv.sql_in</a>
+</li>
+<li>svm_reg_agg()
+: <a class="el" href="online__sv_8sql__in.html#a225bdb74eaa687cef0ddbb35145318fc">online_sv.sql_in</a>
+</li>
+<li>svm_reg_update()
+: <a class="el" href="online__sv_8sql__in.html#a0eb68a44edff3fa53891a6a39de61e57">online_sv.sql_in</a>
+</li>
+<li>svm_regression()
+: <a class="el" href="online__sv_8sql__in.html#acaf1f4aa3eec5710de5c03e368a4b106">online_sv.sql_in</a>
+</li>
+<li>svm_store_model()
+: <a class="el" href="online__sv_8sql__in.html#ad2cd899daaa2036b475f71c28cc031ae">online_sv.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x74.html
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diff --git a/docs/latest/globals_0x74.html b/docs/latest/globals_0x74.html
new file mode 100644
index 0000000..24ba325
--- /dev/null
+++ b/docs/latest/globals_0x74.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x74.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
+<li>t_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac97c5f5015790b59645d69858e127645">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a14fdcfa276fd1a7ea2e3adb41ebe7db4">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ad570d893565618bcbbcbb01b3bb0a9b9">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a3bd1bcc335a2da73d01b40e06f7d2eea">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_pooled()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5c306ba8380ce6567831fef4610e515b">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_pooled_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a0a0a8a579bbf0f0d1efcbf62223e3431">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a1a835d80d1846a10a2c25b91ce81c6d2">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_unpooled()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac50750a0e0797ce24af1dc479b9699e1">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_unpooled_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a8fe7f38d29bf835718adca811e36f15a">hypothesis_tests.sql_in</a>
+</li>
+<li>tanimoto_distance()
+: <a class="el" href="svec__util_8sql__in.html#a98222e58bc519865a26643a61611b717">svec_util.sql_in</a>
+</li>
+<li>tree_display()
+: <a class="el" href="decision__tree_8sql__in.html#a3c51edcc0fe3de029dc39a3f0306155d">decision_tree.sql_in</a>
+</li>
+<li>tree_predict()
+: <a class="el" href="decision__tree_8sql__in.html#abb6f3ecdd2fdcb7661ee10a65419066e">decision_tree.sql_in</a>
+</li>
+<li>tree_surr_display()
+: <a class="el" href="decision__tree_8sql__in.html#a6ba706727adf88f8c07cfe09f71732bb">decision_tree.sql_in</a>
+</li>
+<li>tree_train()
+: <a class="el" href="decision__tree_8sql__in.html#a639431da2553fb86673e82d62cde4458">decision_tree.sql_in</a>
+</li>
+<li>triangular_cdf()
+: <a class="el" href="prob_8sql__in.html#abf9c7d870bcfe68cacaa421749bbdf35">prob.sql_in</a>
+</li>
+<li>triangular_pdf()
+: <a class="el" href="prob_8sql__in.html#a0c511b9748b2f7a21fe56aaf5f66d188">prob.sql_in</a>
+</li>
+<li>triangular_quantile()
+: <a class="el" href="prob_8sql__in.html#a4777540ab1b003ff92d484c4bc26af27">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x75.html
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diff --git a/docs/latest/globals_0x75.html b/docs/latest/globals_0x75.html
new file mode 100644
index 0000000..2e249bb
--- /dev/null
+++ b/docs/latest/globals_0x75.html
@@ -0,0 +1,140 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
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+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
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+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+</div><!-- top -->
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x75.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
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[45/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/cox__prop__hazards_8sql__in.html
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+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>July 2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to cox regression, see the module description <a class="el" href="group__grp__cox__prop__hazards.html">Cox-Proportional Hazards Regression</a> </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a31f48cba79d114f8f251c5aa77b3c32a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_elem_corr_final </td>
+          <td>(</td>
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+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_elem_corr_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>left_state</em>, </td>
+        </tr>
+        <tr>
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+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>right_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
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+      </table>
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+</div>
+</div>
+<a class="anchor" id="a597e43a46842b113412d0940b3d175e4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_elem_corr_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
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+      </table>
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+</div>
+<a class="anchor" id="a8e3917098eeb129096fa871e7e593e1d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_element_max </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
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+        <tr>
+          <td></td>
+          <td>)</td>
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+<a class="anchor" id="abd49fa0a100bfe513baa90e32fe37839"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_element_min </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a41a4e85912f00b5b73207f837dccc359"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __coxph_resid_stat_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>w</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>residual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>hessian</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>m</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3e6c330ccd548a25c3ef0153f0c956e4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__cox_resid_stat_result __coxph_resid_stat_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a51da9d74afff6f4ec5a34bbdbec5c868"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __coxph_resid_stat_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab3b596b440d3850d742ebab0245dbf61"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __coxph_resid_stat_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>w</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>residual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>hessian</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>m</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a97191f842120124d56329b8b20ed837c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __coxph_scale_resid </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>m</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>hessian</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>resid</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac4025de22406c470e32e35376d0f54a4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __zph_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>left_state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="afe87ae70e07135bc657b6c9db58a2820"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __zph_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>left_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>right_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abb83dc1e36c379287e4dfdf14a350647"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __zph_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a85be1c85fcf03e9b1bc25798e6fa9914"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer _compute_grpid </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>splits</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>split_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>reverse</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae94792f3fb4a0697e11db4334f643164"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer _compute_grpid </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>splits</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>split_col</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a138656eb9696e48be0c5a90ee2de5a44"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] _compute_splits </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1d53d501174ed20ace7108db2ca8863d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 _coxph_predict_resp </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>mean_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pred_type</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a063f714c9a2c0f618007f0274febb266"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] _coxph_predict_terms </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>mean_ind_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac5ba4af69415de3372e5a9d9f97a96af"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] _split_final </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2e641566026ce44f191e722db6d4f4d3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] _split_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a842f88fb0d1f17d5410ac30fb1445deb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] _split_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5734f5796a403ebf2e765cba136f54c8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] array_avg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1f5594f3a1199bfd91d7479292e25b5c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] array_avg_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a45bd6774621ad8fe0baa9427f7ce8fd4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] array_avg_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>left</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>right</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0ffc559f8f54d7ea49c8d1a865ef5a37"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] array_avg_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>use_abs</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a8928da50ddcf85de812ab058214324db"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] array_elem_corr_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>array_input</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>common_elem</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1de2b192c73ede707b42726e9469664d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] array_element_max </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2ec0ea5a3faba236a75d4d9cc16e40a4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] array_element_min </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a74fe60f99c3808e38421c71286850ea8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">coxph_result compute_coxph_result </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>l</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>d2l</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>niter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>stds</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad778b289eb19ae0bb2b7e02a89bab3bc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cox_prop_hazards </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>status</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5e52f9a98bce4ec74230eb95c95e68c4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cox_prop_hazards </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_variable</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a03909f1c14894303fca80ea8503ae7f0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar cox_prop_hazards </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>usage_string</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af92ac384ab346034dc42a4049613a47a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar cox_prop_hazards </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3310cf98478b7c1e400e8fb1b3965d30"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">cox_prop_hazards_result cox_prop_hazards_regr </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>indepColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>depColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>maxNumIterations</em> = <code>20</code>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer</em> = <code>&quot;newton&quot;</code>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>precision</em> = <code>0.0001</code>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a79ca0bbf1a3a5c1f83ede9596d4b43cc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">cox_prop_hazards_result cox_prop_hazards_regr </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>indepColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>depColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>status</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a217c18984301c16b39a6deffa8c677df"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">cox_prop_hazards_result cox_prop_hazards_regr </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>indepColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>depColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>maxNumIterations</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6508b2b2b8e0b6a0d0027c3d8b487df8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">cox_prop_hazards_result cox_prop_hazards_regr </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>indepColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>depColumn</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>maxNumIterations</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a682d95d5475ce33e47937067cadc2766"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cox_zph </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>coxph_model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coxph_model_table</td><td>Name of the table that contains the Cox Proportional Hazards model </td></tr>
+    <tr><td class="paramname">output_table</td><td>Name of the output table to contain the statistics to test the proportional hazards assumption</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>An output table with following columns: Columns of the matrix contain the correlation coefficient between transformed , a chi-square, and the two-sided p-value.<ul>
+<li><code>rho FLOAT8[]</code> - Array of correlation coefficient between survival time and the scaled Schoenfeld residuals</li>
+<li><code>chi_square FLOAT8[]</code> - Chi-square test statistic for the</li>
+<li><code>p_value FLOAT8[]</code> - Two-side p-value for the chi-square test </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="aad77f665f0030202444e99f5a0b895d3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar cox_zph </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af3aee9274235a9a44511e8cc57f15bcb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar cox_zph </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac5ae827993064fd6e0ccf13a96262cdc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">coxph_step_result coxph_improved_step_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6a9ae1ea761b31e1522c47a9794cc233"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] coxph_improved_step_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>max_coef</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a41a415076894d3844475208b2163a267"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">coxph_step_result coxph_improved_strata_step_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abb21bacbff7362724d2698568d857555"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] coxph_improved_strata_step_outer </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0ad613a529ee147565a605d6f443a4bd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pred_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>reference</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">model_table</td><td>Name of the table containing the cox model. </td></tr>
+    <tr><td class="paramname">source_table</td><td>Name of the table containing the prediction data. </td></tr>
+    <tr><td class="paramname">output_table</td><td>Name of the table to store the prediction results into. </td></tr>
+    <tr><td class="paramname">pred_type</td><td>Type of prediction. Can be one of 'linear_predictors', 'risk' or 'terms'. </td></tr>
+    <tr><td class="paramname">reference</td><td>Reference level to use for centering predictions. Can be one of 'overall' or 'strata'. </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="a815961d52fb8ce998071eb92056ee1dd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pred_type</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="acd445fe632e93b2c8260b0455478e2e5"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9edc6c578176561159b2ecde7415a751"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar coxph_predict </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3f1a4a5844c5b2629bcba051e75eee70"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar coxph_predict </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3c96655e41fd17c86dfc9ad37b0959d0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] coxph_step_inner_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abaf93fc77a284f0b765a64ef827c1bed"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] coxph_step_outer_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a737450bbfe0f10204b0074a9d45b0cef"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_train </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>right_censoring_status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>strata</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>To include an intercept in the model, set one coordinate in the <code>independentVariables</code> array to 1.</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">source_table</td><td>Name of the source relation containing the training data </td></tr>
+    <tr><td class="paramname">output_table</td><td>Name of the output relation to contain the coxph_train model </td></tr>
+    <tr><td class="paramname">independent_varname</td><td>Name of the independent column </td></tr>
+    <tr><td class="paramname">dependent_varname</td><td>Name of the dependent column measuring time of death </td></tr>
+    <tr><td class="paramname">right_censoring_status</td><td>Name of the column that determines right censoring support </td></tr>
+    <tr><td class="paramname">strata</td><td>Comma-separated list of column names on which to stratify the data </td></tr>
+    <tr><td class="paramname">optimizer_params</td><td>Comma-separated list of parameters for the optimizer function</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>An output table with following columns:<ul>
+<li><code>coef FLOAT8[]</code> - Array of coefficients, \( \boldsymbol \beta \)</li>
+<li><code>log_likelihood FLOAT8</code> - Log-likelihood \(l(\boldsymbol \beta)\)</li>
+<li><code>std_err FLOAT8[]</code> - Array of standard errors, \( \mathit{se}(c_1), \dots, \mathit{se}(c_k) \)</li>
+<li><code>z_stats FLOAT8[]</code> - Array of Wald z-statistics, \( \boldsymbol z \)</li>
+<li><code>p_values FLOAT8[]</code> - Array of Wald p-values, \( \boldsymbol p \)</li>
+<li><code>hessian FLOAT8[]</code> - Hessian Matrix</li>
+<li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated</li>
+</ul>
+</dd></dl>
+<ul>
+<li>Get vector of coefficients \( \boldsymbol \beta \) and all diagnostic statistics:<br />
+ <pre>SELECT <a class="el" href="cox__prop__hazards_8sql__in.html#a76aeb28f6e0fc47fd59c0d6c6c7be063">coxph_train</a>(
+   '<em>source_table</em>', '<em>output_table</em>',
+   '<em>dependent_varname</em>', '<em>independent_varname</em>'
+   '<em>right_censoring_status</em>', '<em>strata</em>'
+);</pre></li>
+<li>Get vector of coefficients \( \boldsymbol \beta \):<br />
+ <pre>SELECT coef from <em>output_table</em></pre></li>
+</ul>
+<p>See <a class="el" href="group__grp__cox__prop__hazards.html">Cox-Proportional Hazards Regression</a> for more details. </p>
+
+</div>
+</div>
+<a class="anchor" id="ae7edb9499e07fdf835c3bbc297a99c01"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar coxph_train </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6f527631dc32178716221cc9dfa1fde4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar coxph_train </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3fd1cd09fd5de41d6dc8aca0a18eec8b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_train </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>right_censoring_status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>strata</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="afd5fb08d419aab4a8b8eb103afa30f29"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_train </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>right_censoring_status</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a76aeb28f6e0fc47fd59c0d6c6c7be063"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void coxph_train </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_variable</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a91e1a4b2a2d13454f9e65dd3d40a329b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] zph_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_505cd743a8a717435eca324f49291a46.html">stats</a></li><li class="navelem"><a class="el" href="cox__prop__hazards_8sql__in.html">cox_prop_hazards.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[05/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__sparse__linear__solver.html
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+<title>MADlib: Sparse Linear Systems</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Sparse Linear Systems<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> &raquo; <a class="el" href="group__grp__linear__solver.html">Linear Solvers</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#sls_usage">Solution Function</a> </li>
+<li class="level1">
+<a href="#sls_opt_params">Optimizer Parameters</a> </li>
+<li class="level1">
+<a href="#sls_output">Output Tables</a> </li>
+<li class="level1">
+<a href="#sls_examples">Examples</a> </li>
+<li>
+<a href="related">Related Topics</a> </li>
+</ul>
+</div><p>The sparse linear systems module implements solution methods for systems of consistent linear equations. Systems of linear equations take the form: </p><p class="formulaDsp">
+\[ Ax = b \]
+</p>
+<p>where \(x \in \mathbb{R}^{n}\), \(A \in \mathbb{R}^{m \times n} \) and \(b \in \mathbb{R}^{m}\). This module accepts sparse matrix input formats for \(A\) and \(b\). We assume that there are no rows of \(A\) where all elements are zero.</p>
+<dl class="section note"><dt>Note</dt><dd>Algorithms with fail if there is an row of the input matrix containing all zeros.</dd></dl>
+<p>The algorithms implemented in this module can handle large sparse square linear systems. Currently, the algorithms implemented in this module solve the linear system using direct or iterative methods.</p>
+<p><a class="anchor" id="sls_usage"></a></p><dl class="section user"><dt>Sparse Linear Systems Solution Function</dt><dd></dd></dl>
+<pre class="syntax">
+linear_solver_sparse( tbl_source_lhs,
+                      tbl_source_rhs,
+                      tbl_result,
+                      lhs_row_id,
+                      lhs_col_id,
+                      lhs_value,
+                      rhs_row_id,
+                      rhs_value,
+                      grouping_cols := NULL,
+                      optimizer := 'direct',
+                      optimizer_params :=
+                      'algorithm = llt'
+                    )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>tbl_source_lhs </dt>
+<dd><p class="startdd">The name of the table containing the left hand side matrix. For the LHS matrix, the input data is expected to be of the following form: </p><pre>
+{TABLE|VIEW} <em>sourceName</em> (
+    ...
+    <em>row_id</em> FLOAT8,
+    <em>col_id</em> FLOAT8,
+    <em>value</em> FLOAT8,
+    ...
+)</pre><p> Each row represents a single equation. The <em>rhs</em> columns refer to the right hand side of the equations and the <em>lhs</em> columns refer to the multipliers on the variables on the left hand side of the same equations. </p>
+<p class="enddd"></p>
+</dd>
+<dt>tbl_source_rhs </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the right hand side vector. For the RHS matrix, the input data is expected to be of the following form: </p><pre class="fragment">{TABLE|VIEW} &lt;em&gt;sourceName&lt;/em&gt; (
+    ...
+    &lt;em&gt;row_id&lt;/em&gt; FLOAT8,
+    &lt;em&gt;value&lt;/em&gt; FLOAT8
+    ...
+)</pre><p> Each row represents a single equation. The <em>rhs</em> columns refer to the right hand side of the equations while the <em>lhs</em> columns refers to the multipliers on the variables on the left hand side of the same equations. </p>
+<p class="enddd"></p>
+</dd>
+<dt>tbl_result </dt>
+<dd><p class="startdd">TEXT. The name of the table where the output is saved. Output is stored in the tabled named by the <em>tbl_result</em> argument. The table contains the following columns. The output contains the following columns: </p><table  class="output">
+<tr>
+<th>solution </th><td>FLOAT8[]. The solution is an array with the variables in the same order as that provided as input in the 'left_hand_side' column name of the 'source_table'   </td></tr>
+<tr>
+<th>residual_norm </th><td>FLOAT8. Scaled residual norm, defined as \( \frac{|Ax - b|}{|b|} \). This value is an indication of the accuracy of the solution.   </td></tr>
+<tr>
+<th>iters </th><td>INTEGER. Number of iterations required by the algorithm (only applicable for iterative algorithms) . The output is NULL for 'direct' methods.   </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>lhs_row_id </dt>
+<dd>TEXT. The name of the column storing the 'row id' of the equations. <dl class="section note"><dt>Note</dt><dd>For a system with N equations, the row_id's must be a continuous range of integers from \( 0 \ldots n-1 \).</dd></dl>
+</dd>
+<dt>lhs_col_id </dt>
+<dd><p class="startdd">TEXT. The name of the column (in tbl_source_lhs) storing the 'col id' of the equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>lhs_value </dt>
+<dd><p class="startdd">TEXT. The name of the column (in tbl_source_lhs) storing the 'value' of the equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>rhs_row_id </dt>
+<dd><p class="startdd">TEXT. The name of the column (in tbl_source_rhs) storing the 'col id' of the equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>rhs_value </dt>
+<dd><p class="startdd">TEXT. The name of the column (in tbl_source_rhs) storing the 'value' of the equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>num_vars </dt>
+<dd><p class="startdd">INTEGER. The number of variables in the linear system equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional)  </dt>
+<dd>TEXT, default: NULL. Group by column names. <dl class="section note"><dt>Note</dt><dd>The grouping feature is currently not implemented and this parameter is only a placeholder.</dd></dl>
+</dd>
+<dt>optimizer (optional)  </dt>
+<dd><p class="startdd">TEXT, default: 'direct'. Type of optimizer.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer_params (optional) </dt>
+<dd>TEXT, default: NULL. Optimizer specific parameters. </dd>
+</dl>
+<p><a class="anchor" id="sls_opt_params"></a></p><dl class="section user"><dt>Optimizer Parameters</dt><dd></dd></dl>
+<p>For each optimizer, there are specific parameters that can be tuned for better performance.</p>
+<dl class="arglist">
+<dt>algorithm (default: ldlt) </dt>
+<dd><p class="startdd"></p>
+<p>There are several algorithms that can be classified as 'direct' methods of solving linear systems. Madlib functions provide various algorithmic options available for users.</p>
+<p>The following table provides a guideline on the choice of algorithm based on conditions on the A matrix, speed of the algorithms and numerical stability.</p>
+<pre class="fragment">    Algorithm          | Conditions on A  | Speed | Memory
+    ----------------------------------------------------------
+    llt                | Sym. Pos Def     |  ++   |  ---
+    ldlt               | Sym. Pos Def     |  ++   |  ---
+
+    For speed '++' is faster than '+', which is faster than '-'.
+    For accuracy '+++' is better than '++'.
+    For memory, '-' uses less memory than '--'.
+
+    Note: ldlt is often preferred over llt
+</pre><p>There are several algorithms that can be classified as 'iterative' methods of solving linear systems. Madlib functions provide various algorithmic options available for users.</p>
+<p>The following table provides a guideline on the choice of algorithm based on conditions on the A matrix, speed of the algorithms and numerical stability.</p>
+<pre class="fragment">    Algorithm            | Conditions on A  | Speed | Memory | Convergence
+    ----------------------------------------------------------------------
+    cg-mem               | Sym. Pos Def     |  +++  |   -    |    ++
+    bicgstab-mem         | Square           |  ++   |   -    |    +
+    precond-cg-mem       | Sym. Pos Def     |  ++   |   -    |    +++
+    precond-bicgstab-mem | Square           |  +    |   -    |    ++
+
+    For memory, '-' uses less memory than '--'.
+    For speed, '++' is faster than '+'.
+</pre><p>Algorithm Details: </p><table  class="output">
+<tr>
+<th>cg-mem</th><td>In memory conjugate gradient with diagonal preconditioners.  </td></tr>
+<tr>
+<th>bicgstab-mem</th><td>Bi-conjugate gradient (equivalent to performing CG on the least squares formulation of Ax=b) with incomplete LU preconditioners.  </td></tr>
+<tr>
+<th>precond-cg-mem</th><td>In memory conjugate gradient with diagonal preconditioners.  </td></tr>
+<tr>
+<th>bicgstab-mem</th><td>Bi-conjugate gradient (equivalent to performing CG on the least squares formulation of Ax=b) with incomplete LU preconditioners.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>toler (default: 1e-5) </dt>
+<dd><p class="startdd">Termination tolerance (applicable only for iterative methods) which determines the stopping criterion (with respect to residual norm) for iterative methods. </p>
+<p class="enddd"></p>
+</dd>
+</dl>
+<p><a class="anchor" id="sls_examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the sparse linear systems solver function. <pre class="example">
+SELECT madlib.linear_solver_sparse();
+</pre></li>
+<li>Create the sample data set. <pre class="example">
+DROP TABLE IF EXISTS sparse_linear_systems_lhs;
+CREATE TABLE sparse_linear_systems_lhs (
+    rid INTEGER NOT NULL,
+    cid  INTEGER,
+    val DOUBLE PRECISION
+);
+DROP TABLE IF EXISTS sparse_linear_systems_rhs;
+CREATE TABLE sparse_linear_systems_rhs (
+    rid INTEGER NOT NULL,
+    val DOUBLE PRECISION
+);
+INSERT INTO sparse_linear_systems_lhs(rid, cid, val) VALUES
+(0, 0, 1),
+(1, 1, 1),
+(2, 2, 1),
+(3, 3, 1);
+INSERT INTO sparse_linear_systems_rhs(rid, val) VALUES
+(0, 10),
+(1, 20),
+(2, 30);
+</pre></li>
+<li>Solve the linear systems with default parameters. <pre class="example">
+SELECT madlib.linear_solver_sparse( 'sparse_linear_systems_lhs',
+                                    'sparse_linear_systems_rhs',
+                                    'output_table',
+                                    'rid',
+                                    'cid',
+                                    'val',
+                                    'rid',
+                                    'val',
+                                    4
+                                  );
+</pre></li>
+<li>View the contents of the output table. <pre class="example">
+\x on
+SELECT * FROM output_table;
+</pre> Result: <pre class="result">
+--------------------+-------------------------------------
+solution            | {10,20,30,0}
+residual_norm       | 0
+iters               | NULL
+</pre></li>
+<li>Choose a different algorithm than the default algorithm. <pre class="example">
+DROP TABLE IF EXISTS output_table;
+SELECT madlib.linear_solver_sparse( 'sparse_linear_systems_lhs',
+                                    'sparse_linear_systems_rhs',
+                                    'output_table',
+                                    'rid',
+                                    'cid',
+                                    'val',
+                                    'rid',
+                                    'val',
+                                    4,
+                                    NULL,
+                                    'direct',
+                                    'algorithm=llt'
+                                  );
+</pre></li>
+<li>Choose a different algorithm than the default algorithm. <pre class="example">
+DROP TABLE IF EXISTS output_table;
+SELECT madlib.linear_solver_sparse(
+                                    'sparse_linear_systems_lhs',
+                                    'sparse_linear_systems_rhs',
+                                    'output_table',
+                                    'rid',
+                                    'cid',
+                                    'val',
+                                    'rid',
+                                    'val',
+                                    4,
+                                    NULL,
+                                    'iterative',
+                                    'algorithm=cg-mem, toler=1e-5'
+                                  );
+</pre></li>
+</ol>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File sparse_linear_sytems.sql_in documenting the SQL functions.</dd></dl>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__stats.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+$(document).ready(function(){initNavTree('group__grp__stats.html','');});
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+<!-- window showing the filter options -->
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+     onmouseover="return searchBox.OnSearchSelectShow()"
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Statistics</div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>Contains statistics modules </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__desc__stats"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__desc__stats.html">Descriptive Statistics</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__inf__stats"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__inf__stats.html">Inferential Statistics</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__prob"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__prob.html">Probability Functions</a></td></tr>
+<tr class="memdesc:group__grp__prob"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides cumulative distribution, density/mass, and quantile functions for a wide range of probability distributions. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__stats.js
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diff --git a/docs/latest/group__grp__stats.js b/docs/latest/group__grp__stats.js
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--- /dev/null
+++ b/docs/latest/group__grp__stats.js
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+var group__grp__stats =
+[
+    [ "Descriptive Statistics", "group__grp__desc__stats.html", "group__grp__desc__stats" ],
+    [ "Inferential Statistics", "group__grp__inf__stats.html", "group__grp__inf__stats" ],
+    [ "Probability Functions", "group__grp__prob.html", null ]
+];
\ No newline at end of file

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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Hypothesis Tests<div class="ingroups"><a class="el" href="group__grp__stats.html">Statistics</a> &raquo; <a class="el" href="group__grp__inf__stats.html">Inferential Statistics</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#input">Input</a> </li>
+<li>
+<a href="#usage">Usage</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Hypothesis tests are used to confirm or reject a <em>“null” hypothesis</em> \( H_0 \) about the distribution of random variables, given realizations of these random variables. Since in general it is not possible to make statements with certainty, one is interested in the probability \( p \) of seeing random variates at least as extreme as the ones observed, assuming that \( H_0 \) is true. If this probability \( p \) is small, \( H_0 \) will be rejected by the test with <em>significance level</em> \( p \). Falsifying \( H_0 \) is the canonic goal when employing a hypothesis test. That is, hypothesis tests are typically used in order to substantiate that instead the <em>alternative hypothesis</em> \( H_1 \) is true.</p>
+<p>Hypothesis tests may be devided into parametric and non-parametric tests. A parametric test assumes certain distributions and makes inferences about parameters of the distributions (like, e.g., the mean of a normal distribution). Formally, there is a given domain of possible parameters \( \Gamma \) and the null hypothesis \( H_0 \) is the event that the true parameter \( \gamma_0 \in \Gamma_0 \), where \( \Gamma_0 \subsetneq \Gamma \). Non-parametric tests, on the other hand, do not assume any particular distribution of the sample (e.g., a non-parametric test may simply test if two distributions are similar).</p>
+<p>The first step of a hypothesis test is to compute a <em>test statistic</em>, which is a function of the random variates, i.e., a random variate itself. A hypothesis test relies on that the distribution of the test statistic is (approximately) known. Now, the \( p \)-value is the probability of seeing a test statistic at least as extreme as the one observed, assuming that \( H_0 \) is true. In a case where the null hypothesis corresponds to a family of distributions (e.g., in a parametric test where \( \Gamma_0 \) is not a singleton set), the \( p \)-value is the supremum, over all possible distributions according to the null hypothesis, of these probabilities.</p>
+<p><a class="anchor" id="input"></a></p><dl class="section user"><dt>Input</dt><dd></dd></dl>
+<p>Input data is assumed to be normalized with all values stored row-wise. In general, the following inputs are expected.</p>
+<p>One-sample tests expect the following form: </p><pre>{TABLE|VIEW} <em>source</em> (
+    ...
+    <em>value</em> DOUBLE PRECISION
+    ...
+)</pre><p>Two-sample tests expect the following form: </p><pre>{TABLE|VIEW} <em>source</em> (
+    ...
+    <em>first</em> BOOLEAN,
+    <em>value</em> DOUBLE PRECISION
+    ...
+)</pre><p> The <code>first</code> column indicates whether a value is from the first (if <code>TRUE</code>) or the second sample (if <code>FALSE</code>).</p>
+<p>Many-sample tests expect the following form: </p><pre>{TABLE|VIEW} <em>source</em> (
+    ...
+    <em>group</em> INTEGER,
+    <em>value</em> DOUBLE PRECISION
+    ...
+)</pre><p><a class="anchor" id="usage"></a></p><dl class="section user"><dt>Usage</dt><dd></dd></dl>
+<p>All tests are implemented as aggregate functions. The non-parametric (rank-based) tests are implemented as ordered aggregate functions and thus necessitate an <code>ORDER BY</code> clause. In the following, the most simple forms of usage are given. Specific function signatures, as described in <a class="el" href="hypothesis__tests_8sql__in.html">hypothesis_tests.sql_in</a>, may require more arguments or a different <code>ORDER BY</code> clause.</p>
+<ul>
+<li>Run a parametric one-sample test: <pre>SELECT <em>test</em>(<em>value</em>) FROM <em>source</em></pre> where '<em>test</em>' can be one of<ul>
+<li>t_test_one</li>
+<li>chi2_gof_test</li>
+</ul>
+</li>
+<li>Run a parametric two-sample/multi-sample test: <pre>SELECT <em>test</em>(<em>first/group</em>, <em>value</em>) FROM <em>source</em></pre> where '<em>test</em>' can be one of<ul>
+<li>f_test</li>
+<li>t_test_two_pooled</li>
+<li>t_test_two_unpooled</li>
+<li>one_way_anova (multi-sample)</li>
+</ul>
+</li>
+<li><p class="startli">Run a non-parametric two-sample/multi-sample test: </p><pre>SELECT <em>test</em>(<em>first/group</em>, <em>value</em> ORDER BY <em>value</em>) FROM <em>source</em></pre><p> where '<em>test</em>' can be one of</p><ul>
+<li>ks_test</li>
+<li>mw_test</li>
+<li>wsr_test (multi-sample)</li>
+</ul>
+<p class="startli"><b>Note:</b>Kolomogov-Smirnov two-sample test is based on the asymptotic theory. The p-value is given by comparing the test statistics with Kolomogov distribution. The p-value is also adjusted for data with heavy tail distribution, which may give different results given by R function's ks.test. See [3] for detailed explantion. The literature is not unanimous about the definitions of the Wilcoxon rank sum and Mann-Whitney tests. There are two possible definitions for the statistic; MADlib outputs the minimum of the two and uses it for significance testing. This might give different results for both mw_test and wsr_test compared to statistical functions in other popular packages (like R's wilcox.test function). See [4] for detailed explanation.</p>
+</li>
+</ul>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ul>
+<li><b>One-sample and two-sample t-test</b> (data is subset of mpg data from <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda352.htm">NIST/SEMATECH</a>)</li>
+</ul>
+<pre class="example">
+DROP TABLE IF EXISTS auto83b;
+CREATE TABLE auto83b (
+    id SERIAL,
+    mpg_us DOUBLE PRECISION,
+    mpg_j DOUBLE PRECISION
+);
+COPY auto83b (mpg_us, mpg_j) FROM stdin DELIMITER '|';
+18|24
+15|27
+18|27
+16|25
+17|31
+15|35
+14|24
+14|19
+21|31
+10|32
+10|24
+11|26
+9| 9
+\N|32
+\N|37
+\N|38
+\N|34
+\N|34
+\N|32
+\N|33
+\N|32
+\N|25
+\N|24
+\N|37
+13|\N
+12|\N
+18|\N
+21|\N
+19|\N
+21|\N
+15|\N
+16|\N
+15|\N
+11|\N
+20|\N
+21|\N
+19|\N
+15|\N
+\.</pre><pre class="example">DROP TABLE IF EXISTS auto83b_one_sample;
+CREATE TABLE auto83b_one_sample AS
+    SELECT mpg_us AS mpg
+    FROM auto83b
+    WHERE mpg_us is not NULL;
+
+DROP TABLE IF EXISTS auto83b_two_sample;
+CREATE TABLE auto83b_two_sample AS
+SELECT TRUE AS is_us, mpg_us AS mpg
+    FROM auto83b
+    WHERE mpg_us is not NULL
+    UNION ALL
+    SELECT FALSE, mpg_j
+    FROM auto83b
+    WHERE mpg_j is not NULL;
+</pre><pre class="example">
+-- One sample tests
+SELECT (t_test_one(mpg - 20)).* FROM auto83b_one_sample;  -- test rejected for mean = 20
+</pre><pre class="result">
+     statistic     | df | p_value_one_sided |  p_value_two_sided
+ ------------------+----+-------------------+----------------------
+  -6.0532478722666 | 26 | 0.999998926789141 | 2.14642171769697e-06
+ </pre><pre class="example">
+SELECT (t_test_one(mpg - 15.7)).* FROM auto83b_one_sample;  -- test not rejected
+</pre><pre class="result">
+       statistic      | df | p_value_one_sided | p_value_two_sided
+ ---------------------+----+-------------------+-------------------
+  0.00521831713126531 | 26 | 0.497938118950661 | 0.995876237901321
+</pre><pre class="example">
+-- Two sample tests
+SELECT (t_test_two_pooled(is_us, mpg)).* FROM auto83b_two_sample;
+</pre> <pre class="result">
+     statistic     | df | p_value_one_sided |  p_value_two_sided
+ -------------------+----+-------------------+----------------------
+  -8.89342267075968 | 49 | 0.999999999995748 | 8.50408632402377e-12
+ </pre><pre class="example">
+SELECT (t_test_two_unpooled(is_us, mpg)).* FROM auto83b_two_sample;
+</pre><pre class="result">
+      statistic     |        df        | p_value_one_sided |  p_value_two_sided
+ -------------------+------------------+-------------------+----------------------
+  -8.61746388524314 | 35.1283818346179 | 0.999999999821218 | 3.57563867403599e-10
+</pre><ul>
+<li><b>F-Test</b> (Uses same data as above t-test)</li>
+</ul>
+<pre class="example">
+SELECT (f_test(is_us, mpg)).* FROM auto83b_two_sample;
+-- Test result indicates that the two distributions have different variances
+</pre> <pre class="result">
+      statistic     | df1 | df2 | p_value_one_sided |  p_value_two_sided
+ -------------------+-----+-----+-------------------+---------------------
+  0.311786921089247 |  26 |  23 | 0.997559863672441 | 0.00488027265511803
+</pre><ul>
+<li><b>Chi-squared goodness-of-fit test</b> (<a href="http://www.statsdirect.com/help/chi_square_tests/chi_good.htm">Data source</a>)</li>
+</ul>
+<pre class="example">
+CREATE TABLE chi2_test_blood_group (
+    id SERIAL,
+    blood_group VARCHAR,
+    observed BIGINT,
+    expected DOUBLE PRECISION
+);
+INSERT INTO chi2_test_blood_group(blood_group, observed, expected) VALUES
+    ('O', 67, 82.28),
+    ('A', 83, 84.15),
+    ('B', 29, 14.96),
+    ('AB', 8, 5.61);
+SELECT (madlib.chi2_gof_test(observed, expected)).* FROM chi2_test_blood_group;
+</pre> <pre class="result">
+     statistic     |       p_value        | df |       phi        | contingency_coef
+ ------------------+----------------------+----+------------------+-------------------
+  17.0481013341976 | 0.000690824622923826 |  3 | 2.06446732440826 | 0.899977280680593
+ </pre><ul>
+<li><b>Chi-squared independence test</b> (<a href="http://itl.nist.gov/div898/software/dataplot/refman1/auxillar/chistest.htm">Data source</a>)</li>
+</ul>
+<pre class="example">
+CREATE TABLE chi2_test_friendly (
+    id_x SERIAL,
+    values INTEGER[]
+);
+INSERT INTO chi2_test_friendly(values) VALUES
+    (array[5, 29, 14, 16]),
+    (array[15, 54, 14, 10]),
+    (array[20, 84, 17, 94]),
+    (array[68, 119, 26, 7]);</pre><pre class="example">-- we expect the table to be unpivoted
+CREATE TABLE chi2_test_friendly_unpivoted AS
+SELECT id_x, id_y, values[id_y] AS observed
+FROM
+    chi2_test_friendly,
+    generate_series(1,4) AS id_y;</pre><pre class="example">-- Compute Chi-squared independence statistic
+SELECT (madlib.chi2_gof_test(observed, expected, deg_freedom)).*
+FROM (
+    -- Compute expected values and degrees of freedom
+    SELECT
+        observed,
+        sum(observed) OVER (PARTITION BY id_x)::DOUBLE PRECISION
+              sum(observed) OVER (PARTITION BY id_y) AS expected
+    FROM chi2_test_friendly_unpivoted
+) p, (
+    SELECT
+        (count(DISTINCT id_x) - 1) * (count(DISTINCT id_y) - 1) AS deg_freedom
+    FROM chi2_test_friendly_unpivoted
+);
+</pre> <pre class="result">
+     statistic     |       p_value        | df |       phi        | contingency_coef
+ ------------------+----------------------+----+------------------+-------------------
+  138.289841626008 | 2.32528678709871e-25 |  9 | 2.93991753313346 | 0.946730727519112
+ </pre><ul>
+<li><b>ANOVA test</b> (<a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc433.htm">Data source</a>)</li>
+</ul>
+<pre class="example">
+CREATE TABLE nist_anova_test (
+    id SERIAL,
+    resistance FLOAT8[]
+);
+COPY nist_anova_test(resistance) FROM stdin;
+{6.9,8.3,8.0}
+{5.4,6.8,10.5}
+{5.8,7.8,8.1}
+{4.6,9.2,6.9}
+{4.0,6.5,9.3}
+
+SELECT (madlib.one_way_anova(level, value)).* FROM (
+    SELECT level, resistance[level] AS value
+    FROM
+        nist_anova_test, (SELECT * FROM generate_series(1,3) level) q1
+) q2;
+</pre> <pre class="result">
+  sum_squares_between | sum_squares_within | df_between | df_within | mean_squares_between | mean_squares_within |    statistic     |      p_value
+ ---------------------+--------------------+------------+-----------+----------------------+---------------------+------------------+--------------------
+     27.8973333333333 |             17.452 |          2 |        12 |     13.9486666666667 |    1.45433333333333 | 9.59110703644281 | 0.0032482226008593
+</pre><ul>
+<li><b>Kolmogorov-Smirnov test</b> (<a href="http://www.physics.csbsju.edu/stats/KS-test.html">Data source</a>)</li>
+</ul>
+<pre class="example">
+CREATE TABLE ks_sample_1 AS
+SELECT
+    TRUE AS first,
+    unnest(ARRAY[0.22, -0.87, -2.39, -1.79, 0.37, -1.54, 1.28, -0.31, -0.74, 1.72, 0.38, -0.17, -0.62, -1.10, 0.30, 0.15, 2.30, 0.19, -0.50, -0.09]) AS value
+UNION ALL
+SELECT
+    FALSE,
+    unnest(ARRAY[-5.13, -2.19, -2.43, -3.83, 0.50, -3.25, 4.32, 1.63, 5.18, -0.43, 7.11, 4.87, -3.10, -5.81, 3.76, 6.31, 2.58, 0.07, 5.76, 3.50]);</pre><pre class="example">SELECT (ks_test(first, value,
+    (SELECT count(value) FROM ks_sample_1 WHERE first),
+    (SELECT count(value) FROM ks_sample_1 WHERE NOT first)
+    ORDER BY value)).*
+FROM ks_sample_1;
+</pre> <pre class="result">
+  statistic |   k_statistic   |      p_value
+ -----------+-----------------+--------------------
+       0.45 | 1.4926782214936 | 0.0232132758544496
+</pre><ul>
+<li><b>Mann-Whitney test</b> (use same data as t-test)</li>
+</ul>
+<pre class="example">
+SELECT (mw_test(is_us, mpg ORDER BY mpg)).* from auto83b_two_sample;
+</pre> <pre class="result">
+      statistic     | u_statistic | p_value_one_sided |  p_value_two_sided
+ -------------------+-------------+-------------------+----------------------
+  -5.50097925755249 |        32.5 | 0.999999981115618 | 3.77687645883758e-08
+</pre><ul>
+<li><b>Wilcoxon signed-rank test</b></li>
+</ul>
+<pre class="example">
+DROP TABLE IF EXISTS test_wsr;
+CREATE TABLE test_wsr (
+    x DOUBLE PRECISION,
+    y DOUBLE PRECISION
+);
+COPY test_wsr (x, y) FROM stdin DELIMITER '|';
+0.32|0.39
+0.4|0.47
+0.11|0.11
+0.47|0.43
+0.32|0.42
+0.35|0.3
+0.32|0.43
+0.63|0.98
+0.5|0.86
+0.6|0.79
+0.38|0.33
+0.46|0.45
+0.2|0.22
+0.31|0.3
+0.62|0.6
+0.52|0.53
+0.77|0.85
+0.23|0.21
+0.3|0.33
+0.7|0.57
+0.41|0.43
+0.53|0.49
+0.19|0.2
+0.31|0.35
+0.48|0.4
+\.
+
+SELECT (wsr_test(
+    x - y,
+    2 * 2^(-52) * greatest(x,y)
+    ORDER BY abs(x - y)
+)).*
+FROM test_wsr;
+</pre> <pre class="result">
+  statistic | rank_sum_pos | rank_sum_neg | num |    z_statistic    | p_value_one_sided | p_value_two_sided
+ -----------+--------------+--------------+-----+-------------------+-------------------+-------------------
+      105.5 |        105.5 |        194.5 |  24 | -1.27318365656729 | 0.898523560667509 | 0.202952878664983
+</pre><p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] M. Hollander, D. Wolfe: <em>Nonparametric Statistical Methods</em>, 2nd edition, Wiley, 1999</p>
+<p>[2] E. Lehmann, J. Romano: <em>Testing Statistical Hypotheses</em>, 3rd edition, Springer, 2005</p>
+<p>[3] M. Stephens: <em>Use of the Kolmogorov-Smirnov, Cramer-Von Mises and related statistics without extensive tables</em>, Journal of the Royal Statistical Society. Series B (Methodological) (1970): 115-122.</p>
+<p>[4] Wikipedia: Mann–Whitney U test calculation, <a href="http://en.wikipedia.org/wiki/Mann-Whitney_test#Calculations">http://en.wikipedia.org/wiki/Mann-Whitney_test#Calculations</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="hypothesis__tests_8sql__in.html" title="SQL functions for statistical hypothesis tests. ">hypothesis_tests.sql_in</a> documenting the SQL functions. </p>
+</div><!-- contents -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+  <div class="headertitle">
+<div class="title">Summary<div class="ingroups"><a class="el" href="group__grp__stats.html">Statistics</a> &raquo; <a class="el" href="group__grp__desc__stats.html">Descriptive Statistics</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#usage">Summary Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>The MADlib <b><a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a></b> function is a generic function that produces summary statistics from any data table. The function invokes various methods from the MADlib library to provide an overview of the data.</p>
+<p><a class="anchor" id="usage"></a></p><dl class="section user"><dt>Summary Function Syntax</dt><dd>The <b><a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a></b> function has the following syntax:</dd></dl>
+<pre class="syntax">
+summary ( source_table,
+          output_table,
+          target_cols,
+          grouping_cols,
+          get_distinct,
+          get_quartiles,
+          ntile_array,
+          how_many_mfv,
+          get_estimates
+        )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>TEXT. The name of the table containing the input data. </dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the table to contain the output summary data.</p>
+<p>The <b><a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a></b> function returns a composite type containing three fields: </p><table  class="output">
+<tr>
+<th>output_table </th><td>TEXT. The name of the output table.  </td></tr>
+<tr>
+<th>row_count </th><td>INTEGER. The number of rows in the output table.  </td></tr>
+<tr>
+<th>duration </th><td>FLOAT8. The time taken (in seconds) to compute the summary.  </td></tr>
+</table>
+<p class="enddd">Summary statistics are saved in a table with the name specifed in the <em>output_table</em> argument. The table can contain the following columns: </p><table  class="output">
+<tr>
+<th>group_by </th><td>Group-by column name. NULL if none provided.  </td></tr>
+<tr>
+<th>group_by_value </th><td>Value of the Group-by column. NULL if there is no grouping.  </td></tr>
+<tr>
+<th>target_column </th><td>Targeted column values for which summary is requested.  </td></tr>
+<tr>
+<th>column_number </th><td>Physical column number for the target column, as described in <em>pg_attribute</em>.  </td></tr>
+<tr>
+<th>data_type </th><td>Data type of the target column. Standard GPDB type descriptors are displayed.  </td></tr>
+<tr>
+<th>row_count </th><td>Number of rows for the target column.  </td></tr>
+<tr>
+<th>distinct_values </th><td>Number of distinct values in the target column. When the <a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a> function is called with the <em>get_estimates</em> argument set to TRUE, this is an estimated statistic based on the Flajolet-Martin distinct count estimator.  </td></tr>
+<tr>
+<th>missing_values </th><td>Number of missing values in the target column.  </td></tr>
+<tr>
+<th>blank_values </th><td>Number of blank values. Blanks are defined by this regular expression:<pre class="fragment">'^\w*$'</pre>  </td></tr>
+<tr>
+<th>fraction_missing </th><td>Percentage of total rows that are missing, as a decimal value, e.g. 0.3.  </td></tr>
+<tr>
+<th>fraction_blank </th><td>Percentage of total rows that are blank, as a decimal value, e.g. 0.3.  </td></tr>
+<tr>
+<th>mean </th><td>Mean value of target column if target is numeric, otherwise NULL.  </td></tr>
+<tr>
+<th>variance </th><td>Variance of target column if target is numeric, otherwise NULL.  </td></tr>
+<tr>
+<th>min </th><td>Minimum value of target column. For strings this is the length of the shortest string.  </td></tr>
+<tr>
+<th>max </th><td>Maximum value of target column. For strings this is the length of the longest string.  </td></tr>
+<tr>
+<th>first_quartile </th><td>First quartile (25th percentile), only for numeric columns. <b>Currently unavailable for PostgreSQL 9.3 or lower</b>.  </td></tr>
+<tr>
+<th>median </th><td>Median value of target column, if target is numeric, otherwise NULL. <b>Currently unavailable for PostgreSQL 9.3 or lower</b>.  </td></tr>
+<tr>
+<th>third_quartile </th><td>Third quartile (25th percentile), only for numeric columns. <b>Currently unavailable for PostgreSQL 9.3 or lower</b>.  </td></tr>
+<tr>
+<th>quantile_array </th><td>Percentile values corresponding to <em>ntile_array</em>. <b>Currently unavailable for PostgreSQL 9.3 or lower</b>.  </td></tr>
+<tr>
+<th>most_frequent_values </th><td>An array containing the most frequently occurring values. The <em>how_many_mfv</em> argument determines the length of the array, 10 by default. If the <a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a> function is called with the <em>get_estimates</em> argument set to TRUE (default), the frequent values computation is performed using a parallel aggregation method that is faster, but in some cases can fail to detect the exact most frequent values.  </td></tr>
+<tr>
+<th>mfv_frequencies </th><td>Array containing the frequency count for each of the most frequent values.   </td></tr>
+</table>
+</dd>
+<dt>target_columns (optional) </dt>
+<dd>TEXT, default NULL. A comma-separated list of columns to summarize. If NULL, summaries are produced for all columns. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>TEXT, default: null. A comma-separated list of columns on which to group results. If NULL, summaries are produced on the complete table. </dd>
+<dt>get_distinct (optional) </dt>
+<dd>BOOLEAN, default TRUE. If true, distinct values are counted. </dd>
+<dt>get_quartiles (optional) </dt>
+<dd>BOOLEAN, default TRUE. If TRUE, qartiles are computed. </dd>
+<dt>ntile_array (optional) </dt>
+<dd>FLOAT8[], default NULL. An array of quantile values to compute. If NULL, quantile values are not computed. </dd>
+<dt>how_many_mfv (optional) </dt>
+<dd>INTEGER, default: 10. The number of most-frequent-values to compute. </dd>
+<dt>get_estimates (optional) </dt>
+<dd>BOOLEAN, default TRUE. If TRUE, estimated values are produced. If FALSE, exact values are calculated. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the <a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a> function. <pre class="example">
+SELECT * FROM madlib.summary();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+CREATE TABLE houses (id INT, tax INT, bedroom INT, bath FLOAT, price INT,
+             size INT, lot INT);
+COPY houses FROM STDIN WITH DELIMITER '|';
+  1 |  590 |       2 |    1 |  50000 |  770 | 22100
+  2 | 1050 |       3 |    2 |  85000 | 1410 | 12000
+  3 |   20 |       3 |    1 |  22500 | 1060 |  3500
+  4 |  870 |       2 |    2 |  90000 | 1300 | 17500
+  5 | 1320 |       3 |    2 | 133000 | 1500 | 30000
+  6 | 1350 |       2 |    1 |  90500 |  820 | 25700
+  7 | 2790 |       3 |  2.5 | 260000 | 2130 | 25000
+  8 |  680 |       2 |    1 | 142500 | 1170 | 22000
+  9 | 1840 |       3 |    2 | 160000 | 1500 | 19000
+ 10 | 3680 |       4 |    2 | 240000 | 2790 | 20000
+ 11 | 1660 |       3 |    1 |  87000 | 1030 | 17500
+ 12 | 1620 |       3 |    2 | 118600 | 1250 | 20000
+ 13 | 3100 |       3 |    2 | 140000 | 1760 | 38000
+ 14 | 2070 |       2 |    3 | 148000 | 1550 | 14000
+ 15 |  650 |       3 |  1.5 |  65000 | 1450 | 12000
+\.
+</pre></li>
+<li>Run the <b><a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a></b> function. <pre class="example">
+SELECT * FROM madlib.summary( 'houses',
+                              'houses_summary',
+                              'tax,bedroom,lot,bath,price,size,lot',
+                              'bedroom',
+                              TRUE,
+                              TRUE,
+                              NULL,
+                              5,
+                              FALSE
+                            );
+</pre> Result: <pre class="result">
+  output_table  | row_count |    duration
+----------------+-----------+----------------
+ houses_summary |        21 | 0.207587003708
+(1 row)
+</pre></li>
+<li>View the summary data. <pre class="example">
+-- Turn on expanded display for readability.
+\x on
+SELECT * FROM houses_summary;
+</pre> Result: <pre class="result">
+&#160;-[ RECORD 1 ]--------+-----------------------------------
+ group_by             | bedroom
+ group_by_value       | 3
+ target_column        | tax
+ column_number        | 2
+ data_type            | int4
+ row_count            | 9
+ distinct_values      | 9
+ missing_values       | 0
+ blank_values         |
+ fraction_missing     | 0
+ fraction_blank       |
+ mean                 | 1561.11111111111
+ variance             | 936736.111111111
+ min                  | 20
+ max                  | 3100
+ most_frequent_values | {20,1320,2790,1840,1660}
+ mfv_frequencies      | {1,1,1,1,1}
+&#160;-[ RECORD 2 ]--------+-----------------------------------
+ group_by             | bedroom
+ group_by_value       | 3
+ target_column        | bath
+ column_number        | 4
+ ...
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd><ul>
+<li>Table names can be optionally schema qualified (current_schemas() would be searched if a schema name is not provided) and table and column names should follow case-sensitivity and quoting rules per the database. (For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"').</li>
+<li>Estimated values are only implemented for the distinct values computation.</li>
+<li>The <em>get_estimates</em> parameter controls computation for two statistics:<ul>
+<li>If <em>get_estimates</em> is TRUE then the distinct value computation is estimated. Further, the most frequent values computation is computed using a "quick and dirty" method that does parallel aggregation in Greenplum Database at the expense of missing some of the most frequent values.</li>
+<li>If <em>get_estimates</em> is FALSE then the distinct values are computed in a slow but exact method. The most frequent values are computed using a faithful implementation that preserves the approximation guarantees of the Cormode/Muthukrishnan method (more information in <a class="el" href="group__grp__mfvsketch.html">MFV (Most Frequent Values)</a>).</li>
+</ul>
+</li>
+</ul>
+</dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="summary_8sql__in.html" title="Summary function for descriptive statistics. ">summary.sql_in</a> documenting the <b><a class="el" href="summary_8sql__in.html#a14ff66aaa0248c5e2741dfaf2e360881">summary()</a></b> function</dd></dl>
+<p><a class="el" href="group__grp__mfvsketch.html">MFV (Most Frequent Values)</a> </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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+<title>MADlib: Supervised Learning</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Supervised Learning</div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>Contains methods which perform supervised learning tasks </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__regml"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__regml.html">Regression Models</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__tree"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__tree.html">Tree Methods</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__crf"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__crf.html">Conditional Random Field</a></td></tr>
+<tr class="memdesc:group__grp__crf"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructs a Conditional Random Fields (CRF) model for labeling sequential data. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
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[32/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+  <td style="padding-left: 0.5em;">
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[40/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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[14/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__decision__tree.html
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+<title>MADlib: Decision Tree</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
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+       class="ui-resizable-handle">
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+<div class="title">Decision Tree<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__tree.html">Tree Methods</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#display">Display Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Decision trees are a supervised learning method that uses a predictive model to predict the value of a target variable, based on several input variables. They use a tree-based representation of the model such that, the interior nodes of the tree correspond to the input variables, the edges of the nodes correspond to values that the input variables can take, and leaf nodes represent values of the target variable, given the values of the input variables, represented by the path from the root to the leaf nodes.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>We implement the decision tree using the CART algorithm, introduced by Breiman et al. [1]. The training function has the following syntax: <pre class="syntax">
+tree_train(
+    training_table_name,
+    output_table_name,
+    id_col_name,
+    dependent_variable,
+    list_of_features,
+    list_of_features_to_exclude,
+    split_criterion,
+    grouping_cols,
+    weights,
+    max_depth,
+    min_split,
+    min_bucket,
+    num_splits,
+    pruning_params,
+    surrogate_params,
+    verbosity
+    )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>training_table_name </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training data</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table_name </dt>
+<dd><p class="startdd">TEXT. The name of the generated table containing the model. If a table with the same name already exists, then the function will return an error.</p>
+<p>The model table produced by the train function contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td>Grouping columns, if provided in input, same types as in the training table. This could be multiple columns depending on the <code>grouping_cols</code> input.  </td></tr>
+<tr>
+<th>tree </th><td>BYTEA8. Trained decision tree model stored in a binary format.  </td></tr>
+<tr>
+<th>cat_levels_in_text </th><td>TEXT[]. Ordered levels of categorical variables  </td></tr>
+<tr>
+<th>cat_n_levels </th><td><p class="starttd">INTEGER[]. Number of levels for each categorical variable </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>tree_depth </th><td><p class="starttd">INTEGER. The maximum depth the tree obtained after training (root has depth 0) </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>pruning_cp </th><td><p class="starttd">DOUBLE PRECISION. The cost-complexity parameter used for pruning the trained tree(s). This would be different from the input cp value if cross-validation is used.  </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p>A summary table named <em>&lt;model_table&gt;_summary</em> is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method </th><td><p class="starttd">TEXT. 'tree_train' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>is_classification </th><td><p class="starttd">BOOLEAN. TRUE if the decision trees are for classification, FALSE if regression </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>source_table </th><td><p class="starttd">TEXT. The data source table name </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>model_table </th><td><p class="starttd">TEXT. The model table name </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>id_col_name </th><td><p class="starttd">TEXT. The ID column name </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">TEXT. The dependent variable </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">TEXT. The independent variables </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>cat_features </th><td>TEXT. The list of categorical feature names as a comma-separated string  </td></tr>
+<tr>
+<th>con_features </th><td><p class="starttd">TEXT. The list of continuous feature names as a comma-separated string </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>grouping_col </th><td><p class="starttd">TEXT. Names of grouping columns </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">INTEGER. Number of groups in decision tree training </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">INTEGER. Number of failed groups in decision tree training </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_processed </th><td><p class="starttd">BIGINT. Total numbers of rows processed in all groups </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_skipped </th><td><p class="starttd">BIGINT. Total numbers of rows skipped in all groups due to missing values or failures </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_var_levels </th><td><p class="starttd">TEXT. For classification, the distinct levels of the dependent variable </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_var_type </th><td><p class="starttd">TEXT. The type of dependent variable </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>input_cp </th><td><p class="starttd">DOUBLE PRECISION. The complexity parameter (cp) used for pruning the trained tree(s) (before cross-validation is run). This is same as the cp value inputed through the <em>pruning_params</em> </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_var_types </th><td><p class="starttd">TEXT. A comma separated string, the types of independent variables </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>id_col_name </dt>
+<dd><p class="startdd">TEXT. Name of the column containing id information in the training data. This is a mandatory argument and is used for prediction and cross-validation. The values are expected to be unique for each row </p>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_variable </dt>
+<dd><p class="startdd">TEXT. Name of the column that contains the output (response) for training. Boolean, integer and text types are considered classification outputs, while double precision values are considered regression outputs. The response variable for a classification tree can be multinomial, but the time and space complexity of train function increases linearly as the number of response classes increases.</p>
+<p class="enddd"></p>
+</dd>
+<dt>list_of_features </dt>
+<dd><p class="startdd">TEXT. Comma-separated string of column names to use as predictors. Can also be a '*' implying all columns are to be used as predictors (except the ones included in the next argument). The types of the features can be mixed where boolean, integer, and text columns are considered categorical and double precision columns are considered continuous. The categorical variables are not encoded and used as is for the training.</p>
+<p>There are no limitations to the number of levels in a categorical variable. It is, however, important to note that we don't test for every combination of levels of a categorical variable for evaluating a split. We order the levels of the variable by the entropy of the varible in predicting the response. The splits at each node is evaluated between these ordered levels </p>
+<p class="enddd"></p>
+</dd>
+<dt>list_of_features_to_exclude </dt>
+<dd><p class="startdd">TEXT. Comma-separated string of column names to exclude from the predictors list. If the <em>dependent_variable</em> is an expression (including cast of a column name), then this list should include all columns present in the <em>dependent_variable</em> expression, otherwise those columns will be included in the features. The names in this parameter should be identical to the names used in the table and quoted appropriately</p>
+<p class="enddd"></p>
+</dd>
+<dt>split_criterion </dt>
+<dd><p class="startdd">TEXT, default = 'gini' for classification, 'mse' for regression. Impurity function to compute the feature to use for the split. Supported criteria are 'gini', 'entropy', 'misclassification' for classification trees. For regression trees, split_criterion of 'mse' is always used (irrespective of the input for this argument) </p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_cols (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. Comma-separated list of column names to group the data by. This will lead to creating multiple decision trees, one for each group</p>
+<p class="enddd"></p>
+</dd>
+<dt>weights (optional) </dt>
+<dd><p class="startdd">TEXT. Column name containing weights for each observation</p>
+<p class="enddd"></p>
+</dd>
+<dt>max_depth (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 10. Maximum depth of any node of the final tree, with the root node counted as depth 0</p>
+<p class="enddd"></p>
+</dd>
+<dt>min_split (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 20. Minimum number of observations that must exist in a node for a split to be attempted. The best value for this parameter depends on the number of tuples in the dataset</p>
+<p class="enddd"></p>
+</dd>
+<dt>min_bucket (optional) </dt>
+<dd><p class="startdd">INTEGER, default: min_split/3. Minimum number of observations in any terminal node. If only one of min_bucket or min_split is specified, min_split is set to min_bucket*3 or min_bucket to min_split/3, as appropriate</p>
+<p class="enddd"></p>
+</dd>
+<dt>num_splits (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 100. Continuous-valued features are binned into discrete quantiles to compute split boundaries. This global parameter is used to compute the resolution of splits for continuous features. Higher number of bins will lead to better prediction, but will also result in higher processing time</p>
+<p class="enddd"></p>
+</dd>
+<dt>pruning_params (optional) </dt>
+<dd><p class="startdd">TEXT. Comma-separated string of key-value pairs giving the parameters for pruning the tree. The parameters currently accepted are: </p><table  class="output">
+<tr>
+<th>cp </th><td><p class="starttd">Default: 0. A split on a node is attempted only if it decreases the overall lack of fit by a factor of 'cp', else the split is pruned away. This value is used to create an initial tree before running cross-validation (see below).</p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>n_folds </th><td><p class="starttd">Default: 0 (i.e. No cross-validation). Number of cross-validation folds to use to compute the best value of <em>cp</em>. To perform cross-validation, a positive value of <em>n_folds</em> (greater than 2) should be given. An additional output table <em>&lt;model_table&gt;_cv</em> is created containing the values of evaluated <em>cp</em> and the cross-validation error. The tree returned in the output table corresponds to the <em>cp</em> with the lowest cross-validation error (we pick the maximum <em>cp</em> if multiple values have same error).</p>
+<p>The list of <em>cp</em> values are automatically computed by parsing through the tree initially trained on the complete dataset. The tree outputted is a subset of this initial tree corresponding to the best computed <em>cp</em>.</p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>surrogate_params </dt>
+<dd><p class="startdd">TEXT. Comma-separated string of key-value pairs controlling the behavior of surrogate splits for each node. A surrogate variable is another predictor variable that is associated (correlated) with the primary predictor variable for a split. The surrogate variable comes into use when the primary predictior value is NULL. This parameter currently accepts the below argument: </p><table  class="output">
+<tr>
+<th>max_surrogates </th><td>Default: 0. Number of surrogates to store for each node  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>verbosity (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Provides verbose output of the results of training </dd>
+</dl>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd><ul>
+<li>Many of the parameters are designed to be similar to the popular R package 'rpart'. An important distinction between rpart and the above MADlib function is that for both response and feature variables, MADlib considers integer values as categorical values, while rpart considers them as continuous.</li>
+<li>When using no surrogates (<em>max_surrogates</em>=0), all rows containing NULL value for any of the features used for training will be ignored from training and prediction.</li>
+<li>When cross-validation is not used (<em>n_folds</em>=0), each tree outputed is pruned by inputed cost-complextity (<em>cp</em>). With cross-validation, inputed <em>cp</em> is the minimum value of all the explored values of 'cp'. During cross-validation, we train an initial tree using the provided <em>cp</em> and explore all possible sub-trees (upto a single-node tree) to compute the optimal sub-tree. The optimal sub-tree and the 'cp' corresponding to this optimal sub-tree is placed in the <em>output_table</em>, with their columns named as <em>tree</em> and <em>pruning_cp</em> respectively.</li>
+</ul>
+</dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>The prediction function is provided to estimate the conditional mean given a new predictor. It has the following syntax: <pre class="syntax">
+tree_predict(tree_model,
+             new_data_table,
+             output_table,
+             type)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>tree_model </dt>
+<dd><p class="startdd">TEXT. Name of the table containing the decision tree model. This should be the output table returned from <em>tree_train</em></p>
+<p class="enddd"></p>
+</dd>
+<dt>new_data_table </dt>
+<dd><p class="startdd">TEXT. Name of the table containing prediction data. This table is expected to contain the same features that were used during training. The table should also contain <em>id_col_name</em> used for identifying each row</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. Name of the table to output prediction results to. If this table already exists then an error is returned. The table contains the <em>id_col_name</em> column giving the 'id' for each prediction and the prediction columns for the dependent variable.</p>
+<p>If <em>type</em> = 'response', then the table has a single additional column with the prediction value of the response. The type of this column depends on the type of the response variable used during training.</p>
+<p>If <em>type</em> = 'prob', then the table has multiple additional columns, one for each possible value of the response variable. The columns are labeled as 'estimated_prob_<em>dep_value</em>', where <em>dep_value</em> represents each value of the response</p>
+<p class="enddd"></p>
+</dd>
+<dt>type </dt>
+<dd>TEXT, optional, default: 'response'. For regression trees, the output is always the predicted value of the dependent variable. For classification trees, the <em>type</em> variable can be 'response', giving the classification prediction as output, or 'prob', giving the class probabilities as output. For each value of the dependent variable, a column with the probabilities is added to the output table  </dd>
+</dl>
+<dl class="section note"><dt>Note</dt><dd>If the <em>new_data_table</em> contains categories of categorical variables not seen in the training data then the prediction for that row will be NULL.</dd></dl>
+<p><a class="anchor" id="display"></a></p><dl class="section user"><dt>Display Function</dt><dd>The display function is provided to output a graph representation of the decision tree. The output can either be in the popular 'dot' format that can be visualized using various programs including those in the GraphViz package, or in a simple text format. The details of the text format is outputted with the tree. <pre class="syntax">
+tree_display(tree_model, dot_format)
+</pre></dd></dl>
+<p>An additional display function is provided to output the surrogate splits chosen for each internal node. </p><pre class="syntax">
+tree_surr_display(tree_model)
+</pre><p>The output contains the list of surrogate splits for each internal node. The nodes are sorted in ascending order by id. This is equivalent to viewing the tree in a breadth-first manner. For each surrogate, we output the surrogate split (variable and threshold) and also give the number of rows that were common between the primary split and the surrogate split. Finally, the number of rows present in the majority branch of the primary split is also presented. Only surrogates that perform better than this majority branch are included in the surrogate list. When the primary variable has a NULL value the surrogate variables are used in order to compute the split for that node. If all surrogates variables are NULL, then the majority branch is used to compute the split for a tuple.</p>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>tree_model_name </dt>
+<dd>TEXT. Name of the table containing the decision tree model </dd>
+<dt>dot_format </dt>
+<dd>BOOLEAN, default = TRUE. Output can either be in a dot format or a text format. If TRUE, the result is in the dot format, else output is in text format </dd>
+</dl>
+<p>The output is always returned as a 'TEXT'. For the dot format, the output can be redirected to a file on the client side and then rendered using visualization programs.</p>
+<p>If the user wants to export the dot format result to an external file, he can use the following method (Note: the user needs to use unaligned table output mode for psql with '-A' flag. And inside psql client, both '\t' and '\o' should be used):</p>
+<pre class="example">
+&gt; # under bash
+&gt; psql -A my_database
+# -- in psql now
+# \t
+# \o test.dot -- export to a file
+# select madlib.tree_display('tree_out');
+# \o
+# \t
+</pre><p>After the desired dot file has been generated, one can then use third-party plotting software to plot the trees in a nice figure: </p><pre class="example">
+&gt; # under bash, convert the dot file into a PDF file
+&gt; dot -Tpdf test.dot &gt; test.pdf
+&gt; xpdf test.pdf&amp;
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd>Decision tree classification example*</dd></dl>
+<ol type="1">
+<li>Prepare input data. <pre class="example">
+DROP TABLE IF EXISTS dt_golf;
+CREATE TABLE dt_golf (
+    id integer NOT NULL,
+    "OUTLOOK" text,
+    temperature double precision,
+    humidity double precision,
+    windy text,
+    class text
+) ;
+</pre> <pre class="example">
+COPY dt_golf (id,"OUTLOOK",temperature,humidity,windy,class) FROM stdin WITH DELIMITER '|';
+1|sunny|85|85|'false'|'Don''t Play'
+2|sunny|80|90|'true'|'Don''t Play'
+3|overcast|83|78|'false'|'Play'
+4|rain|70|96|'false'|'Play'
+5|rain|68|80|'false'|'Play'
+6|rain|65|70|'true'|'Don''t Play'
+7|overcast|64|65|'true'|'Play'
+8|sunny|72|95|'false'|'Don''t Play'
+9|sunny|69|70|'false'|'Play'
+10|rain|75|80|'false'|'Play'
+11|sunny|75|70|'true'|'Play'
+12|overcast|72|90|'true'|'Play'
+13|overcast|81|75|'false'|'Play'
+14|rain|71|80|'true'|'Don''t Play'
+\.
+</pre></li>
+<li>Run Decision tree train function. <pre class="example">
+SELECT madlib.tree_train('dt_golf',         -- source table
+                         'train_output',    -- output model table
+                         'id',              -- id column
+                         'class',           -- response
+                         '"OUTLOOK", temperature, humidity, windy',   -- features
+                         NULL::text,        -- exclude columns
+                         'gini',            -- split criterion
+                         NULL::text,        -- no grouping
+                         NULL::text,        -- no weights
+                         5,                 -- max depth
+                         3,                 -- min split
+                         1,                 -- min bucket
+                         10                 -- number of bins per continuous variable
+                         );
+</pre></li>
+<li>Predict output categories for the same data as was used for input. <pre class="example">
+SELECT madlib.tree_predict('train_output',
+                           'dt_golf',
+                           'prediction_results',
+                           'response');
+SELECT * FROM prediction_results;
+</pre> Result: <pre class="result">
+ id | estimated_class
+&#160;----+-----------------
+  1 | Don't Play
+  2 | Don't Play
+  3 | Play
+  4 | Play
+  5 | Play
+  6 | Don't Play
+  7 | Play
+  8 | Don't Play
+  9 | Play
+ 10 | Play
+ 11 | Play
+ 12 | Play
+ 13 | Play
+ 14 | Don't Play
+(14 rows)
+</pre></li>
+<li>Obtain a dot format display of the tree <pre class="example">
+SELECT madlib.tree_display('train_output');
+</pre> Result: <pre class="result">
+digraph "Classification tree for dt_golf" {
+         subgraph "cluster0"{
+         label=""
+"g0_0" [label="\"OUTLOOK"&lt;={overcast}", shape=ellipse];
+"g0_0" -&gt; "g0_1"[label="yes"];
+"g0_1" [label=""Play"",shape=box];
+"g0_0" -&gt; "g0_2"[label="no"];
+"g0_2" [label="temperature&lt;=75", shape=ellipse];
+"g0_2" -&gt; "g0_5"[label="yes"];
+"g0_2" -&gt; "g0_6"[label="no"];
+"g0_6" [label=""Don't Play"",shape=box];
+"g0_5" [label="temperature&lt;=65", shape=ellipse];
+"g0_5" -&gt; "g0_11"[label="yes"];
+"g0_11" [label=""Don't Play"",shape=box];
+"g0_5" -&gt; "g0_12"[label="no"];
+"g0_12" [label="temperature&lt;=70", shape=ellipse];
+"g0_12" -&gt; "g0_25"[label="yes"];
+"g0_25" [label=""Play"",shape=box];
+"g0_12" -&gt; "g0_26"[label="no"];
+"g0_26" [label="temperature&lt;=72", shape=ellipse];
+"g0_26" -&gt; "g0_53"[label="yes"];
+"g0_53" [label=""Don't Play"",shape=box];
+"g0_26" -&gt; "g0_54"[label="no"];
+"g0_54" [label=""Play"",shape=box];
+&#160;&#160;&#160;} //--- end of subgraph------------
+&#160;} //---end of digraph---------
+</pre></li>
+<li><p class="startli">Obtain a text display of the tree </p><pre class="example">
+SELECT madlib.tree_display('train_output', FALSE);
+</pre><p> Result: </p><pre class="result">
+&#160;-------------------------------------
+&#160;- Each node represented by 'id' inside ().
+&#160;- Leaf nodes have a * while internal nodes have the split condition at the end.
+&#160;- For each internal node (i), it's children will be at (2i+1) and (2i+2).
+&#160;- For each split the first indented child (2i+1) is the 'True' node and
+second indented child (2i+2) is the 'False' node.
+&#160;- Number of (weighted) rows for each response variable inside [].
+&#160;- Order of values = ['"Don\'t Play"', '"Play"']
+&#160;-------------------------------------
+(0)[ 5 9]  "OUTLOOK"&lt;={overcast}
+  (1)[ 0 4]  *
+  (2)[ 5 5]  temperature&lt;=75
+    (5)[ 3 5]  temperature&lt;=65
+      (11)[ 1 0]  *
+      (12)[ 2 5]  temperature&lt;=70
+        (25)[ 0 3]  *
+        (26)[ 2 2]  temperature&lt;=72
+          (53)[ 2 0]  *
+          (54)[ 0 2]  *
+    (6)[ 2 0]  *
+&#160;-------------------------------------
+</pre><p class="startli">Decision tree regression example*</p>
+</li>
+<li>Prepare input data. <pre class="example">
+CREATE TABLE mt_cars (
+    id integer NOT NULL,
+    mpg double precision,
+    cyl integer,
+    disp double precision,
+    hp integer,
+    drat double precision,
+    wt double precision,
+    qsec double precision,
+    vs integer,
+    am integer,
+    gear integer,
+    carb integer
+) ;
+</pre> <pre class="example">
+COPY mt_cars (id,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb) FROM stdin WITH DELIMITER '|' NULL '\null';
+1|18.7|8|360|175|3.15|3.44|17.02|0|0|3|2
+2|21|6|160|110|3.9|2.62|16.46|0|1|4|4
+3|24.4|4|146.7|62|3.69|3.19|20|1|0|4|2
+4|21|6|160|110|3.9|2.875|17.02|0|1|4|4
+5|17.8|6|167.6|123|3.92|3.44|18.9|1|0|4|4
+6|16.4|8|275.8|180|3.078|4.07|17.4|0|0|3|3
+7|22.8|4|108|93|3.85|2.32|18.61|1|1|4|1
+8|17.3|8|275.8|180|3.078|3.73|17.6|0|0|3|3
+9|21.4|\null|258|110|3.08|3.215|19.44|1|0|3|1
+10|15.2|8|275.8|180|3.078|3.78|18|0|0|3|3
+11|18.1|6|225|105|2.768|3.46|20.22|1|0|3|1
+12|32.4|4|78.7|66|4.08|2.20|19.47|1|1|4|1
+13|14.3|8|360|245|3.21|3.578|15.84|0|0|3|4
+14|22.8|4|140.8|95|3.92|3.15|22.9|1|0|4|2
+15|30.4|4|75.7|52|4.93|1.615|18.52|1|1|4|2
+16|19.2|6|167.6|123|3.92|3.44|18.3|1|0|4|4
+17|33.9|4|71.14|65|4.22|1.835|19.9|1|1|4|1
+18|15.2|\null|304|150|3.15|3.435|17.3|0|0|3|2
+19|10.4|8|472|205|2.93|5.25|17.98|0|0|3|4
+20|27.3|4|79|66|4.08|1.935|18.9|1|1|4|1
+21|10.4|8|460|215|3|5.424|17.82|0|0|3|4
+22|26|4|120.3|91|4.43|2.14|16.7|0|1|5|2
+23|14.7|8|440|230|3.23|5.345|17.42|0|0|3|4
+24|30.4|4|95.14|113|3.77|1.513|16.9|1|1|5|2
+25|21.5|4|120.1|97|3.70|2.465|20.01|1|0|3|1
+26|15.8|8|351|264|4.22|3.17|14.5|0|1|5|4
+27|15.5|8|318|150|2.768|3.52|16.87|0|0|3|2
+28|15|8|301|335|3.54|3.578|14.6|0|1|5|8
+29|13.3|8|350|245|3.73|3.84|15.41|0|0|3|4
+30|19.2|8|400|175|3.08|3.845|17.05|0|0|3|2
+31|19.7|6|145|175|3.62|2.77|15.5|0|1|5|6
+32|21.4|4|121|109|4.11|2.78|18.6|1|1|4|2
+\.
+</pre></li>
+<li>Run Decision Tree train function. <pre class="example">
+DROP TABLE IF EXISTS train_output, train_output_summary;
+SELECT madlib.tree_train('mt_cars',
+                         'train_output',
+                         'id',
+                         'mpg',
+                         '*',
+                         'id, hp, drat, am, gear, carb',  -- exclude columns
+                         'mse',
+                         NULL::text,
+                         NULL::text,
+                         10,
+                         8,
+                         3,
+                         10,
+                         NULL,
+                         'max_surrogates=2'
+                         );
+</pre></li>
+<li>Display the decision tree in basic text format. <pre class="example">
+SELECT madlib.tree_display('train_output', FALSE);
+</pre> Result: <pre class="result">
+&#160; -------------------------------------
+&#160;- Each node represented by 'id' inside ().
+&#160;- Each internal nodes has the split condition at the end, while each
+&#160;    leaf node has a * at the end.
+&#160;- For each internal node (i), its child nodes are indented by 1 level
+&#160;    with ids (2i+1) for True node and (2i+2) for False node.
+&#160;- Number of rows and average response value inside []. For a leaf node, this is the prediction.
+&#160;-------------------------------------
+ (0)[32, 20.0906]  cyl in {8,6}
+    (1)[21, 16.6476]  disp &lt;= 258
+       (3)[7, 19.7429]  *
+       (4)[14, 15.1]  qsec &lt;= 17.42
+          (9)[10, 15.81]  qsec &lt;= 16.9
+             (19)[5, 14.78]  *
+             (20)[5, 16.84]  *
+          (10)[4, 13.325]  *
+    (2)[11, 26.6636]  wt &lt;= 2.2
+       (5)[6, 30.0667]  *
+       (6)[5, 22.58]  *
+ &#160;-------------------------------------
+(1 row)
+</pre></li>
+<li>Display the surrogates in the decision tree. <pre class="example">
+SELECT madlib.tree_surr_display('train_output');
+</pre> Result: <pre class="result">
+&#160;-------------------------------------
+       Surrogates for internal nodes
+&#160;-------------------------------------
+ (0) cyl in {8,6}
+      1: disp &gt; 146.7    [common rows = 29]
+      2: vs in {0}    [common rows = 26]
+      [Majority branch = 19 ]
+ (1) disp &lt;= 258
+      1: cyl in {6,4}    [common rows = 19]
+      2: vs in {1}    [common rows = 18]
+      [Majority branch = 14 ]
+ (2) wt &lt;= 2.2
+      1: disp &lt;= 108    [common rows = 9]
+      2: qsec &lt;= 18.52    [common rows = 8]
+      [Majority branch = 6 ]
+ (4) qsec &lt;= 17.42
+      1: disp &gt; 275.8    [common rows = 11]
+      2: vs in {0}    [common rows = 10]
+      [Majority branch = 10 ]
+ (9) qsec &lt;= 16.9
+      1: wt &lt;= 3.84    [common rows = 8]
+      2: disp &lt;= 360    [common rows = 7]
+      [Majority branch = 5 ]
+&#160;-------------------------------------
+(1 row)
+</pre></li>
+</ol>
+<dl class="section note"><dt>Note</dt><dd>The 'cyl' parameter above has two tuples with null values. In the prediction example below, the surrogate splits for the <em>cyl in {8, 6}</em> split are used to predict those two tuples (<em>id = 9</em> and <em>id = 18</em>). The splits are used in descending order till a surrogate variable is found that is not NULL. In this case, the two tuples have non-NULL values for <em>disp</em>, hence the <em>disp &gt; 146.7</em> split is used to make the prediction. If all the surrogate variables had been NULL then the majority branch would have been followed.</dd></dl>
+<ol type="1">
+<li>Predict regression output for the same data and compare with original. <pre class="example">
+DROP TABLE IF EXISTS prediction_results;
+SELECT madlib.tree_predict('train_output',
+                           'mt_cars',
+                           'prediction_results',
+                           'response');
+SELECT s.id, mpg, estimated_mpg FROM prediction_results p, mt_cars s where s.id = p.id;
+</pre> Result: <pre class="result">
+  id | mpg  |  estimated_mpg
+----+------+------------------
+  1 | 18.7 |            16.84
+  2 |   21 | 19.7428571428571
+  3 | 24.4 |            22.58
+  4 |   21 | 19.7428571428571
+  5 | 17.8 | 19.7428571428571
+  6 | 16.4 |            16.84
+  7 | 22.8 |            22.58
+  8 | 17.3 |           13.325
+  9 | 21.4 | 19.7428571428571
+ 10 | 15.2 |           13.325
+ 11 | 18.1 | 19.7428571428571
+ 12 | 32.4 | 30.0666666666667
+ 13 | 14.3 |            14.78
+ 14 | 22.8 |            22.58
+ 15 | 30.4 | 30.0666666666667
+ 16 | 19.2 | 19.7428571428571
+ 17 | 33.9 | 30.0666666666667
+ 18 | 15.2 |            16.84
+ 19 | 10.4 |           13.325
+ 20 | 27.3 | 30.0666666666667
+ 21 | 10.4 |           13.325
+ 22 |   26 | 30.0666666666667
+ 23 | 14.7 |            16.84
+ 24 | 30.4 | 30.0666666666667
+ 25 | 21.5 |            22.58
+ 26 | 15.8 |            14.78
+ 27 | 15.5 |            14.78
+ 28 |   15 |            14.78
+ 29 | 13.3 |            14.78
+ 30 | 19.2 |            16.84
+ 31 | 19.7 | 19.7428571428571
+ 32 | 21.4 |            22.58
+(32 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd>[1] Breiman, Leo; Friedman, J. H.; Olshen, R. A.; Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth &amp; Brooks/Cole Advanced Books &amp; Software.</dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="decision__tree_8sql__in.html">decision_tree.sql_in</a> documenting the training function</p>
+<p><a class="el" href="group__grp__random__forest.html">Random Forest</a></p>
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+  <div class="headertitle">
+<div class="title">Decision Tree (old C4.5 implementation)<div class="ingroups"><a class="el" href="group__grp__deprecated.html">Deprecated Modules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#input">Input</a> </li>
+<li>
+<a href="#train">Training Function</a> </li>
+<li>
+<a href="#classify">Classification Function</a> </li>
+<li>
+<a href="#score">Scoring Function</a> </li>
+<li>
+<a href="#display">Display Tree Function</a> </li>
+<li>
+<a href="#notes">Implementation Notes</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This is an old implementation of decision trees. For a newer implementation, please see <a class="el" href="group__grp__decision__tree.html">Decision Tree</a></em></dd></dl>
+<p>This module provides an implementation of the C4.5 algorithm to grow decision trees.</p>
+<p>The implementation supports:</p><ul>
+<li>Building the decision tree</li>
+<li>Multiple split critera, including: &ndash; Information Gain &ndash; Gini Coefficient &ndash; Gain Ratio</li>
+<li>Decision tree Pruning</li>
+<li>Decision tree classification/scoring</li>
+<li>Decision tree display</li>
+<li>Rule generation</li>
+<li>Continuous and discrete features</li>
+<li>Missing value handling</li>
+</ul>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd></dd></dl>
+<p>Run the training algorithm on the source data: </p><pre class="syntax">
+c45_train( split_criterion,
+           training_table_name,
+           result_tree_table_name,
+           validation_table_name,
+           continuous_feature_names,
+           feature_col_names,
+           id_col_name,
+           class_col_name,
+           confidence_level,
+           how2handle_missing_value,
+           max_tree_depth,
+           node_prune_threshold,
+           node_split_threshold,
+           verbosity
+         )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>split_criterion </dt>
+<dd>The name of the split criterion that should be used for tree construction. The valid values are ‘infogain’, ‘gainratio’, and ‘gini’. It can't be NULL. Information gain(infogain) and gini index(gini) are biased toward multivalued attributes. Gain ratio(gainratio) adjusts for this bias. However, it tends to prefer unbalanced splits in which one partition is much smaller than the others.  </dd>
+<dt>training_table_name </dt>
+<dd>The name of the table/view with the source data. The <b>training data</b> is expected to be of the following form: <pre>{TABLE|VIEW} <em>trainingSource</em> (
+    ...
+    <em>id</em> INT|BIGINT,
+    <em>feature1</em> SUPPORTED_DATA_TYPE,
+    <em>feature2</em> SUPPORTED_DATA_TYPE,
+    <em>feature3</em> SUPPORTED_DATA_TYPE,
+    ....................
+    <em>featureN</em> SUPPORTED_DATA_TYPE,
+    <em>class</em>    SUPPORTED_DATA_TYPE,
+    ...
+)</pre> The detailed list of SUPPORTED_DATA_TYPE is: SMALLINT, INT, BIGINT, FLOAT8, REAL, DECIMAL, INET, CIDR, MACADDR, BOOLEAN, CHAR, VARCHAR, TEXT, "char", DATE, TIME, TIMETZ, TIMESTAMP, TIMESTAMPTZ, and INTERVAL.  </dd>
+<dt>result_tree_table_name </dt>
+<dd>The name of the table to contain the decision tree output. The table stores an abstract object (representing the model) used for further classification. It has the following columns: <table  class="output">
+<tr>
+<th>id  </th></tr>
+<tr>
+<th>tree_location  </th></tr>
+<tr>
+<th>feature  </th></tr>
+<tr>
+<th>probability  </th></tr>
+<tr>
+<th>ebp_coeff  </th></tr>
+<tr>
+<th>maxclass  </th></tr>
+<tr>
+<th>scv  </th></tr>
+<tr>
+<th>live  </th></tr>
+<tr>
+<th>sample_size  </th></tr>
+<tr>
+<th>parent_id  </th></tr>
+<tr>
+<th>lmc_nid  </th></tr>
+<tr>
+<th>lmc_fval  </th></tr>
+<tr>
+<th>is_continuous  </th></tr>
+<tr>
+<th>split_value  </th></tr>
+<tr>
+<th>tid  </th></tr>
+<tr>
+<th>dp_ids  </th></tr>
+</table>
+</dd>
+<dt>validation_table_name </dt>
+<dd>The name of the table/view that contains the validation set used for tree pruning. The default is NULL, in which case we will not do tree pruning.  </dd>
+<dt>continuous_feature_names </dt>
+<dd>A comma-separated list of the names of features whose values are continuous. The default is null, which means there are no continuous features in the training table.  </dd>
+<dt>feature_col_names </dt>
+<dd>A comma-separated list of the names of table columns, each of which defines a feature. The default value is null, which means all the columns in the training table, except columns named ‘id’ and ‘class’, will be used as features.  </dd>
+<dt>id_col_name </dt>
+<dd>The name of the column containing an ID for each record. </dd>
+<dt>class_col_name </dt>
+<dd>The name of the column containing the labeled class.  </dd>
+<dt>confidence_level </dt>
+<dd>A statistical confidence interval of the resubstitution error. </dd>
+<dt>how2handle_missing_value </dt>
+<dd>The way to handle missing value. The valid value is 'explicit' or 'ignore'. </dd>
+<dt>max_tree_depth </dt>
+<dd>Specifies the maximum number of levels in the result DT to avoid overgrown DTs.  </dd>
+<dt>node_prune_threshold </dt>
+<dd>The minimum percentage of the number of records required in a child node. It can't be NULL. The range of it is in [0.0, 1.0]. This threshold only applies to the non-root nodes. Therefore, if its value is 1, then the trained tree only has one node (the root node); if its value is 0, then no nodes will be pruned by this parameter. </dd>
+<dt>node_split_threshold </dt>
+<dd>The minimum percentage of the number of records required in a node in order for a further split to be possible. It can't be NULL. The range of it is in [0.0, 1.0]. If it's value is 1, then the trained tree only has two levels, since only the root node can grow; if its value is 0, then trees can grow extensively. </dd>
+<dt>verbosity </dt>
+<dd>An integer greater than 0 means this function runs in verbose mode. </dd>
+</dl>
+<p><a class="anchor" id="classify"></a></p><dl class="section user"><dt>Classification Function</dt><dd></dd></dl>
+<p>The classification function uses the learned model stored by the training function to create the classification results. </p><pre class="syntax">
+c45_classify( tree_table_name,
+              classification_table_name,
+              result_table_name
+            )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>tree_table_name </dt>
+<dd><p class="startdd">The name of the table containing the trained model.</p>
+<p class="enddd">The data to classify is expected to be in the same form as the training data, except that it does not need a class column.  </p>
+</dd>
+<dt>classification_table_name </dt>
+<dd>The name of the table containing the data to classify. </dd>
+<dt>result_table_name </dt>
+<dd>The name of the output table. </dd>
+</dl>
+<p><a class="anchor" id="score"></a></p><dl class="section user"><dt>Scoring Function</dt><dd>The scoring function scores the learned model against a validation data set. <pre class="syntax">
+c45_score( tree_table_name,
+           validation_table_name,
+           verbosity
+         );
+</pre></dd></dl>
+<p>This gives a ratio of correctly classified items in the validation set.</p>
+<p><a class="anchor" id="display"></a></p><dl class="section user"><dt>Display Tree Function</dt><dd></dd></dl>
+<p>The display tree function displays the learned model in a human-readable format.</p>
+<pre class="syntax">
+c45_display( tree_table_name
+           );
+</pre><p><a class="anchor" id="clean"></a></p><dl class="section user"><dt>Clean Tree Function</dt><dd></dd></dl>
+<p>The clean tree function cleans up the learned model and all metadata. </p><pre class="syntax">
+  c45_clean( tree_table_name
+           );
+</pre><p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Implementation Notes</dt><dd></dd></dl>
+<p>Due to some implementation difference, decisiont tree on HAWQ is much slower than on Greenplum database when running on small data sets. However, for larger data sets, the performance difference is much smaller. For example, in a test with 0.75 million rows of data, decision tree on HAWQ is only one time slower than on GPDB. This is because the overhead due to the different implementation is proportional to the tree size, and is usually negligible as data size increases (The tree size is not likely to increase proportionally with the data size. For example, if a 10-node tree is used to fit a data set with 1000 rows, it is very unlikely to fit another data set with 1 million rows with a 10000-node tree).</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare an input table. <pre class="example">
+SELECT * FROM golf_data ORDER BY id;
+</pre> Result: <pre class="result">
+ id | outlook  | temperature | humidity | windy  |    class
+&#160;---+----------+-------------+----------+--------+--------------
+  1 | sunny    |          85 |       85 |  false |  Do not Play
+  2 | sunny    |          80 |       90 |  true  |  Do not Play
+  3 | overcast |          83 |       78 |  false |  Play
+  4 | rain     |          70 |       96 |  false |  Play
+  5 | rain     |          68 |       80 |  false |  Play
+  6 | rain     |          65 |       70 |  true  |  Do not Play
+  7 | overcast |          64 |       65 |  true  |  Play
+  8 | sunny    |          72 |       95 |  false |  Do not Play
+  9 | sunny    |          69 |       70 |  false |  Play
+ 10 | rain     |          75 |       80 |  false |  Play
+ 11 | sunny    |          75 |       70 |  true  |  Play
+ 12 | overcast |          72 |       90 |  true  |  Play
+ 13 | overcast |          81 |       75 |  false |  Play
+ 14 | rain     |          71 |       80 |  true  |  Do not Play
+(14 rows)
+</pre></li>
+<li>Train the decision tree model. Run the <a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793" title="Cleanup the trained tree table and any relevant tables. ">c45_clean()</a> function first to clean up any model and metadata from previous executions. <pre class="example">
+SELECT * FROM madlib.c45_clean( 'trained_tree_infogain'
+                              );
+SELECT * FROM madlib.c45_train( 'infogain',
+                                'golf_data',
+                                'trained_tree_infogain',
+                                null,
+                                'temperature,humidity',
+                                'outlook,temperature,humidity,windy',
+                                'id',
+                                'class',
+                                100,
+                                'explicit',
+                                5,
+                                0.001,
+                                0.001,
+                                0
+                              );
+</pre> Result: <pre class="result">
+ training_set_size | tree_nodes | tree_depth |  training_time  | split_criterion
+&#160;------------------+------------+------------+-----------------+-----------------
+                14 |          8 |          3 | 00:00:00.871805 | infogain
+(1 row)
+</pre></li>
+<li>View the the tree model table. <pre class="example">
+SELECT * FROM trained_tree_infogain ORDER BY id;
+</pre> Result: <pre class="result">
+ id | tree_location | feature |    probability    | ebp_coeff | maxclass |       scv         | live |sample_size | parent_id | lmc_nid | lmc_fval | is_continuous   | split_value
+&#160;---+---------------+---------+-------------------+-----------+----------+-------------------+------+----------+-----------+---------+----------+-----------------+-------------
+  1 | {0}           |       3 | 0.642857142857143 |         1 |        2 | 0.171033941880327 |    0 |       14 |         0 |       2 |        1 | f               |
+  2 | {0,1}         |       4 |                 1 |         1 |        2 |                 0 |    0 |        4 |         1 |         |          | f               |
+  3 | {0,2}         |       4 |               0.6 |         1 |        2 | 0.673011667009257 |    0 |        5 |         1 |       5 |        1 | f               |
+  4 | {0,3}         |       2 |               0.6 |         1 |        1 | 0.673011667009257 |    0 |        5 |         1 |       7 |        1 | t               |          70
+  5 | {0,2,1}       |       4 |                 1 |         1 |        2 |                 0 |    0 |        3 |         3 |         |          | f               |
+  6 | {0,2,2}       |       4 |                 1 |         1 |        1 |                 0 |    0 |        2 |         3 |         |          | f               |
+  7 | {0,3,1}       |       4 |                 1 |         1 |        2 |                 0 |    0 |        2 |         4 |         |          | f               |
+  8 | {0,3,2}       |       4 |                 1 |         1 |        1 |                 0 |    0 |        3 |         4 |         |          | f               |
+(8 rows)
+</pre></li>
+<li>Display the tree with a human readable format: <pre class="example">
+SELECT madlib.c45_display('trained_tree_infogain');
+</pre> Result: <pre class="result">
+                                      c45_display
+&#160;--------------------------------------------------------------------------------------
+Tree 1
+    Root Node  : class(  Play)   num_elements(14)  predict_prob(0.642857142857143)
+         outlook:  = overcast : class( Play)   num_elements(4)  predict_prob(1)
+         outlook:  = rain : class( Play)   num_elements(5)  predict_prob(0.6)
+             windy:  =  false : class( Play)   num_elements(3)  predict_prob(1)
+             windy:  = true  : class(  Do not Play)   num_elements(2)  predict_prob(1)
+         outlook:  =  sunny      : class(  Do not Play)   num_elements(5)  predict_prob(0.6)
+             humidity:  &lt;= 70 : class( Play)   num_elements(2)  predict_prob(1)
+             humidity:  &gt; 70  : class(  Do not Play)   num_elements(3)  predict_prob(1)
+(1 row)
+</pre></li>
+<li>Classify some data with the learned model. <pre class="example">
+SELECT * FROM madlib.c45_classify ( 'trained_tree_infogain',
+                                    'golf_data',
+                                    'classification_result'
+                                  );
+</pre> Result: <pre class="result">
+ input_set_size |    classification_time
+----------------+-----------------
+             14 | 00:00:00.247713
+(1 row)
+</pre></li>
+<li>Check the classification results. <pre class="example">
+SELECT t.id, t.outlook, t.temperature, t.humidity, t.windy, c.class
+FROM   madlib.classification_result c, golf_data t
+WHERE  t.id=c.id ORDER BY id;
+</pre> Result: <pre class="result">
+ id | outlook  | temperature | humidity | windy  |    class
+&#160;---+----------+-------------+----------+--------+--------------
+  1 | sunny    |          85 |       85 |  false |  Do not Play
+  2 | sunny    |          80 |       90 |  true  |  Do not Play
+  3 | overcast |          83 |       78 |  false |  Play
+  4 | rain     |          70 |       96 |  false |  Play
+  5 | rain     |          68 |       80 |  false |  Play
+  6 | rain     |          65 |       70 |  true  |  Do not Play
+  7 | overcast |          64 |       65 |  true  |  Play
+  8 | sunny    |          72 |       95 |  false |  Do not Play
+  9 | sunny    |          69 |       70 |  false |  Play
+ 10 | rain     |          75 |       80 |  false |  Play
+ 11 | sunny    |          75 |       70 |  true  |  Play
+ 12 | overcast |          72 |       90 |  true  |  Play
+ 13 | overcast |          81 |       75 |  false |  Play
+ 14 | rain     |          71 |       80 |  true  |  Do not Play
+(14 rows)
+</pre></li>
+<li>Score the data against a validation set. <pre class="example">
+SELECT * FROM madlib.c45_score( 'trained_tree_infogain',
+                                'golf_data_validation',
+                                0)
+                              );
+</pre> Result: <pre class="result">
+ c45_score
+&#160;----------
+      1
+(1 row)
+</pre></li>
+<li>Clean up the tree and metadata. <pre class="example">
+SELECT madlib.c45_clean( 'trained_tree_infogain'
+                       );
+</pre> Result: <pre class="result">
+ c45_clean
+&#160;----------
+&#160;
+(1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] <a href="http://en.wikipedia.org/wiki/C4.5_algorithm">http://en.wikipedia.org/wiki/C4.5_algorithm</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="c45_8sql__in.html" title="C4.5 APIs and main controller written in PL/PGSQL. ">c45.sql_in</a> documenting the SQL functions. </dd></dl>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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+<title>MADlib: Dense Linear Systems</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Dense Linear Systems<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> &raquo; <a class="el" href="group__grp__linear__solver.html">Linear Solvers</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#dls_usage">Solution Function</a> </li>
+<li class="level1">
+<a href="#dls_opt_params">Optimizer Parameters</a> </li>
+<li class="level1">
+<a href="#dls_examples">Examples</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>The linear systems module implements solution methods for systems of consistent linear equations. Systems of linear equations take the form: </p><p class="formulaDsp">
+\[ Ax = b \]
+</p>
+<p>where \(x \in \mathbb{R}^{n}\), \(A \in \mathbb{R}^{m \times n} \) and \(b \in \mathbb{R}^{m}\). We assume that there are no rows of \(A\) where all elements are zero. The algorithms implemented in this module can handle large dense linear systems. Currently, the algorithms implemented in this module solve the linear system by a direct decomposition. Hence, these methods are known as <em>direct method</em>.</p>
+<p><a class="anchor" id="dls_usage"></a></p><dl class="section user"><dt>Solution Function</dt><dd><pre class="syntax">
+linear_solver_dense( tbl_source,
+                     tbl_result,
+                     row_id,
+                     LHS,
+                     RHS,
+                     grouping_col,
+                     optimizer,
+                     optimizer_params
+                   )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>tbl_source </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training data. The input data is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>sourceName</em> (
+    ...
+    <em>row_id</em>          FLOAT8,
+    <em>left_hand_side</em>  FLOAT8[],
+    <em>right_hand_side</em> FLOAT8,
+    ...
+)</pre><p>Each row represents a single equation. The <em>right_hand_side</em> column refers to the right hand side of the equations while the <em>left_hand_side</em> column refers to the multipliers on the variables on the left hand side of the same equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>tbl_result </dt>
+<dd><p class="startdd">TEXT. The name of the table where the output is saved. The output is stored in the table named by the <em>tbl_result</em> argument. It contains the following columns: </p><table  class="output">
+<tr>
+<th>solution </th><td>FLOAT8[]. The solution variables in the same order as that provided as input in the 'left_hand_side' column name of the <em>source_table</em>  </td></tr>
+<tr>
+<th>residual_norm </th><td>FLOAT8. The scaled residual norm, defined as \( \frac{|Ax - b|}{|b|} \). This value is an indication of the accuracy of the solution.  </td></tr>
+<tr>
+<th>iters </th><td>INTEGER. Number of iterations required by the algorithm (only applicable for iterative algorithms). The output is NULL for 'direct' methods.   </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>row_id </dt>
+<dd><p class="startdd">TEXT. The name of the column storing the 'row id' of the equations.</p>
+<p>For a system with N equations, the row_id's must be a continuous range of integers from \( 0 \ldots n-1 \). </p>
+<p class="enddd"></p>
+</dd>
+<dt>LHS </dt>
+<dd><p class="startdd">TEXT. The name of the column storing the 'left hand side' of the equations, stored as an array.</p>
+<p class="enddd"></p>
+</dd>
+<dt>RHS </dt>
+<dd><p class="startdd">TEXT. The name of the column storing the 'right hand side' of the equations.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_cols (optional)  </dt>
+<dd>TEXT, default: NULL. Group by column names. <em>Not currently implemented. Any non-NULL value is ignored.</em> </dd>
+<dt>optimizer (optional)  </dt>
+<dd><p class="startdd">TEXT, default: 'direct'. The type of optimizer.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer_params (optional)  </dt>
+<dd>TEXT, default: NULL. Optimizer specific parameters. </dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="dls_opt_params"></a></p><dl class="section user"><dt>Optimizer Parameters</dt><dd></dd></dl>
+<p>For each optimizer, there are specific parameters that can be tuned for better performance.</p>
+<dl class="arglist">
+<dt>algorithm (default: householderqr) </dt>
+<dd><p class="startdd">There are several algorithms that can be classified as 'direct' methods of solving linear systems. MADlib dense linear system solvers provide various algorithmic options for users.</p>
+<p>The following table provides a guideline on the choice of algorithm based on conditions on the A matrix, speed of the algorithms and numerical stability. </p><pre class="fragment"> Algorithm            | Conditions on A  | Speed | Accuracy
+ ----------------------------------------------------------
+ householderqr        | None             |  ++   |  +
+ partialpivlu         | Invertable       |  ++   |  +
+ fullpivlu            | None             |  -    |  +++
+ colpivhouseholderqr  | None             |  +    |  ++
+ fullpivhouseholderqr | None             |  -    |  +++
+ llt                  | Pos. Definite    |  +++  |  +
+ ldlt                 | Pos. or Neg Def  |  +++  |  ++
+</pre><p>For speed '++' is faster than '+', which is faster than '-'. For accuracy '+++' is better than '++'.</p>
+<p class="enddd">More details about the individual algorithms can be found in the <a href="http://eigen.tuxfamily.org/dox-devel/group__TutorialLinearAlgebra.html">Eigen documentation</a>. Eigen is an open source library for linear algebra.  </p>
+</dd>
+</dl>
+<p><a class="anchor" id="dls_examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the linear systems solver function. <pre class="example">
+SELECT madlib.linear_solver_dense();
+</pre></li>
+<li>Create the sample data set. <pre class="example">
+CREATE TABLE linear_systems_test_data( id INTEGER NOT NULL,
+                                       lhs DOUBLE PRECISION[],
+                                       rhs DOUBLE PRECISION
+                                     );
+INSERT INTO linear_systems_test_data(id, lhs, rhs)
+       VALUES
+        (0, ARRAY[1,0,0], 20),
+        (1, ARRAY[0,1,0], 15),
+        (2, ARRAY[0,0,1], 20);
+</pre></li>
+<li>Solve the linear systems with default parameters. <pre class="example">
+SELECT madlib.linear_solver_dense( 'linear_systems_test_data',
+                                   'output_table',
+                                   'id',
+                                   'lhs',
+                                   'rhs'
+                                 );
+</pre></li>
+<li>Obtain the output from the output table. <pre class="example">
+\x on
+SELECT * FROM output_table;
+</pre> Result: <pre class="result">
+--------------------+-------------------------------------
+solution            | {20,15,20}
+residual_norm       | 0
+iters               | NULL
+</pre></li>
+<li>Choose an algorithm different than the default. <pre class="example">
+DROP TABLE IF EXISTS result_table;
+SELECT madlib.linear_solver_dense( 'linear_systems_test_data',
+                                   'result_table',
+                                   'id',
+                                   'lhs',
+                                   'rhs',
+                                   NULL,
+                                   'direct',
+                                   'algorithm=llt'
+                                 );
+</pre></li>
+</ol>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="dense__linear__systems_8sql__in.html" title="SQL functions for linear systems. ">dense_linear_systems.sql_in</a> documenting the SQL functions</dd></dl>
+</div><!-- contents -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__deprecated.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Deprecated Modules</div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of deprecated modules. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__dectree"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__dectree.html">Decision Tree (old C4.5 implementation)</a></td></tr>
+<tr class="memdesc:group__grp__dectree"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generates a decision tree using the C4.5 algorithm. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__svdmf"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__svdmf.html">Matrix Factorization</a></td></tr>
+<tr class="memdesc:group__grp__svdmf"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes low-rank approximation of a sparse matrix. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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[21/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+<div id="doc-content">
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+<iframe src="javascript:void(0)" frameborder="0" 
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+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_k"></a>- k -</h3><ul>
+<li>kolmogorov_cdf()
+: <a class="el" href="prob_8sql__in.html#aeef43f74f583bdff17bd074d9c0d9607">prob.sql_in</a>
+</li>
+<li>ks_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd3f6d1d6dc4203cab3bcc980ec8ed8d">hypothesis_tests.sql_in</a>
+</li>
+<li>ks_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a58ffb5b2b8392e005f4f3e21560df93f">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_l.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_l.html b/docs/latest/globals_func_l.html
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_l.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
+<li>l1norm()
+: <a class="el" href="svec__util_8sql__in.html#a9e9b64c93b29c19adfb670f0171f14bf">svec_util.sql_in</a>
+</li>
+<li>l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a1b6bb42a5176b020aed12734df23aca9">svec_util.sql_in</a>
+</li>
+<li>laplace_cdf()
+: <a class="el" href="prob_8sql__in.html#a64e197de8da3761acdeec9db7e211703">prob.sql_in</a>
+</li>
+<li>laplace_pdf()
+: <a class="el" href="prob_8sql__in.html#a750278ad29d514793f76e159b773f410">prob.sql_in</a>
+</li>
+<li>laplace_quantile()
+: <a class="el" href="prob_8sql__in.html#a77f94fc43d4777fc4f68d18e29454a81">prob.sql_in</a>
+</li>
+<li>lda_get_perplexity()
+: <a class="el" href="lda_8sql__in.html#a25c3ef12d9808d8a38c5fd2630f3b5a9">lda.sql_in</a>
+</li>
+<li>lda_get_topic_desc()
+: <a class="el" href="lda_8sql__in.html#a4ac16d87d50821aadcab0d75d65b3f1b">lda.sql_in</a>
+</li>
+<li>lda_get_topic_word_count()
+: <a class="el" href="lda_8sql__in.html#ace20b6314e5b4e98929a98a6f05d925d">lda.sql_in</a>
+</li>
+<li>lda_get_word_topic_count()
+: <a class="el" href="lda_8sql__in.html#a455699e72328d6b105c61a3c9e7ae5dc">lda.sql_in</a>
+</li>
+<li>lda_predict()
+: <a class="el" href="lda_8sql__in.html#af1fde06c39dd12bb9e5544997f815323">lda.sql_in</a>
+</li>
+<li>lda_train()
+: <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80">lda.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step()
+: <a class="el" href="crf_8sql__in.html#a590cd748d8e18055d851e7d4f4a3ca00">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_final()
+: <a class="el" href="crf_8sql__in.html#a5bf4b2738003096f4eeeccd0eb5e98f0">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_merge_states()
+: <a class="el" href="crf_8sql__in.html#a45f3eaf0c80cdfa99872cd071020d5c7">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_transition()
+: <a class="el" href="crf_8sql__in.html#a2e7104fea95ca76587e7fae9bcb0d7ed">crf.sql_in</a>
+</li>
+<li>lincrf_train()
+: <a class="el" href="crf_8sql__in.html#a336ac57ae3d07e331646d397c7651d3d">crf.sql_in</a>
+</li>
+<li>linear_solver_dense()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a885d6a36c07dc2c048948cb5287a3b92">dense_linear_systems.sql_in</a>
+</li>
+<li>linear_solver_sparse()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3b3b7eea8b91c0a16ede3e7a9c6b17ef">sparse_linear_systems.sql_in</a>
+</li>
+<li>linear_svm_igd_final()
+: <a class="el" href="lsvm_8sql__in.html#a614936d27848149eead4581db57a9236">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_merge()
+: <a class="el" href="lsvm_8sql__in.html#a241eea31c78737ecbb5da6639debec3b">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step()
+: <a class="el" href="lsvm_8sql__in.html#a8cd061b3cd2b9ef62e018c33dde27a4d">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step_serial()
+: <a class="el" href="lsvm_8sql__in.html#a027f40b148849a721001d20f098a4a0e">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_transition()
+: <a class="el" href="lsvm_8sql__in.html#a6f26e0007d7d95b4bb4b72db4f702b25">lsvm.sql_in</a>
+</li>
+<li>linregr()
+: <a class="el" href="linear_8sql__in.html#a5fcd1daa9796dccf5430929c37ea0ee9">linear.sql_in</a>
+</li>
+<li>linregr_final()
+: <a class="el" href="linear_8sql__in.html#a28b69254ba5cd513b35ba6b17efd2c35">linear.sql_in</a>
+</li>
+<li>linregr_merge_states()
+: <a class="el" href="linear_8sql__in.html#a042536820ffbad498a4e1c410f949e35">linear.sql_in</a>
+</li>
+<li>linregr_predict()
+: <a class="el" href="linear_8sql__in.html#a8da0030683d5931b93e5f2bee3fd0544">linear.sql_in</a>
+</li>
+<li>linregr_train()
+: <a class="el" href="linear_8sql__in.html#ad0175a9ecc455d9679f7d251af264848">linear.sql_in</a>
+</li>
+<li>linregr_transition()
+: <a class="el" href="linear_8sql__in.html#aa2b87a3e60272176bfa2fb859f99461a">linear.sql_in</a>
+</li>
+<li>lmf_igd_final()
+: <a class="el" href="lmf_8sql__in.html#a298f46c944cd6813b0360514bf8c9ed1">lmf.sql_in</a>
+</li>
+<li>lmf_igd_merge()
+: <a class="el" href="lmf_8sql__in.html#ae3bb16a76a927e1e59420d4a8e13012f">lmf.sql_in</a>
+</li>
+<li>lmf_igd_run()
+: <a class="el" href="lmf_8sql__in.html#ac1acb1f0e1f7008118f21c83546a4602">lmf.sql_in</a>
+</li>
+<li>lmf_igd_step()
+: <a class="el" href="lmf_8sql__in.html#a54d2ca2048d9a0c29d5593530b932a23">lmf.sql_in</a>
+</li>
+<li>lmf_igd_transition()
+: <a class="el" href="lmf_8sql__in.html#a187574cfe85888b29d6b1658554e7507">lmf.sql_in</a>
+</li>
+<li>logistic()
+: <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e">logistic.sql_in</a>
+</li>
+<li>logistic_cdf()
+: <a class="el" href="prob_8sql__in.html#a140f674876813d5e786a4d8ba8d75c87">prob.sql_in</a>
+</li>
+<li>logistic_pdf()
+: <a class="el" href="prob_8sql__in.html#afa38eb6c61d3c9825d5c172e6c17dbf7">prob.sql_in</a>
+</li>
+<li>logistic_quantile()
+: <a class="el" href="prob_8sql__in.html#a5a77a0bc5884af2a914a955174892ae2">prob.sql_in</a>
+</li>
+<li>lognormal_cdf()
+: <a class="el" href="prob_8sql__in.html#a4c05b347f8feb64e1236d21b850af61e">prob.sql_in</a>
+</li>
+<li>lognormal_pdf()
+: <a class="el" href="prob_8sql__in.html#a7370b797bf450f9aa54d4fea4d64d611">prob.sql_in</a>
+</li>
+<li>lognormal_quantile()
+: <a class="el" href="prob_8sql__in.html#aab3a6de990ae5a81834274a1cf9cad8f">prob.sql_in</a>
+</li>
+<li>logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#a2d1571ffa794176a5dfed9d35e70fed7">cross_validation.sql_in</a>
+</li>
+<li>logregr_predict()
+: <a class="el" href="logistic_8sql__in.html#acc8af7a39e7b0d604c2760d9d6bd0b5e">logistic.sql_in</a>
+</li>
+<li>logregr_predict_prob()
+: <a class="el" href="logistic_8sql__in.html#a75dba5248d75cd6c5d2fb69f944ce479">logistic.sql_in</a>
+</li>
+<li>logregr_train()
+: <a class="el" href="logistic_8sql__in.html#a3c217840f2fbe1dcd53ba15c4111b86a">logistic.sql_in</a>
+</li>
+<li>lsvm_classification()
+: <a class="el" href="lsvm_8sql__in.html#a75d126981ae4bf2e6641627501f0a2a5">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict()
+: <a class="el" href="lsvm_8sql__in.html#a0dee8d0cd54582f06de2935b25b7ecb0">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict_batch()
+: <a class="el" href="lsvm_8sql__in.html#a1c0a002f50250133c0ef1d3c43c6d338">lsvm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_m.html
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diff --git a/docs/latest/globals_func_m.html b/docs/latest/globals_func_m.html
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+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
+<li>marginal_logregr()
+: <a class="el" href="marginal_8sql__in.html#a3286b644d2d51a8f2874896dc1b5d925">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_final()
+: <a class="el" href="marginal_8sql__in.html#ac4e06a28a76651de70768457575bb9e0">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#ae44a992f8bd650b5c1f0c2ffae7bb542">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a2115a795562bc2928c3858fc55104c4b">marginal.sql_in</a>
+</li>
+<li>marginal_mlogregr()
+: <a class="el" href="marginal_8sql__in.html#aaf0b3cc34b99d4b47334bdde2d139b81">marginal.sql_in</a>
+</li>
+<li>margins()
+: <a class="el" href="marginal_8sql__in.html#a6d002a5934d6bc614925ffcf67f24a99">marginal.sql_in</a>
+</li>
+<li>margins_logregr()
+: <a class="el" href="marginal_8sql__in.html#af520629d3624d30cdcdbce247d89ac7d">marginal.sql_in</a>
+</li>
+<li>margins_mlogregr()
+: <a class="el" href="marginal_8sql__in.html#a8311df1f591cff8d30ca8df816c0cfca">marginal.sql_in</a>
+</li>
+<li>matrix_add()
+: <a class="el" href="matrix__ops_8sql__in.html#a1cbb83dad451e7c36033c0f3df0a22e0">matrix_ops.sql_in</a>
+</li>
+<li>matrix_agg()
+: <a class="el" href="linalg_8sql__in.html#a9c439706f35d6cac89f151d553a5f111">linalg.sql_in</a>
+</li>
+<li>matrix_agg_final()
+: <a class="el" href="linalg_8sql__in.html#adb012de82ecdc71ac54f3498e88e59d0">linalg.sql_in</a>
+</li>
+<li>matrix_agg_transition()
+: <a class="el" href="linalg_8sql__in.html#ac2d98e204b4fccab518aa20edef4ec41">linalg.sql_in</a>
+</li>
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+: <a class="el" href="matrix__ops_8sql__in.html#a0cb243e18397fb101f1cf48d9872f805">matrix_ops.sql_in</a>
+</li>
+<li>matrix_block_square()
+: <a class="el" href="matrix__ops_8sql__in.html#af84a929ab1558a06a5f8e9e500bf9051">matrix_ops.sql_in</a>
+</li>
+<li>matrix_block_trans()
+: <a class="el" href="matrix__ops_8sql__in.html#aad2242b0b102d1d80cd2253b0be7e90a">matrix_ops.sql_in</a>
+</li>
+<li>matrix_blockize()
+: <a class="el" href="matrix__ops_8sql__in.html#aa63cfd84aa33c87ad16f52dc7f642848">matrix_ops.sql_in</a>
+</li>
+<li>matrix_column()
+: <a class="el" href="linalg_8sql__in.html#aed3a33f3d3d43a056c5ec591661d34d8">linalg.sql_in</a>
+</li>
+<li>matrix_densify()
+: <a class="el" href="matrix__ops_8sql__in.html#a9f646acba5ebc51b0aa647adeadbb96e">matrix_ops.sql_in</a>
+</li>
+<li>matrix_elem_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#aa93181583456a1f23c3665c051742173">matrix_ops.sql_in</a>
+</li>
+<li>matrix_info()
+: <a class="el" href="matrix__ops_8sql__in.html#a0535b01f414f5d61153abca8b3cd94fa">matrix_ops.sql_in</a>
+</li>
+<li>matrix_mem_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#a91383f59dcb8b7ca2fba89ea350d113b">matrix_ops.sql_in</a>
+</li>
+<li>matrix_mem_trans()
+: <a class="el" href="matrix__ops_8sql__in.html#a0731901b985a90c3aa2f686a7ce95978">matrix_ops.sql_in</a>
+</li>
+<li>matrix_mult()
+: <a class="el" href="matrix__ops_8sql__in.html#ad89a88acce449ee27ba9d3a73950c2d7">matrix_ops.sql_in</a>
+</li>
+<li>matrix_norm()
+: <a class="el" href="matrix__ops_8sql__in.html#aa5eb2864180eb276a786d9b2172aef5f">matrix_ops.sql_in</a>
+</li>
+<li>matrix_scale_and_add()
+: <a class="el" href="matrix__ops_8sql__in.html#a68c05ce6379f28d5e30dd0144fe50565">matrix_ops.sql_in</a>
+</li>
+<li>matrix_sparsify()
+: <a class="el" href="matrix__ops_8sql__in.html#a9ed8df5fc43740c00bfdfd3f934429ef">matrix_ops.sql_in</a>
+</li>
+<li>matrix_square()
+: <a class="el" href="matrix__ops_8sql__in.html#ae4082533d1943e20660dee4c951edf22">matrix_ops.sql_in</a>
+</li>
+<li>matrix_sub()
+: <a class="el" href="matrix__ops_8sql__in.html#a66d586c0126a95e43ecad7d3efbcce69">matrix_ops.sql_in</a>
+</li>
+<li>matrix_trans()
+: <a class="el" href="matrix__ops_8sql__in.html#a058e681612aeaea330e8c72ad35ba550">matrix_ops.sql_in</a>
+</li>
+<li>matrix_unblockize()
+: <a class="el" href="matrix__ops_8sql__in.html#a0a199e7cf9e56acd6a7f3fd78e73fa22">matrix_ops.sql_in</a>
+</li>
+<li>mean()
+: <a class="el" href="svec__util_8sql__in.html#a546b947319c9f48237120a31929bdfd9">svec_util.sql_in</a>
+</li>
+<li>mfvsketch_quick_histogram()
+: <a class="el" href="sketch_8sql__in.html#aa2d4c0af50b242731f841c2dd355fc72">sketch.sql_in</a>
+</li>
+<li>mfvsketch_top_histogram()
+: <a class="el" href="sketch_8sql__in.html#ab915f092b747e3a955cbb699afc9c70c">sketch.sql_in</a>
+</li>
+<li>misclassification_avg()
+: <a class="el" href="cross__validation_8sql__in.html#ad01cd1c97ae1be0ac7cd6c42abb75bb2">cross_validation.sql_in</a>
+</li>
+<li>mlogregr()
+: <a class="el" href="multilogistic_8sql__in.html#a116c95de21b112dedf99035a9b243fd7">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_final()
+: <a class="el" href="marginal_8sql__in.html#ad80d7df29226a2529f9f80607f2a983e">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#a82ac74fd7aec70b7596bc2b4776742fa">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a7b8a7cf298ea51fc16de975e2503615a">marginal.sql_in</a>
+</li>
+<li>mlogregr_predict()
+: <a class="el" href="multilogistic_8sql__in.html#a0fdfed54d63cefe260a0b74b9c7bbad5">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_robust_step_final()
+: <a class="el" href="robust_8sql__in.html#a6bfc968b23887a0a8ddd69a5c3350ba9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#a123149abbf366743e04ecb6fc7f8b9d9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_transition()
+: <a class="el" href="robust_8sql__in.html#a3928ec1e3935e8dc4dbcb9e705805660">robust.sql_in</a>
+</li>
+<li>mlogregr_train()
+: <a class="el" href="multilogistic_8sql__in.html#ab8b5a7eb69a945435cba5a068576f2e4">multilogistic.sql_in</a>
+</li>
+<li>mode()
+: <a class="el" href="utilities_8sql__in.html#ac25ef90f8a8e2f9b2244a57d92086c91">utilities.sql_in</a>
+</li>
+<li>mse_error()
+: <a class="el" href="cross__validation_8sql__in.html#a2cd71e756f90b0c4fa46f508a1ca261e">cross_validation.sql_in</a>
+</li>
+<li>mw_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac18e666088117997da2d22236e982f5e">hypothesis_tests.sql_in</a>
+</li>
+<li>mw_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4e324c82b069ebf7b498012aa83931c5">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
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+</html>

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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+   <div id="projectbrief">User Documentation</div>
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+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
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+$(document).ready(function(){initNavTree('globals_func_n.html','');});
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+<div id="doc-content">
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+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
+<li>negative_binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#ad0a7e4474f828869fb90e62f8e6f04d7">prob.sql_in</a>
+</li>
+<li>negative_binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#ab9cbc30424eba30f2df2a32a7e45f138">prob.sql_in</a>
+</li>
+<li>negative_binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#ad9e541de8b41da2e7b7434f862db4845">prob.sql_in</a>
+</li>
+<li>non_central_beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a1361569bd86e41f796c70f8cb277010e">prob.sql_in</a>
+</li>
+<li>non_central_beta_pdf()
+: <a class="el" href="prob_8sql__in.html#ad4a12c083054f0e2d316ae76c9aaeef7">prob.sql_in</a>
+</li>
+<li>non_central_beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a3073b409eaee3faa6d43df014662c279">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#ab4b7d2cf10bb031328dcc34c6ff494ad">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#aa7a563183224593d1e0d623a3c5489d8">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad694e29187b629ae683ef1235d2b9270">prob.sql_in</a>
+</li>
+<li>non_central_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a00051df630007b530ce86b4ab44a0434">prob.sql_in</a>
+</li>
+<li>non_central_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a3d94edcf90fca1fa52671293a9ea9c2f">prob.sql_in</a>
+</li>
+<li>non_central_f_quantile()
+: <a class="el" href="prob_8sql__in.html#a92b2a978db480a6c78cfb708107ecb92">prob.sql_in</a>
+</li>
+<li>non_central_t_cdf()
+: <a class="el" href="prob_8sql__in.html#afaf4374d2720b230a54713e21ecb1955">prob.sql_in</a>
+</li>
+<li>non_central_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a4799e3bb68a496d9bc1ef1ea85265409">prob.sql_in</a>
+</li>
+<li>non_central_t_quantile()
+: <a class="el" href="prob_8sql__in.html#af50865aba2ece2e23b2af461a02f7d12">prob.sql_in</a>
+</li>
+<li>noop()
+: <a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0">utilities.sql_in</a>
+</li>
+<li>norm1()
+: <a class="el" href="linalg_8sql__in.html#a300300fe4b8576ba0b97b95d8dea3057">linalg.sql_in</a>
+</li>
+<li>norm2()
+: <a class="el" href="linalg_8sql__in.html#a50fdfe30cc0edc6888a909dbb4b4c239">linalg.sql_in</a>
+</li>
+<li>normal_cdf()
+: <a class="el" href="prob_8sql__in.html#a370e31a46781ed8832b31625a683d053">prob.sql_in</a>
+</li>
+<li>normal_pdf()
+: <a class="el" href="prob_8sql__in.html#a63f555f36385d86e229cdca223e39567">prob.sql_in</a>
+</li>
+<li>normal_quantile()
+: <a class="el" href="prob_8sql__in.html#a53d56b672fe4cd1277cb5eac5de5118f">prob.sql_in</a>
+</li>
+<li>normalize()
+: <a class="el" href="svec__util_8sql__in.html#aace16abea7c63f136520fde6c28f1f28">svec_util.sql_in</a>
+</li>
+<li>normalized_avg()
+: <a class="el" href="linalg_8sql__in.html#a0b04663ca206f03e66aed5ea2b4cc461">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#a1c22ee0672d6c550f5bb966f78ce738a">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#ad5863fed5eb4813ba0044abc268d03ce">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_o.html
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diff --git a/docs/latest/globals_func_o.html b/docs/latest/globals_func_o.html
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+<!-- HTML header for doxygen 1.8.4-->
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
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+        <span class="left">
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+$(document).ready(function(){initNavTree('globals_func_o.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
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+</iframe>
+</div>
+
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+&#160;
+
+<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
+<li>one_way_anova()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd5c80afd954294de268030ee73e88cc">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5ac28bec7ff670a7da8b9eae4a8ed4cb">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ad6c05d07183c961961f171b0a925ad93">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#aa694f4ce95280210a3887773bb3f417b">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_p.html
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diff --git a/docs/latest/globals_func_p.html b/docs/latest/globals_func_p.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
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+        <span class="left">
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
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+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_p.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_p"></a>- p -</h3><ul>
+<li>pareto_cdf()
+: <a class="el" href="prob_8sql__in.html#aa1a42ebd68f20f65bc1784b427721b5d">prob.sql_in</a>
+</li>
+<li>pareto_pdf()
+: <a class="el" href="prob_8sql__in.html#a22c56a6e48bc442435b13afac2a1eb37">prob.sql_in</a>
+</li>
+<li>pareto_quantile()
+: <a class="el" href="prob_8sql__in.html#a77779e2b5fa951189ccba6806c503c4d">prob.sql_in</a>
+</li>
+<li>pca_project()
+: <a class="el" href="pca__project_8sql__in.html#a30c86aed79810df5595451481ba74265">pca_project.sql_in</a>
+</li>
+<li>pca_sparse_project()
+: <a class="el" href="pca__project_8sql__in.html#a2ffda9dc6c376df9f31e71b41b7259b3">pca_project.sql_in</a>
+</li>
+<li>pca_sparse_train()
+: <a class="el" href="pca_8sql__in.html#ace5236fb2133c297eca85ab788e6dfc2">pca.sql_in</a>
+</li>
+<li>pca_train()
+: <a class="el" href="pca_8sql__in.html#a2d1cfcf6a7c8a9c69808ec8927b900d1">pca.sql_in</a>
+</li>
+<li>pmml()
+: <a class="el" href="table__to__pmml_8sql__in.html#a3aa36483b5dd5af46f9b564397822817">table_to_pmml.sql_in</a>
+</li>
+<li>poisson_cdf()
+: <a class="el" href="prob_8sql__in.html#ae0b4313d9fe730d6efb3f7c44206f345">prob.sql_in</a>
+</li>
+<li>poisson_pmf()
+: <a class="el" href="prob_8sql__in.html#a82f1edc27261021c73cd080ff2677a9f">prob.sql_in</a>
+</li>
+<li>poisson_quantile()
+: <a class="el" href="prob_8sql__in.html#a032d26db18b2ee1034085f5521939c61">prob.sql_in</a>
+</li>
+<li>poisson_random()
+: <a class="el" href="sample_8sql__in.html#a2051a3f21bc06e3c6e3efcd7492b71c4">sample.sql_in</a>
+</li>
+<li>profile()
+: <a class="el" href="profile_8sql__in.html#a22f0136bb3d7740262b13030a2925a80">profile.sql_in</a>
+</li>
+<li>profile_full()
+: <a class="el" href="profile_8sql__in.html#a879e445d74412b093d411ed9a7180a1e">profile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_q.html
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+       class="ui-resizable-handle">
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+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_q.html','');});
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+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
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+<div class="contents">
+&#160;
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+<h3><a class="anchor" id="index_q"></a>- q -</h3><ul>
+<li>quantile()
+: <a class="el" href="quantile_8sql__in.html#a6955e44659a0817803ab7a11a9a7e8a5">quantile.sql_in</a>
+</li>
+<li>quantile_big()
+: <a class="el" href="quantile_8sql__in.html#a154435ddf6200110013dc05b1a6207fe">quantile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_r.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_r.html b/docs/latest/globals_func_r.html
new file mode 100644
index 0000000..602b864
--- /dev/null
+++ b/docs/latest/globals_func_r.html
@@ -0,0 +1,212 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_r.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_r"></a>- r -</h3><ul>
+<li>rayleigh_cdf()
+: <a class="el" href="prob_8sql__in.html#aab0ddb8a5348cfa387d777043a3cb6d0">prob.sql_in</a>
+</li>
+<li>rayleigh_pdf()
+: <a class="el" href="prob_8sql__in.html#a798541736d9255bdd5c0bd94924d47bc">prob.sql_in</a>
+</li>
+<li>rayleigh_quantile()
+: <a class="el" href="prob_8sql__in.html#acd6757acab1683c735e2b57901494336">prob.sql_in</a>
+</li>
+<li>rb_coxph_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a504aa4de0f53044a065e6b1e8a66a285">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_coxph_step_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a0bd8046f4e0956b8dc503656d5127467">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_coxph_strata_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9826d93b250c1fb6e06d412181545754">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ad8fc6f9cca64b26bc7a12f15abfc6234">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9473298f0225d2ad1f6297f360a639d4">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a4bf2a610417ee174074b2191fb921256">robust_variance_coxph.sql_in</a>
+</li>
+<li>relative_error()
+: <a class="el" href="utilities_8sql__in.html#a4d49e00b4dd6059f61ffb9fdc0a696f7">utilities.sql_in</a>
+</li>
+<li>rf_classify()
+: <a class="el" href="rf_8sql__in.html#a116584e8a5194a4277e964900ee5383d">rf.sql_in</a>
+</li>
+<li>rf_clean()
+: <a class="el" href="rf_8sql__in.html#af33b77b75df225ee65a8acf18705256e">rf.sql_in</a>
+</li>
+<li>rf_display()
+: <a class="el" href="rf_8sql__in.html#a60b3f093409a14331b7601522ac0ac42">rf.sql_in</a>
+</li>
+<li>rf_score()
+: <a class="el" href="rf_8sql__in.html#ac6745c3d4cae3443b217f3ba66d11ac4">rf.sql_in</a>
+</li>
+<li>rf_train()
+: <a class="el" href="rf_8sql__in.html#a3cf718282802b63dc0a0d19b34f6829b">rf.sql_in</a>
+</li>
+<li>robust_linregr()
+: <a class="el" href="robust_8sql__in.html#ade1af85b04294ee404a638aa2bfbf051">robust.sql_in</a>
+</li>
+<li>robust_linregr_final()
+: <a class="el" href="robust_8sql__in.html#a458741a15dc6c2ca627f0d1c1d19b7f8">robust.sql_in</a>
+</li>
+<li>robust_linregr_merge_states()
+: <a class="el" href="robust_8sql__in.html#ad85d9925cbab4b59acd798a7b32671ff">robust.sql_in</a>
+</li>
+<li>robust_linregr_transition()
+: <a class="el" href="robust_8sql__in.html#a3f978229f9a2e229276278985d76c438">robust.sql_in</a>
+</li>
+<li>robust_logregr()
+: <a class="el" href="robust_8sql__in.html#a6a9290aafa048bcb7cdc13410d5814aa">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_final()
+: <a class="el" href="robust_8sql__in.html#af8efdd02ad3155411507bf786095a562">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#aa715a6f6885b56d919a243ec4ff07fde">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_transition()
+: <a class="el" href="robust_8sql__in.html#a840ddaa14013c4445b7a0d55e9489d8d">robust.sql_in</a>
+</li>
+<li>robust_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a209021ac52461f029353cec7fb900cd4">robust.sql_in</a>
+</li>
+<li>robust_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">clustered_variance_coxph.sql_in</a>
+, <a class="el" href="robust__variance__coxph_8sql__in.html#a9d08b7ab1efa1f58543214a311344741">robust_variance_coxph.sql_in</a>
+</li>
+<li>robust_variance_linregr()
+: <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust.sql_in</a>
+</li>
+<li>robust_variance_logregr()
+: <a class="el" href="robust_8sql__in.html#a815cadfeb54447993db9ce2bd2aa709e">robust.sql_in</a>
+</li>
+<li>robust_variance_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a93031a31e4c93d3caa22d963927e75c8">robust.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[28/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x65.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x65.html b/docs/latest/globals_0x65.html
new file mode 100644
index 0000000..82db0af
--- /dev/null
+++ b/docs/latest/globals_0x65.html
@@ -0,0 +1,158 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x65.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
+<li>elastic_net_binomial_predict()
+: <a class="el" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_binomial_prob()
+: <a class="el" href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_gaussian_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_train()
+: <a class="el" href="elastic__net_8sql__in.html#a63c8ed415005cc446a59bdc47a09791d">elastic_net.sql_in</a>
+</li>
+<li>exponential_cdf()
+: <a class="el" href="prob_8sql__in.html#a6d1bf6816f56b8e5ba6bf6ca94752f46">prob.sql_in</a>
+</li>
+<li>exponential_pdf()
+: <a class="el" href="prob_8sql__in.html#a18a5458c4bc85f0c4ea321317f90bdbb">prob.sql_in</a>
+</li>
+<li>exponential_quantile()
+: <a class="el" href="prob_8sql__in.html#ae3687b8e69a402154b829a6531b1b279">prob.sql_in</a>
+</li>
+<li>extreme_value_cdf()
+: <a class="el" href="prob_8sql__in.html#acffffe04c15eccd2e88cdac250bccc68">prob.sql_in</a>
+</li>
+<li>extreme_value_pdf()
+: <a class="el" href="prob_8sql__in.html#a03a3494462f4cb8c9fb6212e72b0b2e9">prob.sql_in</a>
+</li>
+<li>extreme_value_quantile()
+: <a class="el" href="prob_8sql__in.html#aeb5a7d295b83a891774a4fb0ef27c458">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x66.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x66.html b/docs/latest/globals_0x66.html
new file mode 100644
index 0000000..300cef8
--- /dev/null
+++ b/docs/latest/globals_0x66.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+<div id="titlearea">
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+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+<div id="doc-content">
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
+<li>f_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5a946aa469ff6ddf8d276db16fa26ad4">hypothesis_tests.sql_in</a>
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+</li>
+<li>fisher_f_quantile()
+: <a class="el" href="prob_8sql__in.html#ab6ed888a5338a0bee9c55edf4d33847f">prob.sql_in</a>
+</li>
+<li>float8arr_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a2a6e17fd9dafc349c7cfa070486e6ad1">svec.sql_in</a>
+</li>
+<li>float8arr_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af8db439cf1a73749052f06fc30c7d282">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int2()
+: <a class="el" href="svec_8sql__in.html#a5e38a27f556a6ac88549efec37da786f">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int4()
+: <a class="el" href="svec_8sql__in.html#a9c05d02233680a2e96dc6058155e4483">svec.sql_in</a>
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+<li>float8arr_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a4862b4e281323db16e9be4dfd681db2e">svec.sql_in</a>
+</li>
+<li>float8arr_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a0bc8ef7e7b8e37b784c81a940bd817f0">svec.sql_in</a>
+</li>
+<li>float8arr_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a20d4a7b907e967351f6439640482e0f9">svec.sql_in</a>
+</li>
+<li>float8arr_div_svec()
+: <a class="el" href="svec_8sql__in.html#a5c4ac1cc9a2ee3057958070b8e7bffcc">svec.sql_in</a>
+</li>
+<li>float8arr_eq()
+: <a class="el" href="svec_8sql__in.html#a0684988340d533c00523951f9a95107e">svec.sql_in</a>
+</li>
+<li>float8arr_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a4c82ac5de8e026c85e83aae4e0f06220">svec.sql_in</a>
+</li>
+<li>float8arr_minus_svec()
+: <a class="el" href="svec_8sql__in.html#a457f2f9210baba1c13820801c42cd06a">svec.sql_in</a>
+</li>
+<li>float8arr_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#abb0b3f155c45bb370ef7adac49096835">svec.sql_in</a>
+</li>
+<li>float8arr_mult_svec()
+: <a class="el" href="svec_8sql__in.html#a0e7dd7a82b41f4b8344efa21208e1637">svec.sql_in</a>
+</li>
+<li>float8arr_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6bb068ab6ba775619fe787ed845f5a0d">svec.sql_in</a>
+</li>
+<li>float8arr_plus_svec()
+: <a class="el" href="svec_8sql__in.html#a98aa60ec0c58c828b0a72c75d604322e">svec.sql_in</a>
+</li>
+<li>fmsketch_dcount()
+: <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b">sketch.sql_in</a>
+</li>
+<li>forest_predict()
+: <a class="el" href="random__forest_8sql__in.html#aef268338ffa67f54ca1371326bb25fa0">random_forest.sql_in</a>
+</li>
+<li>forest_train()
+: <a class="el" href="random__forest_8sql__in.html#a04cbc760f3790bcf69fe48e008cfba3d">random_forest.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x67.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x67.html b/docs/latest/globals_0x67.html
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+<!-- HTML header for doxygen 1.8.4-->
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+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
+<li>gamma_cdf()
+: <a class="el" href="prob_8sql__in.html#ab6760b0486bad2f1ab0635eb59404e7c">prob.sql_in</a>
+</li>
+<li>gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a6c37e3bda2596accbb46525321a328c4">prob.sql_in</a>
+</li>
+<li>gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#ac48bbd491bd34831415705c3a0b7bf29">prob.sql_in</a>
+</li>
+<li>gamma_random()
+: <a class="el" href="sample_8sql__in.html#afc523f72e64820329acb8874c2a759d3">sample.sql_in</a>
+</li>
+<li>gen_doc_svecs()
+: <a class="el" href="svec__util_8sql__in.html#a6136c83e9c419d783d1ec6db061f8d6e">svec_util.sql_in</a>
+</li>
+<li>gen_rules_from_cfp()
+: <a class="el" href="assoc__rules_8sql__in.html#a9a872579615586ce478d32ba145cc279">assoc_rules.sql_in</a>
+</li>
+<li>geometric_cdf()
+: <a class="el" href="prob_8sql__in.html#a00879bdf7d48ceddedb3b4cc33511497">prob.sql_in</a>
+</li>
+<li>geometric_pmf()
+: <a class="el" href="prob_8sql__in.html#a5e08db93bd448a1e2164e106ce5781a4">prob.sql_in</a>
+</li>
+<li>geometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a62674ca958aec0533cdf0a74a1dadea9">prob.sql_in</a>
+</li>
+<li>get_tree()
+: <a class="el" href="random__forest_8sql__in.html#af22b14c3da4f64b544b6c333a8e97070">random_forest.sql_in</a>
+</li>
+<li>get_tree_surr()
+: <a class="el" href="random__forest_8sql__in.html#a3ba1911b340a431982189e3e1f8c0111">random_forest.sql_in</a>
+</li>
+<li>glm()
+: <a class="el" href="glm_8sql__in.html#a4938524efbef7684ce8a8e2646f126ef">glm.sql_in</a>
+</li>
+<li>glm_predict()
+: <a class="el" href="glm_8sql__in.html#aa42b3ee4e9c39132154dfa81f96ed4ff">glm.sql_in</a>
+</li>
+<li>glm_predict_binomial()
+: <a class="el" href="glm_8sql__in.html#a9a468186034a936c3997cad22b03cafe">glm.sql_in</a>
+</li>
+<li>glm_predict_poisson()
+: <a class="el" href="glm_8sql__in.html#a941d49196e573048852def804c5ce4c4">glm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x68.html
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diff --git a/docs/latest/globals_0x68.html b/docs/latest/globals_0x68.html
new file mode 100644
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--- /dev/null
+++ b/docs/latest/globals_0x68.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
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+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+<h3><a class="anchor" id="index_h"></a>- h -</h3><ul>
+<li>hetero_linregr_final()
+: <a class="el" href="linear_8sql__in.html#a4b2e38f45a8e519af5947fcc2eb02829">linear.sql_in</a>
+</li>
+<li>hetero_linregr_merge_states()
+: <a class="el" href="linear_8sql__in.html#ad10649c8460c337a285cf20f2b87d0c5">linear.sql_in</a>
+</li>
+<li>hetero_linregr_transition()
+: <a class="el" href="linear_8sql__in.html#a4a583204053716bbece6321f5d9332b1">linear.sql_in</a>
+</li>
+<li>heteroskedasticity_test_linregr()
+: <a class="el" href="linear_8sql__in.html#a6d36a88e06a31753f4045a6c4539ca10">linear.sql_in</a>
+</li>
+<li>hypergeometric_cdf()
+: <a class="el" href="prob_8sql__in.html#a5c48e7fa2fc7bcbc69c7f4da663d457f">prob.sql_in</a>
+</li>
+<li>hypergeometric_pmf()
+: <a class="el" href="prob_8sql__in.html#afbd2f8d9fb30fb179f59cc14f1fd8d6d">prob.sql_in</a>
+</li>
+<li>hypergeometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a813cc27fe097e797ed0fb6022c7bb79a">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x69.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x69.html b/docs/latest/globals_0x69.html
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
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+: <a class="el" href="linalg_8sql__in.html#a4b92f7d1178cd4d26f527d8c760c63d9">linalg.sql_in</a>
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+: <a class="el" href="kmeans_8sql__in.html#a4a1b8fc3dd6da4d4491927888d70e7b9">kmeans.sql_in</a>
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+<li>internal_execute_using_kmeans_args()
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+: <a class="el" href="lmf_8sql__in.html#aea3df2d68e7a289da928b8a584ac719e">lmf.sql_in</a>
+</li>
+<li>internal_lincrf_lbfgs_converge()
+: <a class="el" href="crf_8sql__in.html#a47ef6b30d54ac4ef89cb664a681d1067">crf.sql_in</a>
+</li>
+<li>internal_lincrf_lbfgs_result()
+: <a class="el" href="crf_8sql__in.html#aff63c9a56bebf501a2f40971271605c2">crf.sql_in</a>
+</li>
+<li>internal_linear_svm_igd_distance()
+: <a class="el" href="lsvm_8sql__in.html#a81b1bdf8278bf77439ac9e101ae2ae7a">lsvm.sql_in</a>
+</li>
+<li>internal_linear_svm_igd_result()
+: <a class="el" href="lsvm_8sql__in.html#a21dc69a5bcf6e08f6c0d6cb9c4461091">lsvm.sql_in</a>
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+: <a class="el" href="lmf_8sql__in.html#a05860ded070a214128e0b4e64c9c989e">lmf.sql_in</a>
+</li>
+<li>internal_lmf_igd_result()
+: <a class="el" href="lmf_8sql__in.html#a8dede6aa8d024f0bb9acca9a83dfd579">lmf.sql_in</a>
+</li>
+<li>inverse_gamma_cdf()
+: <a class="el" href="prob_8sql__in.html#a85e9c16aa2c6973ddeb7883a5f153d93">prob.sql_in</a>
+</li>
+<li>inverse_gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a126211c2172a43a654288fa72a2349f9">prob.sql_in</a>
+</li>
+<li>inverse_gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#a5876aae01f14729866d4fd52918a65ba">prob.sql_in</a>
+</li>
+<li>isnan()
+: <a class="el" href="utilities_8sql__in.html#a48bed63cab78a5446fa4c122eaae3a40">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x6b.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x6b.html b/docs/latest/globals_0x6b.html
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
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+<script type="text/javascript">
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+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
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+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_k"></a>- k -</h3><ul>
+<li>kolmogorov_cdf()
+: <a class="el" href="prob_8sql__in.html#aeef43f74f583bdff17bd074d9c0d9607">prob.sql_in</a>
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+<li>ks_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd3f6d1d6dc4203cab3bcc980ec8ed8d">hypothesis_tests.sql_in</a>
+</li>
+<li>ks_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a58ffb5b2b8392e005f4f3e21560df93f">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x6c.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x6c.html b/docs/latest/globals_0x6c.html
new file mode 100644
index 0000000..7ca552f
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript">
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+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x6c.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
+<li>l1norm()
+: <a class="el" href="svec__util_8sql__in.html#a9e9b64c93b29c19adfb670f0171f14bf">svec_util.sql_in</a>
+</li>
+<li>l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a1b6bb42a5176b020aed12734df23aca9">svec_util.sql_in</a>
+</li>
+<li>laplace_cdf()
+: <a class="el" href="prob_8sql__in.html#a64e197de8da3761acdeec9db7e211703">prob.sql_in</a>
+</li>
+<li>laplace_pdf()
+: <a class="el" href="prob_8sql__in.html#a750278ad29d514793f76e159b773f410">prob.sql_in</a>
+</li>
+<li>laplace_quantile()
+: <a class="el" href="prob_8sql__in.html#a77f94fc43d4777fc4f68d18e29454a81">prob.sql_in</a>
+</li>
+<li>lda_get_perplexity()
+: <a class="el" href="lda_8sql__in.html#a25c3ef12d9808d8a38c5fd2630f3b5a9">lda.sql_in</a>
+</li>
+<li>lda_get_topic_desc()
+: <a class="el" href="lda_8sql__in.html#a4ac16d87d50821aadcab0d75d65b3f1b">lda.sql_in</a>
+</li>
+<li>lda_get_topic_word_count()
+: <a class="el" href="lda_8sql__in.html#ace20b6314e5b4e98929a98a6f05d925d">lda.sql_in</a>
+</li>
+<li>lda_get_word_topic_count()
+: <a class="el" href="lda_8sql__in.html#a455699e72328d6b105c61a3c9e7ae5dc">lda.sql_in</a>
+</li>
+<li>lda_predict()
+: <a class="el" href="lda_8sql__in.html#af1fde06c39dd12bb9e5544997f815323">lda.sql_in</a>
+</li>
+<li>lda_train()
+: <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80">lda.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step()
+: <a class="el" href="crf_8sql__in.html#a590cd748d8e18055d851e7d4f4a3ca00">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_final()
+: <a class="el" href="crf_8sql__in.html#a5bf4b2738003096f4eeeccd0eb5e98f0">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_merge_states()
+: <a class="el" href="crf_8sql__in.html#a45f3eaf0c80cdfa99872cd071020d5c7">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_transition()
+: <a class="el" href="crf_8sql__in.html#a2e7104fea95ca76587e7fae9bcb0d7ed">crf.sql_in</a>
+</li>
+<li>lincrf_train()
+: <a class="el" href="crf_8sql__in.html#a336ac57ae3d07e331646d397c7651d3d">crf.sql_in</a>
+</li>
+<li>linear_solver_dense()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a885d6a36c07dc2c048948cb5287a3b92">dense_linear_systems.sql_in</a>
+</li>
+<li>linear_solver_sparse()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3b3b7eea8b91c0a16ede3e7a9c6b17ef">sparse_linear_systems.sql_in</a>
+</li>
+<li>linear_svm_igd_final()
+: <a class="el" href="lsvm_8sql__in.html#a614936d27848149eead4581db57a9236">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_merge()
+: <a class="el" href="lsvm_8sql__in.html#a241eea31c78737ecbb5da6639debec3b">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step()
+: <a class="el" href="lsvm_8sql__in.html#a8cd061b3cd2b9ef62e018c33dde27a4d">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step_serial()
+: <a class="el" href="lsvm_8sql__in.html#a027f40b148849a721001d20f098a4a0e">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_transition()
+: <a class="el" href="lsvm_8sql__in.html#a6f26e0007d7d95b4bb4b72db4f702b25">lsvm.sql_in</a>
+</li>
+<li>linregr()
+: <a class="el" href="linear_8sql__in.html#a5fcd1daa9796dccf5430929c37ea0ee9">linear.sql_in</a>
+</li>
+<li>linregr_final()
+: <a class="el" href="linear_8sql__in.html#a28b69254ba5cd513b35ba6b17efd2c35">linear.sql_in</a>
+</li>
+<li>linregr_merge_states()
+: <a class="el" href="linear_8sql__in.html#a042536820ffbad498a4e1c410f949e35">linear.sql_in</a>
+</li>
+<li>linregr_predict()
+: <a class="el" href="linear_8sql__in.html#a8da0030683d5931b93e5f2bee3fd0544">linear.sql_in</a>
+</li>
+<li>linregr_train()
+: <a class="el" href="linear_8sql__in.html#ad0175a9ecc455d9679f7d251af264848">linear.sql_in</a>
+</li>
+<li>linregr_transition()
+: <a class="el" href="linear_8sql__in.html#aa2b87a3e60272176bfa2fb859f99461a">linear.sql_in</a>
+</li>
+<li>lmf_igd_final()
+: <a class="el" href="lmf_8sql__in.html#a298f46c944cd6813b0360514bf8c9ed1">lmf.sql_in</a>
+</li>
+<li>lmf_igd_merge()
+: <a class="el" href="lmf_8sql__in.html#ae3bb16a76a927e1e59420d4a8e13012f">lmf.sql_in</a>
+</li>
+<li>lmf_igd_run()
+: <a class="el" href="lmf_8sql__in.html#ac1acb1f0e1f7008118f21c83546a4602">lmf.sql_in</a>
+</li>
+<li>lmf_igd_step()
+: <a class="el" href="lmf_8sql__in.html#a54d2ca2048d9a0c29d5593530b932a23">lmf.sql_in</a>
+</li>
+<li>lmf_igd_transition()
+: <a class="el" href="lmf_8sql__in.html#a187574cfe85888b29d6b1658554e7507">lmf.sql_in</a>
+</li>
+<li>logistic()
+: <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e">logistic.sql_in</a>
+</li>
+<li>logistic_cdf()
+: <a class="el" href="prob_8sql__in.html#a140f674876813d5e786a4d8ba8d75c87">prob.sql_in</a>
+</li>
+<li>logistic_pdf()
+: <a class="el" href="prob_8sql__in.html#afa38eb6c61d3c9825d5c172e6c17dbf7">prob.sql_in</a>
+</li>
+<li>logistic_quantile()
+: <a class="el" href="prob_8sql__in.html#a5a77a0bc5884af2a914a955174892ae2">prob.sql_in</a>
+</li>
+<li>lognormal_cdf()
+: <a class="el" href="prob_8sql__in.html#a4c05b347f8feb64e1236d21b850af61e">prob.sql_in</a>
+</li>
+<li>lognormal_pdf()
+: <a class="el" href="prob_8sql__in.html#a7370b797bf450f9aa54d4fea4d64d611">prob.sql_in</a>
+</li>
+<li>lognormal_quantile()
+: <a class="el" href="prob_8sql__in.html#aab3a6de990ae5a81834274a1cf9cad8f">prob.sql_in</a>
+</li>
+<li>logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#a2d1571ffa794176a5dfed9d35e70fed7">cross_validation.sql_in</a>
+</li>
+<li>logregr_predict()
+: <a class="el" href="logistic_8sql__in.html#acc8af7a39e7b0d604c2760d9d6bd0b5e">logistic.sql_in</a>
+</li>
+<li>logregr_predict_prob()
+: <a class="el" href="logistic_8sql__in.html#a75dba5248d75cd6c5d2fb69f944ce479">logistic.sql_in</a>
+</li>
+<li>logregr_train()
+: <a class="el" href="logistic_8sql__in.html#a3c217840f2fbe1dcd53ba15c4111b86a">logistic.sql_in</a>
+</li>
+<li>lsvm_classification()
+: <a class="el" href="lsvm_8sql__in.html#a75d126981ae4bf2e6641627501f0a2a5">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict()
+: <a class="el" href="lsvm_8sql__in.html#a0dee8d0cd54582f06de2935b25b7ecb0">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict_batch()
+: <a class="el" href="lsvm_8sql__in.html#a1c0a002f50250133c0ef1d3c43c6d338">lsvm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x6d.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
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+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x6d.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
+<li>marginal_logregr()
+: <a class="el" href="marginal_8sql__in.html#a3286b644d2d51a8f2874896dc1b5d925">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_final()
+: <a class="el" href="marginal_8sql__in.html#ac4e06a28a76651de70768457575bb9e0">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#ae44a992f8bd650b5c1f0c2ffae7bb542">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a2115a795562bc2928c3858fc55104c4b">marginal.sql_in</a>
+</li>
+<li>marginal_mlogregr()
+: <a class="el" href="marginal_8sql__in.html#aaf0b3cc34b99d4b47334bdde2d139b81">marginal.sql_in</a>
+</li>
+<li>margins()
+: <a class="el" href="marginal_8sql__in.html#a6d002a5934d6bc614925ffcf67f24a99">marginal.sql_in</a>
+</li>
+<li>margins_logregr()
+: <a class="el" href="marginal_8sql__in.html#af520629d3624d30cdcdbce247d89ac7d">marginal.sql_in</a>
+</li>
+<li>margins_mlogregr()
+: <a class="el" href="marginal_8sql__in.html#a8311df1f591cff8d30ca8df816c0cfca">marginal.sql_in</a>
+</li>
+<li>matrix_agg_final()
+: <a class="el" href="linalg_8sql__in.html#adb012de82ecdc71ac54f3498e88e59d0">linalg.sql_in</a>
+</li>
+<li>matrix_agg_transition()
+: <a class="el" href="linalg_8sql__in.html#ac2d98e204b4fccab518aa20edef4ec41">linalg.sql_in</a>
+</li>
+<li>matrix_block_mult()
+: <a class="el" href="matrix__op_8sql__in.html#a2569d504e2f91bb4ab84ef483c3eb806">matrix_op.sql_in</a>
+</li>
+<li>matrix_block_square()
+: <a class="el" href="matrix__op_8sql__in.html#a1099e22b2ed3cd995a858827a8efd6e2">matrix_op.sql_in</a>
+</li>
+<li>matrix_block_trans()
+: <a class="el" href="matrix__op_8sql__in.html#a18208524dce36a0e1a3cd4f3d4699d7d">matrix_op.sql_in</a>
+</li>
+<li>matrix_blockize()
+: <a class="el" href="matrix__op_8sql__in.html#aaaef33a34ca7d5b34d19fcc1778d02d6">matrix_op.sql_in</a>
+</li>
+<li>matrix_column()
+: <a class="el" href="linalg_8sql__in.html#a24a4f14f171a12ec31f609af2f07ac18">linalg.sql_in</a>
+</li>
+<li>matrix_densify()
+: <a class="el" href="matrix__op_8sql__in.html#ad56abb5f2271700423a51147b8b890b6">matrix_op.sql_in</a>
+</li>
+<li>matrix_mem_mult()
+: <a class="el" href="matrix__op_8sql__in.html#a91383f59dcb8b7ca2fba89ea350d113b">matrix_op.sql_in</a>
+</li>
+<li>matrix_mem_trans()
+: <a class="el" href="matrix__op_8sql__in.html#a0731901b985a90c3aa2f686a7ce95978">matrix_op.sql_in</a>
+</li>
+<li>matrix_mult()
+: <a class="el" href="matrix__op_8sql__in.html#a716158fc895763243a9b222733cbdd9a">matrix_op.sql_in</a>
+</li>
+<li>matrix_norm()
+: <a class="el" href="matrix__op_8sql__in.html#aa3e1bf84295bb870c097bb293dc56d5b">matrix_op.sql_in</a>
+</li>
+<li>matrix_scale_and_add()
+: <a class="el" href="matrix__op_8sql__in.html#ab4018d7ab7e3c0c667f12f5421d437cd">matrix_op.sql_in</a>
+</li>
+<li>matrix_sparsify()
+: <a class="el" href="matrix__op_8sql__in.html#ac5937bd13fa68864318970bc48425f59">matrix_op.sql_in</a>
+</li>
+<li>matrix_square()
+: <a class="el" href="matrix__op_8sql__in.html#a1cd4cba697f163a4e2ec2fe2ca0c8f46">matrix_op.sql_in</a>
+</li>
+<li>matrix_trans()
+: <a class="el" href="matrix__op_8sql__in.html#a505b024301dd1a7ceb9ee01e1bf572d3">matrix_op.sql_in</a>
+</li>
+<li>matrix_unblockize()
+: <a class="el" href="matrix__op_8sql__in.html#ad48d14a3b941b2c5ba61a76128f9e597">matrix_op.sql_in</a>
+</li>
+<li>mean()
+: <a class="el" href="svec__util_8sql__in.html#a546b947319c9f48237120a31929bdfd9">svec_util.sql_in</a>
+</li>
+<li>mfvsketch_quick_histogram()
+: <a class="el" href="sketch_8sql__in.html#aa2d4c0af50b242731f841c2dd355fc72">sketch.sql_in</a>
+</li>
+<li>mfvsketch_top_histogram()
+: <a class="el" href="sketch_8sql__in.html#ab915f092b747e3a955cbb699afc9c70c">sketch.sql_in</a>
+</li>
+<li>mlogregr()
+: <a class="el" href="multilogistic_8sql__in.html#a116c95de21b112dedf99035a9b243fd7">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_final()
+: <a class="el" href="marginal_8sql__in.html#ad80d7df29226a2529f9f80607f2a983e">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#a82ac74fd7aec70b7596bc2b4776742fa">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a7b8a7cf298ea51fc16de975e2503615a">marginal.sql_in</a>
+</li>
+<li>mlogregr_predict()
+: <a class="el" href="multilogistic_8sql__in.html#a0fdfed54d63cefe260a0b74b9c7bbad5">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_robust_step_final()
+: <a class="el" href="robust_8sql__in.html#a6bfc968b23887a0a8ddd69a5c3350ba9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#a123149abbf366743e04ecb6fc7f8b9d9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_transition()
+: <a class="el" href="robust_8sql__in.html#a3928ec1e3935e8dc4dbcb9e705805660">robust.sql_in</a>
+</li>
+<li>mlogregr_train()
+: <a class="el" href="multilogistic_8sql__in.html#ab8b5a7eb69a945435cba5a068576f2e4">multilogistic.sql_in</a>
+</li>
+<li>mode()
+: <a class="el" href="utilities_8sql__in.html#ac25ef90f8a8e2f9b2244a57d92086c91">utilities.sql_in</a>
+</li>
+<li>mse_error()
+: <a class="el" href="cross__validation_8sql__in.html#a2cd71e756f90b0c4fa46f508a1ca261e">cross_validation.sql_in</a>
+</li>
+<li>mw_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac18e666088117997da2d22236e982f5e">hypothesis_tests.sql_in</a>
+</li>
+<li>mw_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4e324c82b069ebf7b498012aa83931c5">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x6e.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x6e.html b/docs/latest/globals_0x6e.html
new file mode 100644
index 0000000..c8f6e08
--- /dev/null
+++ b/docs/latest/globals_0x6e.html
@@ -0,0 +1,200 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x6e.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
+<li>negative_binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#ad0a7e4474f828869fb90e62f8e6f04d7">prob.sql_in</a>
+</li>
+<li>negative_binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#ab9cbc30424eba30f2df2a32a7e45f138">prob.sql_in</a>
+</li>
+<li>negative_binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#ad9e541de8b41da2e7b7434f862db4845">prob.sql_in</a>
+</li>
+<li>non_central_beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a1361569bd86e41f796c70f8cb277010e">prob.sql_in</a>
+</li>
+<li>non_central_beta_pdf()
+: <a class="el" href="prob_8sql__in.html#ad4a12c083054f0e2d316ae76c9aaeef7">prob.sql_in</a>
+</li>
+<li>non_central_beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a3073b409eaee3faa6d43df014662c279">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#ab4b7d2cf10bb031328dcc34c6ff494ad">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#aa7a563183224593d1e0d623a3c5489d8">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad694e29187b629ae683ef1235d2b9270">prob.sql_in</a>
+</li>
+<li>non_central_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a00051df630007b530ce86b4ab44a0434">prob.sql_in</a>
+</li>
+<li>non_central_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a3d94edcf90fca1fa52671293a9ea9c2f">prob.sql_in</a>
+</li>
+<li>non_central_f_quantile()
+: <a class="el" href="prob_8sql__in.html#a92b2a978db480a6c78cfb708107ecb92">prob.sql_in</a>
+</li>
+<li>non_central_t_cdf()
+: <a class="el" href="prob_8sql__in.html#afaf4374d2720b230a54713e21ecb1955">prob.sql_in</a>
+</li>
+<li>non_central_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a4799e3bb68a496d9bc1ef1ea85265409">prob.sql_in</a>
+</li>
+<li>non_central_t_quantile()
+: <a class="el" href="prob_8sql__in.html#af50865aba2ece2e23b2af461a02f7d12">prob.sql_in</a>
+</li>
+<li>noop()
+: <a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0">utilities.sql_in</a>
+</li>
+<li>norm1()
+: <a class="el" href="linalg_8sql__in.html#a300300fe4b8576ba0b97b95d8dea3057">linalg.sql_in</a>
+</li>
+<li>norm2()
+: <a class="el" href="linalg_8sql__in.html#a50fdfe30cc0edc6888a909dbb4b4c239">linalg.sql_in</a>
+</li>
+<li>normal_cdf()
+: <a class="el" href="prob_8sql__in.html#a370e31a46781ed8832b31625a683d053">prob.sql_in</a>
+</li>
+<li>normal_pdf()
+: <a class="el" href="prob_8sql__in.html#a63f555f36385d86e229cdca223e39567">prob.sql_in</a>
+</li>
+<li>normal_quantile()
+: <a class="el" href="prob_8sql__in.html#a53d56b672fe4cd1277cb5eac5de5118f">prob.sql_in</a>
+</li>
+<li>normalize()
+: <a class="el" href="svec__util_8sql__in.html#aace16abea7c63f136520fde6c28f1f28">svec_util.sql_in</a>
+</li>
+<li>normalized_avg()
+: <a class="el" href="linalg_8sql__in.html#a0b04663ca206f03e66aed5ea2b4cc461">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#a1c22ee0672d6c550f5bb966f78ce738a">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#ad5863fed5eb4813ba0044abc268d03ce">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x6f.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x6f.html b/docs/latest/globals_0x6f.html
new file mode 100644
index 0000000..278d15c
--- /dev/null
+++ b/docs/latest/globals_0x6f.html
@@ -0,0 +1,137 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x6f.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_o"></a>- o -</h3><ul>
+<li>one_way_anova()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd5c80afd954294de268030ee73e88cc">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5ac28bec7ff670a7da8b9eae4a8ed4cb">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ad6c05d07183c961961f171b0a925ad93">hypothesis_tests.sql_in</a>
+</li>
+<li>one_way_anova_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#aa694f4ce95280210a3887773bb3f417b">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

[51/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

Add all files from old site (madlib.net)

Previous site (madlib.net) contained all the documentation and
binary files. This commit moves all the documentation to the apache
repository and creates links to the older binaries. Those binaries
are stored in the older github project (github.com/madlib/madlib).


Project: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/commit/7258f51a
Tree: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/tree/7258f51a
Diff: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/diff/7258f51a

Branch: refs/heads/asf-site
Commit: 7258f51a7ae82326b7eb95aea911a270c746061a
Parents: 04ed412
Author: Frank McQuillan <fm...@pivotal.io>
Authored: Tue Mar 29 17:50:54 2016 -0700
Committer: Rahul Iyer <ra...@gmail.com>
Committed: Tue Mar 29 17:57:30 2016 -0700

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 community.html                                  |  102 +-
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 docs/.htaccess                                  |    2 +
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 .../dir_fe647384fff178c2cff5ce75fb4044e5.html   |  137 +
 docs/latest/distribution_8sql__in.html          |  312 ++
 docs/latest/doc.png                             |  Bin 0 -> 746 bytes
 docs/latest/doxygen.css                         | 1454 ++++++
 docs/latest/doxygen.png                         |  Bin 0 -> 3779 bytes
 docs/latest/dt_8sql__in.html                    | 1932 ++++++++
 docs/latest/dt__preproc_8sql__in.html           |  854 ++++
 docs/latest/dt__utility_8sql__in.html           |  658 +++
 docs/latest/dynsections.js                      |   97 +
 docs/latest/elastic__net_8sql__in.html          | 2393 ++++++++++
 docs/latest/files.html                          |  191 +
 docs/latest/folderclosed.png                    |  Bin 0 -> 616 bytes
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 docs/latest/ftv2mo.png                          |  Bin 0 -> 403 bytes
 docs/latest/ftv2node.png                        |  Bin 0 -> 86 bytes
 docs/latest/ftv2ns.png                          |  Bin 0 -> 388 bytes
 docs/latest/ftv2plastnode.png                   |  Bin 0 -> 229 bytes
 docs/latest/ftv2pnode.png                       |  Bin 0 -> 229 bytes
 docs/latest/ftv2splitbar.png                    |  Bin 0 -> 314 bytes
 docs/latest/ftv2vertline.png                    |  Bin 0 -> 86 bytes
 docs/latest/glm_8sql__in.html                   | 1824 +++++++
 docs/latest/globals.html                        | 1204 +++++
 docs/latest/globals_0x5f.html                   |  880 ++++
 docs/latest/globals_0x61.html                   |  248 +
 docs/latest/globals_0x62.html                   |  170 +
 docs/latest/globals_0x63.html                   |  353 ++
 docs/latest/globals_0x64.html                   |  164 +
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 docs/latest/globals_0x69.html                   |  173 +
 docs/latest/globals_0x6b.html                   |  134 +
 docs/latest/globals_0x6c.html                   |  269 ++
 docs/latest/globals_0x6d.html                   |  248 +
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 docs/latest/globals_0x70.html                   |  167 +
 docs/latest/globals_0x71.html                   |  131 +
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 docs/latest/globals_0x73.html                   |  458 ++
 docs/latest/globals_0x74.html                   |  176 +
 docs/latest/globals_0x75.html                   |  140 +
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 docs/latest/globals_0x77.html                   |  161 +
 docs/latest/globals_0x7a.html                   |  128 +
 docs/latest/globals_a.html                      |  253 +
 docs/latest/globals_b.html                      |  172 +
 docs/latest/globals_c.html                      |  364 ++
 docs/latest/globals_d.html                      |  184 +
 docs/latest/globals_e.html                      |  160 +
 docs/latest/globals_f.html                      |  196 +
 docs/latest/globals_func.html                   | 1204 +++++
 docs/latest/globals_func_0x61.html              |  248 +
 docs/latest/globals_func_0x62.html              |  170 +
 docs/latest/globals_func_0x63.html              |  353 ++
 docs/latest/globals_func_0x64.html              |  164 +
 docs/latest/globals_func_0x65.html              |  158 +
 docs/latest/globals_func_0x66.html              |  194 +
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 docs/latest/globals_func_0x68.html              |  146 +
 docs/latest/globals_func_0x69.html              |  173 +
 docs/latest/globals_func_0x6b.html              |  134 +
 docs/latest/globals_func_0x6c.html              |  269 ++
 docs/latest/globals_func_0x6d.html              |  248 +
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 docs/latest/globals_func_0x72.html              |  210 +
 docs/latest/globals_func_0x73.html              |  458 ++
 docs/latest/globals_func_0x74.html              |  176 +
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 docs/latest/globals_func_b.html                 |  172 +
 docs/latest/globals_func_c.html                 |  364 ++
 docs/latest/globals_func_d.html                 |  184 +
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 docs/latest/globals_func_f.html                 |  196 +
 docs/latest/globals_func_g.html                 |  178 +
 docs/latest/globals_func_h.html                 |  148 +
 docs/latest/globals_func_i.html                 |  175 +
 docs/latest/globals_func_k.html                 |  136 +
 docs/latest/globals_func_l.html                 |  271 ++
 docs/latest/globals_func_m.html                 |  268 ++
 docs/latest/globals_func_n.html                 |  202 +
 docs/latest/globals_func_o.html                 |  139 +
 docs/latest/globals_func_p.html                 |  169 +
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 docs/latest/globals_func_r.html                 |  212 +
 docs/latest/globals_func_s.html                 |  460 ++
 docs/latest/globals_func_t.html                 |  181 +
 docs/latest/globals_func_u.html                 |  142 +
 docs/latest/globals_func_v.html                 |  148 +
 docs/latest/globals_func_w.html                 |  163 +
 docs/latest/globals_func_z.html                 |  130 +
 docs/latest/globals_g.html                      |  178 +
 docs/latest/globals_h.html                      |  148 +
 docs/latest/globals_i.html                      |  175 +
 docs/latest/globals_k.html                      |  136 +
 docs/latest/globals_l.html                      |  271 ++
 docs/latest/globals_m.html                      |  268 ++
 docs/latest/globals_n.html                      |  202 +
 docs/latest/globals_o.html                      |  139 +
 docs/latest/globals_p.html                      |  169 +
 docs/latest/globals_q.html                      |  133 +
 docs/latest/globals_r.html                      |  212 +
 docs/latest/globals_s.html                      |  460 ++
 docs/latest/globals_t.html                      |  181 +
 docs/latest/globals_u.html                      |  142 +
 docs/latest/globals_v.html                      |  148 +
 docs/latest/globals_vars.html                   |  146 +
 docs/latest/globals_w.html                      |  163 +
 docs/latest/globals_z.html                      |  130 +
 docs/latest/graph_legend.html                   |  189 +
 docs/latest/graph_legend.md5                    |    1 +
 docs/latest/graph_legend.svg                    |  114 +
 docs/latest/group__grp__arima.html              |  421 ++
 docs/latest/group__grp__array.html              |  322 ++
 docs/latest/group__grp__arraysmatrix.html       |  176 +
 docs/latest/group__grp__arraysmatrix.js         |    8 +
 docs/latest/group__grp__assoc__rules.html       |  314 ++
 docs/latest/group__grp__association__rules.html |  140 +
 docs/latest/group__grp__association__rules.js   |    4 +
 docs/latest/group__grp__bayes.html              |  489 ++
 docs/latest/group__grp__cg.html                 |  186 +
 docs/latest/group__grp__clustered__errors.html  |  406 ++
 docs/latest/group__grp__clustering.html         |  140 +
 docs/latest/group__grp__clustering.js           |    4 +
 docs/latest/group__grp__compatibility.html      |  124 +
 docs/latest/group__grp__correlation.html        |  248 +
 docs/latest/group__grp__countmin.html           |  265 ++
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 docs/latest/group__grp__crf.html                |  626 +++
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 .../group__grp__dense__linear__solver.html      |  263 ++
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 docs/latest/group__grp__desc__stats.js          |    5 +
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 docs/latest/group__grp__early__stage.js         |    8 +
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 docs/latest/group__grp__glm.js                  |   11 +
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 docs/latest/group__grp__inf__stats.js           |    4 +
 docs/latest/group__grp__kernmach.html           |  361 ++
 docs/latest/group__grp__kmeans.html             |  414 ++
 docs/latest/group__grp__lda.html                |  462 ++
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 docs/latest/group__grp__logreg.html             |  425 ++
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 docs/latest/group__grp__matrix.html             |  556 +++
 .../group__grp__matrix__factorization.html      |  142 +
 .../latest/group__grp__matrix__factorization.js |    5 +
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 docs/latest/group__grp__mdl.js                  |    4 +
 docs/latest/group__grp__mfvsketch.html          |  177 +
 docs/latest/group__grp__mlogreg.html            |  417 ++
 docs/latest/group__grp__multinom.html           |  488 ++
 docs/latest/group__grp__ordinal.html            |  471 ++
 docs/latest/group__grp__path.html               |  272 ++
 docs/latest/group__grp__pca.html                |  143 +
 docs/latest/group__grp__pca.js                  |    5 +
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 docs/latest/group__grp__pca__train.html         |  320 ++
 docs/latest/group__grp__pmml.html               |  333 ++
 docs/latest/group__grp__prob.html               |  165 +
 docs/latest/group__grp__profile.html            |  213 +
 docs/latest/group__grp__quantile.html           |  174 +
 docs/latest/group__grp__random__forest.html     |  717 +++
 .../group__grp__recursive__partitioning.html    |  141 +
 .../group__grp__recursive__partitioning.js      |    5 +
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 docs/latest/group__grp__regml.js                |   13 +
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 docs/latest/group__grp__robust.html             |  441 ++
 docs/latest/group__grp__sample.html             |  151 +
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 .../group__grp__sparse__linear__solver.html     |  355 ++
 docs/latest/group__grp__stats.html              |  144 +
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 docs/latest/group__grp__summary.html            |  311 ++
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 docs/latest/group__grp__svd.html                |  397 ++
 docs/latest/group__grp__svdmf.html              |  207 +
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 docs/master/group__grp__kmeans.html             |  414 ++
 docs/master/group__grp__kmeans.map              |    3 +
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 docs/master/group__grp__kmeans__new.map         |    3 +
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 .../v0.7/hypothesis__tests_8sql__in_source.html | 1109 +++++
 docs/v0.7/index.html                            |  136 +
 docs/v0.7/installdox                            |  112 +
 docs/v0.7/jquery.js                             |   54 +
 docs/v0.7/kmeans_8sql__in.html                  |  623 +++
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 docs/v0.7/multilogistic_8sql__in.html           |  248 +
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 docs/v0.7/online__sv_8sql__in.html              | 1127 +++++
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 docs/v0.7/rf_8sql__in_source.html               | 1264 +++++
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 docs/v0.7/svec_8sql__in.html                    | 2683 +++++++++++
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 docs/v1.0/globals_0x63.html                     |  275 ++
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 docs/v1.0/globals_0x73.html                     |  401 ++
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 docs/v1.0/globals_func_0x63.html                |  275 ++
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 docs/v1.0/globals_func_0x73.html                |  401 ++
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 docs/v1.0/group__grp__crf.html                  |  459 ++
 docs/v1.0/group__grp__crf.map                   |    3 +
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 docs/v1.0/group__grp__dectree.html              |  304 ++
 docs/v1.0/group__grp__dectree.map               |    3 +
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 docs/v1.0/group__grp__desc__stats.js            |    5 +
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 docs/v1.0/group__grp__desc__stats.md5           |    1 +
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 docs/v1.0/group__grp__early__stage.js           |   16 +
 docs/v1.0/group__grp__early__stage.map          |   15 +
 docs/v1.0/group__grp__early__stage.md5          |    1 +
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 docs/v1.0/group__grp__elasticnet.html           |  301 ++
 docs/v1.0/group__grp__elasticnet.map            |    3 +
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 docs/v1.0/group__grp__fmsketch.map              |    3 +
 docs/v1.0/group__grp__fmsketch.md5              |    1 +
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 docs/v1.0/group__grp__glm.js                    |   11 +
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 docs/v1.0/group__grp__glm.md5                   |    1 +
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 docs/v1.0/group__grp__kernmach.html             |  258 +
 docs/v1.0/group__grp__kernmach.map              |    3 +
 docs/v1.0/group__grp__kernmach.md5              |    1 +
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 docs/v1.0/group__grp__lda.html                  |  319 ++
 docs/v1.0/group__grp__lda.map                   |    3 +
 docs/v1.0/group__grp__lda.md5                   |    1 +
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 docs/v1.0/group__grp__linreg.html               |  339 ++
 docs/v1.0/group__grp__linreg.map                |    3 +
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 docs/v1.0/group__grp__logreg.md5                |    1 +
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 docs/v1.0/group__grp__marginal.html             |  313 ++
 docs/v1.0/group__grp__marginal.map              |    3 +
 docs/v1.0/group__grp__marginal.md5              |    1 +
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 docs/v1.0/group__grp__matrix__factorization.js  |    4 +
 docs/v1.0/group__grp__matrix__factorization.map |    3 +
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 docs/v1.0/group__grp__mlogreg.map               |    3 +
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 docs/v1.0/group__grp__rf.html                   |  450 ++
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 docs/v1.1/globals_0x73.html                     |  408 ++
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 docs/v1.1/globals_func_0x62.html                |  186 +
 docs/v1.1/globals_func_0x63.html                |  282 ++
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 docs/v1.1/globals_func_0x72.html                |  198 +
 docs/v1.1/globals_func_0x73.html                |  408 ++
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 docs/v1.1/group__grp__assoc__rules.map          |    3 +
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 docs/v1.1/group__grp__association__rules.html   |  138 +
 docs/v1.1/group__grp__association__rules.js     |    4 +
 docs/v1.1/group__grp__association__rules.map    |    3 +
 docs/v1.1/group__grp__association__rules.md5    |    1 +
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 docs/v1.1/group__grp__bayes.html                |  274 ++
 docs/v1.1/group__grp__bayes.map                 |    3 +
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 docs/v1.1/group__grp__clustered__errors.map     |    3 +
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 docs/v1.1/group__grp__clustering.js             |    4 +
 docs/v1.1/group__grp__clustering.map            |    3 +
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 docs/v1.1/group__grp__compatibility.map         |    3 +
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 docs/v1.1/group__grp__correlation.map           |    3 +
 docs/v1.1/group__grp__correlation.md5           |    1 +
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 docs/v1.1/group__grp__countmin.map              |    3 +
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 docs/v1.1/group__grp__crf.map                   |    3 +
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 docs/v1.1/group__grp__dectree.map               |    3 +
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 docs/v1.1/group__grp__dense__linear__solver.map |    3 +
 docs/v1.1/group__grp__dense__linear__solver.md5 |    1 +
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 docs/v1.1/group__grp__desc__stats.html          |  140 +
 docs/v1.1/group__grp__desc__stats.js            |    5 +
 docs/v1.1/group__grp__desc__stats.map           |    4 +
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 docs/v1.1/group__grp__kmeans.svg                |   30 +
 docs/v1.1/group__grp__lda.html                  |  320 ++
 docs/v1.1/group__grp__lda.map                   |    3 +
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 docs/v1.1/group__grp__linalg.html               |  294 ++
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[43/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/data__preparation_8sql__in.html
----------------------------------------------------------------------
diff --git a/docs/latest/data__preparation_8sql__in.html b/docs/latest/data__preparation_8sql__in.html
new file mode 100644
index 0000000..ad44a49
--- /dev/null
+++ b/docs/latest/data__preparation_8sql__in.html
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: data_preparation.sql_in File Reference</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+               onfocus="searchBox.OnSearchFieldFocus(true)" 
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+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('data__preparation_8sql__in.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">data_preparation.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for dummy coding categorical variables.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a0add50a6ab76682bde7825fc54f4d64b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="data__preparation_8sql__in.html#a0add50a6ab76682bde7825fc54f4d64b">create_indicator_variables</a> (text source_table, text out_table, text categorical_cols, boolean keep_null, text distributed_by)</td></tr>
+<tr class="memdesc:a0add50a6ab76682bde7825fc54f4d64b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create new table containing dummy coded variables for categorical variables.  <a href="#a0add50a6ab76682bde7825fc54f4d64b">More...</a><br /></td></tr>
+<tr class="separator:a0add50a6ab76682bde7825fc54f4d64b"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a05dbb30d8dd8589e75a96b3232cafe65"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="data__preparation_8sql__in.html#a05dbb30d8dd8589e75a96b3232cafe65">create_indicator_variables</a> (text source_table, text out_table, text categorical_cols, boolean keep_null)</td></tr>
+<tr class="memdesc:a05dbb30d8dd8589e75a96b3232cafe65"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create new table containing dummy coded variables for categorical variables.  <a href="#a05dbb30d8dd8589e75a96b3232cafe65">More...</a><br /></td></tr>
+<tr class="separator:a05dbb30d8dd8589e75a96b3232cafe65"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a617b67999ab8c9c07fb0d24101a1115d"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="data__preparation_8sql__in.html#a617b67999ab8c9c07fb0d24101a1115d">create_indicator_variables</a> (text source_table, text out_table, text categorical_cols)</td></tr>
+<tr class="separator:a617b67999ab8c9c07fb0d24101a1115d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a839b64f47a11a35c5df24ee93e36ba26"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="data__preparation_8sql__in.html#a839b64f47a11a35c5df24ee93e36ba26">create_indicator_variables</a> (varchar message)</td></tr>
+<tr class="separator:a839b64f47a11a35c5df24ee93e36ba26"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a5d0ef27bc38be2b4b5f063cf7b560d1f"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="data__preparation_8sql__in.html#a5d0ef27bc38be2b4b5f063cf7b560d1f">create_indicator_variables</a> ()</td></tr>
+<tr class="separator:a5d0ef27bc38be2b4b5f063cf7b560d1f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>June 2014</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>Calculates dummy-coded indicator variables for categorical variables </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a0add50a6ab76682bde7825fc54f4d64b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_indicator_variables </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>categorical_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>keep_null</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>distributed_by</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">source_table</td><td>Name of table containing categorical variable </td></tr>
+    <tr><td class="paramname">out_table</td><td>Name of table to output dummy variables </td></tr>
+    <tr><td class="paramname">categorical_cols</td><td>Comma-separated list of column names to dummy code </td></tr>
+    <tr><td class="paramname">keep_null</td><td>Boolean to determine the behavior for rows with NULL value </td></tr>
+    <tr><td class="paramname">distributed_by</td><td>Comma-separated list of column names to use for distribution of output</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Void </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a05dbb30d8dd8589e75a96b3232cafe65"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_indicator_variables </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>categorical_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>keep_null</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">source_table</td><td>Name of table containing categorical variable </td></tr>
+    <tr><td class="paramname">out_table</td><td>Name of table to output dummy variables </td></tr>
+    <tr><td class="paramname">categorical_cols</td><td>Comma-separated list of column names to dummy code </td></tr>
+    <tr><td class="paramname">keep_null</td><td>Boolean to determine the behavior for rows with NULL value</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Void </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a617b67999ab8c9c07fb0d24101a1115d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_indicator_variables </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>categorical_cols</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a839b64f47a11a35c5df24ee93e36ba26"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar create_indicator_variables </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5d0ef27bc38be2b4b5f063cf7b560d1f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar create_indicator_variables </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_8d53e1c0026e7e34b4cd68f8b91426d6.html">utilities</a></li><li class="navelem"><a class="el" href="data__preparation_8sql__in.html">data_preparation.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[20/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_s.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_s.html b/docs/latest/globals_func_s.html
new file mode 100644
index 0000000..886677a
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@@ -0,0 +1,460 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+</script><script src="../mathjax/MathJax.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
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+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
+<li>sparse_direct_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8d22e4088dedd57a931b465a73a0357a">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a37d42899e8ec22d8642aa2c943689d19">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a81014be318749e53d8c3fd58eece9600">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8175311c8e181a3ac73d9cc87814bbb0">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#ace39ff8e846754e3e0709bfbbbee9a60">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5ff2f1afd945580fc9cb9bedba84695b">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3daa8daf05cda974b4137ce07c694e2e">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5463eaef5dc5e4040acd56a1035ecc91">sparse_linear_systems.sql_in</a>
+</li>
+<li>squared_dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#a00a08e69f27524f2096032214e15b668">linalg.sql_in</a>
+</li>
+<li>students_t_cdf()
+: <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">prob.sql_in</a>
+</li>
+<li>students_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a8815c21670fff9d31946553a84b845b1">prob.sql_in</a>
+</li>
+<li>students_t_quantile()
+: <a class="el" href="prob_8sql__in.html#a7d64add02af21a95d73502b2dd466a75">prob.sql_in</a>
+</li>
+<li>sum()
+: <a class="el" href="array__ops_8sql__in.html#a26e8508a2bae10a6574cec697a270eea">array_ops.sql_in</a>
+</li>
+<li>summary()
+: <a class="el" href="summary_8sql__in.html#ae39edc1569d25b212ca5ebc06703c6d6">summary.sql_in</a>
+</li>
+<li>svd()
+: <a class="el" href="svd_8sql__in.html#a8dbab74f5bde863a63431a6ec6f66a61">svd.sql_in</a>
+</li>
+<li>svd_block()
+: <a class="el" href="svd_8sql__in.html#a96fb5620eed9c936b5bbfe24d7e37ad6">svd.sql_in</a>
+</li>
+<li>svd_sparse()
+: <a class="el" href="svd_8sql__in.html#a511f83e3eba19f6f41f8e3f7122ddb3a">svd.sql_in</a>
+</li>
+<li>svd_sparse_native()
+: <a class="el" href="svd_8sql__in.html#a0225b28fd4b71cf6e7072c034f3296c6">svd.sql_in</a>
+</li>
+<li>svdmf_run()
+: <a class="el" href="svdmf_8sql__in.html#a6cff34415cca23aa0a826cc08a6283f5">svdmf.sql_in</a>
+</li>
+<li>svec_append()
+: <a class="el" href="svec__util_8sql__in.html#a3950b17b103ceeddca520a0f0d097ddc">svec_util.sql_in</a>
+</li>
+<li>svec_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a1f1025124d5423cc1505991414f17582">svec.sql_in</a>
+</li>
+<li>svec_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af42e4b15879949032a68139c59443aac">svec.sql_in</a>
+</li>
+<li>svec_cast_float8arr()
+: <a class="el" href="svec_8sql__in.html#a96025250e64f049a053c5696b3b96e02">svec.sql_in</a>
+</li>
+<li>svec_cast_int2()
+: <a class="el" href="svec_8sql__in.html#af69458bf9b2906ca471d027966b13e6f">svec.sql_in</a>
+</li>
+<li>svec_cast_int4()
+: <a class="el" href="svec_8sql__in.html#abc01ac6b36ea9d689d338fd72abbd540">svec.sql_in</a>
+</li>
+<li>svec_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a9ea167c01fb2999dc5aaf5e8e21f5e8f">svec.sql_in</a>
+</li>
+<li>svec_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a50de0278ff7ceeff33a2ea4ca5fdf1a0">svec.sql_in</a>
+</li>
+<li>svec_cast_positions_float8arr()
+: <a class="el" href="svec_8sql__in.html#a0ee423729fbca5abd46c86fa81d51f23">svec.sql_in</a>
+</li>
+<li>svec_change()
+: <a class="el" href="svec__util_8sql__in.html#a59407764a1cbf1937da39cf39a2f447c">svec_util.sql_in</a>
+</li>
+<li>svec_cmp()
+: <a class="el" href="svec_8sql__in.html#a813762d8bd2a7fda083880e5bd8c355b">svec.sql_in</a>
+</li>
+<li>svec_concat()
+: <a class="el" href="svec_8sql__in.html#a24f184bef083fd9068c28fc94dad8054">svec.sql_in</a>
+</li>
+<li>svec_concat_replicate()
+: <a class="el" href="svec_8sql__in.html#a34cb5f83e0c010aacc719b8b078bfeb9">svec.sql_in</a>
+</li>
+<li>svec_contains()
+: <a class="el" href="svec__util_8sql__in.html#afdef1c6bf65ffc0e838cf10f33ae5557">svec_util.sql_in</a>
+</li>
+<li>svec_count()
+: <a class="el" href="svec__util_8sql__in.html#a6a21e25d3f50053c888a0bd126f831f0">svec_util.sql_in</a>
+</li>
+<li>svec_count_nonzero()
+: <a class="el" href="svec__util_8sql__in.html#abebdcbe45de346aff874db008e842e65">svec_util.sql_in</a>
+</li>
+<li>svec_dimension()
+: <a class="el" href="svec__util_8sql__in.html#ab8d1acbe4e8551755fef217b506bed8a">svec_util.sql_in</a>
+</li>
+<li>svec_div()
+: <a class="el" href="svec_8sql__in.html#a78a38512c860085e31f6153b8118cbe9">svec.sql_in</a>
+</li>
+<li>svec_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a211cf37584f54087dabc1fac14ed12dd">svec.sql_in</a>
+</li>
+<li>svec_dmax()
+: <a class="el" href="svec__util_8sql__in.html#aa05abd8cb32253c07c0f2224861256a5">svec_util.sql_in</a>
+</li>
+<li>svec_dmin()
+: <a class="el" href="svec__util_8sql__in.html#ad1011a13839994adbb1eb3e643c9074d">svec_util.sql_in</a>
+</li>
+<li>svec_dot()
+: <a class="el" href="svec_8sql__in.html#a47b111663b39ec8646891edec8ab3925">svec.sql_in</a>
+</li>
+<li>svec_elsum()
+: <a class="el" href="svec__util_8sql__in.html#a00a7b3260b9fde9b55061e6bf58a028a">svec_util.sql_in</a>
+</li>
+<li>svec_eq()
+: <a class="el" href="svec_8sql__in.html#adceeff7e2f2e43b3e17219bca5d4fea0">svec.sql_in</a>
+</li>
+<li>svec_eq_non_zero()
+: <a class="el" href="svec__util_8sql__in.html#ab3a1cccd709379973f100e5eb35559d2">svec_util.sql_in</a>
+</li>
+<li>svec_from_string()
+: <a class="el" href="svec__util_8sql__in.html#a1f5e8911574cf30cc979478040bd3262">svec_util.sql_in</a>
+</li>
+<li>svec_ge()
+: <a class="el" href="svec_8sql__in.html#ad08b0cadbcf50ff4685228f917cafd1f">svec.sql_in</a>
+</li>
+<li>svec_gt()
+: <a class="el" href="svec_8sql__in.html#a3e5d57b7a4473904e4d053884468e540">svec.sql_in</a>
+</li>
+<li>svec_hash()
+: <a class="el" href="svec__util_8sql__in.html#a0bfa8f8d083282bc9e8caf6f03bb5251">svec_util.sql_in</a>
+</li>
+<li>svec_in()
+: <a class="el" href="svec_8sql__in.html#abe896192db26c02d5c517a5ccf2144f1">svec.sql_in</a>
+</li>
+<li>svec_l1norm()
+: <a class="el" href="svec__util_8sql__in.html#ae60f7aaa5710c765d08b6c3906e0c9f0">svec_util.sql_in</a>
+</li>
+<li>svec_l2_cmp()
+: <a class="el" href="svec_8sql__in.html#a8f72c8cf6d39f08d939f23cf1338a3c6">svec.sql_in</a>
+</li>
+<li>svec_l2_eq()
+: <a class="el" href="svec_8sql__in.html#a9268d26c54c4b2905eb7938df95af8c0">svec.sql_in</a>
+</li>
+<li>svec_l2_ge()
+: <a class="el" href="svec_8sql__in.html#ac22b72fbae2a8f2b35b1c571d9f54dad">svec.sql_in</a>
+</li>
+<li>svec_l2_gt()
+: <a class="el" href="svec_8sql__in.html#ab2414d0d78286884cd199cf44c1d17b9">svec.sql_in</a>
+</li>
+<li>svec_l2_le()
+: <a class="el" href="svec_8sql__in.html#a4045437bce8b9a9bdb0eb3115c97ca99">svec.sql_in</a>
+</li>
+<li>svec_l2_lt()
+: <a class="el" href="svec_8sql__in.html#ab53b85cb3412c9172bf0ee04f6856c69">svec.sql_in</a>
+</li>
+<li>svec_l2_ne()
+: <a class="el" href="svec_8sql__in.html#af35c08aadf164fa3bbc8678765bca643">svec.sql_in</a>
+</li>
+<li>svec_l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a475f0f3e4602e8278b582e099fce2487">svec_util.sql_in</a>
+</li>
+<li>svec_lapply()
+: <a class="el" href="svec__util_8sql__in.html#a0d94c44dde95a00e3d802dee6d7c01eb">svec_util.sql_in</a>
+</li>
+<li>svec_le()
+: <a class="el" href="svec_8sql__in.html#aa6f5715df728db0783b5dcb8db82a2a2">svec.sql_in</a>
+</li>
+<li>svec_log()
+: <a class="el" href="svec__util_8sql__in.html#aab641f9db3deed5bbfa7e050c531e9c1">svec_util.sql_in</a>
+</li>
+<li>svec_lt()
+: <a class="el" href="svec_8sql__in.html#ad7f05a6ce6efc51517bab6ccdb79ee23">svec.sql_in</a>
+</li>
+<li>svec_mean_final()
+: <a class="el" href="svec__util_8sql__in.html#ac3fc45511b50b5d054bf7bbeb952b98d">svec_util.sql_in</a>
+</li>
+<li>svec_mean_prefunc()
+: <a class="el" href="svec__util_8sql__in.html#ab161647a1498c85762779966790eab71">svec_util.sql_in</a>
+</li>
+<li>svec_mean_transition()
+: <a class="el" href="svec__util_8sql__in.html#afcf2f75cd00ab0ba269caf79dcfcc62c">svec_util.sql_in</a>
+</li>
+<li>svec_median()
+: <a class="el" href="svec__util_8sql__in.html#a2284a118fe03465203ecea8c34acefa5">svec_util.sql_in</a>
+</li>
+<li>svec_median_inmemory()
+: <a class="el" href="svec__util_8sql__in.html#a93400e39ecce2f11b9ceb08403420a43">svec_util.sql_in</a>
+</li>
+<li>svec_minus()
+: <a class="el" href="svec_8sql__in.html#a170890a165bf662d343339ffc2a187cf">svec.sql_in</a>
+</li>
+<li>svec_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#adf39975fbbd8a39629569c622db33ab9">svec.sql_in</a>
+</li>
+<li>svec_mult()
+: <a class="el" href="svec_8sql__in.html#a27f0629942dba2e395b005c728d53ac3">svec.sql_in</a>
+</li>
+<li>svec_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6e6916baaf0a7ae0079424ce0565c8b4">svec.sql_in</a>
+</li>
+<li>svec_ne()
+: <a class="el" href="svec_8sql__in.html#a80d9692f67e7cb482c4bda6dbaacd82b">svec.sql_in</a>
+</li>
+<li>svec_nonbase_positions()
+: <a class="el" href="svec__util_8sql__in.html#a5bc053a080fb38601ad91045dc3ffe74">svec_util.sql_in</a>
+</li>
+<li>svec_nonbase_values()
+: <a class="el" href="svec__util_8sql__in.html#a076dcbc0a278bb3b5161b0db2ad8fa44">svec_util.sql_in</a>
+</li>
+<li>svec_out()
+: <a class="el" href="svec_8sql__in.html#a3db4af36acd318030ae57d2bfd19387a">svec.sql_in</a>
+</li>
+<li>svec_pivot()
+: <a class="el" href="svec__util_8sql__in.html#a929bde3bc91b67300d9d370b6a1495f1">svec_util.sql_in</a>
+</li>
+<li>svec_plus()
+: <a class="el" href="svec_8sql__in.html#a612d8920c61e6fdf18dd4e3e9c69c979">svec.sql_in</a>
+</li>
+<li>svec_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#aaeb54c952836c83c7f21a1c62a77955f">svec.sql_in</a>
+</li>
+<li>svec_pow()
+: <a class="el" href="svec_8sql__in.html#ab5166565f3482768db33ee677acc3d96">svec.sql_in</a>
+</li>
+<li>svec_proj()
+: <a class="el" href="svec__util_8sql__in.html#a8787222aec691f94d9808d1369aa401c">svec_util.sql_in</a>
+</li>
+<li>svec_recv()
+: <a class="el" href="svec_8sql__in.html#a585ea8bcb1d0647ef1295187b87405b2">svec.sql_in</a>
+</li>
+<li>svec_return_array()
+: <a class="el" href="svec_8sql__in.html#a7decda276d7ff1e9340dd44e0e7419c0">svec.sql_in</a>
+</li>
+<li>svec_reverse()
+: <a class="el" href="svec__util_8sql__in.html#a4a221435ca91b6fa45114e689ee96010">svec_util.sql_in</a>
+</li>
+<li>svec_send()
+: <a class="el" href="svec_8sql__in.html#a617557435170136b2781070c0a268b83">svec.sql_in</a>
+</li>
+<li>svec_sfv()
+: <a class="el" href="svec__util_8sql__in.html#a375acd521ed9cb05f63b3696dcc10bf4">svec_util.sql_in</a>
+</li>
+<li>svec_sort()
+: <a class="el" href="svec__util_8sql__in.html#a91c23a7ce58932f48f5ab3579ea0309e">svec_util.sql_in</a>
+</li>
+<li>svec_subvec()
+: <a class="el" href="svec__util_8sql__in.html#a5cb3446de5fc117befe88ccb1ebb0e4e">svec_util.sql_in</a>
+</li>
+<li>svec_sum()
+: <a class="el" href="svec__util_8sql__in.html#af1ce6cdd7244ac0832d15c65b7ccc782">svec_util.sql_in</a>
+</li>
+<li>svec_to_string()
+: <a class="el" href="svec__util_8sql__in.html#a1de746ebd23530e72ba4f031ac41d155">svec_util.sql_in</a>
+</li>
+<li>svec_unnest()
+: <a class="el" href="svec__util_8sql__in.html#a87b45a983da4066fa7f35b17ccecf699">svec_util.sql_in</a>
+</li>
+<li>svm_classification()
+: <a class="el" href="online__sv_8sql__in.html#ad90b6bf3b807f22d37b0e2b1893262f0">online_sv.sql_in</a>
+</li>
+<li>svm_cls_agg()
+: <a class="el" href="online__sv_8sql__in.html#a4fd5a4571d959a892ab28dab1fea92f3">online_sv.sql_in</a>
+</li>
+<li>svm_cls_update()
+: <a class="el" href="online__sv_8sql__in.html#a837bd8cfd5f3200e985febf0ff7ec013">online_sv.sql_in</a>
+</li>
+<li>svm_data_normalization()
+: <a class="el" href="online__sv_8sql__in.html#a78bb07d8f4aee80c3bbc8e8176e512be">online_sv.sql_in</a>
+</li>
+<li>svm_dot()
+: <a class="el" href="online__sv_8sql__in.html#ab53cac5790dafd7230359e08c72af4f1">online_sv.sql_in</a>
+</li>
+<li>svm_drop_model()
+: <a class="el" href="online__sv_8sql__in.html#ab54d33f13c0e00faa358e3e3f17c10fb">online_sv.sql_in</a>
+</li>
+<li>svm_gaussian()
+: <a class="el" href="online__sv_8sql__in.html#a9f2a96e1a241ecc66386a78b110777d3">online_sv.sql_in</a>
+</li>
+<li>svm_generate_cls_data()
+: <a class="el" href="online__sv_8sql__in.html#a1cf6d0632772caf6643212a4698d2507">online_sv.sql_in</a>
+</li>
+<li>svm_generate_nd_data()
+: <a class="el" href="online__sv_8sql__in.html#a8be25cfa6134cc12bbc6d31c04712c23">online_sv.sql_in</a>
+</li>
+<li>svm_generate_reg_data()
+: <a class="el" href="online__sv_8sql__in.html#adc9fe3f9b4a04dff13004edbaab8240f">online_sv.sql_in</a>
+</li>
+<li>svm_nd_agg()
+: <a class="el" href="online__sv_8sql__in.html#a9e0abf5bf91bd0833bf80b9d9a5ae8bf">online_sv.sql_in</a>
+</li>
+<li>svm_nd_update()
+: <a class="el" href="online__sv_8sql__in.html#ae6aef7b5654b6da487d0178adf443ee9">online_sv.sql_in</a>
+</li>
+<li>svm_novelty_detection()
+: <a class="el" href="online__sv_8sql__in.html#a75b773bcf4b5242c673d0e54128ed159">online_sv.sql_in</a>
+</li>
+<li>svm_polynomial()
+: <a class="el" href="online__sv_8sql__in.html#a1ac76fdf9623e0a4db47665f2a80be90">online_sv.sql_in</a>
+</li>
+<li>svm_predict()
+: <a class="el" href="online__sv_8sql__in.html#adc8c1c1d5e2deb20f5436790468b052f">online_sv.sql_in</a>
+</li>
+<li>svm_predict_batch()
+: <a class="el" href="online__sv_8sql__in.html#a91ac71354e9dec74e25339bf168c2e5b">online_sv.sql_in</a>
+</li>
+<li>svm_predict_sub()
+: <a class="el" href="online__sv_8sql__in.html#a5988c209ceb6d15b9139eef0c16d7b7d">online_sv.sql_in</a>
+</li>
+<li>svm_reg_agg()
+: <a class="el" href="online__sv_8sql__in.html#a225bdb74eaa687cef0ddbb35145318fc">online_sv.sql_in</a>
+</li>
+<li>svm_reg_update()
+: <a class="el" href="online__sv_8sql__in.html#a0eb68a44edff3fa53891a6a39de61e57">online_sv.sql_in</a>
+</li>
+<li>svm_regression()
+: <a class="el" href="online__sv_8sql__in.html#acaf1f4aa3eec5710de5c03e368a4b106">online_sv.sql_in</a>
+</li>
+<li>svm_store_model()
+: <a class="el" href="online__sv_8sql__in.html#ad2cd899daaa2036b475f71c28cc031ae">online_sv.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_t.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_t.html b/docs/latest/globals_func_t.html
new file mode 100644
index 0000000..2152e71
--- /dev/null
+++ b/docs/latest/globals_func_t.html
@@ -0,0 +1,181 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
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+<script type="text/javascript" src="search/searchdata.js"></script>
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+  $(document).ready(function() { init_search(); });
+</script>
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
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+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
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+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
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+    </div>
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+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_t.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
+<li>t_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac97c5f5015790b59645d69858e127645">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a14fdcfa276fd1a7ea2e3adb41ebe7db4">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ad570d893565618bcbbcbb01b3bb0a9b9">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a3bd1bcc335a2da73d01b40e06f7d2eea">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_pooled()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5c306ba8380ce6567831fef4610e515b">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_pooled_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a0a0a8a579bbf0f0d1efcbf62223e3431">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a1a835d80d1846a10a2c25b91ce81c6d2">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_unpooled()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac50750a0e0797ce24af1dc479b9699e1">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_unpooled_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a8fe7f38d29bf835718adca811e36f15a">hypothesis_tests.sql_in</a>
+</li>
+<li>tanimoto_distance()
+: <a class="el" href="svec__util_8sql__in.html#a98222e58bc519865a26643a61611b717">svec_util.sql_in</a>
+</li>
+<li>term_frequency()
+: <a class="el" href="text__utilities_8sql__in.html#a8a2caae5bf0b45eb1b4b6bbc531bce46">text_utilities.sql_in</a>
+</li>
+<li>tree_display()
+: <a class="el" href="decision__tree_8sql__in.html#a3c51edcc0fe3de029dc39a3f0306155d">decision_tree.sql_in</a>
+</li>
+<li>tree_predict()
+: <a class="el" href="decision__tree_8sql__in.html#ae371963e135c40d8be5b2e342ab3c252">decision_tree.sql_in</a>
+</li>
+<li>tree_surr_display()
+: <a class="el" href="decision__tree_8sql__in.html#a6ba706727adf88f8c07cfe09f71732bb">decision_tree.sql_in</a>
+</li>
+<li>tree_train()
+: <a class="el" href="decision__tree_8sql__in.html#a639431da2553fb86673e82d62cde4458">decision_tree.sql_in</a>
+</li>
+<li>triangular_cdf()
+: <a class="el" href="prob_8sql__in.html#abf9c7d870bcfe68cacaa421749bbdf35">prob.sql_in</a>
+</li>
+<li>triangular_pdf()
+: <a class="el" href="prob_8sql__in.html#a0c511b9748b2f7a21fe56aaf5f66d188">prob.sql_in</a>
+</li>
+<li>triangular_quantile()
+: <a class="el" href="prob_8sql__in.html#a4777540ab1b003ff92d484c4bc26af27">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_u.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_u.html b/docs/latest/globals_func_u.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
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+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+ <tbody>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
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+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_u.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_u"></a>- u -</h3><ul>
+<li>uniform_cdf()
+: <a class="el" href="prob_8sql__in.html#aa3a05f4f2e0ef9eb65e828261ecfbed9">prob.sql_in</a>
+</li>
+<li>uniform_pdf()
+: <a class="el" href="prob_8sql__in.html#ab90fa34d90a9c75747a34c3f210df239">prob.sql_in</a>
+</li>
+<li>uniform_quantile()
+: <a class="el" href="prob_8sql__in.html#a629587a0fdefb588d28b15517ae5cc04">prob.sql_in</a>
+</li>
+<li>utils_normalize_data()
+: <a class="el" href="utils__regularization_8sql__in.html#a3dc216dff38a6d033020af2f7440e66d">utils_regularization.sql_in</a>
+</li>
+<li>utils_var_scales()
+: <a class="el" href="utils__regularization_8sql__in.html#aaae98ce639cd126a308ed4604ac364a9">utils_regularization.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_v.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_v.html b/docs/latest/globals_func_v.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
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+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+<!-- Generated by Doxygen 1.8.10 -->
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+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_v"></a>- v -</h3><ul>
+<li>vcrf_label()
+: <a class="el" href="viterbi_8sql__in.html#a05e8e687442b8f8618738d34010ef68d">viterbi.sql_in</a>
+</li>
+<li>vcrf_top1_label()
+: <a class="el" href="viterbi_8sql__in.html#a77b62d01f74a7bd206f6954c82638ba7">viterbi.sql_in</a>
+</li>
+<li>vcrf_top1_table()
+: <a class="el" href="viterbi_8sql__in.html#a1471ce51491126b282ed8f9a2ac38b25">viterbi.sql_in</a>
+</li>
+<li>vectorized_distribution_agg()
+: <a class="el" href="distribution_8sql__in.html#aa4608a56578c7e93615e6d5b558d0bf9">distribution.sql_in</a>
+</li>
+<li>vectorized_distribution_final()
+: <a class="el" href="distribution_8sql__in.html#a6bca924e0d2a49b101253f3ca9dc4a58">distribution.sql_in</a>
+</li>
+<li>vectorized_distribution_transition()
+: <a class="el" href="distribution_8sql__in.html#abc7f8816bbdb1467435e5ba17a7d08f1">distribution.sql_in</a>
+</li>
+<li>version()
+: <a class="el" href="utilities_8sql__in.html#a9ce7ffa76acc8a0638e9bc48be99f36a">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_w.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_w.html b/docs/latest/globals_func_w.html
new file mode 100644
index 0000000..5510057
--- /dev/null
+++ b/docs/latest/globals_func_w.html
@@ -0,0 +1,163 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_w.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_w"></a>- w -</h3><ul>
+<li>weibull_cdf()
+: <a class="el" href="prob_8sql__in.html#a50e4a1883588cd7a4c1ff1017399e4af">prob.sql_in</a>
+</li>
+<li>weibull_pdf()
+: <a class="el" href="prob_8sql__in.html#a81a876ae2b8598f060dadb179b9324d2">prob.sql_in</a>
+</li>
+<li>weibull_quantile()
+: <a class="el" href="prob_8sql__in.html#aa544631ddeb7a5c06b995b4383c3b612">prob.sql_in</a>
+</li>
+<li>weighted_sample()
+: <a class="el" href="sample_8sql__in.html#ab933c8334edc748aa9ef46b9bb50e60e">sample.sql_in</a>
+</li>
+<li>weighted_sample_final_int64()
+: <a class="el" href="sample_8sql__in.html#a5c2226995c3ef02cb2320b9829bad5eb">sample.sql_in</a>
+</li>
+<li>weighted_sample_final_vector()
+: <a class="el" href="sample_8sql__in.html#a8b2b3bb57b01f47bafe44bea73f39f78">sample.sql_in</a>
+</li>
+<li>weighted_sample_merge_int64()
+: <a class="el" href="sample_8sql__in.html#aa066d40345787e00819963f095f40998">sample.sql_in</a>
+</li>
+<li>weighted_sample_merge_vector()
+: <a class="el" href="sample_8sql__in.html#ac5d00ff9e30dcae8a1b6b8450df3e95e">sample.sql_in</a>
+</li>
+<li>weighted_sample_transition_int64()
+: <a class="el" href="sample_8sql__in.html#af933c6e9d6623c62a671dc74e5bd9d51">sample.sql_in</a>
+</li>
+<li>weighted_sample_transition_vector()
+: <a class="el" href="sample_8sql__in.html#ae7ad8a80a8c2508cc54cf496934017c2">sample.sql_in</a>
+</li>
+<li>wsr_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a8f0431ace27ac78e9e1df9906f1f1c33">hypothesis_tests.sql_in</a>
+</li>
+<li>wsr_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a0d85654832dfa961cd13526c052642f3">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_z.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_z.html b/docs/latest/globals_func_z.html
new file mode 100644
index 0000000..d674df6
--- /dev/null
+++ b/docs/latest/globals_func_z.html
@@ -0,0 +1,130 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
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+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
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+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+</div><!-- top -->
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_z.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_z"></a>- z -</h3><ul>
+<li>zph_agg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a91e1a4b2a2d13454f9e65dd3d40a329b">cox_prop_hazards.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_g.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_g.html b/docs/latest/globals_g.html
new file mode 100644
index 0000000..10a1ef1
--- /dev/null
+++ b/docs/latest/globals_g.html
@@ -0,0 +1,178 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_g.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
+<li>gamma_cdf()
+: <a class="el" href="prob_8sql__in.html#ab6760b0486bad2f1ab0635eb59404e7c">prob.sql_in</a>
+</li>
+<li>gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a6c37e3bda2596accbb46525321a328c4">prob.sql_in</a>
+</li>
+<li>gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#ac48bbd491bd34831415705c3a0b7bf29">prob.sql_in</a>
+</li>
+<li>gamma_random()
+: <a class="el" href="sample_8sql__in.html#afc523f72e64820329acb8874c2a759d3">sample.sql_in</a>
+</li>
+<li>gen_doc_svecs()
+: <a class="el" href="svec__util_8sql__in.html#a6136c83e9c419d783d1ec6db061f8d6e">svec_util.sql_in</a>
+</li>
+<li>gen_rules_from_cfp()
+: <a class="el" href="assoc__rules_8sql__in.html#a9a872579615586ce478d32ba145cc279">assoc_rules.sql_in</a>
+</li>
+<li>geometric_cdf()
+: <a class="el" href="prob_8sql__in.html#a00879bdf7d48ceddedb3b4cc33511497">prob.sql_in</a>
+</li>
+<li>geometric_pmf()
+: <a class="el" href="prob_8sql__in.html#a5e08db93bd448a1e2164e106ce5781a4">prob.sql_in</a>
+</li>
+<li>geometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a62674ca958aec0533cdf0a74a1dadea9">prob.sql_in</a>
+</li>
+<li>get_col()
+: <a class="el" href="linalg_8sql__in.html#a20f34c9e661191e5225cca7bc23252c5">linalg.sql_in</a>
+</li>
+<li>get_row()
+: <a class="el" href="linalg_8sql__in.html#af6b905fcac7746ef0ed0c36df4a1e070">linalg.sql_in</a>
+</li>
+<li>get_tree()
+: <a class="el" href="random__forest_8sql__in.html#af22b14c3da4f64b544b6c333a8e97070">random_forest.sql_in</a>
+</li>
+<li>get_tree_surr()
+: <a class="el" href="random__forest_8sql__in.html#a3ba1911b340a431982189e3e1f8c0111">random_forest.sql_in</a>
+</li>
+<li>glm()
+: <a class="el" href="glm_8sql__in.html#a4938524efbef7684ce8a8e2646f126ef">glm.sql_in</a>
+</li>
+<li>glm_predict()
+: <a class="el" href="glm_8sql__in.html#add7c22acc8eaaa3eace7089eadc5f367">glm.sql_in</a>
+</li>
+<li>glm_predict_binomial()
+: <a class="el" href="glm_8sql__in.html#a9a468186034a936c3997cad22b03cafe">glm.sql_in</a>
+</li>
+<li>glm_predict_poisson()
+: <a class="el" href="glm_8sql__in.html#a941d49196e573048852def804c5ce4c4">glm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_h.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_h.html b/docs/latest/globals_h.html
new file mode 100644
index 0000000..11621bd
--- /dev/null
+++ b/docs/latest/globals_h.html
@@ -0,0 +1,148 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_h.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_h"></a>- h -</h3><ul>
+<li>hetero_linregr_final()
+: <a class="el" href="linear_8sql__in.html#a4b2e38f45a8e519af5947fcc2eb02829">linear.sql_in</a>
+</li>
+<li>hetero_linregr_merge_states()
+: <a class="el" href="linear_8sql__in.html#ad10649c8460c337a285cf20f2b87d0c5">linear.sql_in</a>
+</li>
+<li>hetero_linregr_transition()
+: <a class="el" href="linear_8sql__in.html#a4a583204053716bbece6321f5d9332b1">linear.sql_in</a>
+</li>
+<li>heteroskedasticity_test_linregr()
+: <a class="el" href="linear_8sql__in.html#a6d36a88e06a31753f4045a6c4539ca10">linear.sql_in</a>
+</li>
+<li>hypergeometric_cdf()
+: <a class="el" href="prob_8sql__in.html#a5c48e7fa2fc7bcbc69c7f4da663d457f">prob.sql_in</a>
+</li>
+<li>hypergeometric_pmf()
+: <a class="el" href="prob_8sql__in.html#afbd2f8d9fb30fb179f59cc14f1fd8d6d">prob.sql_in</a>
+</li>
+<li>hypergeometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a813cc27fe097e797ed0fb6022c7bb79a">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_i.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_i.html b/docs/latest/globals_i.html
new file mode 100644
index 0000000..6c5cdcd
--- /dev/null
+++ b/docs/latest/globals_i.html
@@ -0,0 +1,175 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_i.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_i"></a>- i -</h3><ul>
+<li>index_2d_array()
+: <a class="el" href="linalg_8sql__in.html#abe23dd6c6963093863c2a107425f6384">linalg.sql_in</a>
+</li>
+<li>index_weighted_sample()
+: <a class="el" href="sample_8sql__in.html#af0bfa324d198f8059566757f38fe5c93">sample.sql_in</a>
+</li>
+<li>internal_compute_kmeans()
+: <a class="el" href="kmeans_8sql__in.html#a4a1b8fc3dd6da4d4491927888d70e7b9">kmeans.sql_in</a>
+</li>
+<li>internal_compute_lmf_igd()
+: <a class="el" href="lmf_8sql__in.html#aae221f5f5b2f670a93c00202b251857d">lmf.sql_in</a>
+</li>
+<li>internal_execute_using_kmeans_args()
+: <a class="el" href="kmeans_8sql__in.html#abcbeb5878d5a31f600764fbb1fbe5578">kmeans.sql_in</a>
+</li>
+<li>internal_execute_using_lmf_igd_args()
+: <a class="el" href="lmf_8sql__in.html#aea3df2d68e7a289da928b8a584ac719e">lmf.sql_in</a>
+</li>
+<li>internal_lincrf_lbfgs_converge()
+: <a class="el" href="crf_8sql__in.html#a47ef6b30d54ac4ef89cb664a681d1067">crf.sql_in</a>
+</li>
+<li>internal_lincrf_lbfgs_result()
+: <a class="el" href="crf_8sql__in.html#aff63c9a56bebf501a2f40971271605c2">crf.sql_in</a>
+</li>
+<li>internal_linear_svm_igd_distance()
+: <a class="el" href="lsvm_8sql__in.html#a81b1bdf8278bf77439ac9e101ae2ae7a">lsvm.sql_in</a>
+</li>
+<li>internal_linear_svm_igd_result()
+: <a class="el" href="lsvm_8sql__in.html#a21dc69a5bcf6e08f6c0d6cb9c4461091">lsvm.sql_in</a>
+</li>
+<li>internal_lmf_igd_distance()
+: <a class="el" href="lmf_8sql__in.html#a05860ded070a214128e0b4e64c9c989e">lmf.sql_in</a>
+</li>
+<li>internal_lmf_igd_result()
+: <a class="el" href="lmf_8sql__in.html#a8dede6aa8d024f0bb9acca9a83dfd579">lmf.sql_in</a>
+</li>
+<li>inverse_gamma_cdf()
+: <a class="el" href="prob_8sql__in.html#a85e9c16aa2c6973ddeb7883a5f153d93">prob.sql_in</a>
+</li>
+<li>inverse_gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a126211c2172a43a654288fa72a2349f9">prob.sql_in</a>
+</li>
+<li>inverse_gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#a5876aae01f14729866d4fd52918a65ba">prob.sql_in</a>
+</li>
+<li>isnan()
+: <a class="el" href="utilities_8sql__in.html#a48bed63cab78a5446fa4c122eaae3a40">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_k.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_k.html b/docs/latest/globals_k.html
new file mode 100644
index 0000000..8f6788c
--- /dev/null
+++ b/docs/latest/globals_k.html
@@ -0,0 +1,136 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
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+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+        </div>
+</td>
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+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_k.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
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+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_k"></a>- k -</h3><ul>
+<li>kolmogorov_cdf()
+: <a class="el" href="prob_8sql__in.html#aeef43f74f583bdff17bd074d9c0d9607">prob.sql_in</a>
+</li>
+<li>ks_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd3f6d1d6dc4203cab3bcc980ec8ed8d">hypothesis_tests.sql_in</a>
+</li>
+<li>ks_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a58ffb5b2b8392e005f4f3e21560df93f">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[50/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

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diff --git a/community.html b/community.html
index 2fca25f..77d396b 100644
--- a/community.html
+++ b/community.html
@@ -1,35 +1,21 @@
 <!doctype html>
 <html class="no-js" dir="ltr" lang="en-US">
 <head>
-	<meta charset="UTF-8">
-	<meta name="viewport" content="width=1100">
-	<title>Community |  MADlib</title>
-	<link rel="pingback" href="wordpress314/xmlrpc.php">
-	<script>
-		function supportsSVG(){if(document.createElementNS&&document.createElementNS("http://www.w3.org/2000/svg","svg").createSVGRect){return true}}if(supportsSVG()){document.documentElement.className+=" svg"};
-	</script>
-	<script type="text/javascript" src="//use.typekit.net/qbv8hok.js"></script>
-	<script type="text/javascript">try{Typekit.load();}catch(e){}</script>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=1100">
+    <title>MADlib</title>
 
-	<link rel="shortcut icon" href="wordpress314/wp-content/themes/madlib/favicon.ico" />
-	<link rel="alternate" type="application/rss+xml" title="MADlib &raquo; Feed" href="wordpress314/feed/" />
-<link rel="alternate" type="application/rss+xml" title="MADlib &raquo; Comments Feed" href="wordpress314/comments/feed/" />
-<link rel="alternate" type="application/rss+xml" title="MADlib &raquo; Community Comments Feed" href="wordpress314/community/feed/" />
-<link rel='stylesheet' id='style-css'  href='wordpress314/wp-content/themes/madlib/style.css?ver=3.1.4' type='text/css' media='all' />
-<script type='text/javascript' src='wordpress314/wp-includes/js/l10n.js?ver=20101110'></script>
-<script type='text/javascript' src='wordpress314/wp-includes/js/jquery/jquery.js?ver=1.4.4'></script>
-<link rel="EditURI" type="application/rsd+xml" title="RSD" href="wordpress314/xmlrpc.php?rsd" />
-<link rel="wlwmanifest" type="application/wlwmanifest+xml" href="wordpress314/wp-includes/wlwmanifest.xml" /> 
-<link rel='index' title='MADlib' href='wordpress314/' />
-<link rel='prev' title='Home' href='wordpress314/' />
-<link rel='next' title='Download' href='wordpress314/download/' />
-<meta name="generator" content="WordPress 3.1.4" />
-<link rel='canonical' href='wordpress314/community/' />
+    <script src="https://use.typekit.net/qbv8hok.js"></script>
+    <script>try{Typekit.load({ async: true });}catch(e){}</script>
 
+    <link rel="shortcut icon" href="favicon.ico" />
+
+    <link rel='stylesheet' href='style.css' type='text/css' media='all' />
+
+    <script type='text/javascript' src='http://code.jquery.com/jquery-1.10.2.min.js'></script>
+    <script type="text/javascript" src="html5lightbox.js"></script>
+    <script type='text/javascript' src='master.js'></script>
 
-	<script type='text/javascript' src='http://code.jquery.com/jquery-1.10.2.min.js'></script>
-<script type="text/javascript" src="wordpress314/wp-content/themes/madlib/_js/html5lightbox.js"></script>
-<script type='text/javascript' src='wordpress314/wp-content/themes/madlib/_js/master.js?ver=3.1.4'></script>
 </head>
 <body class="page page-id-7 page-template page-template-default">
 	<div class="header">
@@ -42,7 +28,7 @@
 					<li id="menu-item-28" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-28"><a href="product.html">Product</a></li>
 					<li id="menu-item-25" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-25"><a title="Documentation" href="documentation.html">Documentation</a></li>
 					<li id="menu-item-24" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-24"><a href="community.html">Community</a></li>
-					<li id="menu-item-26" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-26 nav-button last"><a href="http://madlib.net/download/">Download pre-Apache releases</a></li>
+					<li id="menu-item-26" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-26 nav-button last"><a href="download.html">Download</a></li>
 					</ul>
 				</div>
 			</div>
@@ -66,18 +52,18 @@
     </div>
     <div class="row">
         <div class="span3 simple-box">
-            <a href="http://gopivotal.com" class="center">
+            <a href="http://pivotal.io/" class="center">
                 <div class="history-logo">
-                    <img src="wordpress314/wp-content/themes/madlib/_media/logos/pivotal.png" alt="">
+                    <img src="_media/logos/pivotal.png" alt="">
                 </div>
-                <h4><small>Providing core development and quality assurance</small></h4>
+                <h4><small>Providing core development and scalability testing</small></h4>
                 <div class="more"><p class="inline-block pointer point-right">Learn More</p></div>
             </a>
         </div>
         <div class="span3 simple-box">
             <a href="http://db.cs.berkeley.edu/jmh/" class="center">
                 <div class="history-logo">
-                    <img src="wordpress314/wp-content/themes/madlib/_media/logos/berkeley.png" alt="">
+                    <img src="_media/logos/berkeley.png" alt="">
                 </div>
                 <h4><small>Providing key research and development of scalable estimators</small></h4>
                 <div class="more"><p class="inline-block pointer point-right">Learn More</p></div>
@@ -86,16 +72,16 @@
         <div class="span3 simple-box">
             <a href="http://cs.stanford.edu/people/chrismre/" class="center">
                 <div class="history-logo">
-                    <img src="wordpress314/wp-content/themes/madlib/_media/logos/stanford.png" alt="">
+                    <img src="_media/logos/stanford.png" alt="">
                 </div>
-                <h4><small>Providing key research in the space of convex optimization</small></h4>
+                <h4><small>Providing key research in the area of convex optimization</small></h4>
                 <div class="more"><p class="inline-block pointer point-right">Learn More</p></div>
             </a>
         </div>
         <div class="span3 simple-box">
             <a href="http://www.cise.ufl.edu/~daisyw/" class="center">
                 <div class="history-logo">
-                    <img src="wordpress314/wp-content/themes/madlib/_media/logos/florida.png" alt="">
+                    <img src="_media/logos/florida.png" alt="">
                 </div>
                 <h4><small>Providing key research into text analytics</small></h4>
                 <div class="more"><p class="inline-block pointer point-right">Learn More</p></div>
@@ -172,9 +158,9 @@
             <p>There are several resources aimed at helping new developers understand how MADlib is designed</p>
             <ul>
                 <li><a href="https://cwiki.apache.org/confluence/display/MADLIB/Architecture">MADlib Architecture</a></li>
-                <li><a href="https://cwiki.apache.org/confluence/display/MADLIB/Contribution+Guidelines">Anatomy of a MADlib Module</a></li>
+                <li><a href="https://cwiki.apache.org/confluence/display/MADLIB/Module+Anatomy">Anatomy of a MADlib Module</a></li>
                 <li><a href="https://cwiki.apache.org/confluence/display/MADLIB/SQL+API+Guide">MADlib SQL API Style Guide</a></li>
-                <li><a href="https://madlib.net/design.pdf">Technical Design Document</a></li>
+                <li><a href="design.pdf">Technical Design Document</a></li>
             </ul>
         </div>
     </div>
@@ -217,35 +203,19 @@
         </div>
     </div>
 </div>			</div>
-<div class="footer">
-	<div class="container">
- <img src='http://incubator.apache.org/images/egg-logo.png'/>
-    <br/>
-
-<p>MADlib is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the <a href="http://incubator.apache.org">Incubator</a>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.</p>
-
-    <br/>
-    <p>
-      Copyright &copy; 2015 <a href='http://www.apache.org/'>The Apache Software Foundation</a>
-      <br>
-      Apache, Apache MADlib (incubating), the Apache feather and the MADlib logo are trademarks of The Apache Software Foundation
-    </p>
-
-	</div>
-</div>
-<script type='text/javascript' src='wordpress314/wp-content/themes/madlib/_js/master.js?ver=3.1.4'></script>
-	<script>
-		var _gaq = _gaq || [];
-		_gaq.push(['_setAccount', 'UA-45382226-1']);
-		_gaq.push(['_trackPageview']);
-		(function() {
-			var ga = document.createElement('script');
-			ga.type = 'text/javascript';
-			ga.async = true;
-			ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
-			var s = document.getElementsByTagName('script')[0];
-			s.parentNode.insertBefore(ga, s);
-		})();
-	</script>
+    <div class="footer">
+      <div class="container">
+            <img src='http://incubator.apache.org/images/egg-logo.png'/>
+          <br/>
+          <br/>
+            <p>MADlib is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the <a href="http://incubator.apache.org">Incubator</a>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.</p>
+          <br/>
+        <p>
+          Copyright &copy; <script> var d = new Date();document.write(d.getFullYear());</script> <a href='http://www.apache.org/'>The Apache Software Foundation</a>
+          <br>
+          Apache, Apache MADlib (incubating), the Apache feather and the MADlib logo are trademarks of The Apache Software Foundation
+        </p>
+      </div>
+    </div>
 </body>
 </html>

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+++ b/docs/.htaccess
@@ -0,0 +1,2 @@
+ErrorDocument 500 http://doc.madlib.net/latest
+ErrorDocument 404 http://doc.madlib.net/latest
\ No newline at end of file

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index 0000000..faf719c
--- /dev/null
+++ b/docs/index.html.legacy
@@ -0,0 +1,7 @@
+<html>
+<head>
+<meta http-equiv="Refresh" content="0; url=./v0.3/" />
+</head>
+<body>
+</body>
+</html>

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+++ b/docs/index.php
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+<?php
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+   <span id="projectnumber">1.8</span>
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">arima.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>Arima function for forecasting of timeseries data.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>August 2013 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="af6f5621d79045e8270e6785ed0d98ce3"></a>
+<div class="memitem">
+<div class="memproto">
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+          <td class="memname">float8 [] __arima_adjust </td>
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+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>curr_tvals</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>prev_tvals</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>p</em>&#160;</td>
+        </tr>
+        <tr>
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+          <td>)</td>
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+          <td class="memname">float8 [] __arima_diff </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>tvals</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
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+          <td class="paramtype">integer&#160;</td>
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+          <td class="memname">__arima_lm_result __arima_lm </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>distid</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
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+          <td class="paramkey"></td>
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+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>phi</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
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+          <td class="paramname"><em>prej</em>&#160;</td>
+        </tr>
+        <tr>
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+          <td>)</td>
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+          <td class="memname">float8 [] __arima_lm_delta </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>jj</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>jz</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>u</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="addf886fb09f74d10dfd1875c641d814b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate __arima_lm_sum_result __arima_lm_result_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="afd8e1b6c57007a47ba64b22718d4e26f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__arima_lm_sum_result __arima_lm_result_ffunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state_data</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a76b2c9d653383c81acde8383bb29aa97"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __arima_lm_result_pfunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa919ba0d4fe79c3b55ee60b7ad17c7de"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __arima_lm_result_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state_data</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>jj</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>jz</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>z2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae9b8675db85344ac26484f8c125f57b6"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate __arima_lm_stat_result __arima_lm_stat_agg </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abb44a6b3bf206e966a9746dde942ddc3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__arima_lm_stat_result __arima_lm_stat_ffunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state_data</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a884a57ef18bd9e1a1b4137269786bf19"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __arima_lm_stat_sfunc </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state_data</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>distid</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>tvals</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>p</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>q</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>phi</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>theta</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>mean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>delta</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6955c191d95e1531655cea4617caf12c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __arima_residual </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>distid</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>tvals</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>p</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>d</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>q</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>phi</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>theta</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>mean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>prez</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a52416f13110c15b6b7fe8736a4eb0202"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void arima_forecast </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>steps_ahead</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ada9e8962693411ec919953bad1ef6007"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text arima_forecast </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a08645edc97c591bce174964ac309a3a0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text arima_forecast </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9af899e0260745b75b1872159fea1281"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void arima_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timestamp_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timeseries_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>include_mean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>non_seasonal_orders</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac5b071de3f011a05b877afd6a415f993"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void arima_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timestamp_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timeseries_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>include_mean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>non_seasonal_orders</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac4d148eefd1d5fbf4e613d5f10df4930"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void arima_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timestamp_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timeseries_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>include_mean</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a47977919832510a148d237b11688aee6"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void arima_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timestamp_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timeseries_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1842458490b5b3a3a80d9663792c5e48"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void arima_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timestamp_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>timeseries_column</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a97025971eb41b5480e279e8b56d5c018"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text arima_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
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+    <a href="http://www.doxygen.org/index.html">
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[17/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Array Operations<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#notes">Implementation Notes</a> </li>
+<li>
+<a href="#list">List of Array Operations</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module provides a set of basic array operations implemented in C. It is a support module for several machine learning algorithms that require fast array operations.</p>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Implementation Notes</dt><dd></dd></dl>
+<p>All functions (except <a class="el" href="svec__util_8sql__in.html#aace16abea7c63f136520fde6c28f1f28">normalize()</a> and array_filter()) described in this module work with 2-D arrays.</p>
+<p>These functions support several numeric types:</p><ul>
+<li>SMALLINT</li>
+<li>INTEGER</li>
+<li>BIGINT</li>
+<li>REAL</li>
+<li>DOUBLE PRECISION (FLOAT8)</li>
+<li>NUMERIC (internally casted into FLOAT8, loss of precisions can happen)</li>
+</ul>
+<p>Several of the function require NO NULL VALUES, while others omit NULLs and return results. See details in description of individual functions.</p>
+<p><a class="anchor" id="list"></a></p><dl class="section user"><dt>Array Operations</dt><dd><table  class="output">
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a91c8d3715142927b3967f05a4fbf1575" title="Adds two arrays. It requires that all the values are NON-NULL. Return type is the same as the input t...">array_add()</a></th><td><p class="starttd">Adds two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.</p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a26e8508a2bae10a6574cec697a270eea" title="Aggregate, element-wise sum of arrays. It requires that all the values are NON-NULL. Return type is the same as the input type. ">sum()</a></th><td><p class="starttd">Aggregate, sums vector element-wisely. It requires that all the values are NON-NULL. Return type is the same as the input type.</p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a2875a161a01c7dcdea9a4997b074eefc" title="Subtracts two arrays. It requires that all the values are NON-NULL. Return type is the same as the in...">array_sub()</a></th><td><p class="starttd">Subtracts two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a652d70c480d484c4a1a92ded384b0dd7" title="Element-wise product of two arrays. It requires that all the values are NON-NULL. Return type is the ...">array_mult()</a></th><td><p class="starttd">Element-wise product of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a6cc05e7052495f8b64692faf40219576" title="Element-wise division of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type. ">array_div()</a></th><td><p class="starttd">Element-wise division of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#acde10964ed23b7c8da515fb84cb8d5e0" title="Dot-product of two arrays. It requires that all the values are NON-NULL. Return type is the same as t...">array_dot()</a></th><td><p class="starttd">Dot-product of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#aedf6cb13eb4803bcc12dc4d95ea8ff4e" title="Checks whether one array contains the other. This function returns TRUE if each non-zero element in t...">array_contains()</a></th><td><p class="starttd">Checks whether one array contains the other. This function returns TRUE if each non-zero element in the right array equals to the element with the same index in the left array. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#ae891429cc50705c530f3e5ca15541849" title="This function finds the maximum value in the array. NULLs are ignored. Return type is the same as the...">array_max()</a></th><td><p class="starttd">This function finds the maximum value in the array. NULLs are ignored. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#aa415256a9064aecc600dfb5e377fb7b1" title="This function finds the maximum value and corresponding index in the array. NULLs are ignored...">array_max_index()</a></th><td><p class="starttd">This function finds the maximum value and corresponding index in the array. NULLs are ignored. Return type is array in format [max, index], and its element type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a6659bf9d9363eb179fab34f81f8ac59b" title="This function finds the minimum value in the array. NULLs are ignored. Return type is the same as the...">array_min()</a></th><td><p class="starttd">This function finds the minimum value in the array. NULLs are ignored. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a813a4d9ffc1c18b1b3e18f6ecdb2051f" title="This function finds the minimum value and corresponding index in the array. NULLs are ignored...">array_min_index()</a></th><td><p class="starttd">This function finds the minimum value and corresponding index in the array. NULLs are ignored. Return type is array in format [min, index], and its element type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec" title="This function finds the sum of the values in the array. NULLs are ignored. Return type is the same as...">array_sum()</a></th><td><p class="starttd">This function finds the sum of the values in the array. NULLs are ignored. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a418de59800833aa95f9b7cbd6b12901c" title="This function finds the sum of the values in the array. NULLs are ignored. Return type is always FLOA...">array_sum_big()</a></th><td><p class="starttd">This function finds the sum of the values in the array. NULLs are ignored. Return type is always FLOAT8 regardless of input. This function is meant to replace <a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec" title="This function finds the sum of the values in the array. NULLs are ignored. Return type is the same as...">array_sum()</a> in cases when a sum may overflow the element type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a407598f9eb70637798b02fd731bfca2c" title="This function finds the mean of the values in the array. NULLs are ignored. ">array_mean()</a></th><td><p class="starttd">This function finds the mean of the values in the array. NULLs are ignored. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a3b6c2d173a611e6d6b184d825c2b336d" title="This function finds the standard deviation of the values in the array. NULLs are ignored. ">array_stddev()</a></th><td><p class="starttd">This function finds the standard deviation of the values in the array. NULLs are ignored. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#ab066e65a41db78b00b4532996b2a6efc" title="This function creates an array of set size (the argument value) of FLOAT8, initializing the values to...">array_of_float()</a></th><td><p class="starttd">This function creates an array of set size (the argument value) of FLOAT8, initializing the values to 0.0. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#ab7d8550e66d2e0bd54b8f0997d93880c" title="This function creates an array of set size (the argument value) of BIGINT, initializing the values to...">array_of_bigint()</a></th><td><p class="starttd">This function creates an array of set size (the argument value) of BIGINT, initializing the values to 0. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#a065a5323f3b742be47e39ad8b4c90fc2" title="This functions set every values in the array to some desired value (provided as the argument)...">array_fill()</a></th><td><p class="starttd">This functions set every value in the array to some desired value (provided as the argument). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>array_filter()</th><td><p class="starttd">This function takes an array as the input and keep only elements that satisfy the operator on specified scalar. It requires that the array is 1-D and all the values are NON-NULL. Return type is the same as the input type. By default, this function removes all zeros. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="array__ops_8sql__in.html#ae6881cc5c86941b6ffca35d7f3cd5c12" title="This function takes an array as the input and executes element-wise multiplication by the scalar prov...">array_scalar_mult()</a></th><td><p class="starttd">This function takes an array as the input and executes element-wise multiplication by the scalar provided as the second argument, returning the resulting array. It requires that all the values are NON-NULL. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>array_scalar_add()</th><td><p class="starttd">This function takes an array as the input and executes element-wise addition of the scalar provided as the second argument, returning the resulting array. It requires that all the values are NON-NULL. Return type is the same as the input type. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>array_sqrt()</th><td><p class="starttd">This function takes an array as the input and finds square root of each element in the array, returning the resulting array. It requires that all the values are NON-NULL. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>array_square()</th><td><p class="starttd">This function takes an array as the input and finds square of each element in the array, returning the resulting array. It requires that all the values are NON-NULL. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="svec__util_8sql__in.html#aace16abea7c63f136520fde6c28f1f28">normalize()</a></th><td>This function normalizes an array as sum of squares to be 1. It requires that the array is 1-D and all the values are NON-NULL.  </td></tr>
+</table>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Create a database table with two integer array columns and add some data. <pre class="example">
+CREATE TABLE array_tbl ( id integer,
+                         array1 integer[],
+                         array2 integer[]
+                       );
+INSERT INTO array_tbl VALUES
+                       ( 1, '{1,2,3,4,5,6,7,8,9}', '{9,8,7,6,5,4,3,2,1}' ),
+                       ( 2, '{1,1,0,1,1,2,3,99,8}','{0,0,0,-5,4,1,1,7,6}' );
+</pre></li>
+<li>Find the minimum, maximum, mean, and standard deviation of the <code>array1</code> column. <pre class="example">
+SELECT id, madlib.array_min(array1), madlib.array_max(array1),
+           madlib.array_min_index(array1), madlib.array_max_index(array1),
+           madlib.array_mean(array1), madlib.array_stddev(array1)
+FROM array_tbl;
+</pre> Result: <pre class="result">
+id | array_min | array_max | array_min_index | array_max_index |    array_mean    |   array_stddev
+----+-----------+-----------+---------------+---------------+------------------+------------------
+  1 |         1 |         9 | {1,1}         | {9,9}         |                5 | 2.73861278752583
+  2 |         0 |        99 | {0,3}         | {99,8}        | 12.8888888888889 | 32.3784050118457(2 rows)
+</pre></li>
+<li>Perform array addition and subtraction. <pre class="example">
+SELECT id, madlib.array_add(array1,array2),
+          madlib.array_sub(array1,array2)
+FROM array_tbl;
+</pre> Result: <pre class="result">
+ id |          array_add           |        array_sub
+&#160;---+------------------------------+-------------------------
+  2 | {1,1,0,-4,5,3,4,106,14}      | {1,1,0,6,-3,1,2,92,2}
+  1 | {10,10,10,10,10,10,10,10,10} | {-8,-6,-4,-2,0,2,4,6,8}
+(2 rows)
+</pre></li>
+<li>Perform element-wise array multiplication and division. The row with <code>id=2</code> is excluded because the divisor array contains zero, which would cause a divide-by-zero error. <pre class="example">
+SELECT id, madlib.array_mult(array1,array2),
+           madlib.array_div(array1,array2)
+FROM array_tbl
+WHERE 0 != ALL(array2);</pre> Result: <pre class="result">
+ id |         array_mult         |      array_div
+&#160;---+----------------------------+---------------------
+  1 | {9,16,21,24,25,24,21,16,9} | {0,0,0,0,1,1,2,4,9}
+(1 row)
+</pre></li>
+<li>Calculate the dot product of the arrays. <pre class="example">
+SELECT id, madlib.array_dot(array1, array2)
+FROM array_tbl;
+</pre> Result: <pre class="result">
+ id | array_dot
+&#160;---+----------
+  2 |       745
+  1 |       165
+(2 rows)
+</pre></li>
+<li>Multiply an array by a scalar 3. <pre class="example">
+SELECT id, madlib.array_scalar_mult(array1,3)
+FROM array_tbl;
+</pre> Result: <pre class="result">
+ id |     array_scalar_mult
+&#160;---+--------------------------
+  1 | {3,6,9,12,15,18,21,24,27}
+  2 | {3,3,0,3,3,6,9,297,24}
+(2 rows)
+</pre></li>
+<li>Construct a nine-element array with each element set to the value 1.3. <pre class="example">
+SELECT madlib.array_fill(madlib.array_of_float(9), 1.3::float);
+</pre> Result: <pre class="result">
+              array_fill
+&#160;--------------------------------------
+ {1.3,1.3,1.3,1.3,1.3,1.3,1.3,1.3,1.3}
+(1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="array__ops_8sql__in.html" title="implementation of array operations in SQL ">array_ops.sql_in</a> for list of functions and usage. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+<div class="title">Arrays and Matrices<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>These modules provide basic mathematical operations to be run on array and matrices.</p>
+<p>For a distributed system, a matrix cannot simply be represented as a 2D array of numbers in memory. <b>We provide two forms of distributed representation of a matrix</b>:</p>
+<ul>
+<li>Dense: The matrix is represented as a distributed collection of 1-D arrays. An example 3x10 matrix would be the below table: <pre>
+ row_id |         row_vec
+--------+-------------------------
+   1    | {9,6,5,8,5,6,6,3,10,8}
+   2    | {8,2,2,6,6,10,2,1,9,9}
+   3    | {3,9,9,9,8,6,3,9,5,6}
+</pre></li>
+<li>Sparse: The matrix is represented using the row and column indices for each non-zero entry of the matrix. Example: <pre>
+ row_id | col_id | value
+--------+--------+-------
+      1 |      1 |     9
+      1 |      5 |     6
+      1 |      6 |     6
+      2 |      1 |     8
+      3 |      1 |     3
+      3 |      2 |     9
+      4 |      7 |     0
+(6 rows)
+</pre> &#160; All matrix operations work with either form of representation.</li>
+</ul>
+<p>In many cases, a matrix function can be <b>decomposed to vector operations applied independently on each row of a matrix (or corresponding rows of two matrices)</b>. We have also provided access to these internal vector operations (<a class="el" href="group__grp__array.html">Array Operations</a>) for greater flexibility. Matrix operations like <em>matrix_add</em> use the corresponding vector operation (<em>array_add</em>) and also include additional validation and formating. Other functions like <em>matrix_mult</em> are complex and use a combination of such vector operations and other SQL operations.</p>
+<p><b>It's important to note</b> that these array functions are only available for the dense format representation of the matrix. In general, the scope of a single array function invocation is limited to only an array (1-dimensional or 2-dimensional) that fits in memory. When such function is executed on a table of arrays, the function is called multiple times - once for each array (or pair of arrays). On contrary, scope of a single matrix function invocation is the complete matrix stored as a distributed table. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
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+<tr class="memdesc:group__grp__array"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides fast array operations supporting other MADlib modules. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__matrix"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__matrix.html">Matrix Operations</a></td></tr>
+<tr class="memdesc:group__grp__matrix"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides fast matrix operations supporting other MADlib modules. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__matrix__factorization"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__matrix__factorization.html">Matrix Factorization</a></td></tr>
+<tr class="memdesc:group__grp__matrix__factorization"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix Factorization methods including Singular Value Decomposition and Low-rank Matrix Factorization. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__linalg"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__linalg.html">Norms and Distance functions</a></td></tr>
+<tr class="memdesc:group__grp__linalg"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides utility functions for basic linear algebra operations. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__svec"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__svec.html">Sparse Vectors</a></td></tr>
+<tr class="memdesc:group__grp__svec"><td class="mdescLeft">&#160;</td><td class="mdescRight">Implements a sparse vector data type that provides compressed storage of vectors that may have many duplicate elements. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: Apriori Algorithm</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
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+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+$(document).ready(function(){initNavTree('group__grp__assoc__rules.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Apriori Algorithm<div class="ingroups"><a class="el" href="group__grp__unsupervised.html">Unsupervised Learning</a> &raquo; <a class="el" href="group__grp__association__rules.html">Association Rules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#rules">Rules</a> </li>
+<li>
+<a href="#algorithm">Apriori Algorithm</a> </li>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module implements the association rules data mining technique on a transactional data set. Given the names of a table and the columns, minimum support and confidence values, this function generates all single and multidimensional association rules that meet the minimum thresholds.</p>
+<p>Association rule mining is a widely used technique for discovering relationships between variables in a large data set (e.g items in a store that are commonly purchased together). The classic market basket analysis example using association rules is the "beer and diapers" rule. According to data mining urban legend, a study of customers' purchase behavior in a supermarket found that men often purchased beer and diapers together. After making this discovery, the managers strategically placed beer and diapers closer together on the shelves and saw a dramatic increase in sales. In addition to market basket analysis, association rules are also used in bioinformatics, web analytics, and several other fields.</p>
+<p>This type of data mining algorithm uses transactional data. Every transaction event has a unique identification, and each transaction consists of a set of items (or itemset). Purchases are considered binary (either it was purchased or not), and this implementation does not take into consideration the quantity of each item. For the MADlib association rules function, it is assumed that the data is stored in two columns with one item and transaction id per row. Transactions with multiple items will span multiple rows with one row per item.</p>
+<pre>
+     tran_id | product
+    ---------+---------
+           1 | 1
+           1 | 2
+           1 | 3
+           1 | 4
+           2 | 3
+           2 | 4
+           2 | 5
+           3 | 1
+           3 | 4
+           3 | 6
+    ...
+</pre><p><a class="anchor" id="rules"></a></p><dl class="section user"><dt>Rules</dt><dd></dd></dl>
+<p>Association rules take the form "If X, then Y", where X and Y are non-empty itemsets. X and Y are called the antecedent and consequent, or the left-hand- side and right-hand-side, of the rule respectively. Using our previous example, the association rule may state "If {diapers}, then {beer}" with .2 support and .85 confidence.</p>
+<p>Given any association rule "If X, then Y", the association rules function will also calculate the following metrics:</p><ul>
+<li>Support: The ratio of transactions that contain X to all transactions, T <p class="formulaDsp">
+\[ S (X) = \frac{Total X}{Total transactions} \]
+</p>
+</li>
+<li>Confidence: The ratio of transactions that contain \( X,Y \) to transactions that contain \( X \). One could view this metric as the conditional probability of \( Y \) , given \( X \) . \( P(Y|X) \) <p class="formulaDsp">
+\[ C (X \Rightarrow Y) = \frac{s(X \cap Y )}{s(X)} \]
+</p>
+</li>
+<li>Lift: The ratio of observed support of \( X,Y \) to the expected support of \( X,Y \) , assuming \( X \) and \( Y \) are independent. <p class="formulaDsp">
+\[ L (X \Rightarrow Y) = \frac{s(X \cap Y )}{s(X) \cdot s(Y)} \]
+</p>
+</li>
+<li><p class="startli">Conviction: The ratio of expected support of \( X \) occurring without \( Y \) assuming \( X \) and \( \neg Y \) are independent, to the observed support of \( X \) occuring without \( Y \). If conviction is greater than 1, then this metric shows that incorrect predictions ( \( X \Rightarrow Y \) ) occur less often than if these two actions were independent. This metric can be viewed as the ratio that the association rule would be incorrect if the actions were independent (i.e. a conviction of 1.5 indicates that if the variables were independent, this rule would be incorrect 50% more often.)</p>
+<p class="formulaDsp">
+\[ Conv (X \Rightarrow Y) = \frac{1 - S(Y)}{1 - C(X \Rightarrow Y)} \]
+</p>
+</li>
+</ul>
+<p><a class="anchor" id="algorithm"></a></p><dl class="section user"><dt>Apriori Algorithm</dt><dd></dd></dl>
+<p>Although there are many algorithms that generate association rules, the classic algorithm used is called Apriori (which we implemented in this module). It is a breadth-first search, as opposed to depth-first searches like eclat. Frequent itemsets of order \( n \) are generated from sets of order \( n - 1 \). Using the downward closure property, all sets must have frequent subsets. There are two steps in this algorithm; generating frequent itemsets, and using these itemsets to construct the association rules. A simplified version of the algorithm is as follows, and assumes a minimum level of support and confidence is provided:</p>
+<p><em>Initial</em> <em>step</em> </p><ol type="1">
+<li>Generate all itemsets of order 1</li>
+<li>Eliminate itemsets that have support is less than minimum support</li>
+</ol>
+<p><em>Main</em> <em>algorithm</em> </p><ol type="1">
+<li>For \( n \ge 2 \), generate itemsets of order \( n \) by combining the itemsets of order \( n - 1 \). This is done by doing the union of two itemsets that have identical items except one.</li>
+<li>Eliminate itemsets that have (n-1) order subsets with insufficient support</li>
+<li>Eliminate itemsets with insufficient support</li>
+<li>Repeat until itemsets cannot be generated</li>
+</ol>
+<p><em>Association</em> <em>rule</em> <em>generation</em> </p>
+<p>Given a frequent itemset \( A \) generated from the Apriori algorithm, and all subsets \( B \) , we generate rules such that \( B \Rightarrow (A - B) \) meets minimum confidence requirements.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd>Association rules can be called with the following syntax. <pre class="syntax">
+assoc_rules( support,
+             confidence,
+             tid_col,
+             item_col,
+             input_table,
+             output_schema,
+             verbose
+           );</pre> This generates all association rules that satisfy the specified minimum <em>support</em> and <em>confidence</em>.</dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>support </dt>
+<dd><p class="startdd">The minimum level of support needed for each itemset to be included in result.</p>
+<p class="enddd"></p>
+</dd>
+<dt>confidence </dt>
+<dd><p class="startdd">The minimum level of confidence needed for each rule to be included in result.</p>
+<p class="enddd"></p>
+</dd>
+<dt>tid_col </dt>
+<dd><p class="startdd">The name of the column storing the transaction ids.</p>
+<p class="enddd"></p>
+</dd>
+<dt>item_col </dt>
+<dd><p class="startdd">The name of the column storing the products.</p>
+<p class="enddd"></p>
+</dd>
+<dt>input_table </dt>
+<dd><p class="startdd">The name of the table containing the input data.</p>
+<p>The input data is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>input_table</em> (
+    <em>trans_id</em> INTEGER,
+    <em>product</em> TEXT
+)</pre><p>The algorithm maps the product names to consecutive integer ids starting at 1. If they are already structured this way, then the ids will not change. </p>
+<p class="enddd"></p>
+</dd>
+<dt>output_schema </dt>
+<dd><p class="startdd">The name of the schema where the final results will be stored. It is expected to be created before calling the function, or using <code>NULL</code> suggests the current schema will be used.</p>
+<p>The results containing the rules, support, confidence, lift, and conviction are stored in the table <code>assoc_rules</code> in the schema specified by <code>output_schema</code>.</p>
+<p>The table has the following columns. </p><table  class="output">
+<tr>
+<th>ruleid </th><td>integer  </td></tr>
+<tr>
+<th>pre </th><td>text  </td></tr>
+<tr>
+<th>post </th><td>text  </td></tr>
+<tr>
+<th>support </th><td>double  </td></tr>
+<tr>
+<th>confidence </th><td>double  </td></tr>
+<tr>
+<th>lift </th><td>double  </td></tr>
+<tr>
+<th>conviction </th><td>double  </td></tr>
+</table>
+<p>On Greenplum Database the table is distributed by the ruleid column.</p>
+<p>The <code>pre</code> and <code>post</code> columns are the itemsets of left and right hand sides of the association rule respectively. The <code>support</code>, <code>confidence</code>, <code>lift</code>, and <code>conviction</code> columns are calculated as mentioned in the about section. </p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose </dt>
+<dd>BOOLEAN, default FALSE. Determines if the output contains comments. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p>Let us take a look at some sample transactional data and generate association rules.</p>
+<ol type="1">
+<li>Create an input dataset. <pre class="example">
+DROP TABLE IF EXISTS test_data;
+CREATE TABLE test_data (
+    trans_id INT,
+    product TEXT
+);
+INSERT INTO test_data VALUES (1, 'beer');
+INSERT INTO test_data VALUES (1, 'diapers');
+INSERT INTO test_data VALUES (1, 'chips');
+INSERT INTO test_data VALUES (2, 'beer');
+INSERT INTO test_data VALUES (2, 'diapers');
+INSERT INTO test_data VALUES (3, 'beer');
+INSERT INTO test_data VALUES (3, 'diapers');
+INSERT INTO test_data VALUES (4, 'beer');
+INSERT INTO test_data VALUES (4, 'chips');
+INSERT INTO test_data VALUES (5, 'beer');
+INSERT INTO test_data VALUES (6, 'beer');
+INSERT INTO test_data VALUES (6, 'diapers');
+INSERT INTO test_data VALUES (6, 'chips');
+INSERT INTO test_data VALUES (7, 'beer');
+INSERT INTO test_data VALUES (7, 'diapers');
+</pre></li>
+<li>Let \( min(support) = .25 \) and \( min(confidence) = .5 \), and the output schema be 'myschema'. For this example, we set verbose to TRUE so that we have some insight into the progress of the function. We can now generate association rules as follows: <pre class="example">
+SELECT * FROM madlib.assoc_rules( .25,
+                                  .5,
+                                  'trans_id',
+                                  'product',
+                                  'test_data',
+                                  'myschema',
+                                  TRUE
+                                );
+</pre> Result: <pre class="result">
+ output_schema | output_table | total_rules | total_time
+---------------+--------------+-------------+-----------------
+ myschema      | assoc_rules  |           7 | 00:00:03.162094
+(1 row)
+</pre> The association rules are stored in the myschema.assoc_rules table: <pre class="example">
+SELECT * FROM myschema.assoc_rules
+ORDER BY support DESC;
+</pre> Result: <pre class="result">
+ ruleid |       pre       |      post      |      support      |    confidence     |       lift        |    conviction
+--------+-----------------+----------------+-------------------+-------------------+-------------------+-------------------
+      4 | {diapers}       | {beer}         | 0.714285714285714 |                 1 |                 1 |                 0
+      2 | {beer}          | {diapers}      | 0.714285714285714 | 0.714285714285714 |                 1 |                 1
+      1 | {chips}         | {beer}         | 0.428571428571429 |                 1 |                 1 |                 0
+      5 | {chips}         | {beer,diapers} | 0.285714285714286 | 0.666666666666667 | 0.933333333333333 | 0.857142857142857
+      6 | {chips,beer}    | {diapers}      | 0.285714285714286 | 0.666666666666667 | 0.933333333333333 | 0.857142857142857
+      7 | {chips,diapers} | {beer}         | 0.285714285714286 |                 1 |                 1 |                 0
+      3 | {chips}         | {diapers}      | 0.285714285714286 | 0.666666666666667 | 0.933333333333333 | 0.857142857142857
+(7 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<p>The association rules function always creates a table named <code>assoc_rules</code>. Make a copy of this table before running the function again if you would like to keep multiple association rule tables.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="assoc__rules_8sql__in.html" title="The assoc_rules function computes association rules for a given set of data. The data is assumed to h...">assoc_rules.sql_in</a> documenting the SQL function. </p>
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+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Association Rules<div class="ingroups"><a class="el" href="group__grp__unsupervised.html">Unsupervised Learning</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods used to uncover interesting patterns in transactional datasets. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__assoc__rules"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__assoc__rules.html">Apriori Algorithm</a></td></tr>
+<tr class="memdesc:group__grp__assoc__rules"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes association rules for a given set of data. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
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+    [ "Apriori Algorithm", "group__grp__assoc__rules.html", null ]
+];
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+<title>MADlib: Naive Bayes Classification</title>
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+<div class="title">Naive Bayes Classification<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#train">Training Function(s)</a> </li>
+<li>
+<a href="#classify">Classify Function(s)</a> </li>
+<li>
+<a href="#probabilities">Probabilities Function(s)</a> </li>
+<li>
+<a href="#adhoc">Ad Hoc Computation</a> </li>
+<li>
+<a href="#notes">Implementation Notes</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#background">Technical Background</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>Naive Bayes refers to a stochastic model where all independent variables \( a_1, \dots, a_n \) (often referred to as attributes in this context) independently contribute to the probability that a data point belongs to a certain class \( c \).</p>
+<p>Naives Bayes classification estimates feature probabilities and class priors using maximum likelihood or Laplacian smoothing. For numeric attributes, Gaussian smoothing can be used to estimate the feature probabilities.These parameters are then used to classify new data.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function(s)</dt><dd></dd></dl>
+<p>For data with only categorical attributes, precompute feature probabilities and class priors using the following function:</p>
+<pre class="syntax">
+create_nb_prepared_data_tables ( trainingSource,
+                                 trainingClassColumn,
+                                 trainingAttrColumn,
+                                 numAttrs,
+                                 featureProbsName,
+                                 classPriorsName
+                               )
+</pre><p>For data containing both categorical and numeric attributes, use the following form to precompute the Gaussian parameters (mean and variance) for numeric attributes alongside the feature probabilities for categorical attributes and class priors.</p>
+<pre class="syntax">
+create_nb_prepared_data_tables ( trainingSource,
+                                 trainingClassColumn,
+                                 trainingAttrColumn,
+                                 numericAttrsColumnIndices,
+                                 numAttrs,
+                                 featureProbsName,
+                                 numericAttrParamsName,
+                                 classPriorsName
+                               )
+</pre><p>The <em>trainingSource</em> is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>trainingSource</em> (
+    ...
+    <em>trainingClassColumn</em> INTEGER,
+    <em>trainingAttrColumn</em> INTEGER[] OR NUMERIC[] OR FLOAT8[],
+    ...
+)</pre><p><em>numericAttrsColumnIndices</em> should be of type TEXT, specified as an array of indices (starting from 1) in the <em>trainingAttrColumn</em> attributes-array that correspond to numeric attributes.</p>
+<p>The two output tables are:</p><ul>
+<li><em>featureProbsName</em> &ndash; stores feature probabilities</li>
+<li><em>classPriorsName</em> &ndash; stores the class priors</li>
+</ul>
+<p>In addition to the above, if the function specifying numeric attributes is used, an additional table <em>numericAttrParamsName</em> is created which stores the Gaussian parameters for the numeric attributes.</p>
+<p><a class="anchor" id="classify"></a></p><dl class="section user"><dt>Classify Function(s)</dt><dd></dd></dl>
+<p>Perform Naive Bayes classification: </p><pre class="syntax">
+create_nb_classify_view ( featureProbsName,
+                          classPriorsName,
+                          classifySource,
+                          classifyKeyColumn,
+                          classifyAttrColumn,
+                          numAttrs,
+                          destName
+                        )
+</pre><p>For data with numeric attributes, use the following version:</p>
+<pre class="syntax">
+create_nb_classify_view ( featureProbsName,
+                          classPriorsName,
+                          classifySource,
+                          classifyKeyColumn,
+                          classifyAttrColumn,
+                          numAttrs,
+                          numericAttrParamsName,
+                          destName
+                        )
+</pre><p>The <b>data to classify</b> is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>classifySource</em> (
+    ...
+    <em>classifyKeyColumn</em> ANYTYPE,
+    <em>classifyAttrColumn</em> INTEGER[],
+    ...
+)</pre><p>This function creates the view <code><em>destName</em></code> mapping <em>classifyKeyColumn</em> to the Naive Bayes classification. </p><pre class="result">
+key | nb_classification
+&#160;---+------------------
+...
+</pre><p><a class="anchor" id="probabilities"></a></p><dl class="section user"><dt>Probabilities Function(s)</dt><dd></dd></dl>
+<p>Compute Naive Bayes probabilities. </p><pre class="syntax">
+create_nb_probs_view( featureProbsName,
+                      classPriorsName,
+                      classifySource,
+                      classifyKeyColumn,
+                      classifyAttrColumn,
+                      numAttrs,
+                      destName
+                    )
+</pre><p>For data with numeric attributes , use the following version:</p>
+<pre class="syntax">
+create_nb_probs_view( featureProbsName,
+                      classPriorsName,
+                      classifySource,
+                      classifyKeyColumn,
+                      classifyAttrColumn,
+                      numAttrs,
+                      numericAttrParamsName,
+                      destName
+                    )
+</pre><p>This creates the view <code><em>destName</em></code> mapping <em>classifyKeyColumn</em> and every single class to the Naive Bayes probability: </p><pre class="result">
+key | class | nb_prob
+&#160;---+-------+--------
+...
+</pre><p><a class="anchor" id="adhoc"></a></p><dl class="section user"><dt>Ad Hoc Computation Function</dt><dd></dd></dl>
+<p>With ad hoc execution (no precomputation), the functions <a class="el" href="bayes_8sql__in.html#a798402280fc6db710957ae3ab58767e0" title="Create a view with columns (key, nb_classification) ">create_nb_classify_view()</a> and <a class="el" href="bayes_8sql__in.html#a163afffd0c845d325f060f74bcf02243" title="Create view with columns (key, class, nb_prob) ">create_nb_probs_view()</a> can be used in an ad-hoc fashion without the precomputation step. In this case, replace the function arguments</p>
+<pre>'<em>featureProbsName</em>', '<em>classPriorsName</em>'</pre><p> with </p><pre>'<em>trainingSource</em>', '<em>trainingClassColumn</em>', '<em>trainingAttrColumn</em>'</pre><p> for data without any any numeric attributes and with </p><pre>'<em>trainingSource</em>', '<em>trainingClassColumn</em>', '<em>trainingAttrColumn</em>', '<em>numericAttrsColumnIndices</em>'</pre><p> for data containing numeric attributes as well.</p>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Implementation Notes</dt><dd><ul>
+<li>The probabilities computed on the platforms of PostgreSQL and Greenplum database have a small difference due to the nature of floating point computation. Usually this is not important. However, if a data point has <p class="formulaDsp">
+\[ P(C=c_i \mid A) \approx P(C=c_j \mid A) \]
+</p>
+ for two classes, this data point might be classified into diferent classes on PostgreSQL and Greenplum. This leads to the differences in classifications on PostgreSQL and Greenplum for some data sets, but this should not affect the quality of the results.</li>
+<li>When two classes have equal and highest probability among all classes, the classification result is an array of these two classes, but the order of the two classes is random.</li>
+<li>The current implementation of Naive Bayes classification is suitable for discontinuous (categorial) attributes as well as continuous (numeric) attributes.<br />
+For continuous data, a typical assumption, usually used for small datasets, is that the continuous values associated with each class are distributed according to a Gaussian distribution, and the probabilities \( P(A_i = a \mid C=c) \) are estimated using the Gaussian Distribution formula: <p class="formulaDsp">
+\[ P(A_i=a \mid C=c) = \frac{1}{\sqrt{2\pi\sigma^{2}_c}}exp\left(-\frac{(a-\mu_c)^{2}}{2\sigma^{2}_c}\right) \]
+</p>
+ where \(\mu_c\) and \(\sigma^{2}_c\) are the population mean and variance of the attribute for the class \(c\).<br />
+Another common technique for handling continuous values, which is better for large data sets, is to use binning to discretize the values, and convert the continuous data into categorical bins. This approach is currently not implemented.</li>
+<li>One can provide floating point data to the Naive Bayes classification function. If the corresponding attribute index is not specified in <em>numericAttrsColumnIndices</em>, floating point numbers will be used as symbolic substitutions for categorial data. In this case, the classification would work best if there are sufficient data points for each floating point attribute. However, if floating point numbers are used as continuous data without the attribute being marked as of type numeric in <em>numericAttrsColumnIndices</em>, no warning is raised and the result may not be as expected.</li>
+</ul>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p>The following is an extremely simplified example of the above option #1 which can by verified by hand.</p>
+<ol type="1">
+<li>The training and the classification data. <pre class="example">
+SELECT * FROM training;
+</pre> Result: <pre class="result">
+ id | class | attributes
+&#160;---+-------+------------
+  1 |     1 | {1,2,3}
+  2 |     1 | {1,2,1}
+  3 |     1 | {1,4,3}
+  4 |     2 | {1,2,2}
+  5 |     2 | {0,2,2}
+  6 |     2 | {0,1,3}
+(6 rows)
+</pre> <pre class="example">
+SELECT * FROM toclassify;
+</pre> Result: <pre class="result">
+ id | attributes
+&#160;---+------------
+  1 | {0,2,1}
+  2 | {1,2,3}
+(2 rows)
+</pre></li>
+<li>Precompute feature probabilities and class priors. <pre class="example">
+SELECT madlib.create_nb_prepared_data_tables( 'training',
+                                              'class',
+                                              'attributes',
+                                              3,
+                                              'nb_feature_probs',
+                                              'nb_class_priors'
+                                            );
+</pre></li>
+<li>Optionally check the contents of the precomputed tables. <pre class="example">
+SELECT * FROM nb_class_priors;
+</pre> Result: <pre class="result">
+ class | class_cnt | all_cnt
+&#160;------+-----------+---------
+     1 |         3 |       6
+     2 |         3 |       6
+(2 rows)
+</pre> <pre class="example">
+SELECT * FROM nb_feature_probs;
+</pre> Result: <pre class="result">
+ class | attr | value | cnt | attr_cnt
+&#160;------+------+-------+-----+----------
+     1 |    1 |     0 |   0 |        2
+     1 |    1 |     1 |   3 |        2
+     1 |    2 |     1 |   0 |        3
+     1 |    2 |     2 |   2 |        3
+...
+</pre></li>
+<li>Create the view with Naive Bayes classification and check the results. <pre class="example">
+SELECT madlib.create_nb_classify_view( 'nb_feature_probs',
+                                       'nb_class_priors',
+                                       'toclassify',
+                                       'id',
+                                       'attributes',
+                                       3,
+                                       'nb_classify_view_fast'
+                                     );
+&#160;
+SELECT * FROM nb_classify_view_fast;
+</pre> Result: <pre class="result">
+ key | nb_classification
+&#160;----+-------------------
+   1 | {2}
+   2 | {1}
+(2 rows)
+</pre></li>
+<li>Look at the probabilities for each class (note that we use "Laplacian smoothing"), <pre class="example">
+SELECT madlib.create_nb_probs_view( 'nb_feature_probs',
+                                    'nb_class_priors',
+                                    'toclassify',
+                                    'id',
+                                    'attributes',
+                                    3,
+                                    'nb_probs_view_fast'
+                                  );
+&#160;
+SELECT * FROM nb_probs_view_fast;
+</pre> Result: <pre class="result">
+ key | class | nb_prob
+&#160;----+-------+---------
+   1 |     1 |     0.4
+   1 |     2 |     0.6
+   2 |     1 |    0.75
+   2 |     2 |    0.25
+(4 rows)
+</pre></li>
+</ol>
+<p>The following is an example of using a dataset with both numeric and categorical attributes</p>
+<ol type="1">
+<li>The training and the classification data. Attributes {height(numeric),weight(numeric),shoe size(categorical)}, Class{sex(1=male,2=female)} <pre class="example">
+SELECT * FROM gaussian_data;
+</pre> Result: <pre class="result">
+ id | sex |  attributes   
+&#160;----+-----+---------------
+  1 |   1 | {6,180,12}
+  2 |   1 | {5.92,190,12}
+  3 |   1 | {5.58,170,11}
+  4 |   1 | {5.92,165,11}
+  5 |   2 | {5,100,6}
+  6 |   2 | {5.5,150,6}
+  7 |   2 | {5.42,130,7}
+  8 |   2 | {5.75,150,8}
+(8 rows)
+</pre> <pre class="example">
+SELECT * FROM gaussian_test;
+</pre> Result: <pre class="result">
+ id | sex |  attributes  
+----+-----+--------------
+  9 |   1 | {5.8,180,11}
+ 10 |   2 | {5,160,6}
+(2 rows)
+</pre></li>
+<li>Precompute feature probabilities and class priors. <pre class="example">
+SELECT madlib.create_nb_prepared_data_tables( 'gaussian_data',
+                                              'sex',
+                                              'attributes',
+                                              'ARRAY[1,2]',
+                                              3,
+                                              'categ_feature_probs',
+                                              'numeric_attr_params',
+                                              'class_priors'
+                                            );
+</pre></li>
+<li>Optionally check the contents of the precomputed tables. <pre class="example">
+SELECT * FROM class_priors;
+</pre> Result: <pre class="result">
+class | class_cnt | all_cnt 
+&#160;-------+-----------+---------
+     1 |         4 |       8
+     2 |         4 |       8
+(2 rows)
+</pre> <pre class="example">
+SELECT * FROM categ_feature_probs;
+</pre> Result: <pre class="result">
+ class | attr | value | cnt | attr_cnt 
+-------+------+-------+-----+----------
+     2 |    3 |     6 |   2 |        5
+     1 |    3 |    12 |   2 |        5
+     2 |    3 |     7 |   1 |        5
+     1 |    3 |    11 |   2 |        5
+     2 |    3 |     8 |   1 |        5
+     2 |    3 |    12 |   0 |        5
+     1 |    3 |     6 |   0 |        5
+     2 |    3 |    11 |   0 |        5
+     1 |    3 |     8 |   0 |        5
+     1 |    3 |     7 |   0 |        5
+(10 rows)
+</pre> <pre class="example">
+SELECT * FROM numeric_attr_params;
+</pre> Result: <pre class="result">
+class | attr |      attr_mean       |        attr_var        
+-------+------+----------------------+------------------------
+     1 |    1 |   5.8550000000000000 | 0.03503333333333333333
+     1 |    2 | 176.2500000000000000 |   122.9166666666666667
+     2 |    1 |   5.4175000000000000 | 0.09722500000000000000
+     2 |    2 | 132.5000000000000000 |   558.3333333333333333
+(4 rows)
+</pre></li>
+<li>Create the view with Naive Bayes classification and check the results. <pre class="example">
+SELECT madlib.create_nb_classify_view( 'categ_feature_probs',
+                                       'class_priors',
+                                       'gaussian_test',
+                                       'id',
+                                       'attributes',
+                                       3,
+                                       'numeric_attr_params',
+                                       'classify_view'
+                                     );
+&#160;
+SELECT * FROM classify_view;
+</pre> Result: <pre class="result">
+ key | nb_classification
+&#160;----+-------------------
+   9 | {1}
+   10 | {2}
+(2 rows)
+</pre></li>
+<li>Look at the probabilities for each class <pre class="example">
+SELECT madlib.create_nb_probs_view( 'categ_feature_probs',
+                                       'class_priors',
+                                       'gaussian_test',
+                                       'id',
+                                       'attributes',
+                                       3,
+                                       'numeric_attr_params',
+                                       'probs_view'
+                                  );
+&#160;
+SELECT * FROM probs_view;
+</pre> Result: <pre class="result">
+ key | class |       nb_prob        
+-----+-------+----------------------
+   9 |     1 |    0.993556745948775
+   9 |     2 |  0.00644325405122553
+  10 |     1 | 5.74057538627122e-05
+  10 |     2 |    0.999942594246137
+(4 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>In detail, <b>Bayes'</b> theorem states that </p><p class="formulaDsp">
+\[ \Pr(C = c \mid A_1 = a_1, \dots, A_n = a_n) = \frac{\Pr(C = c) \cdot \Pr(A_1 = a_1, \dots, A_n = a_n \mid C = c)} {\Pr(A_1 = a_1, \dots, A_n = a_n)} \,, \]
+</p>
+<p> and the <b>naive</b> assumption is that </p><p class="formulaDsp">
+\[ \Pr(A_1 = a_1, \dots, A_n = a_n \mid C = c) = \prod_{i=1}^n \Pr(A_i = a_i \mid C = c) \,. \]
+</p>
+<p> Naives Bayes classification estimates feature probabilities and class priors using maximum likelihood or Laplacian smoothing. These parameters are then used to classifying new data.</p>
+<p>A Naive Bayes classifier computes the following formula: </p><p class="formulaDsp">
+\[ \text{classify}(a_1, ..., a_n) = \arg\max_c \left\{ \Pr(C = c) \cdot \prod_{i=1}^n \Pr(A_i = a_i \mid C = c) \right\} \]
+</p>
+<p> where \( c \) ranges over all classes in the training data and probabilites are estimated with relative frequencies from the training set. There are different ways to estimate the feature probabilities \( P(A_i = a \mid C = c) \). The maximum likelihood estimate takes the relative frequencies. That is: </p><p class="formulaDsp">
+\[ P(A_i = a \mid C = c) = \frac{\#(c,i,a)}{\#c} \]
+</p>
+<p> where</p><ul>
+<li>\( \#(c,i,a) \) denotes the # of training samples where attribute \( i \) is \( a \) and class is \( c \)</li>
+<li>\( \#c \) denotes the # of training samples where class is \( c \).</li>
+</ul>
+<p>Since the maximum likelihood sometimes results in estimates of "0", you might want to use a "smoothed" estimate. To do this, you add a number of "virtual" samples and make the assumption that these samples are evenly distributed among the values assumed by attribute \( i \) (that is, the set of all values observed for attribute \( a \) for any class):</p>
+<p class="formulaDsp">
+\[ P(A_i = a \mid C = c) = \frac{\#(c,i,a) + s}{\#c + s \cdot \#i} \]
+</p>
+<p> where</p><ul>
+<li>\( \#i \) denotes the # of distinct values for attribute \( i \) (for all classes)</li>
+<li>\( s \geq 0 \) denotes the smoothing factor.</li>
+</ul>
+<p>The case \( s = 1 \) is known as "Laplace smoothing". The case \( s = 0 \) trivially reduces to maximum-likelihood estimates.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Tom Mitchell: Machine Learning, McGraw Hill, 1997. Book chapter <em>Generativ and Discriminative Classifiers: Naive Bayes and Logistic Regression</em> available at: <a href="http://www.cs.cmu.edu/~tom/NewChapters.html">http://www.cs.cmu.edu/~tom/NewChapters.html</a></p>
+<p>[2] Wikipedia, Naive Bayes classifier, <a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier">http://en.wikipedia.org/wiki/Naive_Bayes_classifier</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="bayes_8sql__in.html" title="SQL functions for naive Bayes. ">bayes.sql_in</a> documenting the SQL functions.</dd></dl>
+</div><!-- contents -->
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[16/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__cg.html
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+<title>MADlib: Conjugate Gradient</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+  <div class="headertitle">
+<div class="title">Conjugate Gradient<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>This function uses the iterative conjugate gradient method [1] to find a solution to the function: </p><p class="formulaDsp">
+\[ \boldsymbol Ax = \boldsymbol b \]
+</p>
+<p> where \( \boldsymbol A \) is a symmetric, positive definite matrix and \(x\) and \( \boldsymbol b \) are vectors.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd>Conjugate gradient returns x as an array. It has the following syntax.</dd></dl>
+<pre class="syntax">
+conjugate_gradient( table_name,
+                    name_of_row_values_col,
+                    name_of_row_number_col,
+                    aray_of_b_values,
+                    desired_precision
+                  )
+</pre><p>Matrix \( \boldsymbol A \) is assumed to be stored in a table where each row consists of at least two columns: array containing values of a given row, row number: </p><pre>{TABLE|VIEW} <em>matrix_A</em> (
+    <em>row_number</em> FLOAT,
+    <em>row_values</em> FLOAT[],
+)</pre><p> The number of elements in each row should be the same.</p>
+<p>\( \boldsymbol b \) is passed as a FLOAT[] to the function.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>Construct matrix A according to structure. <pre class="example">
+SELECT * FROM data;
+</pre> Result: <pre class="result">
+ row_num | row_val
+&#160;--------+---------
+       1 | {2,1}
+       2 | {1,4}
+(2 rows)
+</pre></li>
+<li>Call the conjugate gradient function. <pre class="example">
+SELECT conjugate_gradient( 'data',
+                           'row_val',
+                           'row_num',
+                           '{2,1}',
+                           1E-6,1
+                         );
+</pre> <pre class="result">
+INFO:  COMPUTE RESIDUAL ERROR 14.5655661859659
+INFO:  ERROR 0.144934004246004
+INFO:  ERROR 3.12963615962926e-31
+INFO:  TEST FINAL ERROR 2.90029642185163e-29
+    conjugate_gradient
+&#160;--------------------------
+ {1,-1.31838984174237e-15}
+(1 row)
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd>[1] "Conjugate gradient method" Wikipedia - <a href="http://en.wikipedia.org/wiki/Conjugate_gradient_method">http://en.wikipedia.org/wiki/Conjugate_gradient_method</a></dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="conjugate__gradient_8sql__in.html" title="SQL function computing Conjugate Gradient. ">conjugate_gradient.sql_in</a> documenting the SQL function. </dd></dl>
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+  <div class="headertitle">
+<div class="title">Clustered Variance<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#train_linregr">Clustered Variance Linear Regression Training Function</a> </li>
+<li>
+<a href="#train_logregr">Clustered Variance Logistic Regression Training Function</a> </li>
+<li>
+<a href="#train_mlogregr">Clustered Variance Multinomial Logistic Regression Training Function</a> </li>
+<li>
+<a href="#train_cox">Clustered Variance for Cox Proportional Hazards model</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#background">Technical Background</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>The Clustered Variance module adjusts standard errors for clustering. For example, replicating a dataset 100 times should not increase the precision of parameter estimates, but performing this procedure with the IID assumption will actually do this. Another example is in economics of education research, it is reasonable to expect that the error terms for children in the same class are not independent. Clustering standard errors can correct for this.</p>
+<p>The MADlib Clustered Variance module includes functions to calculate linear, logistic, and multinomial logistic regression problems.</p>
+<p><a class="anchor" id="train_linregr"></a></p><dl class="section user"><dt>Clustered Variance Linear Regression Training Function</dt><dd></dd></dl>
+<p>The clustered variance linear regression training function has the following syntax. </p><pre class="syntax">
+clustered_variance_linregr ( source_table,
+                             out_table,
+                             dependent_varname,
+                             independent_varname,
+                             clustervar,
+                             grouping_cols
+                           )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the input data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>out_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table contains the following columns. </p><table  class="output">
+<tr>
+<th>coef </th><td>DOUBLE PRECISION[]. Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>std_err </th><td>DOUBLE PRECISION[]. Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>t_stats </th><td>DOUBLE PRECISION[]. Vector of the t-stats of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>DOUBLE PRECISION[]. Vector of the p-values of the coefficients.  </td></tr>
+</table>
+<p>A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by linregr_train function. Please refer to the documentation for linear regression for details.</p>
+<p></p>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>TEXT. An expression to evaluate for the dependent variable. </dd>
+<dt>independent_varname </dt>
+<dd>TEXT. An Expression to evalue for the independent variables. </dd>
+<dt>clustervar </dt>
+<dd>TEXT. A comma-separated list of the columns to use as cluster variables. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>TEXT, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL GROUP BY clause. When this value is null, no grouping is used and a single result model is generated. </dd>
+</dl>
+<p><a class="anchor" id="train_logregr"></a></p><dl class="section user"><dt>Clustered Variance Logistic Regression Training Function</dt><dd></dd></dl>
+<p>The clustered variance logistic regression training function has the following syntax. </p><pre class="syntax">
+clustered_variance_logregr( source_table,
+                            out_table,
+                            dependent_varname,
+                            independent_varname,
+                            clustervar,
+                            grouping_cols,
+                            max_iter,
+                            optimizer,
+                            tolerance,
+                            verbose_mode
+                          )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>TEXT. The name of the table containing the input data. </dd>
+<dt>out_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table has the following columns: </p><table  class="output">
+<tr>
+<th>coef </th><td>Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>std_err </th><td>Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>z_stats </th><td>Vector of the z-stats of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>Vector of the p-values of the coefficients.  </td></tr>
+</table>
+<p>A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by logregr_train function. Please refer to the documentation for logistic regression for details.</p>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>TEXT. An expression to evaluate for the dependent variable. </dd>
+<dt>independent_varname </dt>
+<dd>TEXT. An expression to evaluate for the independent variable. </dd>
+<dt>clustervar </dt>
+<dd>TEXT. A comma-separated list of columns to use as cluster variables. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>TEXT, default: NULL. <em>Not yet implemented. Any non-NULL values are ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL GROUP BY clause. When this value is NULL, no grouping is used and a single result model is generated. </dd>
+<dt>max_iter (optional) </dt>
+<dd>INTEGER, default: 20. The maximum number of iterations that are allowed. </dd>
+<dt>optimizer (optional) </dt>
+<dd>TEXT, default: 'irls'. The name of the optimizer to use: <ul>
+<li>
+'newton' or 'irls': Iteratively reweighted least squares </li>
+<li>
+'cg': conjugate gradient </li>
+<li>
+'igd': incremental gradient descent. </li>
+</ul>
+</dd>
+<dt>tolerance (optional) </dt>
+<dd>FLOAT8, default: 0.0001 The difference between log-likelihood values in successive iterations that should indicate convergence. A zero disables the convergence criterion, so that execution stops after <em>n</em> Iterations have completed. </dd>
+<dt>verbose_mode (optional) </dt>
+<dd>BOOLEAN, default FALSE. Provides verbose_mode output of the results of training. </dd>
+</dl>
+<p><a class="anchor" id="train_mlogregr"></a></p><dl class="section user"><dt>Clustered Variance Multinomial Logistic Regression Training Function</dt><dd></dd></dl>
+<pre class="syntax">
+clustered_variance_mlogregr( source_table,
+                             out_table,
+                             dependent_varname,
+                             independent_varname,
+                             cluster_varname,
+                             ref_category,
+                             grouping_cols,
+                             optimizer_params,
+                             verbose_mode
+                           )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>TEXT. The name of the table containing the input data. </dd>
+<dt>out_table </dt>
+<dd><p class="startdd">TEXT. The name of the table where the regression model will be stored. The output table has the following columns: </p><table  class="output">
+<tr>
+<th>category </th><td>The category.  </td></tr>
+<tr>
+<th>ref_category </th><td>The refererence category used for modeling.  </td></tr>
+<tr>
+<th>coef </th><td>Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>std_err </th><td>Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>z_stats </th><td>Vector of the z-stats of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>Vector of the p-values of the coefficients.  </td></tr>
+</table>
+<p class="enddd">A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by mlogregr_train function. Please refer to the documentation for multinomial logistic regression for details.  </p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>TEXT. An expression to evaluate for the dependent variable. </dd>
+<dt>independent_varname </dt>
+<dd>TEXT. An expression to evaluate for the independent variable. </dd>
+<dt>cluster_varname </dt>
+<dd>TEXT. A comma-separated list of columns to use as cluster variables. </dd>
+<dt>ref_category (optional) </dt>
+<dd>INTEGER. Reference category in the range [0, num_category). </dd>
+<dt>groupingvarng_cols (optional) </dt>
+<dd>TEXT, default: NULL. <em>Not yet implemented. Any non-NULL values are ignored.</em> A comma-separated list of columns to use as grouping variables. </dd>
+<dt>optimizer_params (optional) </dt>
+<dd>TEXT, default: NULL, which uses the default values of optimizer parameters: max_iter=20, optimizer='newton', tolerance=1e-4. It should be a string that contains pairs of 'key=value' separated by commas. </dd>
+<dt>verbose_mode (optional) </dt>
+<dd>BOOLEAN, default FALSE. If TRUE, detailed information is printed when computing logistic regression. </dd>
+</dl>
+<p><a class="anchor" id="train_cox"></a></p><dl class="section user"><dt>Clustered Variance for Cox Proportional Hazards model</dt><dd></dd></dl>
+<p>The clustered robust variance estimator function for the Cox Proportional Hazards model has the following syntax. </p><pre class="syntax">
+clustered_variance_coxph(model_table, output_table, clustervar)
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>model_table </dt>
+<dd>TEXT. The name of the model table, which is exactaly the same as the 'output_table' parameter of <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> function. </dd>
+<dt>output_table </dt>
+<dd>TEXT. The name of the table where the output is saved. It has the following columns: <table  class="output">
+<tr>
+<th>coef </th><td>FLOAT8[]. Vector of the coefficients.  </td></tr>
+<tr>
+<th>loglikelihood </th><td>FLOAT8. Log-likelihood value of the MLE estimate.  </td></tr>
+<tr>
+<th>std_err </th><td>FLOAT8[]. Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>clustervar </th><td>TEXT. A comma-separated list of columns to use as cluster variables.  </td></tr>
+<tr>
+<th>clustered_se </th><td>FLOAT8[]. Vector of the robust standard errors of the coefficients.  </td></tr>
+<tr>
+<th>clustered_z </th><td>FLOAT8[]. Vector of the robust z-stats of the coefficients.  </td></tr>
+<tr>
+<th>clustered_p </th><td>FLOAT8[]. Vector of the robust p-values of the coefficients.  </td></tr>
+<tr>
+<th>hessian </th><td>FLOAT8[]. The Hessian matrix.  </td></tr>
+</table>
+</dd>
+<dt>clustervar </dt>
+<dd>TEXT. A comma-separated list of columns to use as cluster variables. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the clustered variance linear regression function. <pre class="example">
+SELECT madlib.clustered_variance_linregr();
+</pre></li>
+<li>Run the linear regression function and view the results. <pre class="example">
+DROP TABLE IF EXISTS out_table;
+SELECT madlib.clustered_variance_linregr( 'abalone',
+                                          'out_table',
+                                          'rings',
+                                          'ARRAY[1, diameter, length, width]',
+                                          'sex',
+                                          NULL
+                                        );
+SELECT * FROM out_table;
+</pre></li>
+<li>View online help for the clustered variance logistic regression function. <pre class="example">
+SELECT madlib.clustered_variance_logregr();
+</pre></li>
+<li>Run the logistic regression function and view the results. <pre class="example">
+DROP TABLE IF EXISTS out_table;
+SELECT madlib.clustered_variance_logregr( 'abalone',
+                                          'out_table',
+                                          'rings &lt; 10',
+                                          'ARRAY[1, diameter, length, width]',
+                                          'sex'
+                                        );
+SELECT * FROM out_table;
+</pre></li>
+<li>View online help for the clustered variance multinomial logistic regression function. <pre class="example">
+SELECT madlib.clustered_variance_mlogregr();
+</pre></li>
+<li>Run the multinomial logistic regression and view the results. <pre class="example">
+DROP TABLE IF EXISTS out_table;
+SELECT madlib.clustered_variance_mlogregr( 'abalone',
+                                           'out_table',
+                                           'CASE WHEN rings &lt; 10 THEN 1 ELSE 0 END',
+                                           'ARRAY[1, diameter, length, width]',
+                                           'sex',
+                                           0
+                                         );
+SELECT * FROM out_table;
+</pre></li>
+<li>Run the Cox Proportional Hazards regression and compute the clustered robust estimator. <pre class="example">
+DROP TABLE IF EXISTS lung_cl_out;
+DROP TABLE IF EXISTS lung_out;
+DROP TABLE IF EXISTS lung_out_summary;
+SELECT madlib.coxph_train('lung',
+                          'lung_out',
+                          'time',
+                          'array[age, "ph.ecog"]',
+                          'TRUE',
+                          NULL,
+                          NULL);
+SELECT madlib.clustered_variance_coxph('lung_out',
+                                       'lung_cl_out',
+                                       '"ph.karno"');
+SELECT * FROM lung_cl_out;
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<ul>
+<li>Note that we need to manually include an intercept term in the independent variable expression. The NULL value of <em>groupingvar</em> means that there is no grouping in the calculation.</li>
+</ul>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Assume that the data can be separated into \(m\) clusters. Usually this can be done by grouping the data table according to one or multiple columns.</p>
+<p>The estimator has a similar form to the usual sandwich estimator </p><p class="formulaDsp">
+\[ S(\vec{c}) = B(\vec{c}) M(\vec{c}) B(\vec{c}) \]
+</p>
+<p>The bread part is the same as Huber-White sandwich estimator </p><p class="formulaDsp">
+\begin{eqnarray} B(\vec{c}) &amp; = &amp; \left(-\sum_{i=1}^{n} H(y_i, \vec{x}_i, \vec{c})\right)^{-1}\\ &amp; = &amp; \left(-\sum_{i=1}^{n}\frac{\partial^2 l(y_i, \vec{x}_i, \vec{c})}{\partial c_\alpha \partial c_\beta}\right)^{-1} \end{eqnarray}
+</p>
+<p> where \(H\) is the hessian matrix, which is the second derivative of the target function </p><p class="formulaDsp">
+\[ L(\vec{c}) = \sum_{i=1}^n l(y_i, \vec{x}_i, \vec{c})\ . \]
+</p>
+<p>The meat part is different </p><p class="formulaDsp">
+\[ M(\vec{c}) = \bf{A}^T\bf{A} \]
+</p>
+<p> where the \(m\)-th row of \(\bf{A}\) is </p><p class="formulaDsp">
+\[ A_m = \sum_{i\in G_m}\frac{\partial l(y_i,\vec{x}_i,\vec{c})}{\partial \vec{c}} \]
+</p>
+<p> where \(G_m\) is the set of rows that belong to the same cluster.</p>
+<p>We can compute the quantities of \(B\) and \(A\) for each cluster during one scan through the data table in an aggregate function. Then sum over all clusters to the full \(B\) and \(A\) in the outside of the aggregate function. At last, the matrix mulplitications are done in a separate function on the master node.</p>
+<p>When multinomial logistic regression is computed before the multinomial clustered variance calculation, it uses a default reference category of zero and the regression coefficients are included in the output table. The regression coefficients in the output are in the same order as multinomial logistic regression function, which is described below. For a problem with \( K \) dependent variables \( (1, ..., K) \) and \( J \) categories \( (0, ..., J-1) \), let \( {m_{k,j}} \) denote the coefficient for dependent variable \( k \) and category \( j \). The output is \( {m_{k_1, j_0}, m_{k_1, j_1} \ldots m_{k_1, j_{J-1}}, m_{k_2, j_0}, m_{k_2, j_1} \ldots m_{k_K, j_{J-1}}} \). The order is NOT CONSISTENT with the multinomial regression marginal effect calculation with function <em>marginal_mlogregr</em>. This is deliberate because the interfaces of all multinomial regressions (robust, clustered, ...) will be moved to match that used in marginal.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Standard, Robust, and Clustered Standard Errors Computed in R, <a href="http://diffuseprior.wordpress.com/2012/06/15/standard-robust-and-clustered-standard-errors-computed-in-r/">http://diffuseprior.wordpress.com/2012/06/15/standard-robust-and-clustered-standard-errors-computed-in-r/</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="clustered__variance_8sql__in.html">clustered_variance.sql_in</a> documenting the clustered variance SQL functions.</dd></dl>
+<p>File <a class="el" href="clustered__variance__coxph_8sql__in.html" title="SQL functions for clustered robust cox proportional hazards regression. ">clustered_variance_coxph.sql_in</a> documenting the clustered variance for Cox proportional hazards SQL functions.</p>
+</div><!-- contents -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods for clustering data </p>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Pearson's Correlation<div class="ingroups"><a class="el" href="group__grp__stats.html">Statistics</a> &raquo; <a class="el" href="group__grp__desc__stats.html">Descriptive Statistics</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#usage">Correlation Function</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#seealso">See Also</a> </li>
+</ul>
+</div><p>A correlation function is the degree and direction of association of two variables&mdash;how well one random variable can be predicted from the other. The coefficient of correlation varies from -1 to 1. A coefficient of 1 implies perfect correlation, 0 means no correlation, and -1 means perfect anti-correlation.</p>
+<p>This function provides a cross-correlation matrix for all pairs of numeric columns in a <em>source_table</em>. A correlation matrix describes correlation among \( M \) variables. It is a square symmetrical \( M \)x \(M \) matrix with the \( (ij) \)th element equal to the correlation coefficient between the \(i\)th and the \(j\)th variable. The diagonal elements (correlations of variables with themselves) are always equal to 1.0.</p>
+<p><a class="anchor" id="usage"></a></p><dl class="section user"><dt>Correlation Function</dt><dd></dd></dl>
+<p>The correlation function has the following syntax: </p><pre class="syntax">
+correlation( source_table,
+             output_table,
+             target_cols,
+             verbose
+           )
+</pre><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the data containing the input data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the table where the cross-correlation matrix will be saved. The output is a table with N+2 columns and N rows, where N is the number of target columns. It contains the following columns. </p><table  class="output">
+<tr>
+<th>column_position </th><td>The first column is a sequential counter indicating the position of the variable in the '<em>output_table</em>'.  </td></tr>
+<tr>
+<th>variable </th><td>The second column contains the row-header for the variables.  </td></tr>
+<tr>
+<th>&lt;...&gt; </th><td>The remainder of the table is the NxN correlation matrix for the pairs of numeric 'source_table' columns.  </td></tr>
+</table>
+<p>The output table is arranged as a lower-triangular matrix with the upper triangle set to NULL and the diagonal elements set to 1.0. To obtain the result from the '<em>output_table</em>' in this matrix format ensure to order the elements using the '<em>column_position</em>', as shown in the example below. </p><pre class="example">
+SELECT * FROM output_table ORDER BY column_position;
+</pre><p>In addition to output table, a summary table named &lt;output_table&gt;_summary is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method</th><td>'correlation' </td></tr>
+<tr>
+<th>source_table</th><td>VARCHAR. The data source table name. </td></tr>
+<tr>
+<th>output_table</th><td>VARCHAR. The output table name. </td></tr>
+<tr>
+<th>column_names</th><td>VARCHAR. Column names used for correlation computation, comma-separated string. </td></tr>
+<tr>
+<th>mean_vector</th><td>FLOAT8[]. Vector where each is the mean of a column. </td></tr>
+<tr>
+<th>total_rows_processed </th><td>BIGINT. Total numbers of rows processed.  </td></tr>
+<tr>
+<th>total_rows_skipped </th><td>BIGINT. Total numbers of rows skipped due to missing values.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>target_cols (optional) </dt>
+<dd><p class="startdd">TEXT, default: '*'. A comma-separated list of the columns to correlate. If NULL or <code>'*'</code>, results are produced for all numeric columns.</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd><p class="startdd">BOOLEAN, default: FALSE. Print verbose debugging information if TRUE.</p>
+<p class="enddd"></p>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the correlation function. <pre class="example">
+SELECT madlib.correlation();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+DROP TABLE IF EXISTS example_data;
+CREATE TABLE example_data(
+    id SERIAL, outlook TEXT,
+    temperature FLOAT8, humidity FLOAT8,
+    windy TEXT, class TEXT);
+INSERT INTO example_data VALUES
+(1, 'sunny', 85, 85, 'false', 'Dont Play'),
+(2, 'sunny', 80, 90, 'true', 'Dont Play'),
+(3, 'overcast', 83, 78, 'false', 'Play'),
+(4, 'rain', 70, 96, 'false', 'Play'),
+(5, 'rain', 68, 80, 'false', 'Play'),
+(6, 'rain', 65, 70, 'true', 'Dont Play'),
+(7, 'overcast', 64, 65, 'true', 'Play'),
+(8, 'sunny', 72, 95, 'false', 'Dont Play'),
+(9, 'sunny', 69, 70, 'false', 'Play'),
+(10, 'rain', 75, 80, 'false', 'Play'),
+(11, 'sunny', 75, 70, 'true', 'Play'),
+(12, 'overcast', 72, 90, 'true', 'Play'),
+(13, 'overcast', 81, 75, 'false', 'Play'),
+(14, 'rain', 71, 80, 'true', 'Dont Play'),
+(15, NULL, 100, 100, 'true', NULL),
+(16, NULL, 110, 100, 'true', NULL);
+</pre></li>
+<li>Run the <a class="el" href="correlation_8sql__in.html#a17cb16646fa7ebdf7ce7dd8c58349ce5">correlation()</a> function on the data set. <pre class="example">
+-- Correlate all numeric columns
+SELECT madlib.correlation( 'example_data',
+                           'example_data_output'
+                         );
+-- Setting target_cols to NULL or '*' also correlates all numeric columns
+SELECT madlib.correlation( 'example_data',
+                           'example_data_output',
+                           '*'
+                         );
+-- Correlate only the temperature and humidity columns
+SELECT madlib.correlation( 'example_data',
+                           'example_data_output',
+                           'temperature, humidity'
+                         );
+</pre></li>
+<li>View the correlation matrix. <pre class="example">
+SELECT * FROM example_data_output ORDER BY column_position;
+</pre> Result: <pre class="result">
+ column_position |  variable   |    temperature    | humidity
+-----------------+-------------+-------------------+----------
+               1 | temperature |               1.0 |
+               2 | humidity    | 0.616876934548786 |      1.0
+(2 rows)
+</pre></li>
+</ol>
+<dl class="section user"><dt>Notes</dt><dd>Current implementation ignores a row that contains NULL entirely. This means any correlation in such a row (with NULLs) does not contribute to the final answer.</dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="correlation_8sql__in.html" title="SQL functions for correlation computation. ">correlation.sql_in</a> documenting the SQL functions</p>
+<p><a class="el" href="group__grp__summary.html">Summary</a> for general descriptive statistics for a table </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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+<title>MADlib: CountMin (Cormode-Muthukrishnan)</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
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+  <div class="headertitle">
+<div class="title">CountMin (Cormode-Muthukrishnan)<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a> &raquo; <a class="el" href="group__grp__sketches.html">Cardinality Estimators</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>This module implements Cormode-Muthukrishnan <em>CountMin</em> sketches on integer values, implemented as a user-defined aggregate. It also provides scalar functions over the sketches to produce approximate counts, order statistics, and histograms.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Syntax</dt><dd><ul>
+<li>Get a sketch of a selected column specified by <em>col_name</em>. <pre class="syntax">
+cmsketch( col_name )
+</pre></li>
+<li>Get the number of rows where <em>col_name = p</em>, computed from the sketch obtained from <code>cmsketch</code>. <pre class="syntax">
+cmsketch_count( cmsketch,
+                p
+              )
+</pre></li>
+<li>Get the number of rows where <em>col_name</em> is between <em>m</em> and <em>n</em> inclusive. <pre class="syntax">
+cmsketch_rangecount( cmsketch,
+                     m,
+                     n
+                   )
+</pre></li>
+<li>Get the <em>k</em>th percentile of <em>col_name</em> where <em>count</em> specifies number of rows. <em>k</em> should be an integer between 1 to 99. <pre class="syntax">
+cmsketch_centile( cmsketch,
+                  k,
+                  count
+                )
+</pre></li>
+<li>Get the median of col_name where <em>count</em> specifies number of rows. This is equivalent to <code><a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">cmsketch_centile</a>(<em>cmsketch</em>,50,<em>count</em>)</code>. <pre class="syntax">
+cmsketch_median( cmsketch,
+                 count
+               )
+</pre></li>
+<li>Get an n-bucket histogram for values between min and max for the column where each bucket has approximately the same width. The output is a text string containing triples {lo, hi, count} representing the buckets; counts are approximate. <pre class="syntax">
+cmsketch_width_histogram( cmsketch,
+                          min,
+                          max,
+                          n
+                        )
+</pre></li>
+<li>Get an n-bucket histogram for the column where each bucket has approximately the same count. The output is a text string containing triples {lo, hi, count} representing the buckets; counts are approximate. Note that an equi-depth histogram is equivalent to a spanning set of equi-spaced centiles. <pre class="syntax">
+cmsketch_depth_histogram( cmsketch,
+                          n
+                        )
+</pre></li>
+</ul>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Generate some data. <pre class="example">
+CREATE TABLE data(class INT, a1 INT);
+INSERT INTO data SELECT 1,1 FROM generate_series(1,10000);
+INSERT INTO data SELECT 1,2 FROM generate_series(1,15000);
+INSERT INTO data SELECT 1,3 FROM generate_series(1,10000);
+INSERT INTO data SELECT 2,5 FROM generate_series(1,1000);
+INSERT INTO data SELECT 2,6 FROM generate_series(1,1000);
+</pre></li>
+<li>Count number of rows where a1 = 2 in each class. <pre class="example">
+SELECT class,
+       cmsketch_count(
+                       cmsketch( a1 ),
+                       2
+                      )
+FROM data GROUP BY data.class;
+</pre> Result: <pre class="result">
+ class | cmsketch_count
+&#160;------+----------------
+     2 |              0
+     1 |          15000
+(2 rows)
+</pre></li>
+<li>Count number of rows where a1 is between 3 and 6. <pre class="example">
+SELECT class,
+       cmsketch_rangecount(
+                            cmsketch(a1),
+                            3,
+                            6
+                          )
+FROM data GROUP BY data.class;
+</pre> Result: <pre class="result">
+ class | cmsketch_rangecount
+&#160;------+---------------------
+     2 |                2000
+     1 |               10000
+(2 rows)
+</pre></li>
+<li>Compute the 90th percentile of all of a1. <pre class="example">
+SELECT cmsketch_centile(
+                         cmsketch( a1 ),
+                         90,
+                         count(*)
+                       )
+FROM data;
+</pre> Result: <pre class="result">
+ cmsketch_centile
+&#160;-----------------
+                3
+(1 row)
+</pre></li>
+<li>Produce an equi-width histogram with 2 bins between 0 and 10. <pre class="example">
+SELECT cmsketch_width_histogram(
+                                 cmsketch( a1 ),
+                                 0,
+                                 10,
+                                 2
+                               )
+FROM data;
+</pre> Result: <pre class="result">
+      cmsketch_width_histogram
+&#160;-----------------------------------
+ [[0L, 4L, 35000], [5L, 10L, 2000]]
+(1 row)
+</pre></li>
+<li>Produce an equi-depth histogram of a1 with 2 bins of approximately equal depth. <pre class="example">
+SELECT cmsketch_depth_histogram(
+                                 cmsketch( a1 ),
+                                 2
+                               )
+FROM data;
+</pre> Result: <pre class="result">
+                       cmsketch_depth_histogram
+&#160;----------------------------------------------------------------------
+ [[-9223372036854775807L, 1, 10000], [2, 9223372036854775807L, 27000]]
+(1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. LATIN 2004, J. Algorithm 55(1): 58-75 (2005) . <a href="http://dimacs.rutgers.edu/~graham/pubs/html/CormodeMuthukrishnan04CMLatin.html">http://dimacs.rutgers.edu/~graham/pubs/html/CormodeMuthukrishnan04CMLatin.html</a></p>
+<p>[2] G. Cormode. Encyclopedia entry on 'Count-Min Sketch'. In L. Liu and M. T. Ozsu, editors, Encyclopedia of Database Systems, pages 511-516. Springer, 2009. <a href="http://dimacs.rutgers.edu/~graham/pubs/html/Cormode09b.html">http://dimacs.rutgers.edu/~graham/pubs/html/Cormode09b.html</a></p>
+<p><a class="anchor" id="related"></a>File <a class="el" href="sketch_8sql__in.html" title="SQL functions for sketch-based approximations of descriptive statistics. ">sketch.sql_in</a> documenting the SQL functions.</p>
+<p>Module <a class="el" href="group__grp__quantile.html">Quantile</a> for a different implementation of quantile function. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[33/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">elastic_net.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for elastic net regularization.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
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+<tr class="separator:a735038a5090c112505c740a90a203e83"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:add7e2592fb0bd9f2eb1d8b176c80b5d0"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a6f6ee3def49b5e9a1d3375fe41427dc0"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a3578608204ac9b2d3442ff42977f632b"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:aaddbfdfea4207d38ab7e2cd3db876bdc"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a96db4ff4ba3ea363fafbf6c036c19fae"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a93bfe398c91709e7d55ed94d18c6ac23"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a2284166bacd4b45fb34d50b2d7aec4d4"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:af83bb77c4eb5c9f5750f35d56fd39117"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a69a71f6e1f8fb3f7d8ad8b987bd432db"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aba9d8b5ca783ad4c3d551fd7b2797ade"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="elastic__net_8sql__in.html#aba9d8b5ca783ad4c3d551fd7b2797ade">__binomial_fista_state_diff</a> (float8[] state1, float8[] state2)</td></tr>
+<tr class="separator:aba9d8b5ca783ad4c3d551fd7b2797ade"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a1e0cc178e025d8eebb0637d97a02f624"><td class="memItemLeft" align="right" valign="top">__elastic_net_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="elastic__net_8sql__in.html#a1e0cc178e025d8eebb0637d97a02f624">__binomial_fista_result</a> (float8[] in_state)</td></tr>
+<tr class="separator:a1e0cc178e025d8eebb0637d97a02f624"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>July 2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to elastic net, see the module description <a class="el" href="group__grp__elasticnet.html">Elastic Net Regularization</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="af83bb77c4eb5c9f5750f35d56fd39117"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __binomial_fista_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2284166bacd4b45fb34d50b2d7aec4d4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __binomial_fista_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1e0cc178e025d8eebb0637d97a02f624"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__elastic_net_result __binomial_fista_result </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>in_state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aba9d8b5ca783ad4c3d551fd7b2797ade"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __binomial_fista_state_diff </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a69a71f6e1f8fb3f7d8ad8b987bd432db"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __binomial_fista_step </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a43f125953d105e8ad2243f7c722cf753"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __binomial_fista_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>pre_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>dimension</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>total_rows</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>max_stepsize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>eta</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>use_active_set</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>is_active</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>random_stepsize</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a39c4af5547dc7f28c189fe5ef8d09ba5"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __binomial_igd_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae4aed277de996eab9023335413fe9e28"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __binomial_igd_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a93bfe398c91709e7d55ed94d18c6ac23"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__elastic_net_result __binomial_igd_result </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>in_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>feature_sq</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2968538b152e7f38e71bcff8e55b768e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __binomial_igd_state_diff </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a791a9701c20c65d1e86a1d5c3462b99d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __binomial_igd_step </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac1ec74fff164d85cae14969bb0890e11"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __binomial_igd_step_single_seg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa0f3feb438f90eef90ddfc9ad4f3e33e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __binomial_igd_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>pre_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>dimension</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>stepsize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>total_rows</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>xmean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>ymean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>step_decay</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a465ca3aeaca43ab1294f82146eb2fc31"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __elastic_net_binomial_loglikelihood </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coefficients</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>intercept</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9cf4bc6bc719c054cf99436965bbab0c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __gaussian_fista_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a35dad39bc1dce3573be2c9cc3f5458b2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __gaussian_fista_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9f5ed35bd3467280e7d68604046ccebd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__elastic_net_result __gaussian_fista_result </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>in_state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab9b58c238fa59cd8b38a07d3cbfef514"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __gaussian_fista_state_diff </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4566cf3ca4587153ab7b57fd741582fe"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __gaussian_fista_step </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a87bf3c26c925cfe8d214cc4f8c784613"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __gaussian_fista_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>pre_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>dimension</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>total_rows</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>max_stepsize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>eta</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>use_active_set</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>is_active</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>random_stepsize</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4c5b88fb94d8035e988178a2b67ffa43"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __gaussian_igd_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aba6849a59e2178eb0629d3bfc5abe5da"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __gaussian_igd_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a407f4a6063a4eb931a39b23f52d675f1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__elastic_net_result __gaussian_igd_result </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>in_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>feature_sq</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>threshold</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a244ef9698a82bbd2d28cdce326f8e514"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __gaussian_igd_state_diff </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a493a8cfa6faf0264c7cf9aa80dc9ffb2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __gaussian_igd_step </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abd46990f9fd7d216bc7f9e8115f10408"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __gaussian_igd_step_single_seg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad8a31dceade418034918a37c34101c18"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __gaussian_igd_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>pre_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>dimension</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>stepsize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>total_rows</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>xmean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>ymean</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>step_decay</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa78cde79f1f2caa7c5b38f933001d793"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean elastic_net_binomial_predict </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coefficients</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>intercept</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coefficients</td><td>Logistic fitting coefficients </td></tr>
+    <tr><td class="paramname">intercept</td><td>Logistic fitting intercept </td></tr>
+    <tr><td class="paramname">ind_var</td><td>Features (independent variables)</td></tr>
+  </table>
+  </dd>
+</dl>
+<p>returns a boolean value </p>
+
+</div>
+</div>
+<a class="anchor" id="a308718fd5234bc1007b971a639aadf71"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 elastic_net_binomial_prob </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coefficients</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>intercept</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coefficients</td><td>Logistic fitting coefficients </td></tr>
+    <tr><td class="paramname">intercept</td><td>Logistic fitting intercept </td></tr>
+    <tr><td class="paramname">ind_var</td><td>Features (independent variables)</td></tr>
+  </table>
+  </dd>
+</dl>
+<p>returns a double value, which is the probability of this data point being True class </p>
+
+</div>
+</div>
+<a class="anchor" id="a96db4ff4ba3ea363fafbf6c036c19fae"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 elastic_net_gaussian_predict </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coefficients</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>intercept</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coefficients</td><td>Linear fitting coefficients </td></tr>
+    <tr><td class="paramname">intercept</td><td>Linear fitting intercept </td></tr>
+    <tr><td class="paramname">ind_var</td><td>Features (independent variables)</td></tr>
+  </table>
+  </dd>
+</dl>
+<p>returns a double value </p>
+
+</div>
+</div>
+<a class="anchor" id="a3578608204ac9b2d3442ff42977f632b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void elastic_net_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_model</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_new_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_predict</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tbl_model</td><td>The result from elastic_net_train </td></tr>
+    <tr><td class="paramname">tbl_new_source</td><td>Data table </td></tr>
+    <tr><td class="paramname">col_id</td><td>Unique ID associated with each row </td></tr>
+    <tr><td class="paramname">tbl_predict</td><td>Prediction result </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="aaddbfdfea4207d38ab7e2cd3db876bdc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 elastic_net_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regress_family</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coefficients</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>intercept</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>ind_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">regress_family</td><td>model family </td></tr>
+    <tr><td class="paramname">coefficients</td><td>The fitting coefficients </td></tr>
+    <tr><td class="paramname">intercept</td><td>The fitting intercept </td></tr>
+    <tr><td class="paramname">ind_var</td><td>Features (independent variables) </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>float8</dd></dl>
+<p>Note: Please use function <a class="el" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae" title="Prediction for linear models use learned coefficients for a given example. ">elastic_net_gaussian_predict()</a> or <a class="el" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793" title="Prediction for logistic models use learned coefficients for a given example. ">elastic_net_binomial_predict()</a> instead if you could. This function is not supported in HAWQ and may be deprecated in the future, as users are confused between this function and the table output function with the same name.</p>
+<p>When regress_family is 'binomial' or 'logistic', this function returns 1 for True and 0 for False </p>
+
+</div>
+</div>
+<a class="anchor" id="a735038a5090c112505c740a90a203e83"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void elastic_net_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regress_family</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>standardize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>excluded</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tbl_source</td><td>Name of data source table </td></tr>
+    <tr><td class="paramname">tbl_result</td><td>Name of the table to store the results </td></tr>
+    <tr><td class="paramname">col_ind_var</td><td>Name of independent variable column, independent variable is an array </td></tr>
+    <tr><td class="paramname">col_dep_var</td><td>Name of dependent variable column </td></tr>
+    <tr><td class="paramname">regress_family</td><td>Response type (gaussian or binomial) </td></tr>
+    <tr><td class="paramname">alpha</td><td>The elastic net parameter, [0, 1] </td></tr>
+    <tr><td class="paramname">lambda_value</td><td>The regularization parameter </td></tr>
+    <tr><td class="paramname">standardize</td><td>Whether to normalize the variables (default True) </td></tr>
+    <tr><td class="paramname">grouping_col</td><td>List of columns on which to apply grouping (currently only a placeholder) </td></tr>
+    <tr><td class="paramname">optimizer</td><td>The optimization algorithm, 'fista' or 'igd'. Default is 'fista' </td></tr>
+    <tr><td class="paramname">optimizer_params</td><td>Parameters of the above optimizer, the format is 'arg = value, ...'. Default is NULL </td></tr>
+    <tr><td class="paramname">excluded</td><td>Which columns to exclude? Default is NULL (applicable only if col_ind_var is set as * or a column of array, column names as 'col1, col2, ...' if col_ind_var is '*'; element indices as '1,2,3, ...' if col_ind_var is a column of array) </td></tr>
+    <tr><td class="paramname">max_iter</td><td>Maximum number of iterations to run the algorithm (default value of 10000) </td></tr>
+    <tr><td class="paramname">tolerance</td><td>Iteration stopping criteria. Default is 1e-6 </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="a148de1ad5e1e60b9df3d3af590c06579"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void elastic_net_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regress_family</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>standardization</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>excluded</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a63c8ed415005cc446a59bdc47a09791d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void elastic_net_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regress_family</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>standardization</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>excluded</em>&#160;</td>
+        </tr>
+        <tr>
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+</div>
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+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void elastic_net_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_result</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regress_family</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>lambda_value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>standardization</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
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+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7e698f9a0c7a1aa43f4bf9e169dc8b35"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void elastic_net_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tbl_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regress_family</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>alpha</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey">

<TRUNCATED>

[42/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/decision__tree_8sql__in.html
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diff --git a/docs/latest/decision__tree_8sql__in.html b/docs/latest/decision__tree_8sql__in.html
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+<!-- HTML header for doxygen 1.8.4-->
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+<title>MADlib: decision_tree.sql_in File Reference</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
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+$(document).ready(function(){initNavTree('decision__tree_8sql__in.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
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+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">decision_tree.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:ad5e9443cce27ebc70d7b2c2381d17e06"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#ad5e9443cce27ebc70d7b2c2381d17e06">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols, text weights, integer max_depth, integer min_split, integer min_bucket, integer n_bins, text pruning_params, text surrogate_params, boolean verbose_mode)</td></tr>
+<tr class="memdesc:ad5e9443cce27ebc70d7b2c2381d17e06"><td class="mdescLeft">&#160;</td><td class="mdescRight">Training of decision tree.  <a href="#ad5e9443cce27ebc70d7b2c2381d17e06">More...</a><br /></td></tr>
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+<tr class="memdesc:ae371963e135c40d8be5b2e342ab3c252"><td class="mdescLeft">&#160;</td><td class="mdescRight">Use decision tree model to make predictions.  <a href="#ae371963e135c40d8be5b2e342ab3c252">More...</a><br /></td></tr>
+<tr class="separator:ae371963e135c40d8be5b2e342ab3c252"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:abb6f3ecdd2fdcb7661ee10a65419066e"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a744d4c5564a3ba18c9774fa040f32d18"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="memdesc:a6ba706727adf88f8c07cfe09f71732bb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display decision tree in dot or text format.  <a href="#a6ba706727adf88f8c07cfe09f71732bb">More...</a><br /></td></tr>
+<tr class="separator:a6ba706727adf88f8c07cfe09f71732bb"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a97c730e746e1a8ea658efe433c9d1be5"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a97c730e746e1a8ea658efe433c9d1be5">tree_surr_display</a> ()</td></tr>
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+<tr class="memdesc:a3c51edcc0fe3de029dc39a3f0306155d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Display decision tree in dot or text format.  <a href="#a3c51edcc0fe3de029dc39a3f0306155d">More...</a><br /></td></tr>
+<tr class="separator:a3c51edcc0fe3de029dc39a3f0306155d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a5168f7f604b0ad1c9e581f0e3f52698e"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a5168f7f604b0ad1c9e581f0e3f52698e">tree_display</a> (text model_table)</td></tr>
+<tr class="separator:a5168f7f604b0ad1c9e581f0e3f52698e"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:adc9d02213b4d906976805a9681ea13fb"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#adc9d02213b4d906976805a9681ea13fb">tree_display</a> ()</td></tr>
+<tr class="separator:adc9d02213b4d906976805a9681ea13fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a0dd7cec9ec3db8482ae60a3f274c3488"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a0dd7cec9ec3db8482ae60a3f274c3488">_display_decision_tree</a> (bytea8 tree, text[] cat_features, text[] con_features, text[] cat_levels_in_text, integer[] cat_n_levels, text[] dependent_levels, text id_prefix)</td></tr>
+<tr class="separator:a0dd7cec9ec3db8482ae60a3f274c3488"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a071ad5dd0cf985d9e5258968fa8c214d"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a071ad5dd0cf985d9e5258968fa8c214d">_display_decision_tree_surrogate</a> (bytea8 tree, text[] cat_features, text[] con_features, text[] cat_levels_in_text, integer[] cat_n_levels)</td></tr>
+<tr class="separator:a071ad5dd0cf985d9e5258968fa8c214d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:af030ab954fd282bb08e13134e087fafb"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#af030ab954fd282bb08e13134e087fafb">_display_text_decision_tree</a> (bytea8 tree, text[] cat_features, text[] con_features, text[] cat_levels_in_text, integer[] cat_n_levels, text[] dependent_levels)</td></tr>
+<tr class="separator:af030ab954fd282bb08e13134e087fafb"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a658412807f7b28e9e367a1421cb8ad96"><td class="memItemLeft" align="right" valign="top">set&lt; _cat_levels_type &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a658412807f7b28e9e367a1421cb8ad96">_gen_cat_levels_set</a> (text[] grp_keys, integer[] cat_n_levels, integer n_cat, text[] cat_sorted_origin)</td></tr>
+<tr class="separator:a658412807f7b28e9e367a1421cb8ad96"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a29db1b54ffa60f40bb533c1229332789"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:ab561b3e2a8a66b6ec763d44ea042aeca"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:add62455af9b9f9a075df8221269a4872"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#add62455af9b9f9a075df8221269a4872">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols, text weights, integer max_depth, integer min_split, integer min_bucket, integer n_bins)</td></tr>
+<tr class="separator:add62455af9b9f9a075df8221269a4872"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a635b82bc9c1fcd63e5a46546d3e2ba70"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a635b82bc9c1fcd63e5a46546d3e2ba70">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols, text weights, integer max_depth, integer min_split, integer min_bucket)</td></tr>
+<tr class="separator:a635b82bc9c1fcd63e5a46546d3e2ba70"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a93591ca5193e5ac9f8c3ac52e1dfb1ba"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a93591ca5193e5ac9f8c3ac52e1dfb1ba">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols, text weights, integer max_depth, integer min_split)</td></tr>
+<tr class="separator:a93591ca5193e5ac9f8c3ac52e1dfb1ba"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a558361a337c93731b856523e20f20e72"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a558361a337c93731b856523e20f20e72">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols, text weights, integer max_depth)</td></tr>
+<tr class="separator:a558361a337c93731b856523e20f20e72"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a10b882e4d6417023ae6b92f79e88ac29"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a10b882e4d6417023ae6b92f79e88ac29">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols, text weights)</td></tr>
+<tr class="separator:a10b882e4d6417023ae6b92f79e88ac29"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ac49c038b5668aeb901d22a5a81c8f338"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#ac49c038b5668aeb901d22a5a81c8f338">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion, text grouping_cols)</td></tr>
+<tr class="separator:ac49c038b5668aeb901d22a5a81c8f338"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a9cfac8d89b1a13d22a20eb79d4d7e7ce"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="decision__tree_8sql__in.html#a9cfac8d89b1a13d22a20eb79d4d7e7ce">tree_train</a> (text training_table_name, text output_table_name, text id_col_name, text dependent_variable, text list_of_features, text list_of_features_to_exclude, text split_criterion)</td></tr>
+<tr class="separator:a9cfac8d89b1a13d22a20eb79d4d7e7ce"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:af70d0b6bba29f94ebd49acc48b4f9bb1"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a639431da2553fb86673e82d62cde4458"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a31a5134efecabf1864017fe614b45911"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __build_tree </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>is_classification</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>dep_is_bool</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>boolean_cats</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_split</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_bucket</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_bins</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cp_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>max_n_surr</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>msg_level</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>k</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a71a536f94609625aabceaaa8a9030ac6"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __tree_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pred_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>use_existing_tables</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>k</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a93896cc01cd324ff6dec20d3791b8500"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 _compute_leaf_stats </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aec006330e98f6c322f30c05cb07518c0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _compute_leaf_stats_merge </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aadad9dcc5dd62e335305e99dc1a1b68e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _compute_leaf_stats_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>response</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>weight</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_levels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>con_splits</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>n_response_labels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>weights_as_rows</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a52020e1216a5ca6b255aa9fefa5ed0f1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 _compute_surr_stats </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af17fb0c819380de1c090731dba54ec40"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _compute_surr_stats_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree_state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_levels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>con_splits</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>dup_count</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0dd7cec9ec3db8482ae60a3f274c3488"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text _display_decision_tree </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_levels_in_text</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_n_levels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>dependent_levels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_prefix</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a071ad5dd0cf985d9e5258968fa8c214d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text _display_decision_tree_surrogate </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_levels_in_text</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_n_levels</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="af030ab954fd282bb08e13134e087fafb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text _display_text_decision_tree </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_levels_in_text</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_n_levels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>dependent_levels</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aaabc1efd8cc211886572692062f74732"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 _dst_compute_con_splits </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1ce0c3bc05f5f6657ce61f2890b27a33"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _dst_compute_con_splits_final </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac6533f633d95f6c41a628a751b356ce1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _dst_compute_con_splits_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>con_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_per_seg</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>num_splits</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a71e8f6ae4227a3cd3734d8c8f7ef54fe"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 _dst_compute_entropy </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac81d2cf47356702c5bc30790f505c05f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 _dst_compute_entropy_final </td>
+          <td>(</td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a992c692244ec9f1dd38557bc8cf5c2ef"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer [] _dst_compute_entropy_merge </td>
+          <td>(</td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a88589c4a865221cd97af52284e222195"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer [] _dst_compute_entropy_transition </td>
+          <td>(</td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>encoded_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>num_dep_var</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a164580846362686fe4ab55db7c4fa52a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">_tree_result_type _dt_apply </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>con_splits</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>min_split</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>min_bucket</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>subsample</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>num_random_features</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a05665f86bc28346cdf12e4b4d05aa3d9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _dt_surr_apply </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>con_splits</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a658412807f7b28e9e367a1421cb8ad96"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;_cat_levels_type&gt; _gen_cat_levels_set </td>
+          <td>(</td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>grp_keys</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_n_levels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_cat</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_sorted_origin</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad63f7b9a78190275509744f9517dd370"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 _initialize_decision_tree </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>is_regression_tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>impurity_function</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>num_response_labels</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">smallint&#160;</td>
+          <td class="paramname"><em>max_n_surr</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a247bb7f7065a506279627028b49af5e9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer [] _map_catlevel_to_int </td>
+          <td>(</td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_values_in_text</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cat_levels_in_text</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_n_levels</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a25ef01785744c7cf005e6b517602b497"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] _predict_dt_prob </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>con_features</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a59b5f555e1f8d7c57830c9cfe3401615"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 _predict_dt_response </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer[]&#160;</td>
+          <td class="paramname"><em>cat_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>con_features</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a32e1056ceed38cc5da2e022e455b7de6"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">_flattened_tree _print_decision_tree </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>tree</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3c51edcc0fe3de029dc39a3f0306155d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar tree_display </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>dot_format</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_model</td><td>Name of the table containing the decision tree model </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="a5168f7f604b0ad1c9e581f0e3f52698e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar tree_display </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adc9d02213b4d906976805a9681ea13fb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar tree_display </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae371963e135c40d8be5b2e342ab3c252"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pred_type</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">model</td><td>Name of the table containing the decision tree model </td></tr>
+    <tr><td class="paramname">source</td><td>Name of table containing prediction data </td></tr>
+    <tr><td class="paramname">output</td><td>Name of table to output prediction results </td></tr>
+    <tr><td class="paramname">pred_type</td><td>OPTIONAL (Default = 'response'). For regression trees, 'response', implies output is the predicted value. For classification trees, this can be 'response', giving the classification prediction as output, or ‘prob’, giving the class probabilities as output (for two classes, only a single probability value is output that corresponds to the first class when the two classes are sorted by name; in case of more than two classes, an array of class probabilities (a probability of each class) is output).</td></tr>
+  </table>
+  </dd>
+</dl>
+<p>See <a class="el" href="group__grp__decision__tree.html">Decision Tree</a> for more details. </p>
+
+</div>
+</div>
+<a class="anchor" id="ad8afbe21c3b73daff7322c3fe906a804"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abb6f3ecdd2fdcb7661ee10a65419066e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text tree_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a744d4c5564a3ba18c9774fa040f32d18"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text tree_predict </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6ba706727adf88f8c07cfe09f71732bb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar tree_surr_display </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">tree_model</td><td>Name of the table containing the decision tree model </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="a97c730e746e1a8ea658efe433c9d1be5"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar tree_surr_display </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad5e9443cce27ebc70d7b2c2381d17e06"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features_to_exclude</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_split</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_bucket</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_bins</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pruning_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>surrogate_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>verbose_mode</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">split_criterion</td><td>Various options to compute the feature to split a node. Available options are 'gini', 'cross-entropy', and 'misclassification'. The "cart" algorithm provides an additional option of 'mse'. </td></tr>
+    <tr><td class="paramname">training_table_name</td><td>Name of the table containing data. </td></tr>
+    <tr><td class="paramname">output_table_name</td><td>Name of the table to output the model. </td></tr>
+    <tr><td class="paramname">id_col_name</td><td>Name of column containing the id information in training data. </td></tr>
+    <tr><td class="paramname">dependent_variable</td><td>Name of the column that contains the output for training. Boolean, integer and text are considered classification outputs, while float values are considered regression outputs. </td></tr>
+    <tr><td class="paramname">list_of_features</td><td>List of column names (comma-separated string) to use as predictors. Can also be a ‘*’ implying all columns are to be used as predictors (except the ones included in the next argument). Boolean, integer, and text columns are considered categorical columns. </td></tr>
+    <tr><td class="paramname">list_of_features_to_exclude</td><td>OPTIONAL. List of column names (comma-separated string) to exlude from the predictors list. </td></tr>
+    <tr><td class="paramname">grouping_cols</td><td>OPTIONAL. List of column names (comma-separated string) to group the data by. This will lead to creating multiple decision trees, one for each group. </td></tr>
+    <tr><td class="paramname">weights</td><td>OPTIONAL. Column name containing weights for each observation. </td></tr>
+    <tr><td class="paramname">max_depth</td><td>OPTIONAL (Default = 10). Set the maximum depth of any node of the final tree, with the root node counted as depth 0. </td></tr>
+    <tr><td class="paramname">min_split</td><td>OPTIONAL (Default = 20). Minimum number of observations that must exist in a node for a split to be attempted. </td></tr>
+    <tr><td class="paramname">min_bucket</td><td>OPTIONAL (Default = minsplit/3). Minimum number of observations in any terminal node. If only one of minbucket or minsplit is specified, minsplit is set to minbucket*3 or minbucket to minsplit/3, as appropriate. </td></tr>
+    <tr><td class="paramname">n_bins</td><td>optional (default = 100) number of bins to use during binning. continuous-valued features are binned into discrete bins (per the quartile values) to compute split bound- aries. this global parameter is used to compute the resolution of the bins. higher number of bins will lead to higher processing time. </td></tr>
+    <tr><td class="paramname">pruning_params</td><td>(default = 'cp=0.01') pruning parameter string containing key-value pairs. the keys can be: cp (default = 0.01) a complexity parameter that determines that a split is attempted only if it decreases the overall lack of fit by a factor of ‘cp’. n_folds (default = 0) number of cross-validation folds </td></tr>
+    <tr><td class="paramname">verbose_mode</td><td>optional (default = false) prints status information on the splits performed and any other information useful for debugging.</td></tr>
+  </table>
+  </dd>
+</dl>
+<p>see <a class="el" href="group__grp__decision__tree.html">Decision Tree</a> for more details. </p>
+
+</div>
+</div>
+<a class="anchor" id="a73a43da31b61ee81d5208860af2d3ab1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text tree_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a530812f8c3662705e24f8bf24d4eb3a5"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text tree_train </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a29db1b54ffa60f40bb533c1229332789"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features_to_exclude</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_split</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_bucket</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_bins</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pruning_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>surrogate_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab561b3e2a8a66b6ec763d44ea042aeca"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features_to_exclude</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_split</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_bucket</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_bins</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>pruning_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="add62455af9b9f9a075df8221269a4872"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features_to_exclude</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_depth</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_split</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>min_bucket</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_bins</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a635b82bc9c1fcd63e5a46546d3e2ba70"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void tree_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>training_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_variable</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>list_of_features_to_exclude</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>split_criterion</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>weights</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+ 

<TRUNCATED>

[18/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_t.html
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diff --git a/docs/latest/globals_t.html b/docs/latest/globals_t.html
new file mode 100644
index 0000000..8f33b5b
--- /dev/null
+++ b/docs/latest/globals_t.html
@@ -0,0 +1,181 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_t.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_t"></a>- t -</h3><ul>
+<li>t_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac97c5f5015790b59645d69858e127645">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a14fdcfa276fd1a7ea2e3adb41ebe7db4">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ad570d893565618bcbbcbb01b3bb0a9b9">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_one_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a3bd1bcc335a2da73d01b40e06f7d2eea">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_pooled()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5c306ba8380ce6567831fef4610e515b">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_pooled_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a0a0a8a579bbf0f0d1efcbf62223e3431">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a1a835d80d1846a10a2c25b91ce81c6d2">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_unpooled()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac50750a0e0797ce24af1dc479b9699e1">hypothesis_tests.sql_in</a>
+</li>
+<li>t_test_two_unpooled_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a8fe7f38d29bf835718adca811e36f15a">hypothesis_tests.sql_in</a>
+</li>
+<li>tanimoto_distance()
+: <a class="el" href="svec__util_8sql__in.html#a98222e58bc519865a26643a61611b717">svec_util.sql_in</a>
+</li>
+<li>term_frequency()
+: <a class="el" href="text__utilities_8sql__in.html#a8a2caae5bf0b45eb1b4b6bbc531bce46">text_utilities.sql_in</a>
+</li>
+<li>tree_display()
+: <a class="el" href="decision__tree_8sql__in.html#a3c51edcc0fe3de029dc39a3f0306155d">decision_tree.sql_in</a>
+</li>
+<li>tree_predict()
+: <a class="el" href="decision__tree_8sql__in.html#ae371963e135c40d8be5b2e342ab3c252">decision_tree.sql_in</a>
+</li>
+<li>tree_surr_display()
+: <a class="el" href="decision__tree_8sql__in.html#a6ba706727adf88f8c07cfe09f71732bb">decision_tree.sql_in</a>
+</li>
+<li>tree_train()
+: <a class="el" href="decision__tree_8sql__in.html#a639431da2553fb86673e82d62cde4458">decision_tree.sql_in</a>
+</li>
+<li>triangular_cdf()
+: <a class="el" href="prob_8sql__in.html#abf9c7d870bcfe68cacaa421749bbdf35">prob.sql_in</a>
+</li>
+<li>triangular_pdf()
+: <a class="el" href="prob_8sql__in.html#a0c511b9748b2f7a21fe56aaf5f66d188">prob.sql_in</a>
+</li>
+<li>triangular_quantile()
+: <a class="el" href="prob_8sql__in.html#a4777540ab1b003ff92d484c4bc26af27">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_u.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_u.html b/docs/latest/globals_u.html
new file mode 100644
index 0000000..2c89d75
--- /dev/null
+++ b/docs/latest/globals_u.html
@@ -0,0 +1,142 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
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+<li>uniform_pdf()
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+<li>uniform_quantile()
+: <a class="el" href="prob_8sql__in.html#a629587a0fdefb588d28b15517ae5cc04">prob.sql_in</a>
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+<li>utils_normalize_data()
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+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_v.html
----------------------------------------------------------------------
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+<title>MADlib: File Members</title>
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_vars.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_vars.html b/docs/latest/globals_vars.html
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+: <a class="el" href="marginal_8sql__in.html#ac55303dc7709bf4e4cf5ffdec8b1baa9">marginal.sql_in</a>
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+<li>VOLATILE
+: <a class="el" href="marginal_8sql__in.html#aaab9ccd1374164e7e25057cdfc616273">marginal.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Jul 3 2014 14:09:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_w.html
----------------------------------------------------------------------
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+<title>MADlib: File Members</title>
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+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
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+<div id="titlearea">
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+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
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+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
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+: <a class="el" href="prob_8sql__in.html#a50e4a1883588cd7a4c1ff1017399e4af">prob.sql_in</a>
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+: <a class="el" href="prob_8sql__in.html#aa544631ddeb7a5c06b995b4383c3b612">prob.sql_in</a>
+</li>
+<li>weighted_sample()
+: <a class="el" href="sample_8sql__in.html#ab933c8334edc748aa9ef46b9bb50e60e">sample.sql_in</a>
+</li>
+<li>weighted_sample_final_int64()
+: <a class="el" href="sample_8sql__in.html#a5c2226995c3ef02cb2320b9829bad5eb">sample.sql_in</a>
+</li>
+<li>weighted_sample_final_vector()
+: <a class="el" href="sample_8sql__in.html#a8b2b3bb57b01f47bafe44bea73f39f78">sample.sql_in</a>
+</li>
+<li>weighted_sample_merge_int64()
+: <a class="el" href="sample_8sql__in.html#aa066d40345787e00819963f095f40998">sample.sql_in</a>
+</li>
+<li>weighted_sample_merge_vector()
+: <a class="el" href="sample_8sql__in.html#ac5d00ff9e30dcae8a1b6b8450df3e95e">sample.sql_in</a>
+</li>
+<li>weighted_sample_transition_int64()
+: <a class="el" href="sample_8sql__in.html#af933c6e9d6623c62a671dc74e5bd9d51">sample.sql_in</a>
+</li>
+<li>weighted_sample_transition_vector()
+: <a class="el" href="sample_8sql__in.html#ae7ad8a80a8c2508cc54cf496934017c2">sample.sql_in</a>
+</li>
+<li>wsr_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a8f0431ace27ac78e9e1df9906f1f1c33">hypothesis_tests.sql_in</a>
+</li>
+<li>wsr_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a0d85654832dfa961cd13526c052642f3">hypothesis_tests.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_z.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_z.html b/docs/latest/globals_z.html
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+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
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+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+</div><!-- doc-content -->
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+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/graph_legend.html
----------------------------------------------------------------------
diff --git a/docs/latest/graph_legend.html b/docs/latest/graph_legend.html
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
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+   <div id="projectname">
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+<title>MADlib: ARIMA</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+  <div class="headertitle">
+<div class="title">ARIMA<div class="ingroups"><a class="el" href="group__grp__tsa.html">Time Series Analysis</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#forecast">Forecasting Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Given a time series of data X, the Autoregressive Integrated Moving Average (ARIMA) model is a tool for understanding and, perhaps, predicting future values in the series. The model consists of three parts, an autoregressive (AR) part, a moving average (MA) part, and an integrated (I) part where an initial differencing step can be applied to remove any non-stationarity in the signal. The model is generally referred to as an ARIMA(p, d, q) model where parameters p, d, and q are non-negative integers that refer to the order of the autoregressive, integrated, and moving average parts of the model respectively.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd></dd></dl>
+<p>The ARIMA training function has the following syntax. </p><pre class="syntax">
+arima_train( input_table,
+       output_table,
+       timestamp_column,
+       timeseries_column,
+       grouping_columns,
+       include_mean,
+       non_seasonal_orders,
+       optimizer_params
+     )
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>input_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing time series data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the table to store the ARIMA model. Three tables are created, with names based on the value of the <em>output_table</em> argument in the training function:</p>
+<ol type="1">
+<li><em>output_table</em>: Table containing the ARIMA model. Contains the following columns: <table  class="output">
+<tr>
+<th>mean </th><td>Model mean (only if 'include_mean' is TRUE)  </td></tr>
+<tr>
+<th>mean_std_error </th><td>Standard errors for mean  </td></tr>
+<tr>
+<th>ar_params </th><td>Auto-regressions parameters of the ARIMA model  </td></tr>
+<tr>
+<th>ar_std_errors </th><td>Standard errors for AR parameters  </td></tr>
+<tr>
+<th>ma_params </th><td>Moving average parameters of the ARIMA model  </td></tr>
+<tr>
+<th>ma_std_errors </th><td>Standard errors for MA parameters  </td></tr>
+</table>
+</li>
+<li><em>output_table</em>_summary: Table containing descriptive statistics of the ARIMA model. Contains the following columns: <table  class="output">
+<tr>
+<th>input_table </th><td>Table name with the source data  </td></tr>
+<tr>
+<th>timestamp_col </th><td>Column name in the source table that contains the timestamp index to data  </td></tr>
+<tr>
+<th>timeseries_col </th><td>Column name in the source table that contains the data values  </td></tr>
+<tr>
+<th>non_seasonal_orders </th><td>Orders of the non-seasonal ARIMA model  </td></tr>
+<tr>
+<th>include_mean </th><td>TRUE if intercept was included in ARIMA model  </td></tr>
+<tr>
+<th>residual_variance </th><td>Variance of the residuals  </td></tr>
+<tr>
+<th>log_likelihood </th><td>Log likelihood value (when using MLE)  </td></tr>
+<tr>
+<th>iter_num </th><td>The number of iterations executed  </td></tr>
+<tr>
+<th>exec_time </th><td>Total time taken to train the model  </td></tr>
+</table>
+</li>
+<li><em>output_table</em>_residual: Table containing the residuals for each data point in 'input_table'. Contains the following columns: <table  class="output">
+<tr>
+<th>timestamp_col </th><td>Same as the 'timestamp_col' parameter (all indices from source table included except the first <em>d</em> elements, where <em>d</em> is the differencing order value from 'non_seasonal_orders')   </td></tr>
+<tr>
+<th>residual </th><td>Residual value for each data point  </td></tr>
+</table>
+</li>
+</ol>
+<p></p>
+<p class="enddd"></p>
+</dd>
+<dt>timestamp_column </dt>
+<dd><p class="startdd">TEXT. The name of the column containing the timestamp (index) data. This could be a serial index (INTEGER) or date/time value (TIMESTAMP).</p>
+<p class="enddd"></p>
+</dd>
+<dt>timeseries_column </dt>
+<dd><p class="startdd">TEXT. The name of the column containing the time series data. This data is currently restricted to DOUBLE PRECISION.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_columns (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em></p>
+<p>A comma-separated list of column names used to group the input dataset into discrete groups, training one ARIMA model per group. It is similar to the SQL <code>GROUP BY</code> clause. When this value is null, no grouping is used and a single result model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>include_mean (optional) </dt>
+<dd><p class="startdd">BOOLEAN, default: FALSE. Mean value of the data series is added in the ARIMA model if this variable is True. </p>
+<p class="enddd"></p>
+</dd>
+<dt>non_seasonal_orders (optional) </dt>
+<dd><p class="startdd">INTEGER[], default: 'ARRAY[1,1,1]'. Orders of the ARIMA model. The orders are [p, d, q], where parameters p, d, and q are non-negative integers that refer to the order of the autoregressive, integrated, and moving average parts of the model respectively. </p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer_params (optional) </dt>
+<dd>TEXT. Comma-separated list of optimizer-specific parameters of the form ‘name=value'. The order of the parameters does not matter. The following parameters are recognized:<ul>
+<li><b>max_iter:</b> Maximum number of iterations to run learning algorithm (Default = 100)</li>
+<li><b>tau:</b> Computes the initial step size for gradient algorithm (Default = 0.001)</li>
+<li><b>e1:</b> Algorithm-specific threshold for convergence (Default = 1e-15)</li>
+<li><b>e2:</b> Algorithm-specific threshold for convergence (Default = 1e-15)</li>
+<li><b>e3:</b> Algorithm-specific threshold for convergence (Default = 1e-15)</li>
+<li><b>hessian_delta:</b> Delta parameter to compute a numerical approximation of the Hessian matrix (Default = 1e-6)  </li>
+</ul>
+</dd>
+</dl>
+<p><a class="anchor" id="forecast"></a></p><dl class="section user"><dt>Forecasting Function</dt><dd></dd></dl>
+<p>The ARIMA forecast function has the following syntax. </p><pre class="syntax">
+arima_forecast( model_table,
+                output_table,
+                steps_ahead
+              )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the ARIMA model trained on the time series dataset.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the table to store the forecasted values. The output table produced by the forecast function contains the following columns. </p><table  class="output">
+<tr>
+<th>group_by_cols </th><td>Grouping column values (if grouping parameter is provided)  </td></tr>
+<tr>
+<th>step_ahead </th><td>Time step for the forecast  </td></tr>
+<tr>
+<th>forecast_value </th><td>Forecast of the current time step  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>steps_ahead </dt>
+<dd>INTEGER. The number of steps to forecast at the end of the time series. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>View online help for the ARIMA training function. <pre class="example">
+SELECT madlib.arima_train();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+DROP TABLE IF EXISTS arima_beer;
+CREATE TABLE arima_beer (time_id integer NOT NULL, value double precision NOT NULL );
+COPY arima_beer (time_id, value) FROM stdin WITH DELIMITER '|';
+1  | 93.2
+2  | 96.0
+3  | 95.2
+4  | 77.0
+5  | 70.9
+6  | 64.7
+7  | 70.0
+8  | 77.2
+9  | 79.5
+10 | 100.5
+11 | 100.7
+12 | 107.0
+13 | 95.9
+14 | 82.7
+15 | 83.2
+16 | 80.0
+17 | 80.4
+18 | 67.5
+19 | 75.7
+20 | 71.0
+21 | 89.2
+22 | 101.0
+23 | 105.2
+24 | 114.0
+25 | 96.2
+26 | 84.4
+27 | 91.2
+28 | 81.9
+29 | 80.5
+30 | 70.4
+31 | 74.7
+32 | 75.9
+33 | 86.2
+34 | 98.7
+35 | 100.9
+36 | 113.7
+37 | 89.7
+38 | 84.4
+39 | 87.2
+40 | 85.5
+\.
+</pre></li>
+<li>Train an ARIMA model. <pre class="example">
+-- Train ARIMA model with 'grouping_columns'=NULL, 'include_mean'=TRUE,
+--   and 'non_seasonal_orders'=[1,1,1]
+SELECT madlib.arima_train( 'arima_beer',
+                           'arima_beer_output',
+                           'time_id',
+                           'value',
+                           NULL,
+                           FALSE,
+                           ARRAY[1, 1, 1]
+                         );
+</pre></li>
+<li>Examine the ARIMA model. <pre class="example">
+\x ON
+SELECT * FROM arima_beer_output;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-+------------------
+ar_params     | {0.221954769696}
+ar_std_errors | {0.575367782602}
+ma_params     | {-0.140623564576}
+ma_std_errors | {0.533445214346}
+</pre></li>
+<li>View the summary statistics table. <pre class="example">
+SELECT * FROM arima_beer_output_summary;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-------+---------------
+input_table         | arima_beer
+timestamp_col       | time_id
+timeseries_col      | value
+non_seasonal_orders | {1,1,1}
+include_mean        | f
+residual_variance   | 100.989970539
+log_likelihood      | -145.331516396
+iter_num            | 28
+exec_time (s)       | 2.75
+</pre></li>
+<li>View the residuals. <pre class="example">
+\x OFF
+SELECT * FROM arima_beer_output_residual;
+</pre> Result: <pre class="result">
+ time_id |      residual
+---------+--------------------
+       2 |                  0
+       4 |   -18.222328834394
+       6 |  -5.49616627282665
+...
+      35 |   1.06298837051437
+      37 |  -25.0886854003757
+      39 |   3.48401666299571
+(40 rows)
+</pre></li>
+<li>Use the ARIMA forecast function to forecast 10 future values. <pre class="example">
+SELECT madlib.arima_forecast( 'arima_beer_output',
+                              'arima_beer_forecast_output',
+                              10
+                            );
+SELECT * FROM arima_beer_forecast_output;
+</pre> Result: <pre class="result">
+ steps_ahead | forecast_value
+-------------+----------------
+           1 |  85.3802343659
+           3 |  85.3477516875
+           5 |  85.3461514635
+           7 |  85.3460726302
+           9 |  85.3460687465
+           2 |  85.3536518121
+           4 |  85.3464421267
+           6 |  85.3460869494
+           8 |  85.3460694519
+          10 |    85.34606859
+(10 rows)
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd>An ARIMA model is an <em>a</em>uto-<em>r</em>egressive <em>i</em>ntegrated <em>m</em>oving <em>a</em>verage model. An ARIMA model is typically expressed in the form <p class="formulaDsp">
+\[ (1 - \phi(B)) Y_t = (1 + \theta(B)) Z_t, \]
+</p>
+</dd></dl>
+<p>where \(B\) is the backshift operator. The time \( t \) is from \( 1 \) to \( N \).</p>
+<p>ARIMA models involve the following variables:</p><ul>
+<li>The values of the time series: \( X_t \).</li>
+<li>Parameters of the model: \( p \), \( q \), and \( d \); \( d \) is the differencing order, \( p \) is the order of the AR operator, and \( q \) is the order of the MA operator.</li>
+<li>The AR operator: \( \phi(B) \).</li>
+<li>The MA operator: \( \theta(B) \).</li>
+<li>The lag difference: \( Y_{t} \), where \( Y_{t} = (1-B)^{d}(X_{t} - \mu) \).</li>
+<li>The mean value: \( \mu \), which is set to be zero for \( d&gt;0 \) and estimated from the data when d=0.</li>
+<li>The error terms: \( Z_t \).</li>
+</ul>
+<p>The auto regression operator models the prediction for the next observation as some linear combination of the previous observations. More formally, an AR operator of order \( p \) is defined as</p>
+<p class="formulaDsp">
+\[ \phi(B) Y_t= \phi_1 Y_{t-1} + \dots + \phi_{p} Y_{t-p} \]
+</p>
+<p>The moving average operator is similar, and it models the prediction for the next observation as a linear combination of the errors in the previous prediction errors. More formally, the MA operator of order \( q \) is defined as</p>
+<p class="formulaDsp">
+\[ \theta(B) Z_t = \theta_{1} Z_{t-1} + \dots + \theta_{q} Z_{t-q}. \]
+</p>
+<p>We estimate the parameters using the Levenberg-Marquardt Algorithm. In mathematics and computing, the Levenberg-Marquardt algorithm (LMA), also known as the damped least-squares (DLS) method, provides a numerical solution to the problem of minimizing a function, generally nonlinear, over a space of parameters of the function.</p>
+<p>Like other numeric minimization algorithms, LMA is an iterative procedure. To start a minimization, the user has to provide an initial guess for the parameter vector, $p$, as well as some tuning parameters \(\tau, \epsilon_1, \epsilon_2, \epsilon_3,\).</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Rob J Hyndman and George Athanasopoulos: Forecasting: principles and practice, <a href="http://otexts.com/fpp/">http://otexts.com/fpp/</a></p>
+<p>[2] Robert H. Shumway, David S. Stoffer: Time Series Analysis and Its Applications With R Examples, Third edition Springer Texts in Statistics, 2010</p>
+<p>[3] Henri Gavin: The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems, 2011</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="arima_8sql__in.html" title="Arima function for forecasting of timeseries data. ">arima.sql_in</a> documenting the ARIMA functions </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[36/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/doxygen.png
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[47/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/clustered__variance_8sql__in.html
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+<tr class="separator:adcf2cf1ec7b2a376be25ed3a0a466e67"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:adec204a306e05ef8506b3b03e8e37ba3"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a7d90589980d2779a149a81513bab79e9"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="clustered__variance_8sql__in.html#a7d90589980d2779a149a81513bab79e9">clustered_variance_mlogregr</a> (text source_table, text out_table, text dependent_varname, text independent_varname, text cluster_varname, integer ref_category, text grouping_cols, integer max_iter)</td></tr>
+<tr class="separator:a7d90589980d2779a149a81513bab79e9"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a2d33f7a6a98dfc92c6d4a017ceabbd9a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] __array_add </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a329c662f02c985ed68b886cf2bd29f37"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] __array_sum </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad772976f85e2f1dd3927f84a7f96e9ae"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__clustered_agg_result __clustered_err_lin_final </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a876867228d62f422f10c03dc93d1e9eb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __clustered_err_lin_merge </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad4b627f3a2aa513b8809e3fc2f5e4c5f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate __clustered_agg_result __clustered_err_lin_step </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab3548ff761b9d720e4db767dcf419e66"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __clustered_err_lin_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a790988e40da04d13e5b56197d35546d8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__clustered_agg_result __clustered_err_log_final </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab3dcbdb7edc8575a660656a2ce680d21"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __clustered_err_log_merge </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6f17f900dfce2d0a53a9f9ab06631386"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate __clustered_agg_result __clustered_err_log_step </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adbddf55ce3fbb5c99024298566a33cf2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __clustered_err_log_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adcf2cf1ec7b2a376be25ed3a0a466e67"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__clustered_agg_result __clustered_err_mlog_final </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a12b136a5f17b60527a0b38d0dd553c97"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __clustered_err_mlog_merge </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad2112683f96b35cee6fcaa44a2831a49"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate __clustered_agg_result __clustered_err_mlog_step </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aacb9639e3031010d9d69ab57ba2a5b1f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __clustered_err_mlog_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>num_cat</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_cat</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abebc8d70763c7264857b4b14d3ddf280"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__clustered_lin_result __clustered_lin_compute_stats </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>meatvec</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>breadvec</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>mcluster</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numrows</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a621c23b2be30469873870797b1dab995"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__clustered_log_result __clustered_log_compute_stats </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>meatvec</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>breadvec</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>mcluster</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numrows</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5497d5be990c0efb11508b23f128f154"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__clustered_mlog_result __clustered_mlog_compute_stats </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>meatvec</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>breadvec</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>mcluster</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numrows</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abaa6702a3482f65199ce64b8a14316d7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_linregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">source_table</td><td>Data table name </td></tr>
+    <tr><td class="paramname">out_table</td><td>The result table </td></tr>
+    <tr><td class="paramname">depvar</td><td>Dependent variable expression </td></tr>
+    <tr><td class="paramname">indvar</td><td>Independent variable expression </td></tr>
+    <tr><td class="paramname">clustervar</td><td>The expressions used to clustering </td></tr>
+    <tr><td class="paramname">grouping_col</td><td>The names of grouping columns </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="adb6227023444552a37f3ea52502b1c37"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_linregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0548d26efc4f0b22c41516dabcd7d32b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text clustered_variance_linregr </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ade1843a5cf8c7c72fb1aae07d82d5a4e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text clustered_variance_linregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>msg</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0debc923b0494f8259938d9c6f5cdbcd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>verbose_mode</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5d526752e92cbe1282bf581c49f9c31c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab060fb03fdcab9e702024eee940ba61c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab58eebaf13ef21e59e50362fe9ad658f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a8d8bd2b7d26981e7de10597222d99f7c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5c10605246186fc87442274b66948f52"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>depvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>indvar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3d702174c793b0c4afc5453d553f0e29"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4450b02e72471fa85f441b9abdc65ff8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text clustered_variance_logregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>msg</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa72cfa6f9259cceea3e36b6db45a7624"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>verbose_mode</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac614f56394b00ad9286c7c83a060b079"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
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+          <td class="paramname"><em>grouping_cols</em>, </td>
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+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>optimizer_params</em>&#160;</td>
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+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
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+
+</div>
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+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
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+        </tr>
+      </table>
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+
+</div>
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+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7b55f727b8beea7cf06c1561bbffb6ca"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
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+<a class="anchor" id="a133a97d8eee41ed57ed0b16a592774e4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>msg</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
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+<a class="anchor" id="ab6a6297525bcd589311d7e085b830123"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>verbose_mode</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
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+</div>
+<a class="anchor" id="aa864a6c22942e33348725c6cf5a79a14"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>tolerance</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="adec204a306e05ef8506b3b03e8e37ba3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7d90589980d2779a149a81513bab79e9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_mlogregr </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cluster_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>ref_category</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iter</em>&#160;</td>
+        </tr>
+        <tr>
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+          <td>)</td>
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+</div><div class="memdoc">
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+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_ac52a4b89b7b1b1591f2952b5cbd041a.html">regress</a></li><li class="navelem"><a class="el" href="clustered__variance_8sql__in.html">clustered_variance.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
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+</html>

[41/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/dense__linear__systems_8sql__in.html
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diff --git a/docs/latest/dense__linear__systems_8sql__in.html b/docs/latest/dense__linear__systems_8sql__in.html
new file mode 100644
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+++ b/docs/latest/dense__linear__systems_8sql__in.html
@@ -0,0 +1,617 @@
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+<title>MADlib: dense_linear_systems.sql_in File Reference</title>
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+  <td style="padding-left: 0.5em;">
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+<a href="#func-members">Functions</a>  </div>
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+<div class="title">dense_linear_systems.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for linear systems.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:ad34728809ab3121705342cf91b34c6dd"><td class="memItemLeft" align="right" valign="top">bytea8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#ad34728809ab3121705342cf91b34c6dd">dense_residual_norm_transition</a> (bytea8 state, float8[] a, float8 b, float8[] x)</td></tr>
+<tr class="separator:ad34728809ab3121705342cf91b34c6dd"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a73ebde08b8395fd7bddbe1dc37b9539f"><td class="memItemLeft" align="right" valign="top">bytea8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a73ebde08b8395fd7bddbe1dc37b9539f">dense_residual_norm_merge_states</a> (bytea8 state1, bytea8 state2)</td></tr>
+<tr class="separator:a73ebde08b8395fd7bddbe1dc37b9539f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a01935dd8c4aef8c92e77b7f0f905fa4b"><td class="memItemLeft" align="right" valign="top">residual_norm_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a01935dd8c4aef8c92e77b7f0f905fa4b">dense_residual_norm_final</a> (bytea8 state)</td></tr>
+<tr class="separator:a01935dd8c4aef8c92e77b7f0f905fa4b"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a52f2bcdd0d038f19e64a44d4bb07fefb"><td class="memItemLeft" align="right" valign="top">aggregate residual_norm_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a52f2bcdd0d038f19e64a44d4bb07fefb">dense_residual_norm</a> (float8[] left_hand_side, float8 right_hand_side, float8[] solution)</td></tr>
+<tr class="memdesc:a52f2bcdd0d038f19e64a44d4bb07fefb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the residual after solving the dense linear systems.  <a href="#a52f2bcdd0d038f19e64a44d4bb07fefb">More...</a><br /></td></tr>
+<tr class="separator:a52f2bcdd0d038f19e64a44d4bb07fefb"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:acbfc47693d0b50ccfc6b7f7c38209b18"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#acbfc47693d0b50ccfc6b7f7c38209b18">dense_direct_linear_system_transition</a> (float8[] state, integer row_id, float8[] a, float8 b, integer num_rows, integer algorithm)</td></tr>
+<tr class="separator:acbfc47693d0b50ccfc6b7f7c38209b18"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a607403f90e1921435183d031825e5e54"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a607403f90e1921435183d031825e5e54">dense_direct_linear_system_merge_states</a> (float8[] state1, float8[] state2)</td></tr>
+<tr class="separator:a607403f90e1921435183d031825e5e54"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a63a3ed2c811216d3d88c120005a13875"><td class="memItemLeft" align="right" valign="top">dense_linear_solver_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a63a3ed2c811216d3d88c120005a13875">dense_direct_linear_system_final</a> (float8[] state)</td></tr>
+<tr class="separator:a63a3ed2c811216d3d88c120005a13875"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a379f260d8dfbf5c2be05c60776974188"><td class="memItemLeft" align="right" valign="top">aggregate dense_linear_solver_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a379f260d8dfbf5c2be05c60776974188">dense_direct_linear_system</a> (integer row_id, float8[] left_hand_side, float8 right_hand_side, integer numEquations, integer algorithm)</td></tr>
+<tr class="memdesc:a379f260d8dfbf5c2be05c60776974188"><td class="mdescLeft">&#160;</td><td class="mdescRight">Solve a system of linear equations using the direct method.  <a href="#a379f260d8dfbf5c2be05c60776974188">More...</a><br /></td></tr>
+<tr class="separator:a379f260d8dfbf5c2be05c60776974188"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a885d6a36c07dc2c048948cb5287a3b92"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a885d6a36c07dc2c048948cb5287a3b92">linear_solver_dense</a> (varchar input_string)</td></tr>
+<tr class="memdesc:a885d6a36c07dc2c048948cb5287a3b92"><td class="mdescLeft">&#160;</td><td class="mdescRight">Help function, to print out the supported families.  <a href="#a885d6a36c07dc2c048948cb5287a3b92">More...</a><br /></td></tr>
+<tr class="separator:a885d6a36c07dc2c048948cb5287a3b92"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a2272a21f30096a79cd543fe66a0aee19"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a2272a21f30096a79cd543fe66a0aee19">linear_solver_dense</a> ()</td></tr>
+<tr class="separator:a2272a21f30096a79cd543fe66a0aee19"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ab7de234e67060a4fc48978e430642baa"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#ab7de234e67060a4fc48978e430642baa">linear_solver_dense</a> (varchar source_table, varchar out_table, varchar row_id, varchar left_hand_side, varchar right_hand_side, varchar grouping_cols, varchar optimizer, varchar optimizer_options)</td></tr>
+<tr class="memdesc:ab7de234e67060a4fc48978e430642baa"><td class="mdescLeft">&#160;</td><td class="mdescRight">A wrapper function for the various marginal linear_systemsion analyzes.  <a href="#ab7de234e67060a4fc48978e430642baa">More...</a><br /></td></tr>
+<tr class="separator:ab7de234e67060a4fc48978e430642baa"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a003b5a21cffde500fa808f1c6e9c14ae"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dense__linear__systems_8sql__in.html#a003b5a21cffde500fa808f1c6e9c14ae">linear_solver_dense</a> (varchar source_table, varchar out_table, varchar row_id, varchar left_hand_side, varchar right_hand_side)</td></tr>
+<tr class="memdesc:a003b5a21cffde500fa808f1c6e9c14ae"><td class="mdescLeft">&#160;</td><td class="mdescRight">Marginal effects with default variables.  <a href="#a003b5a21cffde500fa808f1c6e9c14ae">More...</a><br /></td></tr>
+<tr class="separator:a003b5a21cffde500fa808f1c6e9c14ae"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>July 2013</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>Computes the solution of a consistent linear system, for more details see the module description at <a class="el" href="group__grp__dense__linear__solver.html">Dense Linear Systems</a> </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a379f260d8dfbf5c2be05c60776974188"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate dense_linear_solver_result dense_direct_linear_system </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>row_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>left_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>right_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>numEquations</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>algorithm</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">row_id</td><td>Column containing the row_id </td></tr>
+    <tr><td class="paramname">left_hand_side</td><td>Column containing the left hand side of the system </td></tr>
+    <tr><td class="paramname">right_hand_side</td><td>Column containing the right hand side of the system </td></tr>
+    <tr><td class="paramname">numEquations</td><td>Number of equations </td></tr>
+    <tr><td class="paramname">algorithm</td><td>Algorithm used for the dense linear solver</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value:<ul>
+<li><code>solution FLOAT8[] </code> - Array of marginal effects</li>
+<li><code>residual_norm FLOAT8</code> - Norm of the residual</li>
+<li><code>iters INTEGER</code> - Iterations taken</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Get all the diagnostic statistics:<br />
+ <pre> SELECT linear_system_dense(<em>row_id</em>,
+                                  <em>left_hand_side</em>,
+                                  <em> right_hand_side </em>,
+                                  <em> numEquations </em>)
+ FROM <em>dataTable</em>;
+</pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a63a3ed2c811216d3d88c120005a13875"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">dense_linear_solver_result dense_direct_linear_system_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a607403f90e1921435183d031825e5e54"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] dense_direct_linear_system_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="acbfc47693d0b50ccfc6b7f7c38209b18"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] dense_direct_linear_system_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>row_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>a</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>b</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>num_rows</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>algorithm</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a52f2bcdd0d038f19e64a44d4bb07fefb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate residual_norm_result dense_residual_norm </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>left_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>right_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>solution</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">left_hand_side</td><td>Column containing the left hand side of the system </td></tr>
+    <tr><td class="paramname">right_hand_side</td><td>Column containing the right hand side of the system </td></tr>
+    <tr><td class="paramname">solution</td><td>Solution of the linear system</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>residual_norm FLOAT8:</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Get all the diagnostic statistics:<br />
+ <pre> SELECT dense_residual_norm(<em>row_id</em>,
+                                  <em>left_hand_side</em>,
+                                  <em> right_hand_side </em>,
+                                  <em> solution </em>)
+ FROM <em>dataTable</em>;
+</pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a01935dd8c4aef8c92e77b7f0f905fa4b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">residual_norm_result dense_residual_norm_final </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a73ebde08b8395fd7bddbe1dc37b9539f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 dense_residual_norm_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad34728809ab3121705342cf91b34c6dd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 dense_residual_norm_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>a</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>b</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a885d6a36c07dc2c048948cb5287a3b92"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar linear_solver_dense </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>input_string</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2272a21f30096a79cd543fe66a0aee19"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar linear_solver_dense </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab7de234e67060a4fc48978e430642baa"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void linear_solver_dense </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>row_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>left_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>right_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>grouping_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optimizer_options</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">source_table</td><td>String identifying the input table </td></tr>
+    <tr><td class="paramname">out_table</td><td>String identifying the output table to be created </td></tr>
+    <tr><td class="paramname">row_id</td><td>Column containing the row_id </td></tr>
+    <tr><td class="paramname">left_hand_side</td><td>Column containing the left hand side of the system </td></tr>
+    <tr><td class="paramname">right_hand_side</td><td>Column containing the right hand side of the system </td></tr>
+    <tr><td class="paramname">grouping_cols</td><td>Columns to group by </td></tr>
+    <tr><td class="paramname">optimizer</td><td>Optimizer to be used </td></tr>
+    <tr><td class="paramname">optimizer_options</td><td>Optimal parameters for the algorithms</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>void</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd>For function summary information. Run sql&gt; select linear_solver_dense('help'); OR sql&gt; select <a class="el" href="dense__linear__systems_8sql__in.html#a885d6a36c07dc2c048948cb5287a3b92" title="Help function, to print out the supported families. ">linear_solver_dense()</a>; OR sql&gt; select linear_solver_dense('?'); For function usage information. Run sql&gt; select linear_solver_dense('usage'); </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a003b5a21cffde500fa808f1c6e9c14ae"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void linear_solver_dense </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>out_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>row_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>left_hand_side</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>right_hand_side</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+   <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="title">src Directory Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="subdirs"></a>
+Directories</h2></td></tr>
+<tr class="memitem:dir_757021f59fa4b40e61a20490993d949a"><td class="memItemLeft" align="right" valign="top">directory &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dir_757021f59fa4b40e61a20490993d949a.html">pg_gp</a></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+</div><!-- contents -->
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+  <ul>
+    <li class="navelem"><a class="el" href="dir_704eb8350b43e1ca74c0f90ed1ba450e.html">methods</a></li><li class="navelem"><a class="el" href="dir_883e487a38cc988e0c441456a570d389.html">kernel_machines</a></li><li class="navelem"><a class="el" href="dir_085c2d6b775d67156938ac94f000f710.html">src</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:46 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="subdirs"></a>
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+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:46 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
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[03/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__tree.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+<div id="doc-content">
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+  <div class="headertitle">
+<div class="title">Developer Database Functions<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#utilities">Utility Functions</a> </li>
+<li>
+<a href="#rel;ated">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>The utility module consists of useful utility functions to assist data scientists in using the product. Several of these functions can be used while implementing new algorithms.</p>
+<p><a class="anchor" id="utilities"></a></p><dl class="section user"><dt>Utility Functions</dt><dd></dd></dl>
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+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a9ce7ffa76acc8a0638e9bc48be99f36a" title="Return MADlib build information. ">version()</a> </th><td><p class="starttd">Return MADlib build information.  </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd" title="Raise an exception if the given condition is not satisfied. ">assert()</a> </th><td><p class="starttd">Raise an exception if the given condition is not satisfied. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0" title="Check if a SQL statement raises an error. ">check_if_raises_error()</a> </th><td><p class="starttd">Check if a SQL statement raises an error. </p>
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+<tr>
+<th><a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470" title="Check if a column exists in a table. ">check_if_col_exists()</a> </th><td><p class="starttd">Check if a column exists in a table. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a48bed63cab78a5446fa4c122eaae3a40" title="Check if a floating-point number is NaN (not a number) ">isnan()</a> </th><td><p class="starttd">Check if a floating-point number is NaN (not a number) </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000" title="Create the temporary schema if it does not exist yet. ">create_schema_pg_temp()</a> </th><td><p class="starttd">Create the temporary schema if it does not exist yet. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0" title="Create volatile noop function. ">noop()</a> </th><td><p class="starttd">Create volatile noop function. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a" title="Drop all tables matching pattern &#39;madlib_temp&#39; in a given schema. ">cleanup_madlib_temp_tables()</a> </th><td><p class="starttd">Drop all tables matching pattern 'madlib_temp' in a given schema. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p>Note: If the function <a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a" title="Drop all tables matching pattern &#39;madlib_temp&#39; in a given schema. ">cleanup_madlib_temp_tables()</a> gives an Out-of-memory error, then the number of tables to be dropped is too high to execute in one transaction. In such a case, please follow the instructions provided with the error to execute the command in multiple transactions.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="utilities_8sql__in.html" title="SQL functions for carrying out routine tasks. ">utilities.sql_in</a> documenting the SQL functions. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
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+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
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+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Cross Validation<div class="ingroups"><a class="el" href="group__grp__mdl.html">Model Evaluation</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#cvfunction">Cross-Validation Function</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#background">Technical Background</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Estimates the fit of a predictive model given a data set and specifications for the training, prediction, and error estimation functions.</p>
+<p>Cross validation, sometimes called rotation estimation, is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and you want to estimate how accurately a predictive model will perform in practice.</p>
+<p>The cross-validation function provided by this module is very flexible and can work with algorithms you want to cross validate, including algorithms you write yourself. Among the inputs to the cross-validation function are specifications of the modelling, prediction, and error metric functions. These three-part specifications include the name of the function, an array of arguments to pass to the function, and an array of the data types of the arguments. This makes it possible to use functions from other MADlib modules or user-defined functions that you supply.</p>
+<ul>
+<li>The modelling (training) function takes in a given data set with independent and dependent variables and produces a model, which is stored in an output table.</li>
+<li>The prediction function takes in the model generated by the modelling function and a different data set with independent variables, and produces a prediction of the dependent variables based on the model, which is stored in an output table. The prediction function should take a unique ID column name in the data table as one of the inputs, so that the prediction result can be compared with the validation values. Note: Prediction function in some MADlib modules do not save results into an output table. These prediction functions are not suitable for cross-validation.</li>
+<li>The error metric function compares the prediction results with the known values of the dependent variables in the data set that was fed into the prediction function. It computes the error metric using the specified error metric function, storing the results in a table.</li>
+</ul>
+<p>Other inputs include the output table name, k value for the k-fold cross validation, and how many folds to try. For example, you can choose to run a simple validation instead of a full cross validation.</p>
+<p><a class="anchor" id="cvfunction"></a></p><dl class="section user"><dt>Cross-Validation Function</dt><dd></dd></dl>
+<pre class="syntax">
+cross_validation_general( modelling_func,
+                          modelling_params,
+                          modelling_params_type,
+                          param_explored,
+                          explore_values,
+                          predict_func,
+                          predict_params,
+                          predict_params_type,
+                          metric_func,
+                          metric_params,
+                          metric_params_type,
+                          data_tbl,
+                          data_id,
+                          id_is_random,
+                          validation_result,
+                          data_cols,
+                          fold_num
+                        )</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>modelling_func </dt>
+<dd>VARCHAR. The name of the function that trains the model. </dd>
+<dt>modelling_params </dt>
+<dd>VARCHAR[]. An array of parameters to supply to the modelling function. </dd>
+<dt>modelling_params_type </dt>
+<dd>VARCHAR[]. An array of data type names for each of the parameters supplied to the modelling function. </dd>
+<dt>param_explored </dt>
+<dd>VARCHAR. The name of the parameter that will be checked to find the optimum value. The name must appear in the <em>modelling_params</em> array. </dd>
+<dt>explore_values </dt>
+<dd>VARCHAR. The name of the parameter whose values are to be studied. </dd>
+<dt>predict_func </dt>
+<dd>VARCHAR. The name of the prediction function. </dd>
+<dt>predict_params </dt>
+<dd>VARCHAR[]. An array of parameters to supply to the prediction function. </dd>
+<dt>predict_params_type </dt>
+<dd>VARCHAR[]. An array of data type names for each of the parameters supplied to the prediction function. </dd>
+<dt>metric_func </dt>
+<dd>VARCHAR. The name of the function for measuring errors. </dd>
+<dt>metric_params </dt>
+<dd>VARCHAR[]. An array of parameters to supply to the error metric function. </dd>
+<dt>metric_params_type </dt>
+<dd>VARCHAR[]. An array of data type names for each of the parameters supplied to the metric function. </dd>
+<dt>data_tbl </dt>
+<dd>VARCHAR. The name of the data table that will be split into training and validation parts. </dd>
+<dt>data_id </dt>
+<dd><p class="startdd">VARCHAR. The name of the column containing a unique ID associated with each row, or NULL if the table has no such column.</p>
+<p class="enddd">Ideally, the data set has a unique ID for each row, so that it is easier to partition the data set into the training part and the validation part. Set the <em>id_is_random</em> argument to inform the cross-validation function whether the ID value is randomly assigned to each row. If it is not randomly assigned, the cross-validation function generates a random ID for each row.  </p>
+</dd>
+<dt>id_is_random </dt>
+<dd>BOOLEAN. TRUE if the provided ID is randomly assigned to each row. </dd>
+<dt>validation_result </dt>
+<dd>VARCHAR. The name of the table to store the output of the cross-validation function. The output table has the following columns: <table  class="output">
+<tr>
+<th>param_explored </th><td>The name of the parameter checked to find the optimum value. This is the same name specified in the <em>param_explored</em> argument of the <em><a class="el" href="cross__validation_8sql__in.html#a2a7791b05f51e8748ab7b6ccf328a7e2">cross_validation_general()</a></em> function.  </td></tr>
+<tr>
+<th>average error </th><td>The average of the errors computed by the error metric function.  </td></tr>
+<tr>
+<th>standard deviation of error </th><td>The standard deviation of the errors.  </td></tr>
+</table>
+</dd>
+<dt>data_cols </dt>
+<dd><p class="startdd">A comma-separated list of names of data columns to use in the calculation. When its value is NULL, the function will automatically figure out all the column names of the data table. This is only used if the <em>data_id</em> argument is NULL, otherwise it is ignored.</p>
+<p>If the data set has no unique ID for each row, the cross-validation function copies the data set to a temporary table with a randomly assigned ID column. Setting this argument to the list of independent and dependent variables that are to be used in the calculation minimizes the copying workload by only copying the required data.</p>
+<p class="enddd">The new temporary table is dropped after the computation has finished.  </p>
+</dd>
+<dt><em>fold_num</em> </dt>
+<dd>INTEGER, default: 10. Value of k. How many folds validation? Each validation uses 1/fold_num fraction of the data for validation. </dd>
+</dl>
+<p>The parameter arrays for the modelling, prediction and metric functions can include the following special keywords:</p>
+<ul>
+<li><em>%data%</em> &ndash; The argument position for training/validation data</li>
+<li><em>%model%</em> &ndash; The argument position for the output/input of modelling/prediction function</li>
+<li><em>%id%</em> &ndash; The argument position of the unique ID column (user-provided or generated by the cross-validation function, as described above)</li>
+<li><em>%prediction%</em> &ndash; The argument position for the output/input of prediction/metric function</li>
+<li><em>%error%</em> &ndash; The argument position for the output of the metric function</li>
+</ul>
+<p><b>Note</b>: If the argument <em>explore_values</em> is NULL or has zero length, then the cross-validation function will only run a data folding.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p>This example uses cross validation with an elastic net regression to find the best value of the regularization parameter.</p>
+<ol type="1">
+<li>Populate the table <code>cvtest</code> with 101 dimensional independent variables <code>val</code>, and dependent variable <code>dep</code>.</li>
+<li>Run the general cross-validation function. <pre class="example">
+SELECT madlib.cross_validation_general
+    ( 'madlib.elastic_net_train',
+        '{%data%, %model%, dep, indep, gaussian, 1, lambda, TRUE, NULL, fista,
+          "{eta = 2, max_stepsize = 2, use_active_set = t}",
+          NULL, 2000, 1e-6}'::varchar[],
+        '{varchar, varchar, varchar, varchar, varchar, double precision,
+          double precision, boolean, varchar, varchar, varchar, varchar,
+          integer, double precision}'::varchar[],
+      'lambda',
+      '{0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20,
+        0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.34, 0.36}'::varchar[],
+      'madlib.elastic_net_predict',
+        '{%model%, %data%, %id%, %prediction%}'::varchar[],
+        '{text, text, text, text}'::varchar[],
+      'madlib.mse_error',
+        '{%prediction%, %data%, %id%, dep, %error%}'::varchar[],
+        '{varchar, varchar, varchar, varchar, varchar}'::varchar[],
+      'cvtest',
+      NULL::varchar,
+      FALSE,
+      'valid_rst_tbl',
+      '{indep, dep}'::varchar[],
+      10
+);
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<p><em>max_locks_per_transaction</em>, which usually is set to the default value of 64, limits the number of tables that can be dropped inside a single transaction (the cross-validation function). Thus, the number of different values of <em>param_explored</em> (or the length of the <em>explored_values</em> array) cannot be too large. For 10-fold cross validation, the limit of <code>length(<em>explored_values</em>)</code> is around 40. If the limit is exceeded, you may get an "out of shared memory" error because <em>max_locks_per_transaction</em> is exceeded.</p>
+<p>One way to overcome this limitation is to run the cross-validation function multiple times, with each run covering a different region of values of the parameter.</p>
+<p>In the future, MADlib may implement cross-validation functions for each individual applicable module, where it is possible to optimize the calculation to avoid dropping tables and prevent exceeding the <em>max_locks_per_transaction</em> limitation. Since module-specific cross-validation functions depend upon the implementation details of the modules to perform the optimization, they will not be as flexible as the generalized cross-validation function provided here.</p>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>One round of cross validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross validation are performed using different partitions, and the validation results are averaged over the rounds.</p>
+<p>In k-fold cross validation, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k&minus;1 subsamples are used as training data. The cross-validation process is repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. The k results from the folds can be averaged (or otherwise combined) to produce a single estimation. The advantage of this method over repeated random sub-sampling is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross validation is commonly used, but in general k remains an unfixed parameter.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="cross__validation_8sql__in.html" title="SQL functions for cross validation. ">cross_validation.sql_in</a> documenting the SQL functions. </p>
+</div><!-- contents -->
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[39/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+  <td style="padding-left: 0.5em;">
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+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<tr class="memdesc:crf_8sql__in"><td class="mdescLeft">&#160;</td><td class="mdescRight">SQL functions for conditional random field. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:crf__data__loader_8sql__in"><td class="memItemLeft" align="right" valign="top">file &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__data__loader_8sql__in.html">crf_data_loader.sql_in</a></td></tr>
+<tr class="memdesc:crf__data__loader_8sql__in"><td class="mdescLeft">&#160;</td><td class="mdescRight">Create database tables and import POS/NER training/testing data to the database. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:crf__feature__gen_8sql__in"><td class="memItemLeft" align="right" valign="top">file &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html">crf_feature_gen.sql_in</a></td></tr>
+<tr class="memdesc:crf__feature__gen_8sql__in"><td class="mdescLeft">&#160;</td><td class="mdescRight">SQL function for POS/NER feature extraction. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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[22/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x76.html
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+</li>
+<li>argmax()
+: <a class="el" href="bayes_8sql__in.html#ad47c68a2b8aeedf66d509f0c32a9732d">bayes.sql_in</a>
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+</li>
+<li>array_collapse()
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+</li>
+<li>array_contains()
+: <a class="el" href="array__ops_8sql__in.html#aedf6cb13eb4803bcc12dc4d95ea8ff4e">array_ops.sql_in</a>
+</li>
+<li>array_div()
+: <a class="el" href="array__ops_8sql__in.html#a6cc05e7052495f8b64692faf40219576">array_ops.sql_in</a>
+</li>
+<li>array_dot()
+: <a class="el" href="array__ops_8sql__in.html#acde10964ed23b7c8da515fb84cb8d5e0">array_ops.sql_in</a>
+</li>
+<li>array_elem_corr_agg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a8928da50ddcf85de812ab058214324db">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_max()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a1de2b192c73ede707b42726e9469664d">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_min()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a2ec0ea5a3faba236a75d4d9cc16e40a4">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_fill()
+: <a class="el" href="array__ops_8sql__in.html#a065a5323f3b742be47e39ad8b4c90fc2">array_ops.sql_in</a>
+</li>
+<li>array_filter()
+: <a class="el" href="array__ops_8sql__in.html#acc295a568878940ffc3e2c9a75990efb">array_ops.sql_in</a>
+</li>
+<li>array_max()
+: <a class="el" href="array__ops_8sql__in.html#ae891429cc50705c530f3e5ca15541849">array_ops.sql_in</a>
+</li>
+<li>array_mean()
+: <a class="el" href="array__ops_8sql__in.html#a407598f9eb70637798b02fd731bfca2c">array_ops.sql_in</a>
+</li>
+<li>array_min()
+: <a class="el" href="array__ops_8sql__in.html#a6659bf9d9363eb179fab34f81f8ac59b">array_ops.sql_in</a>
+</li>
+<li>array_mult()
+: <a class="el" href="array__ops_8sql__in.html#a652d70c480d484c4a1a92ded384b0dd7">array_ops.sql_in</a>
+</li>
+<li>array_of_bigint()
+: <a class="el" href="array__ops_8sql__in.html#ab7d8550e66d2e0bd54b8f0997d93880c">array_ops.sql_in</a>
+</li>
+<li>array_of_float()
+: <a class="el" href="array__ops_8sql__in.html#ab066e65a41db78b00b4532996b2a6efc">array_ops.sql_in</a>
+</li>
+<li>array_scalar_add()
+: <a class="el" href="array__ops_8sql__in.html#a0b6ffe59b12c3dee076c3059f9ab363f">array_ops.sql_in</a>
+</li>
+<li>array_scalar_mult()
+: <a class="el" href="array__ops_8sql__in.html#ae6881cc5c86941b6ffca35d7f3cd5c12">array_ops.sql_in</a>
+</li>
+<li>array_stddev()
+: <a class="el" href="array__ops_8sql__in.html#a3b6c2d173a611e6d6b184d825c2b336d">array_ops.sql_in</a>
+</li>
+<li>array_sub()
+: <a class="el" href="array__ops_8sql__in.html#a2875a161a01c7dcdea9a4997b074eefc">array_ops.sql_in</a>
+</li>
+<li>array_sum()
+: <a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec">array_ops.sql_in</a>
+</li>
+<li>array_sum_big()
+: <a class="el" href="array__ops_8sql__in.html#a418de59800833aa95f9b7cbd6b12901c">array_ops.sql_in</a>
+</li>
+<li>array_to_1d()
+: <a class="el" href="linalg_8sql__in.html#a71304c333a999e69ac1430c285c35181">linalg.sql_in</a>
+</li>
+<li>array_to_2d()
+: <a class="el" href="linalg_8sql__in.html#a0799a4ccb93a699a280861126c3e1a74">linalg.sql_in</a>
+</li>
+<li>array_union()
+: <a class="el" href="crf_8sql__in.html#a73693c959db4ca3dbfd1b9debcca2c8f">crf.sql_in</a>
+</li>
+<li>assert()
+: <a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd">utilities.sql_in</a>
+</li>
+<li>assoc_rules()
+: <a class="el" href="assoc__rules_8sql__in.html#af9456adb6dad01e452415b9a0a5371dc">assoc_rules.sql_in</a>
+</li>
+<li>avg()
+: <a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4">linalg.sql_in</a>
+</li>
+<li>avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#ad468b52a7caa9b47a0650d39e32d9c50">linalg.sql_in</a>
+</li>
+<li>avg_vector_merge()
+: <a class="el" href="linalg_8sql__in.html#a5ac255c13e1f2d91274b829ff0383e44">linalg.sql_in</a>
+</li>
+<li>avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#aaea269f4746f952faadffed9d9b107d7">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_b.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_b.html b/docs/latest/globals_func_b.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+<table cellspacing="0" cellpadding="0">
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+  </div>
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+$(document).ready(function(){initNavTree('globals_func_b.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
+<li>bernoulli_cdf()
+: <a class="el" href="prob_8sql__in.html#aea21a931dc5578a570e3370af3d8d43a">prob.sql_in</a>
+</li>
+<li>bernoulli_pmf()
+: <a class="el" href="prob_8sql__in.html#a434b3ad1f3964835834dc2a942b820ef">prob.sql_in</a>
+</li>
+<li>bernoulli_quantile()
+: <a class="el" href="prob_8sql__in.html#a7133c2e86fd2f6384416ee0e4fd3a60b">prob.sql_in</a>
+</li>
+<li>beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a72e1cca872da35592075dbcfb18aed3f">prob.sql_in</a>
+</li>
+<li>beta_pdf()
+: <a class="el" href="prob_8sql__in.html#aa105049e6e3bb9b3891b0ed1b343e28e">prob.sql_in</a>
+</li>
+<li>beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a32433aa742c0504d33e98e28a3e2f190">prob.sql_in</a>
+</li>
+<li>big_or()
+: <a class="el" href="sketch_8sql__in.html#a335fba86bbb3fb5a3284808cc64b8f16">sketch.sql_in</a>
+</li>
+<li>binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#aa5000bad6e2e4af1c8cbfec7ea884476">prob.sql_in</a>
+</li>
+<li>binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#aa0614475b8685bf8e37533d2ac5bb116">prob.sql_in</a>
+</li>
+<li>binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#a49f421c58d2e1abd63b83d71af9edf21">prob.sql_in</a>
+</li>
+<li>bool_to_text()
+: <a class="el" href="utilities_8sql__in.html#abc867482053e6692047d051b679953a5">utilities.sql_in</a>
+</li>
+<li>bytea8in()
+: <a class="el" href="utilities_8sql__in.html#a60c4765ce8dc639cbb0ad4c389f5f1ad">utilities.sql_in</a>
+</li>
+<li>bytea8out()
+: <a class="el" href="utilities_8sql__in.html#adff923d42d4bdb2b631d6cafd5f37a43">utilities.sql_in</a>
+</li>
+<li>bytea8recv()
+: <a class="el" href="utilities_8sql__in.html#a244a7ff30f2788a6f4816417982eb13b">utilities.sql_in</a>
+</li>
+<li>bytea8send()
+: <a class="el" href="utilities_8sql__in.html#aeb90c740e8e6b67aa60a11c430fa9050">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_c.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_c.html b/docs/latest/globals_func_c.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
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+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+       class="ui-resizable-handle">
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+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_c.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
+<li>c45_classify()
+: <a class="el" href="c45_8sql__in.html#afe136e52f498f2ff9e2b91e38e29d670">c45.sql_in</a>
+</li>
+<li>c45_clean()
+: <a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793">c45.sql_in</a>
+</li>
+<li>c45_display()
+: <a class="el" href="c45_8sql__in.html#a32d2bcbc016c990991d77b6f6148306d">c45.sql_in</a>
+</li>
+<li>c45_genrule()
+: <a class="el" href="c45_8sql__in.html#ac71787c47795b3b0b133cdbd37438242">c45.sql_in</a>
+</li>
+<li>c45_score()
+: <a class="el" href="c45_8sql__in.html#a1b634db47e9006d114da0987e80b9601">c45.sql_in</a>
+</li>
+<li>c45_train()
+: <a class="el" href="c45_8sql__in.html#a4fbee855d22101d15d195d573189eb98">c45.sql_in</a>
+</li>
+<li>cauchy_cdf()
+: <a class="el" href="prob_8sql__in.html#a2d8874c2a5679403a473bfedb14467a4">prob.sql_in</a>
+</li>
+<li>cauchy_pdf()
+: <a class="el" href="prob_8sql__in.html#aebfad9365a7fc7a553c3b5c7931f2450">prob.sql_in</a>
+</li>
+<li>cauchy_quantile()
+: <a class="el" href="prob_8sql__in.html#ae8aa9b741e89c8d9236a682d218006e0">prob.sql_in</a>
+</li>
+<li>check_if_col_exists()
+: <a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470">utilities.sql_in</a>
+</li>
+<li>check_if_raises_error()
+: <a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0">utilities.sql_in</a>
+</li>
+<li>chi2_gof_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4c912b329fb103a44253932a653d4e40">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a61c31dfde998db18afd6552239b872c4">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a2b8265718a16ec65e89d2ab512f6a3e1">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a7c580537666776f1bd4b9d4a0a6b6438">hypothesis_tests.sql_in</a>
+</li>
+<li>chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#a230513b6b549d5b445cbacbdbab42c15">prob.sql_in</a>
+</li>
+<li>chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#a90bccc717d7052e83bafd7f160a783b1">prob.sql_in</a>
+</li>
+<li>chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad125307fe65a33b60f6dd524037d4548">prob.sql_in</a>
+</li>
+<li>cleanup_madlib_temp_tables()
+: <a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a">utilities.sql_in</a>
+</li>
+<li>cleanup_madlib_temp_tables_script()
+: <a class="el" href="utilities_8sql__in.html#a156eb9d1c814592e59600980977a64eb">utilities.sql_in</a>
+</li>
+<li>closest_column()
+: <a class="el" href="linalg_8sql__in.html#a31c8d60f9a631c27f5f91964e0108da9">linalg.sql_in</a>
+</li>
+<li>closest_columns()
+: <a class="el" href="linalg_8sql__in.html#ad864339591086b635d12015db993b5bc">linalg.sql_in</a>
+</li>
+<li>clustered_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a91f46572f245f34c9fe92d0ec806a998">clustered_variance_coxph.sql_in</a>
+</li>
+<li>clustered_variance_linregr()
+: <a class="el" href="clustered__variance_8sql__in.html#abaa6702a3482f65199ce64b8a14316d7">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_logregr()
+: <a class="el" href="clustered__variance_8sql__in.html#a0debc923b0494f8259938d9c6f5cdbcd">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_mlogregr()
+: <a class="el" href="clustered__variance_8sql__in.html#aa72cfa6f9259cceea3e36b6db45a7624">clustered_variance.sql_in</a>
+</li>
+<li>cmsketch()
+: <a class="el" href="sketch_8sql__in.html#ae6671bfb7474db7d8b5c9dada29f36ab">sketch.sql_in</a>
+</li>
+<li>cmsketch_centile()
+: <a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">sketch.sql_in</a>
+</li>
+<li>cmsketch_count()
+: <a class="el" href="sketch_8sql__in.html#a3498d2c778d1289154f61d34e84c609e">sketch.sql_in</a>
+</li>
+<li>cmsketch_depth_histogram()
+: <a class="el" href="sketch_8sql__in.html#a9e6d30f20b724b96249cc4a0f67a279e">sketch.sql_in</a>
+</li>
+<li>cmsketch_median()
+: <a class="el" href="sketch_8sql__in.html#a0ef6428fa7ba4b7f7b1f633e6f8003ea">sketch.sql_in</a>
+</li>
+<li>cmsketch_rangecount()
+: <a class="el" href="sketch_8sql__in.html#aeff9e36cfb3338c4e405d4ac77d3968c">sketch.sql_in</a>
+</li>
+<li>cmsketch_width_histogram()
+: <a class="el" href="sketch_8sql__in.html#a8482f62849adf40a2c7df78c23ea33a4">sketch.sql_in</a>
+</li>
+<li>compute_coxph_result()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a74fe60f99c3808e38421c71286850ea8">cox_prop_hazards.sql_in</a>
+</li>
+<li>compute_lincrf()
+: <a class="el" href="crf_8sql__in.html#a20c096420d878cdd66dac6a232483737">crf.sql_in</a>
+</li>
+<li>conjugate_gradient()
+: <a class="el" href="conjugate__gradient_8sql__in.html#ab94e8a12cc9c2d0dac35168721ec8585">conjugate_gradient.sql_in</a>
+</li>
+<li>correlation()
+: <a class="el" href="correlation_8sql__in.html#a17cb16646fa7ebdf7ce7dd8c58349ce5">correlation.sql_in</a>
+</li>
+<li>correlation_agg()
+: <a class="el" href="correlation_8sql__in.html#a1c3081444f6bafc88a9b377f2af32968">correlation.sql_in</a>
+</li>
+<li>correlation_final()
+: <a class="el" href="correlation_8sql__in.html#a85a1a9b0ae5e608fd235f08ae01ff966">correlation.sql_in</a>
+</li>
+<li>correlation_merge()
+: <a class="el" href="correlation_8sql__in.html#ac53cf4dd28185715a1798176bce44b1b">correlation.sql_in</a>
+</li>
+<li>correlation_transition()
+: <a class="el" href="correlation_8sql__in.html#ae6223d0ba3e2b8a2d298ee78a293be6a">correlation.sql_in</a>
+</li>
+<li>cosine_similarity()
+: <a class="el" href="linalg_8sql__in.html#a1782f2ba00d9f9fab20894a576079f87">linalg.sql_in</a>
+</li>
+<li>cox_prop_hazards()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ad778b289eb19ae0bb2b7e02a89bab3bc">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_prop_hazards_regr()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3310cf98478b7c1e400e8fb1b3965d30">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_zph()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a682d95d5475ce33e47937067cadc2766">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_a_b()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a5f5ca89e3265e170c325125e61ef191d">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_final()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a2373d116ad1e2b2090f8c23a0c15edb5">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_merge()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a62bff141ced5972df28ceecdfe522888">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_transition()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a7081022015c00a14ecca27e017a4458b">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_clustered_stats()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a713f413fc571244b7cec6c0084a61eb9">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_w()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a523e61b6d56c8ff59fa8ae3121073a49">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a2743e0cecadd7f3fa2a16f9d7d40564b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#aeb95cacc36c5d6c8cc25c8d37418c71b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_merge()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ab122003102dd22274a76aa1a830c69dd">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a03a39bc1fee64cd9b8b6a373758819da">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_improved_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ac5ae827993064fd6e0ccf13a96262cdc">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_step_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a6a9ae1ea761b31e1522c47a9794cc233">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a41a415076894d3844475208b2163a267">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_outer()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abb21bacbff7362724d2698568d857555">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_predict()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#acd445fe632e93b2c8260b0455478e2e5">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_inner_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3c96655e41fd17c86dfc9ad37b0959d0">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_outer_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abaf93fc77a284f0b765a64ef827c1bed">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_train()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3fd1cd09fd5de41d6dc8aca0a18eec8b">cox_prop_hazards.sql_in</a>
+</li>
+<li>create_indicator_variables()
+: <a class="el" href="data__preparation_8sql__in.html#a617b67999ab8c9c07fb0d24101a1115d">data_preparation.sql_in</a>
+</li>
+<li>create_nb_classify_view()
+: <a class="el" href="bayes_8sql__in.html#a76a03f1d7ff2b1891cb310668077b386">bayes.sql_in</a>
+</li>
+<li>create_nb_prepared_data_tables()
+: <a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2">bayes.sql_in</a>
+</li>
+<li>create_nb_probs_view()
+: <a class="el" href="bayes_8sql__in.html#a6d1186aa04f4d4e3ac44035467818d3c">bayes.sql_in</a>
+</li>
+<li>create_schema_pg_temp()
+: <a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000">utilities.sql_in</a>
+</li>
+<li>crf_test_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#abf2a39a0b84a5e7ae215db28b3a87f81">crf_data_loader.sql_in</a>
+</li>
+<li>crf_test_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_feature_gen.sql_in</a>
+</li>
+<li>crf_train_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#aaa609ec1d1d4cff0f335fa358091d5c3">crf_data_loader.sql_in</a>
+</li>
+<li>crf_train_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_feature_gen.sql_in</a>
+</li>
+<li>cross_validation_general()
+: <a class="el" href="cross__validation_8sql__in.html#a5efbb27898e99f7685816d9d5dfd082c">cross_validation.sql_in</a>
+</li>
+<li>crossprod()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#a588d5855d1673f7bcbc2a1c983081f76">pivotalr_arrayops.sql_in</a>
+</li>
+<li>crossprod_sym()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#aa1e36bf44b3206314c186bba80179c9c">pivotalr_arrayops.sql_in</a>
+</li>
+<li>cv_linregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#aa572f1f57c0dd106b30948928161d8cc">cross_validation.sql_in</a>
+</li>
+<li>cv_linregr_train()
+: <a class="el" href="cross__validation_8sql__in.html#aa151eb3fa9acc7f4cc33236e22ad4362">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#ac1b5c57473ff672af45191c8d53f46ed">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#ac7dbc115f0c4624ccbf62b2d5494388d">cross_validation.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_d.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
+<li>deconstruct_2d_array()
+: <a class="el" href="linalg_8sql__in.html#a21d0a134f1674f71b3e872aacc5f3789">linalg.sql_in</a>
+</li>
+<li>dense_direct_linear_system()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a379f260d8dfbf5c2be05c60776974188">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a63a3ed2c811216d3d88c120005a13875">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a607403f90e1921435183d031825e5e54">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#acbfc47693d0b50ccfc6b7f7c38209b18">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a52f2bcdd0d038f19e64a44d4bb07fefb">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a01935dd8c4aef8c92e77b7f0f905fa4b">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a73ebde08b8395fd7bddbe1dc37b9539f">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#ad34728809ab3121705342cf91b34c6dd">dense_linear_systems.sql_in</a>
+</li>
+<li>discrete_distribution_agg()
+: <a class="el" href="distribution_8sql__in.html#af9418a44cb502919d54c59e909e19606">distribution.sql_in</a>
+</li>
+<li>discrete_distribution_final()
+: <a class="el" href="distribution_8sql__in.html#a99a03822b9063a37df8641cfcf2e450b">distribution.sql_in</a>
+</li>
+<li>discrete_distribution_transition()
+: <a class="el" href="distribution_8sql__in.html#aa7e45a9ebd2b5ad656e61aa9276116d3">distribution.sql_in</a>
+</li>
+<li>dist_angle()
+: <a class="el" href="linalg_8sql__in.html#a8c7b9281a72ff22caf06161701b27e84">linalg.sql_in</a>
+</li>
+<li>dist_inf_norm()
+: <a class="el" href="linalg_8sql__in.html#a31fa9f2f5b45507c09f136464fdad1db">linalg.sql_in</a>
+</li>
+<li>dist_jaccard()
+: <a class="el" href="linalg_8sql__in.html#ac1397ac9f4a35b3b67c3be05b5e1a828">linalg.sql_in</a>
+</li>
+<li>dist_norm1()
+: <a class="el" href="linalg_8sql__in.html#aad193850e79c4b9d811ca9bc53e13476">linalg.sql_in</a>
+</li>
+<li>dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#aa58e51526edea6ea98db30b6f250adb4">linalg.sql_in</a>
+</li>
+<li>dist_pnorm()
+: <a class="el" href="linalg_8sql__in.html#ad9cc156ae57bf7c0a2fe90798259105a">linalg.sql_in</a>
+</li>
+<li>dist_tanimoto()
+: <a class="el" href="linalg_8sql__in.html#afa13b4c6122b99422d666dedea136c18">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_e.html
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diff --git a/docs/latest/globals_func_e.html b/docs/latest/globals_func_e.html
new file mode 100644
index 0000000..922bc1d
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+++ b/docs/latest/globals_func_e.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
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+<!-- Generated by Doxygen 1.8.10 -->
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+  <div id="nav-tree">
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+       class="ui-resizable-handle">
+  </div>
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+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_e.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
+<li>elastic_net_binomial_predict()
+: <a class="el" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_binomial_prob()
+: <a class="el" href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_gaussian_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_train()
+: <a class="el" href="elastic__net_8sql__in.html#a63c8ed415005cc446a59bdc47a09791d">elastic_net.sql_in</a>
+</li>
+<li>exponential_cdf()
+: <a class="el" href="prob_8sql__in.html#a6d1bf6816f56b8e5ba6bf6ca94752f46">prob.sql_in</a>
+</li>
+<li>exponential_pdf()
+: <a class="el" href="prob_8sql__in.html#a18a5458c4bc85f0c4ea321317f90bdbb">prob.sql_in</a>
+</li>
+<li>exponential_quantile()
+: <a class="el" href="prob_8sql__in.html#ae3687b8e69a402154b829a6531b1b279">prob.sql_in</a>
+</li>
+<li>extreme_value_cdf()
+: <a class="el" href="prob_8sql__in.html#acffffe04c15eccd2e88cdac250bccc68">prob.sql_in</a>
+</li>
+<li>extreme_value_pdf()
+: <a class="el" href="prob_8sql__in.html#a03a3494462f4cb8c9fb6212e72b0b2e9">prob.sql_in</a>
+</li>
+<li>extreme_value_quantile()
+: <a class="el" href="prob_8sql__in.html#aeb5a7d295b83a891774a4fb0ef27c458">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_f.html
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diff --git a/docs/latest/globals_func_f.html b/docs/latest/globals_func_f.html
new file mode 100644
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
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+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+          </span><span class="right">
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+          </span>
+        </div>
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+    </div>
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+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_f.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
+<li>f_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5a946aa469ff6ddf8d276db16fa26ad4">hypothesis_tests.sql_in</a>
+</li>
+<li>f_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abc6006e8da028dd93ac48b8fd9ae8786">hypothesis_tests.sql_in</a>
+</li>
+<li>fisher_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a6c5b3e35531e44098f9d0cbef14cb8a6">prob.sql_in</a>
+</li>
+<li>fisher_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a1c7937426379a8913519a6abc5a38ac2">prob.sql_in</a>
+</li>
+<li>fisher_f_quantile()
+: <a class="el" href="prob_8sql__in.html#ab6ed888a5338a0bee9c55edf4d33847f">prob.sql_in</a>
+</li>
+<li>float8arr_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a2a6e17fd9dafc349c7cfa070486e6ad1">svec.sql_in</a>
+</li>
+<li>float8arr_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af8db439cf1a73749052f06fc30c7d282">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int2()
+: <a class="el" href="svec_8sql__in.html#a5e38a27f556a6ac88549efec37da786f">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int4()
+: <a class="el" href="svec_8sql__in.html#a9c05d02233680a2e96dc6058155e4483">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a4862b4e281323db16e9be4dfd681db2e">svec.sql_in</a>
+</li>
+<li>float8arr_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a0bc8ef7e7b8e37b784c81a940bd817f0">svec.sql_in</a>
+</li>
+<li>float8arr_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a20d4a7b907e967351f6439640482e0f9">svec.sql_in</a>
+</li>
+<li>float8arr_div_svec()
+: <a class="el" href="svec_8sql__in.html#a5c4ac1cc9a2ee3057958070b8e7bffcc">svec.sql_in</a>
+</li>
+<li>float8arr_eq()
+: <a class="el" href="svec_8sql__in.html#a0684988340d533c00523951f9a95107e">svec.sql_in</a>
+</li>
+<li>float8arr_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a4c82ac5de8e026c85e83aae4e0f06220">svec.sql_in</a>
+</li>
+<li>float8arr_minus_svec()
+: <a class="el" href="svec_8sql__in.html#a457f2f9210baba1c13820801c42cd06a">svec.sql_in</a>
+</li>
+<li>float8arr_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#abb0b3f155c45bb370ef7adac49096835">svec.sql_in</a>
+</li>
+<li>float8arr_mult_svec()
+: <a class="el" href="svec_8sql__in.html#a0e7dd7a82b41f4b8344efa21208e1637">svec.sql_in</a>
+</li>
+<li>float8arr_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6bb068ab6ba775619fe787ed845f5a0d">svec.sql_in</a>
+</li>
+<li>float8arr_plus_svec()
+: <a class="el" href="svec_8sql__in.html#a98aa60ec0c58c828b0a72c75d604322e">svec.sql_in</a>
+</li>
+<li>fmsketch_dcount()
+: <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b">sketch.sql_in</a>
+</li>
+<li>forest_predict()
+: <a class="el" href="random__forest_8sql__in.html#aef268338ffa67f54ca1371326bb25fa0">random_forest.sql_in</a>
+</li>
+<li>forest_train()
+: <a class="el" href="random__forest_8sql__in.html#a04cbc760f3790bcf69fe48e008cfba3d">random_forest.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_g.html
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diff --git a/docs/latest/globals_func_g.html b/docs/latest/globals_func_g.html
new file mode 100644
index 0000000..2d4db69
--- /dev/null
+++ b/docs/latest/globals_func_g.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
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+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_g.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
+<li>gamma_cdf()
+: <a class="el" href="prob_8sql__in.html#ab6760b0486bad2f1ab0635eb59404e7c">prob.sql_in</a>
+</li>
+<li>gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a6c37e3bda2596accbb46525321a328c4">prob.sql_in</a>
+</li>
+<li>gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#ac48bbd491bd34831415705c3a0b7bf29">prob.sql_in</a>
+</li>
+<li>gamma_random()
+: <a class="el" href="sample_8sql__in.html#afc523f72e64820329acb8874c2a759d3">sample.sql_in</a>
+</li>
+<li>gen_doc_svecs()
+: <a class="el" href="svec__util_8sql__in.html#a6136c83e9c419d783d1ec6db061f8d6e">svec_util.sql_in</a>
+</li>
+<li>gen_rules_from_cfp()
+: <a class="el" href="assoc__rules_8sql__in.html#a9a872579615586ce478d32ba145cc279">assoc_rules.sql_in</a>
+</li>
+<li>geometric_cdf()
+: <a class="el" href="prob_8sql__in.html#a00879bdf7d48ceddedb3b4cc33511497">prob.sql_in</a>
+</li>
+<li>geometric_pmf()
+: <a class="el" href="prob_8sql__in.html#a5e08db93bd448a1e2164e106ce5781a4">prob.sql_in</a>
+</li>
+<li>geometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a62674ca958aec0533cdf0a74a1dadea9">prob.sql_in</a>
+</li>
+<li>get_col()
+: <a class="el" href="linalg_8sql__in.html#a20f34c9e661191e5225cca7bc23252c5">linalg.sql_in</a>
+</li>
+<li>get_row()
+: <a class="el" href="linalg_8sql__in.html#af6b905fcac7746ef0ed0c36df4a1e070">linalg.sql_in</a>
+</li>
+<li>get_tree()
+: <a class="el" href="random__forest_8sql__in.html#af22b14c3da4f64b544b6c333a8e97070">random_forest.sql_in</a>
+</li>
+<li>get_tree_surr()
+: <a class="el" href="random__forest_8sql__in.html#a3ba1911b340a431982189e3e1f8c0111">random_forest.sql_in</a>
+</li>
+<li>glm()
+: <a class="el" href="glm_8sql__in.html#a4938524efbef7684ce8a8e2646f126ef">glm.sql_in</a>
+</li>
+<li>glm_predict()
+: <a class="el" href="glm_8sql__in.html#add7c22acc8eaaa3eace7089eadc5f367">glm.sql_in</a>
+</li>
+<li>glm_predict_binomial()
+: <a class="el" href="glm_8sql__in.html#a9a468186034a936c3997cad22b03cafe">glm.sql_in</a>
+</li>
+<li>glm_predict_poisson()
+: <a class="el" href="glm_8sql__in.html#a941d49196e573048852def804c5ce4c4">glm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[30/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals.html b/docs/latest/globals.html
new file mode 100644
index 0000000..1f75a87
--- /dev/null
+++ b/docs/latest/globals.html
@@ -0,0 +1,1204 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
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+  $(window).load(resizeHeight);
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+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
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+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index__"></a>- _ -</h3><ul>
+<li>__arima_adjust()
+: <a class="el" href="arima_8sql__in.html#af6f5621d79045e8270e6785ed0d98ce3">arima.sql_in</a>
+</li>
+<li>__arima_diff()
+: <a class="el" href="arima_8sql__in.html#ab686e554403a61a5404d83e692735ada">arima.sql_in</a>
+</li>
+<li>__arima_lm()
+: <a class="el" href="arima_8sql__in.html#aadd3c7627043810a7e650e80b7fafd52">arima.sql_in</a>
+</li>
+<li>__arima_lm_delta()
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+</li>
+<li>__arima_lm_result_agg()
+: <a class="el" href="arima_8sql__in.html#addf886fb09f74d10dfd1875c641d814b">arima.sql_in</a>
+</li>
+<li>__arima_lm_result_ffunc()
+: <a class="el" href="arima_8sql__in.html#afd8e1b6c57007a47ba64b22718d4e26f">arima.sql_in</a>
+</li>
+<li>__arima_lm_result_pfunc()
+: <a class="el" href="arima_8sql__in.html#a76b2c9d653383c81acde8383bb29aa97">arima.sql_in</a>
+</li>
+<li>__arima_lm_result_sfunc()
+: <a class="el" href="arima_8sql__in.html#aa919ba0d4fe79c3b55ee60b7ad17c7de">arima.sql_in</a>
+</li>
+<li>__arima_lm_stat_agg()
+: <a class="el" href="arima_8sql__in.html#ae9b8675db85344ac26484f8c125f57b6">arima.sql_in</a>
+</li>
+<li>__arima_lm_stat_ffunc()
+: <a class="el" href="arima_8sql__in.html#abb44a6b3bf206e966a9746dde942ddc3">arima.sql_in</a>
+</li>
+<li>__arima_lm_stat_sfunc()
+: <a class="el" href="arima_8sql__in.html#a884a57ef18bd9e1a1b4137269786bf19">arima.sql_in</a>
+</li>
+<li>__arima_residual()
+: <a class="el" href="arima_8sql__in.html#a6955c191d95e1531655cea4617caf12c">arima.sql_in</a>
+</li>
+<li>__array_add()
+: <a class="el" href="clustered__variance_8sql__in.html#a2d33f7a6a98dfc92c6d4a017ceabbd9a">clustered_variance.sql_in</a>
+</li>
+<li>__array_elem_corr_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a31f48cba79d114f8f251c5aa77b3c32a">cox_prop_hazards.sql_in</a>
+</li>
+<li>__array_elem_corr_merge()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a189426c7d99c600856205d15d2fe445c">cox_prop_hazards.sql_in</a>
+</li>
+<li>__array_elem_corr_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a597e43a46842b113412d0940b3d175e4">cox_prop_hazards.sql_in</a>
+</li>
+<li>__array_elem_in()
+: <a class="el" href="dt__utility_8sql__in.html#aa1341c743f3dabd5a96f36cda5564679">dt_utility.sql_in</a>
+</li>
+<li>__array_element_max()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a8e3917098eeb129096fa871e7e593e1d">cox_prop_hazards.sql_in</a>
+</li>
+<li>__array_element_min()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abd49fa0a100bfe513baa90e32fe37839">cox_prop_hazards.sql_in</a>
+</li>
+<li>__array_indexed_agg()
+: <a class="el" href="dt_8sql__in.html#aa8642bda8d691e9a96de16c22528a21a">dt.sql_in</a>
+</li>
+<li>__array_indexed_agg_ffunc()
+: <a class="el" href="dt_8sql__in.html#adb30a86c68851825eaea7ab5177535d9">dt.sql_in</a>
+</li>
+<li>__array_indexed_agg_prefunc()
+: <a class="el" href="dt_8sql__in.html#a5b11d5994aa54ec795bcd5ad1a4d48db">dt.sql_in</a>
+</li>
+<li>__array_indexed_agg_sfunc()
+: <a class="el" href="dt_8sql__in.html#ab68e6854d41c1096d864fdac1f144227">dt.sql_in</a>
+</li>
+<li>__array_search()
+: <a class="el" href="dt__utility_8sql__in.html#a213c48be09bf0227f2feba174761c354">dt_utility.sql_in</a>
+</li>
+<li>__array_sort()
+: <a class="el" href="dt__utility_8sql__in.html#a4d1e4d0afa6cf17b02795e9cdd05ea27">dt_utility.sql_in</a>
+</li>
+<li>__array_sum()
+: <a class="el" href="clustered__variance_8sql__in.html#a329c662f02c985ed68b886cf2bd29f37">clustered_variance.sql_in</a>
+</li>
+<li>__assert()
+: <a class="el" href="dt__utility_8sql__in.html#a61821817abe0e95f52f6fac2315e566d">dt_utility.sql_in</a>
+</li>
+<li>__assert_table()
+: <a class="el" href="dt__utility_8sql__in.html#a50de3c039d3e5457c79bccb9fd5cbfb0">dt_utility.sql_in</a>
+</li>
+<li>__best_scv_aggr()
+: <a class="el" href="dt_8sql__in.html#a0788025ac1212c14d1e7caa0d0fbaf26">dt.sql_in</a>
+</li>
+<li>__best_scv_prefunc()
+: <a class="el" href="dt_8sql__in.html#ab27e49b2e2fd5d0ab7cfeb1e0fe99434">dt.sql_in</a>
+</li>
+<li>__best_scv_sfunc()
+: <a class="el" href="dt_8sql__in.html#accc1e10002e877e0d8f3797fceff593e">dt.sql_in</a>
+</li>
+<li>__bigint_array_add()
+: <a class="el" href="dt_8sql__in.html#af6c95704957abbd43777161ddd0dbedf">dt.sql_in</a>
+</li>
+<li>__bigint_array_sum()
+: <a class="el" href="dt_8sql__in.html#a0080a73793982aa08420843ede8a8d19">dt.sql_in</a>
+</li>
+<li>__binomial_fista_final()
+: <a class="el" href="elastic__net_8sql__in.html#af83bb77c4eb5c9f5750f35d56fd39117">elastic_net.sql_in</a>
+</li>
+<li>__binomial_fista_merge()
+: <a class="el" href="elastic__net_8sql__in.html#a2284166bacd4b45fb34d50b2d7aec4d4">elastic_net.sql_in</a>
+</li>
+<li>__binomial_fista_result()
+: <a class="el" href="elastic__net_8sql__in.html#a1e0cc178e025d8eebb0637d97a02f624">elastic_net.sql_in</a>
+</li>
+<li>__binomial_fista_state_diff()
+: <a class="el" href="elastic__net_8sql__in.html#aba9d8b5ca783ad4c3d551fd7b2797ade">elastic_net.sql_in</a>
+</li>
+<li>__binomial_fista_step()
+: <a class="el" href="elastic__net_8sql__in.html#a69a71f6e1f8fb3f7d8ad8b987bd432db">elastic_net.sql_in</a>
+</li>
+<li>__binomial_fista_transition()
+: <a class="el" href="elastic__net_8sql__in.html#a43f125953d105e8ad2243f7c722cf753">elastic_net.sql_in</a>
+</li>
+<li>__binomial_igd_final()
+: <a class="el" href="elastic__net_8sql__in.html#a39c4af5547dc7f28c189fe5ef8d09ba5">elastic_net.sql_in</a>
+</li>
+<li>__binomial_igd_merge()
+: <a class="el" href="elastic__net_8sql__in.html#ae4aed277de996eab9023335413fe9e28">elastic_net.sql_in</a>
+</li>
+<li>__binomial_igd_result()
+: <a class="el" href="elastic__net_8sql__in.html#a93bfe398c91709e7d55ed94d18c6ac23">elastic_net.sql_in</a>
+</li>
+<li>__binomial_igd_state_diff()
+: <a class="el" href="elastic__net_8sql__in.html#a2968538b152e7f38e71bcff8e55b768e">elastic_net.sql_in</a>
+</li>
+<li>__binomial_igd_step()
+: <a class="el" href="elastic__net_8sql__in.html#a791a9701c20c65d1e86a1d5c3462b99d">elastic_net.sql_in</a>
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+: <a class="el" href="decision__tree_8sql__in.html#a25ef01785744c7cf005e6b517602b497">decision_tree.sql_in</a>
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+: <a class="el" href="random__forest_8sql__in.html#a9b188037fd90e41b9590d8296351b645">random_forest.sql_in</a>
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+<li>_rf_con_imp_score()
+: <a class="el" href="random__forest_8sql__in.html#aa237f4a451150d5a44f7e871a62b4621">random_forest.sql_in</a>
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+<li>_split_final()
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+<li>_split_merge()
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+<li>_split_transition()
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+<li>_string_to_array()
+: <a class="el" href="utilities_8sql__in.html#a967afc3041b28c8ceedcef8d25c26b9b">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[15/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__cox__prop__hazards.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: Cox-Proportional Hazards Regression</title>
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+  <div class="headertitle">
+<div class="title">Cox-Proportional Hazards Regression<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#training">Training Function</a> </li>
+<li class="level1">
+<a href="#cox_zph">PHA Test Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Proportional-Hazard models enable the comparison of various survival models. These survival models are functions describing the probability of a one-item event (prototypically, this event is death) with respect to time. The interval of time before the occurrence of death can be called the survival time. Let T be a random variable representing the survival time, with a cumulative probability function P(t). Informally, P(t) is the probability that death has happened before time t.</p>
+<p><a class="anchor" id="training"></a></p><dl class="section user"><dt>Training Function</dt><dd></dd></dl>
+<p>Following is the syntax for the <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> training function: </p><pre class="syntax">
+coxph_train( source_table,
+             output_table,
+             dependent_variable,
+             independent_variable,
+             right_censoring_status,
+             strata,
+             optimizer_params
+           )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>TEXT. The name of the table containing input data. </dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the table where the output model is saved. The output is saved in the table named by the <em>output_table</em> argument. It has the following columns: </p><table  class="output">
+<tr>
+<th>coef </th><td>FLOAT8[]. Vector of the coefficients.  </td></tr>
+<tr>
+<th>loglikelihood </th><td>FLOAT8. Log-likelihood value of the MLE estimate.  </td></tr>
+<tr>
+<th>std_err </th><td>FLOAT8[]. Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>stats </th><td>FLOAT8[]. Vector of the statistics of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>FLOAT8[]. Vector of the p-values of the coefficients.  </td></tr>
+<tr>
+<th>hessian </th><td>FLOAT8[]. The Hessian matrix computed using the final solution.  </td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The number of iterations performed by the optimizer.  </td></tr>
+</table>
+<p>Additionally, a summary output table is generated that contains a summary of the parameters used for building the Cox model. It is stored in a table named &lt;output_table&gt;_summary. It has the following columns: </p><table  class="output">
+<tr>
+<th>source_table </th><td>The source table name.  </td></tr>
+<tr>
+<th>dependent_variable </th><td>The dependent variable name.  </td></tr>
+<tr>
+<th>independent_variable </th><td>The independent variable name.  </td></tr>
+<tr>
+<th>right_censoring_status </th><td>The right censoring status  </td></tr>
+<tr>
+<th>strata </th><td>The stratification columns  </td></tr>
+<tr>
+<th>num_processed </th><td>The number of rows that were actually used in the computation.  </td></tr>
+<tr>
+<th>num_missing_rows_skipped </th><td>The number of rows that were skipped in the computation due to NULL values in them.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_variable </dt>
+<dd>TEXT. A string containing the name of a column that contains an array of numeric values, or a string expression in the format 'ARRAY[1, x1, x2, x3]', where <em>x1</em>, <em>x2</em> and <em>x3</em> are column names. Dependent variables refer to the time of death. There is no need to pre-sort the data. </dd>
+<dt>independent_variable </dt>
+<dd>TEXT. The name of the independent variable. </dd>
+<dt>right_censoring_status (optional) </dt>
+<dd>TEXT, default: TRUE for all observations. A string containing an expression that evaluates to the right-censoring status for the observation&mdash;TRUE if the observation is not censored and FALSE if the observation is censored. The string could contain the name of the column containing the right-censoring status, a fixed Boolean expression (i.e., 'true', 'false', '0', '1') that applies to all observations, or a Boolean expression such as 'column_name &lt; 10' that can be evaluated for each observation. </dd>
+<dt>strata (optional) </dt>
+<dd>VARCHAR, default: NULL, which does not do any stratifications. A string of comma-separated column names that are the strata ID variables used to do stratification. </dd>
+<dt>optimizer_params (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: NULL, which uses the default values of optimizer parameters: max_iter=100, optimizer=newton, tolerance=1e-8, array_agg_size=10000000, sample_size=1000000. It should be a string that contains 'key=value' pairs separated by commas. The meanings of these parameters are:</p>
+<ul>
+<li>max_iter &mdash; The maximum number of iterations. The computation stops if the number of iterations exceeds this, which usually means that there is no convergence.</li>
+<li>optimizer &mdash; The optimization method. Right now, "newton" is the only one supported.</li>
+<li>tolerance &mdash; The stopping criteria. When the difference between the log-likelihoods of two consecutive iterations is smaller than this number, the computation has already converged and stops.</li>
+<li>array_agg_size &mdash; To speed up the computation, the original data table is cut into multiple pieces, and each pieces of the data is aggregated into one big row. In the process of computation, the whole big row is loaded into memory and thus speed up the computation. This parameter controls approximately how many numbers we want to put into one big row. Larger value of array_agg_size may speed up more, but the size of the big row cannot exceed 1GB due to the restriction of PostgreSQL databases.</li>
+<li>sample_size &mdash; To cut the data into approximate equal pieces, we first sample the data, and then find out the break points using this sampled data. A larger sample_size produces more accurate break points.  </li>
+</ul>
+</dd>
+</dl>
+<p><a class="anchor" id="cox_zph"></a></p><dl class="section user"><dt>Proportional Hazards Assumption Test Function</dt><dd></dd></dl>
+<p>The <a class="el" href="cox__prop__hazards_8sql__in.html#a682d95d5475ce33e47937067cadc2766" title="Test the proportional hazards assumption for a Cox regression model fit (coxph_train) ...">cox_zph()</a> function tests the proportional hazards assumption (PHA) of a Cox regression.</p>
+<p>Proportional-hazard models enable the comparison of various survival models. These PH models, however, assume that the hazard for a given individual is a fixed proportion of the hazard for any other individual, and the ratio of the hazards is constant across time. MADlib does not currently have support for performing any transformation of the time to compute the correlation.</p>
+<p>The <a class="el" href="cox__prop__hazards_8sql__in.html#a682d95d5475ce33e47937067cadc2766" title="Test the proportional hazards assumption for a Cox regression model fit (coxph_train) ...">cox_zph()</a> function is used to test this assumption by computing the correlation of the residual of the <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> model with time.</p>
+<p>Following is the syntax for the <a class="el" href="cox__prop__hazards_8sql__in.html#a682d95d5475ce33e47937067cadc2766" title="Test the proportional hazards assumption for a Cox regression model fit (coxph_train) ...">cox_zph()</a> function: </p><pre class="syntax">
+cox_zph(cox_model_table, output_table)
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>cox_model_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the Cox Proportional-Hazards model.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd>TEXT. The name of the table where the test statistics are saved. The output table is named by the <em>output_table</em> argument and has the following columns: <table  class="output">
+<tr>
+<th>covariate </th><td>TEXT. The independent variables.  </td></tr>
+<tr>
+<th>rho </th><td>FLOAT8[]. Vector of the correlation coefficients between survival time and the scaled Schoenfeld residuals.  </td></tr>
+<tr>
+<th>chi_square </th><td>FLOAT8[]. Chi-square test statistic for the correlation analysis.  </td></tr>
+<tr>
+<th>p_value </th><td>FLOAT8[]. Two-side p-value for the chi-square statistic.  </td></tr>
+</table>
+</dd>
+</dl>
+<p>Additionally, the residual values are outputted to the table named <em>output_table</em>_residual. The table contains the following columns: </p><table  class="output">
+<tr>
+<th>&lt;dep_column_name&gt; </th><td>FLOAT8. Time values (dependent variable) present in the original source table.   </td></tr>
+<tr>
+<th>residual </th><td>FLOAT8[]. Difference between the original covariate values and the expectation of the covariates obtained from the coxph_train model.  </td></tr>
+<tr>
+<th>scaled_residual </th><td>Residual values scaled by the variance of the coefficients.  </td></tr>
+</table>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<ul>
+<li>Table names can be optionally schema qualified (current_schemas() is used if a schema name is not provided) and table and column names should follow case-sensitivity and quoting rules per the database. For instance, 'mytable' and 'MyTable' both resolve to the same entity&mdash;'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"'.</li>
+<li>The <a class="el" href="cox__prop__hazards_8sql__in.html#a3310cf98478b7c1e400e8fb1b3965d30">cox_prop_hazards_regr()</a> and <a class="el" href="cox__prop__hazards_8sql__in.html#ad778b289eb19ae0bb2b7e02a89bab3bc" title="Cox regression training function. ">cox_prop_hazards()</a> functions have been deprecated; <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> should be used instead.</li>
+</ul>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>The prediction function is provided to calculate the linear predictionors, risk or the linear terms for the given prediction data. It has the following syntax: <pre class="syntax">
+coxph_predict(model_table,
+              source_table,
+              id_col_name,
+              output_table,
+              pred_type,
+              reference)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. Name of the table containing the cox model.</p>
+<p class="enddd"></p>
+</dd>
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. Name of the table containing the prediction data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>id_col_name </dt>
+<dd><p class="startdd">TEXT. Name of the id column in the source table.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. Name of the table to store the prediction results in. The output table is named by the <em>output_table</em> argument and has the following columns: </p><table  class="output">
+<tr>
+<th>id </th><td>TEXT. The id column name from the source table.  </td></tr>
+<tr>
+<th>predicted_result </th><td>DOUBLE PRECISION. Result of prediction based of the value of the prediction type parameter.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>pred_type </dt>
+<dd><p class="startdd">TEXT, OPTIONAL. Type of prediction. This can be one of 'linear_predictors', 'risk', or 'terms'. DEFAULT='linear_predictors'.</p><ul>
+<li>'linear_predictors' calculates the dot product of the independent variables and the coefficients.</li>
+<li>'risk' is the exponentiated value of the linear prediction.</li>
+<li>'terms' correspond to the linear terms obtained by multiplying the independent variables with their corresponding coefficients values (without further calculating the sum of these terms) </li>
+</ul>
+<p class="enddd"></p>
+</dd>
+<dt>reference </dt>
+<dd>TEXT, OPTIONAL. Reference level to use for centering predictions. Can be one of 'strata', 'overall'. DEFAULT='strata'. Note that R uses 'sample' instead of 'overall' when referring to the overall mean value of the covariates as being the reference level. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the proportional hazards training method. <pre class="example">
+SELECT madlib.coxph_train();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+DROP TABLE IF EXISTS sample_data;
+CREATE TABLE sample_data (
+    id INTEGER NOT NULL,
+    grp DOUBLE PRECISION,
+    wbc DOUBLE PRECISION,
+    timedeath INTEGER,
+    status BOOLEAN
+);
+COPY sample_data FROM STDIN WITH DELIMITER '|';
+  0 |   0 | 1.45 |        35 | t
+  1 |   0 | 1.47 |        34 | t
+  3 |   0 |  2.2 |        32 | t
+  4 |   0 | 1.78 |        25 | t
+  5 |   0 | 2.57 |        23 | t
+  6 |   0 | 2.32 |        22 | t
+  7 |   0 | 2.01 |        20 | t
+  8 |   0 | 2.05 |        19 | t
+  9 |   0 | 2.16 |        17 | t
+ 10 |   0 |  3.6 |        16 | t
+ 11 |   1 |  2.3 |        15 | t
+ 12 |   0 | 2.88 |        13 | t
+ 13 |   1 |  1.5 |        12 | t
+ 14 |   0 |  2.6 |        11 | t
+ 15 |   0 |  2.7 |        10 | t
+ 16 |   0 |  2.8 |         9 | t
+ 17 |   1 | 2.32 |         8 | t
+ 18 |   0 | 4.43 |         7 | t
+ 19 |   0 | 2.31 |         6 | t
+ 20 |   1 | 3.49 |         5 | t
+ 21 |   1 | 2.42 |         4 | t
+ 22 |   1 | 4.01 |         3 | t
+ 23 |   1 | 4.91 |         2 | t
+ 24 |   1 |    5 |         1 | t
+\.
+</pre></li>
+<li>Run the Cox regression function. <pre class="example">
+SELECT madlib.coxph_train( 'sample_data',
+                           'sample_cox',
+                           'timedeath',
+                           'ARRAY[grp,wbc]',
+                           'status'
+                         );
+</pre></li>
+<li>View the results of the regression. <pre class="example">
+\x on
+SELECT * FROM sample_cox;
+</pre> Results: <pre class="result">
+-[ RECORD 1 ]--+----------------------------------------------------------------------------
+coef           | {2.54407073265254,1.67172094779487}
+loglikelihood  | -37.8532498733
+std_err        | {0.677180599294897,0.387195514577534}
+z_stats        | {3.7568570855419,4.31751114064138}
+p_values       | {0.000172060691513886,1.5779844638453e-05}
+hessian        | {{2.78043065745617,-2.25848560642414},{-2.25848560642414,8.50472838284472}}
+num_iterations | 5
+</pre></li>
+<li>Computing predictions using cox model. (This example uses the original data table to perform the prediction. Typically a different test dataset with the same features as the original training dataset would be used.) <pre class="example">
+\x off
+-- Display the linear predictors for the original dataset
+SELECT madlib.coxph_predict('sample_cox',
+                            'sample_data',
+                            'id',
+                            'sample_pred');
+</pre> <pre class="result">
+SELECT * FROM sample_pred;
+ id |  predicted_value
+----+--------------------
+  0 |  -2.97110918125034
+  4 |  -2.41944126847803
+  6 |   -1.5167119566688
+  8 |  -1.96807661257341
+ 10 |  0.623090856508638
+ 12 |  -0.58054822590367
+ 14 |  -1.04863009128623
+ 16 | -0.714285901727259
+ 18 |   2.01061924317838
+ 20 |   2.98327228490375
+ 22 |   3.85256717775708
+ 24 |     5.507570916074
+  1 |  -2.93767476229444
+  3 |  -1.71731847040418
+  5 |  -1.09878171972008
+  7 |  -2.03494545048521
+  9 |  -1.78418730831598
+ 15 | -0.881457996506747
+ 19 |  -1.53342916614675
+ 11 |  0.993924357027849
+ 13 | -0.343452401208048
+ 17 |   1.02735877598375
+ 21 |   1.19453087076323
+ 23 |   5.35711603077246
+(24 rows)
+</pre> <pre class="example">
+-- Display the relative risk for the original dataset
+SELECT madlib.coxph_predict('sample_cox',
+                            'sample_data',
+                            'id',
+                            'sample_pred',
+                            'risk');
+</pre> <pre class="result">
+ id |  predicted_value
+ ----+--------------------
+  1 | 0.0529887971503509
+  3 |  0.179546963459175
+  5 |   0.33327686110022
+  7 |  0.130687611255372
+  9 |  0.167933483703554
+ 15 |  0.414178600294289
+ 19 |  0.215794402223054
+ 11 |   2.70181658768287
+ 13 |  0.709317242984782
+ 17 |   2.79367735395696
+ 21 |   3.30200833843654
+ 23 |   212.112338046551
+  0 | 0.0512464372091503
+  4 | 0.0889713146524469
+  6 |  0.219432204682557
+  8 |  0.139725343898993
+ 10 |   1.86468261037506
+ 12 |  0.559591499901241
+ 14 |  0.350417460388247
+ 16 |  0.489541567796517
+ 18 |   7.46794038691975
+ 20 |   19.7523463121038
+ 22 |   47.1138577624204
+ 24 |   246.551504798816
+(24 rows)
+</pre></li>
+<li>Run the test for Proportional Hazards assumption to obtain correlation between residuals and time. <pre class="example">
+SELECT madlib.cox_zph( 'sample_cox',
+                       'sample_zph_output'
+                     );
+</pre></li>
+<li>View results of the PHA test. <pre class="example">
+SELECT * FROM sample_zph_output;
+</pre> Results: <pre class="result">
+-[ RECORD 1 ]-----------------------------------------
+covariate  | ARRAY[grp,wbc]
+rho        | {0.00237308357328641,0.0375600568840431}
+chi_square | {0.000100675718191977,0.0232317400546175}
+p_value    | {0.991994376850758,0.878855984657948}
+</pre></li>
+</ol>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Generally, proportional-hazard models start with a list of \( \boldsymbol n \) observations, each with \( \boldsymbol m \) covariates and a time of death. From this \( \boldsymbol n \times m \) matrix, we would like to derive the correlation between the covariates and the hazard function. This amounts to finding the parameters \( \boldsymbol \beta \) that best fit the model described below.</p>
+<p>Let us define:</p><ul>
+<li>\( \boldsymbol t \in \mathbf R^{m} \) denote the vector of observed dependent variables, with \( n \) rows.</li>
+<li>\( X \in \mathbf R^{m} \) denote the design matrix with \( m \) columns and \( n \) rows, containing all observed vectors of independent variables \( \boldsymbol x_i \) as rows.</li>
+<li>\( R(t_i) \) denote the set of observations still alive at time \( t_i \)</li>
+</ul>
+<p>Note that this model <b>does not</b> include a <b>constant term</b>, and the data cannot contain a column of 1s.</p>
+<p>By definition, </p><p class="formulaDsp">
+\[ P[T_k = t_i | \boldsymbol R(t_i)] = \frac{e^{\beta^T x_k} }{ \sum_{j \in R(t_i)} e^{\beta^T x_j}}. \,. \]
+</p>
+<p>The <b>partial likelihood </b>function can now be generated as the product of conditional probabilities: </p><p class="formulaDsp">
+\[ \mathcal L = \prod_{i = 1}^n \left( \frac{e^{\beta^T x_i}}{ \sum_{j \in R(t_i)} e^{\beta^T x_j}} \right). \]
+</p>
+<p>The log-likelihood form of this equation is </p><p class="formulaDsp">
+\[ L = \sum_{i = 1}^n \left[ \beta^T x_i - \log\left(\sum_{j \in R(t_i)} e^{\beta^T x_j }\right) \right]. \]
+</p>
+<p>Using this score function and Hessian matrix, the partial likelihood can be maximized using the <b> Newton-Raphson algorithm</b>. <b>Breslow's method</b> is used to resolved tied times of deaths. The time of death for two records are considered "equal" if they differ by less than 1.0e-6</p>
+<p>The inverse of the Hessian matrix, evaluated at the estimate of \( \boldsymbol \beta \), can be used as an <b>approximate variance-covariance matrix </b> for the estimate, and used to produce approximate <b>standard errors</b> for the regression coefficients.</p>
+<p class="formulaDsp">
+\[ \mathit{se}(c_i) = \left( (H)^{-1} \right)_{ii} \,. \]
+</p>
+<p> The Wald z-statistic is </p><p class="formulaDsp">
+\[ z_i = \frac{c_i}{\mathit{se}(c_i)} \,. \]
+</p>
+<p>The Wald \( p \)-value for coefficient \( i \) gives the probability (under the assumptions inherent in the Wald test) of seeing a value at least as extreme as the one observed, provided that the null hypothesis ( \( c_i = 0 \)) is true. Letting \( F \) denote the cumulative density function of a standard normal distribution, the Wald \( p \)-value for coefficient \( i \) is therefore </p><p class="formulaDsp">
+\[ p_i = \Pr(|Z| \geq |z_i|) = 2 \cdot (1 - F( |z_i| )) \]
+</p>
+<p> where \( Z \) is a standard normally distributed random variable.</p>
+<p>The condition number is computed as \( \kappa(H) \) during the iteration immediately <em>preceding</em> convergence (i.e., \( A \) is computed using the coefficients of the previous iteration). A large condition number (say, more than 1000) indicates the presence of significant multicollinearity.</p>
+<p><a class="anchor" id="Literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>A somewhat random selection of nice write-ups, with valuable pointers into further literature:</p>
+<p>[1] John Fox: Cox Proportional-Hazards Regression for Survival Data, Appendix to An R and S-PLUS companion to Applied Regression Feb 2012, <a href="http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf">http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf</a></p>
+<p>[2] Stephen J Walters: What is a Cox model? <a href="http://www.medicine.ox.ac.uk/bandolier/painres/download/whatis/cox_model.pdf">http://www.medicine.ox.ac.uk/bandolier/painres/download/whatis/cox_model.pdf</a></p>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<p>If number of ties in the source table is very large, a memory allocation error may be raised. The limitation is about \((10^8 / m)\), where \(m\) is number of featrues. For instance, if there are 100 featrues, the number of ties should be fewer than 1 million.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="cox__prop__hazards_8sql__in.html" title="SQL functions for cox proportional hazards. ">cox_prop_hazards.sql_in</a> documenting the functions</p>
+</div><!-- contents -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+  <td style="padding-left: 0.5em;">
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Conditional Random Field<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#train_feature">Training Feature Generation</a> </li>
+<li>
+<a href="#train">CRF Training Function</a> </li>
+<li>
+<a href="#test_feature">Testing Feature Generation</a> </li>
+<li>
+<a href="#inference">Inference using Viterbi</a> </li>
+<li>
+<a href="#usage">Using CRF</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#background">Technical Background</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>A conditional random field (CRF) is a type of discriminative, undirected probabilistic graphical model. A linear-chain CRF is a special type of CRF that assumes the current state depends only on the previous state.</p>
+<p>Feature extraction modules are provided for text-analysis tasks such as part-of-speech (POS) tagging and named-entity resolution (NER). Currently, six feature types are implemented:</p>
+<ul>
+<li>Edge Feature: transition feature that encodes the transition feature weight from current label to next label.</li>
+<li>Start Feature: fired when the current token is the first token in a sequence.</li>
+<li>End Feature: fired when the current token is the last token in a sequence.</li>
+<li>Word Feature: fired when the current token is observed in the trained dictionary.</li>
+<li>Unknown Feature: fired when the current token is not observed in the trained dictionary for at least a certain number of times (default 1).</li>
+<li>Regex Feature: fired when the current token can be matched by a regular expression.</li>
+</ul>
+<p>A Viterbi implementation is also provided to get the best label sequence and the conditional probability \( \Pr( \text{best label sequence} \mid \text{sequence}) \).</p>
+<p>Following steps are required for CRF Learning and Inference:</p><ol type="1">
+<li><a href="#train_feature">Training Feature Generation</a></li>
+<li><a href="#train">CRF Training</a></li>
+<li><a href="#test_feature">Testing Feature Generation</a></li>
+<li><a href="#inference">Inference using Viterbi</a></li>
+</ol>
+<p><a class="anchor" id="train_feature"></a></p><dl class="section user"><dt>Training Feature Generation</dt><dd>The function takes <code>train_segment_tbl</code> and <code>regex_tbl</code> as input and does feature generation generating three tables <code>dictionary_tbl</code>, <code>train_feature_tbl</code> and <code>train_featureset_tbl</code>, that are required as an input for CRF training. <pre class="syntax">
+crf_train_fgen(train_segment_tbl,
+               regex_tbl,
+               label_tbl,
+               dictionary_tbl,
+               train_feature_tbl,
+               train_featureset_tbl)
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>train_segment_tbl </dt>
+<dd>TEXT. Name of the training segment table. The table is expected to have the following columns: <table  class="output">
+<tr>
+<th>doc_id </th><td>INTEGER. Document id column  </td></tr>
+<tr>
+<th>start_pos </th><td>INTEGER. Index of a particular term in the respective document  </td></tr>
+<tr>
+<th>seg_text </th><td>TEXT. Term at the respective <code>start_pos</code> in the document  </td></tr>
+<tr>
+<th>label </th><td>INTEGER. Label id for the term corresponding to the actual label from <code>label_tbl</code>   </td></tr>
+</table>
+</dd>
+<dt>regex_tbl </dt>
+<dd>TEXT. Name of the regular expression table. The table is expected to have the following columns: <table  class="output">
+<tr>
+<th>pattern </th><td>TEXT. Regular Expression  </td></tr>
+<tr>
+<th>name </th><td>TEXT. Regular Expression name  </td></tr>
+</table>
+</dd>
+<dt>label_tbl </dt>
+<dd>TEXT. Name of the table containing unique labels and their id's. The table is expected to have the following columns: <table  class="output">
+<tr>
+<th>id </th><td>INTEGER. Unique label id. NOTE: Must range from 0 to total number of labels in the table - 1.   </td></tr>
+<tr>
+<th>label </th><td>TEXT. Label name  </td></tr>
+</table>
+</dd>
+<dt>dictionary_tbl </dt>
+<dd>TEXT. Name of the dictionary table to be created containing unique terms along with their counts. The table will have the following columns: <table  class="output">
+<tr>
+<th>token </th><td>TEXT. Contains all the unique terms found in <code>train_segment_tbl</code>   </td></tr>
+<tr>
+<th>total </th><td>INTEGER. Respective counts for the terms  </td></tr>
+</table>
+</dd>
+<dt>train_feature_tbl</dt>
+<dd></dd>
+<dt></dt>
+<dd><p class="startdd">TEXT. Name of the training feature table to be created. The table will have the following columns: </p><table  class="output">
+<tr>
+<th>doc_id </th><td>INTEGER. Document id  </td></tr>
+<tr>
+<th>f_size </th><td>INTEGER. Feature set size. This value will be same for all the tuples in the table  </td></tr>
+<tr>
+<th>sparse_r </th><td>DOUBLE PRECISION[]. Array union of individual single state features (previous label, label, feature index, start position, training existance indicator), ordered by their start position.  </td></tr>
+<tr>
+<th>dense_m </th><td>DOUBLE PRECISION[]. Array union of (previous label, label, feature index, start position, training existance indicator) of edge features ordered by start position.  </td></tr>
+<tr>
+<th>sparse_m </th><td>DOUBLE PRECISION[]. Array union of (feature index, previous label, label) of edge features ordered by feature index.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>train_featureset_tbl </dt>
+<dd>TEXT. Name of the table to be created containing distinct featuresets generated from training feature extraction. The table will have the following columns: <table  class="output">
+<tr>
+<th>f_index </th><td>INTEGER. Column containing distinct featureset ids  </td></tr>
+<tr>
+<th>f_name </th><td>TEXT. Feature name   </td></tr>
+<tr>
+<th>feature </th><td>ARRAY. Feature value. The value is of the form [L1, L2] <br />
+ - If L1 = -1: represents single state feature with L2 being the current label id. <br />
+ - If L1 != -1: represents transition feature with L1 be the previous label and L2 be the current label.    </td></tr>
+</table>
+</dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Linear Chain CRF Training Function</dt><dd>The function takes <code>train_feature_tbl</code> and <code>train_featureset_tbl</code> tables generated in the training feature generation steps as input along with other required parameters and produces two output tables <code>crf_stats_tbl</code> and <code>crf_weights_tbl</code>.</dd></dl>
+<pre class="syntax">
+lincrf_train(train_feature_tbl,
+             train_featureset_tbl,
+             label_tbl,
+             crf_stats_tbl,
+             crf_weights_tbl
+             max_iterations
+            )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>train_feature_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the feature table generated during training feature generation</p>
+<p class="enddd"></p>
+</dd>
+<dt>train_featureset_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the featureset table generated during training feature generation</p>
+<p class="enddd"></p>
+</dd>
+<dt>label_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the label table used</p>
+<p class="enddd"></p>
+</dd>
+<dt>crf_stats_table </dt>
+<dd>TEXT. Name of the table to be created containing statistics for CRF training. The table has the following columns: <table  class="output">
+<tr>
+<th>coef </th><td>DOUBLE PRECISION[]. Array of coefficients  </td></tr>
+<tr>
+<th>log_likelihood </th><td>DOUBLE. Log-likelihood  </td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The number of iterations at which the algorithm terminated  </td></tr>
+</table>
+</dd>
+<dt>crf_weights_table </dt>
+<dd><p class="startdd">TEXT. Name of the table to be created creating learned feature weights. The table has the following columns: </p><table  class="output">
+<tr>
+<th>id </th><td>INTEGER. Feature set id  </td></tr>
+<tr>
+<th>name </th><td>TEXT. Feature name  </td></tr>
+<tr>
+<th>prev_label_id </th><td>INTEGER. Label for the previous token encountered  </td></tr>
+<tr>
+<th>label_id </th><td>INTEGER. Label of the token with the respective feature  </td></tr>
+<tr>
+<th>weight </th><td>DOUBLE PRECISION. Weight for the respective feature set  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>max_iterations </dt>
+<dd>INTEGER. The maximum number of iterations </dd>
+</dl>
+<p><a class="anchor" id="test_feature"></a></p><dl class="section user"><dt>Testing Feature Generation</dt><dd></dd></dl>
+<pre class="syntax">
+crf_test_fgen(test_segment_tbl,
+              dictionary_tbl,
+              label_tbl,
+              regex_tbl,
+              crf_weights_tbl,
+              viterbi_mtbl,
+              viterbi_rtbl
+             )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>test_segment_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the testing segment table. The table is expected to have the following columns: </p><table  class="output">
+<tr>
+<th>doc_id </th><td>INTEGER. Document id column  </td></tr>
+<tr>
+<th>start_pos </th><td>INTEGER. Index of a particular term in the respective document  </td></tr>
+<tr>
+<th>seg_text </th><td>TEXT. Term at the respective <code>start_pos</code> in the document  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dictionary_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the dictionary table created during training feature generation (<code>crf_train_fgen</code>)</p>
+<p class="enddd"></p>
+</dd>
+<dt>label_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the label table</p>
+<p class="enddd"></p>
+</dd>
+<dt>regex_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the regular expression table</p>
+<p class="enddd"></p>
+</dd>
+<dt>crf_weights_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the weights table generated during CRF training (<code>lincrf_train</code>)</p>
+<p class="enddd"></p>
+</dd>
+<dt>viterbi_mtbl </dt>
+<dd><p class="startdd">TEXT. Name of the Viterbi M table to be created</p>
+<p class="enddd"></p>
+</dd>
+<dt>viterbi_rtbl </dt>
+<dd>TEXT. Name of the Viterbi R table to be created </dd>
+</dl>
+<p><a class="anchor" id="inference"></a></p><dl class="section user"><dt>Inference using Viterbi</dt><dd><pre class="syntax">
+vcrf_label(test_segment_tbl,
+           viterbi_mtbl,
+           viterbi_rtbl,
+           label_tbl,
+           result_tbl)
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>test_segment_tbl </dt>
+<dd>TEXT. Name of the testing segment table. For required table schema, please refer to arguments in previous section </dd>
+<dt>viterbi_mtbl </dt>
+<dd>TEXT. Name of the table <code>viterbi_mtbl</code> generated from testing feature generation <code>crf_test_fgen</code>. </dd>
+<dt>viterbi_rtbl </dt>
+<dd>TEXT. Name of the table <code>viterbi_rtbl</code> generated from testing feature generation <code>crf_test_fgen</code>. </dd>
+<dt>label_tbl </dt>
+<dd>TEXT. Name of the label table. </dd>
+<dt>result_tbl </dt>
+<dd>TEXT. Name of the result table to be created containing extracted best label sequences. </dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="usage"></a></p><dl class="section user"><dt>Using CRF</dt><dd></dd></dl>
+<p>Generate text features, calculate their weights, and output the best label sequence for test data:<br />
+</p><ol type="1">
+<li>Perform feature generation on training data i.e. <code>train_segment_tbl</code> generating <code>train_feature_tbl</code> and <code>train_featureset_tbl</code>. <pre>SELECT madlib.crf_train_fgen(
+         '<em>train_segment_tbl</em>',
+         '<em>regex_tbl</em>',
+         '<em>label_tbl</em>',
+         '<em>dictionary_tbl</em>',
+         '<em>train_feature_tbl</em>',
+         '<em>train_featureset_tbl</em>');</pre></li>
+<li>Use linear-chain CRF for training providing <code>train_feature_tbl</code> and <code>train_featureset_tbl</code> generated from previous step as an input. <pre>SELECT madlib.lincrf_train(
+         '<em>train_feature_tbl</em>',
+         '<em>train_featureset_tbl</em>',
+         '<em>label_tbl</em>',
+         '<em>crf_stats_tbl</em>',
+         '<em>crf_weights_tbl</em>',
+         <em>max_iterations</em>);</pre></li>
+<li>Perform feature generation on testing data <code>test_segment_tbl</code> generating <code>viterbi_mtbl</code> and <code>viterbi_rtbl</code> required for inferencing. <pre>SELECT madlib.crf_test_fgen(
+         '<em>test_segment_tbl</em>',
+         '<em>dictionary_tbl</em>',
+         '<em>label_tbl</em>',
+         '<em>regex_tbl</em>',
+         '<em>crf_weights_tbl</em>',
+         '<em>viterbi_mtbl</em>',
+         '<em>viterbi_rtbl</em>');</pre></li>
+<li>Run the Viterbi function to get the best label sequence and the conditional probability \( \Pr( \text{best label sequence} \mid \text{sequence}) \). <pre>SELECT madlib.vcrf_label(
+         '<em>test_segment_tbl</em>',
+         '<em>viterbi_mtbl</em>',
+         '<em>viterbi_rtbl</em>',
+         '<em>label_tbl</em>',
+         '<em>result_tbl</em>');</pre></li>
+</ol>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd>This example uses a trivial training and test data set.</dd></dl>
+<ol type="1">
+<li>Load the label table, the regular expressions table, and the training segment table: <pre class="example">
+SELECT * FROM crf_label ORDER BY id;
+</pre> Result: <pre class="result">
+ id | label
+&#160;---+-------
+  0 | #
+  1 | $
+  2 | ''
+...
+  8 | CC
+  9 | CD
+ 10 | DT
+ 11 | EX
+ 12 | FW
+ 13 | IN
+ 14 | JJ
+...
+</pre> The regular expressions table: <pre class="example">
+SELECT * from crf_regex;
+</pre> <pre class="result">
+    pattern    |         name
+&#160;--------------+----------------------
+ ^.+ing$       | endsWithIng
+ ^[A-Z][a-z]+$ | InitCapital
+ ^[A-Z]+$      | isAllCapital
+ ^.*[0-9]+.*$  | containsDigit
+...
+</pre> The training segment table: <pre class="example">
+SELECT * from train_segmenttbl ORDER BY doc_id, start_pos;
+</pre> <pre class="result">
+ doc_id | start_pos |  seg_text  | label
+&#160;-------+-----------+------------+-------
+      0 |         0 | Confidence |    18
+      0 |         1 | in         |    13
+      0 |         2 | the        |    10
+      0 |         3 | pound      |    18
+      0 |         4 | is         |    38
+      0 |         5 | widely     |    26
+...
+      1 |         0 | Chancellor |    19
+      1 |         1 | of         |    13
+      1 |         2 | the        |    10
+      1 |         3 | Exchequer  |    19
+      1 |         4 | Nigel      |    19
+...
+</pre></li>
+<li>Generate the training features: <pre class="example">
+SELECT crf_train_fgen( 'train_segmenttbl',
+                       'crf_regex',
+                       'crf_label',
+                       'crf_dictionary',
+                       'train_featuretbl',
+                       'train_featureset'
+                     );
+SELECT * from crf_dictionary;
+</pre> Result: <pre class="result">
+     token       | total
+&#160;----------------+-------
+ Hawthorne       |     1
+ Mercedes-Benzes |     1
+ Wolf            |     3
+ best-known      |     1
+ hairline        |     1
+ accepting       |     2
+ purchases       |    14
+ trash           |     5
+ co-venture      |     1
+ restaurants     |     7
+...
+</pre> <pre class="example">
+SELECT * from train_featuretbl;
+</pre> Result: <pre class="result">
+ doc_id | f_size |            sparse_r           |             dense_m             |       sparse_m
+&#160;-------+--------+-------------------------------+---------------------------------+-----------------------
+      2 |     87 | {-1,13,12,0,1,-1,13,9,0,1,..} | {13,31,79,1,1,31,29,70,2,1,...} | {51,26,2,69,29,17,...}
+      1 |     87 | {-1,13,0,0,1,-1,13,9,0,1,...} | {13,0,62,1,1,0,13,54,2,1,13,..} | {51,26,2,69,29,17,...}
+</pre> <pre class="example">
+SELECT * from train_featureset;
+</pre> <pre class="result">
+ f_index |    f_name     | feature
+&#160;--------+---------------+---------
+       1 | R_endsWithED  | {-1,29}
+      13 | W_outweigh    | {-1,26}
+      29 | U             | {-1,5}
+      31 | U             | {-1,29}
+      33 | U             | {-1,12}
+      35 | W_a           | {-1,2}
+      37 | W_possible    | {-1,6}
+      15 | W_signaled    | {-1,29}
+      17 | End.          | {-1,43}
+      49 | W_'s          | {-1,16}
+      63 | W_acquire     | {-1,26}
+      51 | E.            | {26,2}
+      69 | E.            | {29,17}
+      71 | E.            | {2,11}
+      83 | W_the         | {-1,2}
+      85 | E.            | {16,11}
+       4 | W_return      | {-1,11}
+...
+</pre></li>
+<li>Train using linear CRF: <pre class="example">
+SELECT lincrf_train( 'train_featuretbl',
+                     'train_featureset',
+                     'crf_label',
+                     'crf_stats_tbl',
+                     'crf_weights_tbl',
+                     20
+             );
+</pre> <pre class="result">
+                                lincrf_train
+&#160;-----------------------------------------------------------------------------------
+ CRF Train successful. Results stored in the specified CRF stats and weights table
+ lincrf
+</pre> View the feature weight table. <pre class="example">
+SELECT * from crf_weights_tbl;
+</pre> Result: <pre class="result">
+ id |     name      | prev_label_id | label_id |      weight
+&#160;---+---------------+---------------+----------+-------------------
+  1 | R_endsWithED  |            -1 |       29 |  1.54128249293937
+ 13 | W_outweigh    |            -1 |       26 |  1.70691232223653
+ 29 | U             |            -1 |        5 |  1.40708515869008
+ 31 | U             |            -1 |       29 | 0.830356200936407
+ 33 | U             |            -1 |       12 | 0.769587378281239
+ 35 | W_a           |            -1 |        2 |  2.68470625883726
+ 37 | W_possible    |            -1 |        6 |  3.41773107604468
+ 15 | W_signaled    |            -1 |       29 |  1.68187039165771
+ 17 | End.          |            -1 |       43 |  3.07687845517082
+ 49 | W_'s          |            -1 |       16 |  2.61430312229883
+ 63 | W_acquire     |            -1 |       26 |  1.67247047385797
+ 51 | E.            |            26 |        2 |   3.0114240119435
+ 69 | E.            |            29 |       17 |  2.82385531733866
+ 71 | E.            |             2 |       11 |  3.00970493772732
+ 83 | W_the         |            -1 |        2 |  2.58742315259326
+...
+</pre></li>
+<li>To find the best labels for a test set using the trained linear CRF model, repeat steps #1-2 and generate the test features, except instead of creating a new dictionary, use the dictionary generated from the training set. <pre class="example">
+SELECT * from test_segmenttbl ORDER BY doc_id, start_pos;
+</pre> Result: <pre class="result">
+ doc_id | start_pos |   seg_text
+&#160;-------+-----------+---------------
+      0 |         0 | Rockwell
+      0 |         1 | International
+      0 |         2 | Corp.
+      0 |         3 | 's
+      0 |         4 | Tulsa
+      0 |         5 | unit
+      0 |         6 | said
+...
+      1 |         0 | Rockwell
+      1 |         1 | said
+      1 |         2 | the
+      1 |         3 | agreement
+      1 |         4 | calls
+...
+</pre> <pre class="example">
+SELECT crf_test_fgen( 'test_segmenttbl',
+                      'crf_dictionary',
+                      'crf_label',
+                      'crf_regex',
+                      'crf_weights_tbl',
+                      'viterbi_mtbl',
+                      'viterbi_rtbl'
+                    );
+</pre></li>
+<li>Calculate the best label sequence and save in the table <code>extracted_best_labels</code>. <pre class="example">
+SELECT vcrf_label( 'test_segmenttbl',
+                   'viterbi_mtbl',
+                   'viterbi_rtbl',
+                   'crf_label',
+                   'extracted_best_labels'
+                 );
+</pre> View the best labels. <pre class="example">
+SELECT * FROM extracted_best_labels;
+</pre> Result: <pre class="result">
+ doc_id | start_pos |   seg_text    | label | id | max_pos |   prob
+&#160;-------+-----------+---------------+-------+----+---------+----------
+      0 |         0 | Rockwell      | NNP   | 19 |      27 | 0.000269
+      0 |         1 | International | NNP   | 19 |      27 | 0.000269
+      0 |         2 | Corp.         | NNP   | 19 |      27 | 0.000269
+      0 |         3 | 's            | NNP   | 19 |      27 | 0.000269
+...
+      1 |         0 | Rockwell      | NNP   | 19 |      16 | 0.000168
+      1 |         1 | said          | NNP   | 19 |      16 | 0.000168
+      1 |         2 | the           | DT    | 10 |      16 | 0.000168
+      1 |         3 | agreement     | JJ    | 14 |      16 | 0.000168
+      1 |         4 | calls         | NNS   | 21 |      16 | 0.000168
+...
+</pre></li>
+</ol>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Specifically, a linear-chain CRF is a distribution defined by </p><p class="formulaDsp">
+\[ p_\lambda(\boldsymbol y | \boldsymbol x) = \frac{\exp{\sum_{m=1}^M \lambda_m F_m(\boldsymbol x, \boldsymbol y)}}{Z_\lambda(\boldsymbol x)} \,. \]
+</p>
+<p>where</p><ul>
+<li>\( F_m(\boldsymbol x, \boldsymbol y) = \sum_{i=1}^n f_m(y_i,y_{i-1},x_i) \) is a global feature function that is a sum along a sequence \( \boldsymbol x \) of length \( n \)</li>
+<li>\( f_m(y_i,y_{i-1},x_i) \) is a local feature function dependent on the current token label \( y_i \), the previous token label \( y_{i-1} \), and the observation \( x_i \)</li>
+<li>\( \lambda_m \) is the corresponding feature weight</li>
+<li>\( Z_\lambda(\boldsymbol x) \) is an instance-specific normalizer <p class="formulaDsp">
+\[ Z_\lambda(\boldsymbol x) = \sum_{\boldsymbol y&#39;} \exp{\sum_{m=1}^M \lambda_m F_m(\boldsymbol x, \boldsymbol y&#39;)} \]
+</p>
+</li>
+</ul>
+<p>A linear-chain CRF estimates the weights \( \lambda_m \) by maximizing the log-likelihood of a given training set \( T=\{(x_k,y_k)\}_{k=1}^N \).</p>
+<p>The log-likelihood is defined as </p><p class="formulaDsp">
+\[ \ell_{\lambda}=\sum_k \log p_\lambda(y_k|x_k) =\sum_k[\sum_{m=1}^M \lambda_m F_m(x_k,y_k) - \log Z_\lambda(x_k)] \]
+</p>
+<p>and the zero of its gradient </p><p class="formulaDsp">
+\[ \nabla \ell_{\lambda}=\sum_k[F(x_k,y_k)-E_{p_\lambda(Y|x_k)}[F(x_k,Y)]] \]
+</p>
+<p>is found since the maximum likelihood is reached when the empirical average of the global feature vector equals its model expectation. The MADlib implementation uses limited-memory BFGS (L-BFGS), a limited-memory variation of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) update, a quasi-Newton method for unconstrained optimization.</p>
+<p>\(E_{p_\lambda(Y|x)}[F(x,Y)]\) is found by using a variant of the forward-backward algorithm: </p><p class="formulaDsp">
+\[ E_{p_\lambda(Y|x)}[F(x,Y)] = \sum_y p_\lambda(y|x)F(x,y) = \sum_i\frac{\alpha_{i-1}(f_i*M_i)\beta_i^T}{Z_\lambda(x)} \]
+</p>
+ <p class="formulaDsp">
+\[ Z_\lambda(x) = \alpha_n.1^T \]
+</p>
+<p> where \(\alpha_i\) and \( \beta_i\) are the forward and backward state cost vectors defined by </p><p class="formulaDsp">
+\[ \alpha_i = \begin{cases} \alpha_{i-1}M_i, &amp; 0&lt;i&lt;=n\\ 1, &amp; i=0 \end{cases}\\ \]
+</p>
+ <p class="formulaDsp">
+\[ \beta_i^T = \begin{cases} M_{i+1}\beta_{i+1}^T, &amp; 1&lt;=i&lt;n\\ 1, &amp; i=n \end{cases} \]
+</p>
+<p>To avoid overfitting, we penalize the likelihood with a spherical Gaussian weight prior: </p><p class="formulaDsp">
+\[ \ell_{\lambda}^\prime=\sum_k[\sum_{m=1}^M \lambda_m F_m(x_k,y_k) - \log Z_\lambda(x_k)] - \frac{\lVert \lambda \rVert^2}{2\sigma ^2} \]
+</p>
+<p class="formulaDsp">
+\[ \nabla \ell_{\lambda}^\prime=\sum_k[F(x_k,y_k) - E_{p_\lambda(Y|x_k)}[F(x_k,Y)]] - \frac{\lambda}{\sigma ^2} \]
+</p>
+<dl class="section user"><dt>Literature</dt><dd>[1] F. Sha, F. Pereira. Shallow Parsing with Conditional Random Fields, <a href="http://www-bcf.usc.edu/~feisha/pubs/shallow03.pdf">http://www-bcf.usc.edu/~feisha/pubs/shallow03.pdf</a></dd></dl>
+<p>[2] Wikipedia, Conditional Random Field, <a href="http://en.wikipedia.org/wiki/Conditional_random_field">http://en.wikipedia.org/wiki/Conditional_random_field</a></p>
+<p>[3] A. Jaiswal, S.Tawari, I. Mansuri, K. Mittal, C. Tiwari (2012), CRF, <a href="http://crf.sourceforge.net/">http://crf.sourceforge.net/</a></p>
+<p>[4] D. Wang, ViterbiCRF, <a href="http://www.cs.berkeley.edu/~daisyw/ViterbiCRF.html">http://www.cs.berkeley.edu/~daisyw/ViterbiCRF.html</a></p>
+<p>[5] Wikipedia, Viterbi Algorithm, <a href="http://en.wikipedia.org/wiki/Viterbi_algorithm">http://en.wikipedia.org/wiki/Viterbi_algorithm</a></p>
+<p>[6] J. Nocedal. Updating Quasi-Newton Matrices with Limited Storage (1980), Mathematics of Computation 35, pp. 773-782</p>
+<p>[7] J. Nocedal, Software for Large-scale Unconstrained Optimization, <a href="http://users.eecs.northwestern.edu/~nocedal/lbfgs.html">http://users.eecs.northwestern.edu/~nocedal/lbfgs.html</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="crf_8sql__in.html" title="SQL functions for conditional random field. ">crf.sql_in</a> <a class="el" href="crf__feature__gen_8sql__in.html" title="SQL function for POS/NER feature extraction. ">crf_feature_gen.sql_in</a> <a class="el" href="viterbi_8sql__in.html" title="concatenate a set of input values into arrays to feed into viterbi c function and create a human read...">viterbi.sql_in</a> (documenting the SQL functions) </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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+</html>

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+<title>MADlib: Encoding Categorical Variables</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Encoding Categorical Variables<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#categorical">Coding systems for categorical variables</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+</ul>
+</div><p><a class="anchor" id="categorical"></a></p><dl class="section user"><dt>Coding systems for categorical variables</dt><dd>Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot be entered into the regression equation just as they are. For example, if you have a variable called race that is coded 1 = Hispanic, 2 = Asian, 3 = Black, 4 = White, then entering race in your regression will look at the linear effect of race, which is probably not what you intended. Instead, categorical variables like this need to be recoded into a series of indicator variables which can then be entered into the regression model. There are a variety of coding systems (also called as contrasts) that can be used when coding categorical variables. including dummy, effects, orthogonal, and helmert coding.</dd></dl>
+<p>We currently only support the dummy coding technique. Dummy coding is used when a researcher wants to compare other groups of the predictor variable with one specific group of the predictor variable. Often, the specific group to compare with is called the reference group.</p>
+<pre class="syntax">
+create_indicator_variables(
+    source_table,
+    output_table,
+    categorical_cols,
+    keep_null,
+    distributed_by
+    )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>VARCHAR. Name of the source table, containing data for categorical variables. </dd>
+<dt>output_table </dt>
+<dd>VARCHAR. Name of result table. The output table has the same columns as the original table, adding new indicator variable columns for each categorical column. The column name for the indicator variable is <em>'categorical column name'</em>_<em>'categorical value'</em>.  </dd>
+<dt>categorical_cols  </dt>
+<dd>VARCHAR. Comma-separated string of column names of categorical variables that need to be dummy-coded. </dd>
+<dt>keep_null (optional) </dt>
+<dd>BOOLEAN. default: FALSE. Whether 'NULL' should be treated as one of the categories of the categorical variable. If True, then an indicator variable is created corresponding to the NULL value. If False, then all indicator variables for that record will be set to NULL.  </dd>
+<dt>distributed_by (optional) </dt>
+<dd>VARCHAR. default: NULL. Columns to use for the distribution policy of the output table. When NULL, the distribution policy of 'source_table' will be used. This argument is not available for POSTGRESQL platforms. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Use a subset of the abalone dataset. <pre class="example">
+DROP TABLE IF EXISTS abalone;
+CREATE TABLE abalone (
+    sex character varying,
+    length double precision,
+    diameter double precision,
+    height double precision
+);
+COPY abalone (sex, length, diameter, height) FROM stdin WITH DELIMITER '|' NULL as '@';
+M| 0.455 |   0.365 | 0.095
+F| 0.53  |   0.42  | 0.135
+M| 0.35  |   0.265 | 0.09
+F| 0.53  |   0.415 | 0.15
+M| 0.44  |   0.365 | 0.125
+F| 0.545 |   0.425 | 0.125
+I| 0.33  |   0.255 | 0.08
+F| 0.55  |   0.44  | 0.15
+I| 0.425 |   0.30  | 0.095
+F| 0.525 |   0.38  | 0.140
+M| 0.475 |   0.37  | 0.125
+F| 0.535 |   0.405 | 0.145
+M| 0.43  |   0.358 | 0.11
+F| 0.47  |   0.355 | 0.100
+M| 0.49  |   0.38  | 0.135
+F| 0.44  |   0.340 | 0.100
+M| 0.5   |   0.400 | 0.13
+F| 0.565 |   0.44  | 0.155
+I| 0.355 |   0.280 | 0.085
+F| 0.550 |   0.415 | 0.135
+| 0.475 |   0.37  | 0.125
+\.
+</pre></li>
+<li>Create new table with dummy-coded indicator variables <pre class="example">
+drop table if exists abalone_out;
+select madlib.create_indicator_variables ('abalone', 'abalone_out', 'sex');
+select * from abalone_out;
+</pre> <pre class="result">
+ sex  | length | diameter | height | sex_F  | sex_I  | sex_M
+&#160; -----+--------+----------+--------+--------+--------+-------
+ F    |   0.53 |     0.42 |  0.135 |      1 |      0 |     0
+ F    |   0.53 |    0.415 |   0.15 |      1 |      0 |     0
+ F    |  0.545 |    0.425 |  0.125 |      1 |      0 |     0
+ F    |   0.55 |     0.44 |   0.15 |      1 |      0 |     0
+ F    |  0.525 |     0.38 |   0.14 |      1 |      0 |     0
+ F    |  0.535 |    0.405 |  0.145 |      1 |      0 |     0
+ F    |   0.47 |    0.355 |    0.1 |      1 |      0 |     0
+ F    |   0.44 |     0.34 |    0.1 |      1 |      0 |     0
+ F    |  0.565 |     0.44 |  0.155 |      1 |      0 |     0
+ F    |   0.55 |    0.415 |  0.135 |      1 |      0 |     0
+ M    |  0.455 |    0.365 |  0.095 |      0 |      0 |     1
+ M    |   0.35 |    0.265 |   0.09 |      0 |      0 |     0
+ M    |   0.44 |    0.365 |  0.125 |      0 |      0 |     0
+ I    |   0.33 |    0.255 |   0.08 |      0 |      1 |     0
+ I    |  0.425 |      0.3 |  0.095 |      0 |      1 |     0
+ M    |  0.475 |     0.37 |  0.125 |      0 |      0 |     0
+ M    |   0.43 |    0.358 |   0.11 |      0 |      0 |     0
+ M    |   0.49 |     0.38 |  0.135 |      0 |      0 |     0
+ M    |    0.5 |      0.4 |   0.13 |      0 |      0 |     0
+ I    |  0.355 |     0.28 |  0.085 |      0 |      1 |     0
+ NULL |   0.55 |    0.415 |  0.135 |   NULL |   NULL |  NULL
+</pre></li>
+<li>Create indicator variable for 'NULL' value (note the additional column '"sex_NULL"') <pre class="example">
+drop table if exists abalone_out;
+select madlib.create_indicator_variables'abalone', 'abalone_out', 'sex', True);
+select * from abalone_out;
+</pre> <pre class="result">
+ sex  | length | diameter | height | sex_F  | sex_I  | sex_M | sex_NULL
+&#160; ---&mdash;+-----&mdash;+-------&mdash;+-----&mdash;+-----&mdash;+-----&mdash;+----&mdash;+----&mdash;
+ F    |   0.53 |     0.42 |  0.135 |      1 |      0 |     0 |     0
+ F    |   0.53 |    0.415 |   0.15 |      1 |      0 |     0 |     0
+ F    |  0.545 |    0.425 |  0.125 |      1 |      0 |     0 |     0
+ F    |   0.55 |     0.44 |   0.15 |      1 |      0 |     0 |     0
+ F    |  0.525 |     0.38 |   0.14 |      1 |      0 |     0 |     0
+ F    |  0.535 |    0.405 |  0.145 |      1 |      0 |     0 |     0
+ F    |   0.47 |    0.355 |    0.1 |      1 |      0 |     0 |     0
+ F    |   0.44 |     0.34 |    0.1 |      1 |      0 |     0 |     0
+ F    |  0.565 |     0.44 |  0.155 |      1 |      0 |     0 |     0
+ F    |   0.55 |    0.415 |  0.135 |      1 |      0 |     0 |     0
+ M    |  0.455 |    0.365 |  0.095 |      0 |      0 |     1 |     0
+ M    |   0.35 |    0.265 |   0.09 |      0 |      0 |     0 |     0
+ M    |   0.44 |    0.365 |  0.125 |      0 |      0 |     0 |     0
+ I    |   0.33 |    0.255 |   0.08 |      0 |      1 |     0 |     0
+ I    |  0.425 |      0.3 |  0.095 |      0 |      1 |     0 |     0
+ M    |  0.475 |     0.37 |  0.125 |      0 |      0 |     0 |     0
+ M    |   0.43 |    0.358 |   0.11 |      0 |      0 |     0 |     0
+ M    |   0.49 |     0.38 |  0.135 |      0 |      0 |     0 |     0
+ M    |    0.5 |      0.4 |   0.13 |      0 |      0 |     0 |     0
+ I    |  0.355 |     0.28 |  0.085 |      0 |      1 |     0 |     0
+ NULL |   0.55 |    0.415 |  0.135 |      0 |      0 |     0 |     1
+</pre> </li>
+</ol>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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new file mode 100644
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+  <div class="headertitle">
+<div class="title">Data Types and Transforms</div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>Data types and operations </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__arraysmatrix"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a></td></tr>
+<tr class="memdesc:group__grp__arraysmatrix"><td class="mdescLeft">&#160;</td><td class="mdescRight">Mathematical operations for arrays and matrices. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__datatrans.js
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--- /dev/null
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+    [ "Arrays and Matrices", "group__grp__arraysmatrix.html", "group__grp__arraysmatrix" ]
+];
\ No newline at end of file

[31/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+<div class="title">glm.sql_in File Reference</div>  </div>
+</div><!--header-->
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+
+<p>SQL functions for GLM (Poisson)  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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+<tr class="memitem:ace09cf40c436db6c065ef2606781cc93"><td class="memItemLeft" align="right" valign="top">bytea8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="glm_8sql__in.html#ace09cf40c436db6c065ef2606781cc93">__glm_merge_states</a> (bytea8 state1, bytea8 state2)</td></tr>
+<tr class="separator:ace09cf40c436db6c065ef2606781cc93"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a504fa161f5dde9155dd962456597a5c6"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:aeb1962c5b1bdd12ae3111bc93fa2967e"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a9a468186034a936c3997cad22b03cafe"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="glm_8sql__in.html#a9a468186034a936c3997cad22b03cafe">glm_predict_binomial</a> (text message)</td></tr>
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+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>June 2014</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to GLM (Poisson), see the module description grp_poisson. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
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+<a class="anchor" id="a60fb24d4a39f4f98666320b8fc11965c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 __glm_inverse_gaussian_inverse_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>previous_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a8473dca909f252c2a7d60368ffbb5977"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_inverse_gaussian_inverse_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a68be2c4fabab9720636435dcebed5c10"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 __glm_inverse_gaussian_log_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>previous_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3f4c78c4c52b01251307f24e077e92ff"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_inverse_gaussian_log_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9b884108c457cb5881770176115f2661"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 __glm_inverse_gaussian_sqr_inverse_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>previous_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5d4b8d33917e42124a44b30db557940a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_inverse_gaussian_sqr_inverse_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7e84e1d4351a72ea9ecc61db175891ea"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 __glm_loglik_diff </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ace09cf40c436db6c065ef2606781cc93"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aff5070482e51bf7ce3704febfa8f3b58"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 __glm_poisson_identity_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>previous_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a041ee0e1e80ea5cf07f39b8c13f8accb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_poisson_identity_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a504fa161f5dde9155dd962456597a5c6"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 __glm_poisson_log_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>previous_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a48ccc5647dc8d0b6211ae3445d7d533e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_poisson_log_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a051510c3a79c019749fd3f04db41c438"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate bytea8 __glm_poisson_sqrt_agg </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>y</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>previous_state</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa6b93dd92e49df7e3202db15d9e50c6c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bytea8 __glm_poisson_sqrt_transition </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3b7f6999ae949831f33e355ec28b1f79"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__glm_result_type __glm_result_t_stats </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0fb8358e0b92c9d9883eaea281a1ba5f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__glm_result_type __glm_result_z_stats </td>
+          <td>(</td>
+          <td class="paramtype">bytea8&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3f8eb219013e05675626acb8cf4612cc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void glm </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>family_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optim_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>verbose</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a79c8b3660f49977fd3537de1db2e60ba"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void glm </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>family_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>grouping_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>optim_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a98f75815c048dd6ec06842f00ef08f2a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void glm </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>family_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>grouping_col</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4938524efbef7684ce8a8e2646f126ef"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void glm </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>dependent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>independent_varname</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>family_params</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5887e1ae0feabbbfc93e67ab4447f4bd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text glm </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac70223628f2b8fc5e86e295f9d6512ae"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text glm </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a93d7d8b202654b86917bf9e997d0ad6f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 glm_predict </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>link</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coef</td><td>Coefficients obtained by running generalized linear model. </td></tr>
+    <tr><td class="paramname">col_ind</td><td>Predictor variable array </td></tr>
+    <tr><td class="paramname">link</td><td>Link function used in training. Can be one of probit/logit </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Numeric value of the predicted mean</dd></dl>
+<p>This function computes the dot product of the independent variables and the coefficients. This requires the length of the two vectors to be the same. </p>
+
+</div>
+</div>
+<a class="anchor" id="add7c22acc8eaaa3eace7089eadc5f367"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text glm_predict </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa42b3ee4e9c39132154dfa81f96ed4ff"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text glm_predict </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac645a2cc2aa22eac90dee923593eb72d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean glm_predict_binomial </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>link</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coef</td><td>Coefficients obtained by running generalized linear model. </td></tr>
+    <tr><td class="paramname">col_ind</td><td>Predictor variable array </td></tr>
+    <tr><td class="paramname">link</td><td>Link function used in training. Can be one of probit/logit. </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>True/False Boolean value corresponding to output category True if predicted probability &gt;= 0.5, False otherwise</dd></dl>
+<p>This function computes the dot product of the independent variables and the coefficients. This requires the length of the two vectors to be the same. </p>
+
+</div>
+</div>
+<a class="anchor" id="a9a468186034a936c3997cad22b03cafe"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text glm_predict_binomial </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a941d49196e573048852def804c5ce4c4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 glm_predict_poisson </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>link</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coef</td><td>Coefficients obtained by running generalized linear model. </td></tr>
+    <tr><td class="paramname">col_ind</td><td>Predictor variable array </td></tr>
+    <tr><td class="paramname">link</td><td>Link function used in training </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Numeric value of the predicted count, obtained by rounding the predicted mean to the nearest integral value.</dd></dl>
+<p>This function computes the dot product of the independent variables and the coefficients. This requires the length of the two vectors to be the same. </p>
+
+</div>
+</div>
+<a class="anchor" id="aeb1962c5b1bdd12ae3111bc93fa2967e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text glm_predict_poisson </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_e502304d8bc7f1ef2456a474c6d92acd.html">glm</a></li><li class="navelem"><a class="el" href="glm_8sql__in.html">glm.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[08/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__path.html
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diff --git a/docs/latest/group__grp__path.html b/docs/latest/group__grp__path.html
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@@ -0,0 +1,272 @@
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+<title>MADlib: Grp_path</title>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+          </span><span class="right">
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Grp_path</div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+</ul>
+</div><p>The goal of the MADlib path function is to perform regular pattern matching over a sequence of rows, and to extract useful information about the matches. The useful information could be a simple count of matches or something more involved like aggregation.</p>
+<p>There are many use cases where path functions are typically used:</p><ul>
+<li>Web analytics (clickstream)</li>
+<li>Marketing revenue attribution</li>
+<li>Telephone calling patterns</li>
+<li>Stock market trading sequences</li>
+<li>Predictive maintenance</li>
+<li>Genomics sequencing</li>
+</ul>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd><pre class="syntax">
+path(
+    source_table,
+    output_table,
+    partition_expr,
+    order_expr,
+    pattern,
+    symbol,
+    aggregate_func,
+    persist_rows
+)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the source table, containing data for path analysis.</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the result table. This table contains columns predicated by the <em>result</em> argument (shown below).</p>
+<p class="enddd"></p>
+</dd>
+<dt>partition_expr </dt>
+<dd><p class="startdd">VARCHAR. The 'partition_expr' can be a list of columns or expressions (separated by comma) to divide all rows into groups, or partitions, that share the same values of the partition expression(s). For each row, the matching is applied across the rows that fall into the same partition. This can be NULL or '' to indicate the matching is to be applied on the whole table.</p>
+<p class="enddd"></p>
+</dd>
+<dt>order_expr </dt>
+<dd><p class="startdd">VARCHAR. This expression controls the order in which rows are processed or matched in a partition. </p>
+<p class="enddd"></p>
+</dd>
+<dt>pattern </dt>
+<dd><p class="startdd">VARCHAR. The PATTERN clause defines the pattern that path searches for. You express the PATTERN using symbols and operators. Symbols defined using more than 1 characters need to be wrapped in parentheses '()'. The following pattern-matching metacharacters are supported: </p><ul>
+<li>
+| denotes alternation (either of two alternatives).  </li>
+<li>
+? denotes repetition of the previous item zero or one time.  </li>
+<li>
+* denotes repetition of the previous item zero or more times.  </li>
+<li>
++ denotes repetition of the previous item one or more times.  </li>
+<li>
+{m} denotes repetition of the previous item exactly m times.  </li>
+<li>
+{m,} denotes repetition of the previous item m or more times.  </li>
+<li>
+{m,n} denotes repetition of the previous item at least m and not more than n times.  </li>
+<li>
+Parentheses () can be used to group items into a single logical item. </li>
+</ul>
+<p class="enddd"></p>
+</dd>
+<dt>symbol </dt>
+<dd><p class="startdd">VARCHAR. A symbol represents a row of a particular type that you’re searching for as part of a row sequence. In the SYMBOLS clause, you write a predicate to define the type of row that matches the symbol. </p>
+<p class="enddd"></p>
+</dd>
+<dt>aggregate_func </dt>
+<dd><p class="startdd">VARCHAR. A comma-separated list of window functions and aggregates to be applied on the matched window. </p>
+<p class="enddd"></p>
+</dd>
+<dt>persist_rows </dt>
+<dd><p class="startdd">BOOLEAN. If TRUE the matched rows are persisted in another table. This table is named as &lt;output_table&gt;_tuples (the string "_tuples" is added as suffix to the value of <em>output_table</em>). </p>
+<p class="enddd"></p>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ul>
+<li>Build sample dataset <pre class="example">
+CREATE TABLE data (id integer, sessionid integer, starttime timestamp, topic varchar, portfolio integer);
+COPY data FROM STDIN DELIMITER AS '|';
+1|1|1/01/00 5:00 AM|Real-time Equity pricing|769
+1|2|1/01/00 5:30 AM|Real-time Index pricing|9898
+1|3|1/01/00 6:00 AM|Real-time Index pricing|9898
+1|4|1/01/00 5:00 AM|Calendar|98977
+1|5|1/01/00 10:00 AM|Real-time Equity pricing|769
+1|6|1/01/00 10:30 AM|Real-time Equity pricing|9898
+1|7|1/01/00 12:00 PM|Calendar|1325
+1|8|1/01/00 12:00 PM|Calendar|6777
+1|9|1/01/00 4:05 PM|Real-time Equity pricing|769
+1|10|1/01/00 4:10 PM|Report Dates and Analyst Recommendations|34346
+1|11|1/01/00 4:15 PM|Real-time Index pricing|9898
+1|12|1/01/00 4:20 PM|Snapshot Futures pricing|5568
+1|13|1/01/00 4:21 PM|Real-time Equity pricing|769
+1|14|1/01/00 4:25 PM|Bond security master|789
+1|15|1/01/00 10:00 PM|Bond pricing|55
+1|16|1/01/00 10:00 PM|Bond pricing|55
+1|17|1/01/00 10:00 PM|Bond pricing|55
+1|18|1/01/00 10:00 PM|Bond Yield Analysis|655
+1|19|1/01/00 10:00 PM|Bond pricing|55
+1|20|1/01/00 10:00 PM|Bond pricing|55
+2|1|1/04/00 12:00 AM|Real-time Equity pricing|769
+2|2|1/04/00 12:00 AM|Real-time Equity pricing|234234
+2|3|1/04/00 12:00 AM|Calendar|1325
+2|4|1/04/00 12:00 AM|Calendar|6777
+2|5|1/04/00 12:00 AM|Real-time Equity pricing|769
+2|6|1/04/00 12:00 AM|Report Dates and Analyst Recommendations|34346
+2|7|1/04/00 12:00 AM|Real-time Index pricing|9898
+2|8|1/04/00 12:00 AM|Snapshot Futures pricing|5568
+2|9|1/04/00 12:00 AM|Real-time Equity pricing|769
+2|10|1/04/00 12:00 AM|Bond security master|789
+2|11|1/04/00 12:00 AM|Bond pricing|55
+2|12|1/04/00 12:00 AM|Bond pricing|55
+2|13|1/04/00 12:00 AM|Bond pricing|55
+2|14|1/04/00 12:00 AM|Bond pricing|55
+\.
+&#160;
+CREATE TABLE trades AS
+SELECT *, starttimestamp::date startdate,
+        CASE WHEN  THEN 'before'
+            WHEN  THEN 'market'
+            WHEN  THEN 'close'
+            WHEN  THEN 'after'
+        END tradingperiod
+FROM sessiontable
+</pre></li>
+<li>Compare within each day for the same Topic/Portfolio across every user <pre class="example">
+    SELECT madlib.path(
+        'trades',
+        'trades_out',
+        'startdate, topic, portfolio'  -- each day of activity is looked at independently
+        'starttime'                     -- order by time
+        'BEFORE*.MARKET+.CLOSE+.AFTER*' -- at least one event during each of MARKET and CLOSE, but gather up the rest
+        'BEFORE:=starttimestamp::time &gt;= ''0:00:00'' and starttimestamp::time &lt; ''9:30:00''::time,
+         MARKET:=starttimestamp::time &gt;= ''9:30:00'' and starttimestamp::time &lt; ''16:00:00''::time,
+         CLOSE:= starttimestamp::time &lt;= ''16:00:00'' and starttimestamp::time &lt; ''16:30:00''::time,
+         AFTER:= starttimestamp::time &lt;= ''16:30:00'' and starttimestamp::time &lt; ''24:00:00''::time
+        ',
+        'first(startdate) as starttime, array_agg(id) as all_users, count(*) as num_matches'
+    )
+</pre> </li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Tue Mar 22 2016 17:55:59 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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+  <td style="padding-left: 0.5em;">
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+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Dimensionality Reduction<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods for dimensionality reduction. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__pca__train"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__pca__train.html">Principal Component Analysis</a></td></tr>
+<tr class="memdesc:group__grp__pca__train"><td class="mdescLeft">&#160;</td><td class="mdescRight">Produces a model that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__pca__project"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__pca__project.html">Principal Component Projection</a></td></tr>
+<tr class="memdesc:group__grp__pca__project"><td class="mdescLeft">&#160;</td><td class="mdescRight">Projects a higher dimensional data point to a lower dimensional subspace spanned by principal components learned through the PCA training procedure. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+var group__grp__pca =
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+    [ "Principal Component Analysis", "group__grp__pca__train.html", null ],
+    [ "Principal Component Projection", "group__grp__pca__project.html", null ]
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+<title>MADlib: Principal Component Projection</title>
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+  <td style="padding-left: 0.5em;">
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+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Principal Component Projection<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> &raquo; <a class="el" href="group__grp__pca.html">Dimensionality Reduction</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#project">Projection Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#notes">Notes</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Principal component projection is a mathematical procedure that projects high dimensional data onto a lower dimensional space. This lower dimensional space is defined by the \( k \) principal components with the highest variance in the training data. More details on the mathematics of PCA can be found in <a class="el" href="pca_8sql__in.html#a31abf88e67a446a4f789764aa2c61e85">pca_train</a> and some details about the principal component projection calculations can be found in the <a class="el" href="group__grp__mlogreg.html#background">Technical Background</a>.</p>
+<p><a class="anchor" id="project"></a></p><dl class="section user"><dt>Projection Function</dt><dd>The projection functions have the following formats: <pre class="syntax">
+madlib.pca_project( source_table,
+                    pc_table,
+                    out_table,
+                    row_id,
+                    residual_table,
+                    result_summary_table
+                  )
+</pre> and <pre class="syntax">
+madlib.pca_sparse_project( source_table,
+                           pc_table,
+                           out_table,
+                           row_id,
+                           col_id,
+                           val_id,
+                           row_dim,
+                           col_dim,
+                           residual_table,
+                           result_summary_table
+                         )
+</pre></dd></dl>
+<dl class="section user"><dt>Arguments</dt><dd><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. Source table name. Identical to <a class="el" href="pca_8sql__in.html#a31abf88e67a446a4f789764aa2c61e85">pca_train</a>, the input data matrix should have \( N \) rows and \( M \) columns, where \( N \) is the number of data points, and \( M \) is the number of features for each data point.</p>
+<p>The input table for <em> pca_project </em> is expected to be in the one of the two standard MADlib dense matrix formats, and the sparse input table for <em> pca_sparse_project </em> should be in the standard MADlib sparse matrix format. These formats are described in the documentation for <a class="el" href="pca_8sql__in.html#a31abf88e67a446a4f789764aa2c61e85">pca_train</a>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>pc_table </dt>
+<dd><p class="startdd">TEXT. Table name for the table containing principal components. </p>
+<p class="enddd"></p>
+</dd>
+<dt>out_table </dt>
+<dd><p class="startdd">TEXT. Name of the table that will contain the low-dimensional representation of the input data.</p>
+<p>The <em>out_table</em> encodes a dense matrix with the projection onto the principal components. The table has the following columns:</p>
+<table  class="output">
+<tr>
+<th>row_id </th><td>Row id of the output matrix.  </td></tr>
+<tr>
+<th>row_vec </th><td>A vector containing elements in the row of the matrix.  </td></tr>
+</table>
+<p></p>
+<p class="enddd"></p>
+</dd>
+<dt>row_id </dt>
+<dd><p class="startdd">TEXT. Column name containing the row IDs in the input source table.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_id </dt>
+<dd><p class="startdd">TEXT. Name of 'col_id' column in sparse matrix representation (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>val_id </dt>
+<dd><p class="startdd">TEXT. Name of 'val_id' column in sparse matrix representation (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>row_dim </dt>
+<dd><p class="startdd">INTEGER. The number of rows in the sparse matrix (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>col_dim </dt>
+<dd><p class="startdd">INTEGER. The number of columns in the sparse matrix (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>residual_table (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. Name of the optional residual table.</p>
+<p>The <em>residual_table</em> encodes a dense residual matrix. The table has the following columns:</p>
+<table  class="output">
+<tr>
+<th>row_id </th><td>Row id of the output matrix.  </td></tr>
+<tr>
+<th>row_vec </th><td>A vector containing elements in the row of the residual matrix.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>result_summary_table (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. Name of the optional summary table.</p>
+<p class="enddd">The <em>result_summary_table</em> contains information about the performance time of the PCA projection. The table has the following columns: </p><table  class="output">
+<tr>
+<th>exec_time </th><td>Wall clock time (ms) of the function.  </td></tr>
+<tr>
+<th>residual_norm </th><td>Absolute error of the residuals.  </td></tr>
+<tr>
+<th>relative_residual_norm </th><td>Relative error of the residuals.  </td></tr>
+</table>
+</dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>View online help for the PCA projection function. <pre class="example">
+SELECT madlib.pca_project();
+</pre></li>
+<li>Create the sample data. <pre class="example">
+DROP TABLE IF EXISTS mat;
+CREATE TABLE mat (
+    row_id integer,
+    row_vec double precision[]
+);
+COPY mat (row_id, row_vec) FROM stdin;
+1   {1,2,5}
+0   {4,7,5}
+3   {9,2,4}
+2   {7,4,4}
+5   {0,5,5}
+4   {8,5,7}
+\.
+</pre></li>
+<li>Run the PCA function and keep only the top two PCs: <pre class="example">
+DROP TABLE IF EXISTS result_table;
+SELECT pca_train ( 'mat',
+                   'result_table',
+                   'row_id',
+                   2
+                 );
+</pre></li>
+<li>Project the original data into a low-dimensional representation. <pre class="example">
+DROP TABLE IF EXISTS residual_table, result_summary_table, out_table;
+SELECT pca_project( 'mat',
+                    'result_table',
+                    'out_table'
+                    'row_id',
+                    'residual_table',
+                    'result_summary_table'
+                  );
+</pre></li>
+<li>Check the error in the projection. <pre class="example">
+SELECT * FROM result_summary_table;
+</pre> Result: <pre class="result">
+   exec_time   | residual_norm | relative_residual_norm
+---------------+---------------+------------------------
+ 5685.40501595 | 2.19726255664 |         0.099262204234
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd><ul>
+<li>This function is intended to operate on the principal component tables generated by <em> pca_train </em> or <em> pca_sparse_train</em>. The MADlib PCA functions generate a table containing the column-means in addition to a table containing the principal components. If this table is not found by the MADlib projection function, it will trigger an error. As long the principal component tables are created with MADlib functions, then the column-means table will be automatically found by the MADlib projection functions.</li>
+<li>Because of the centering step in PCA projection (see "Technical Background"), sparse matrices almost always become dense during the projection process. Thus, this implementation automatically densifies sparse matrix input, and there should be no expected performance improvement in using sparse matrix input over dense matrix input.</li>
+<li>Table names can be optionally schema qualified (current_schemas() is searched if a schema name is not provided) and all table and column names should follow case-sensitivity and quoting rules per the database. (For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"').</li>
+</ul>
+</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Given a table containing some principal components \( \boldsymbol P \) and some input data \( \boldsymbol X \), the low-dimensional representation \( {\boldsymbol X}&#39; \) is computed as </p><p class="formulaDsp">
+\begin{align*} {\boldsymbol {\hat{X}}} &amp; = {\boldsymbol X} - \vec{e} \hat{x}^T \\ {\boldsymbol X}&#39; &amp; = {\boldsymbol {\hat {X}}} {\boldsymbol P}. \end{align*}
+</p>
+<p> where \(\hat{x} \) is the column means of \( \boldsymbol X \) and \( \vec{e} \) is the vector of all ones. This step is equivalent to centering the data around the origin.</p>
+<p>The residual table \( \boldsymbol R \) is a measure of how well the low-dimensional representation approximates the true input data, and is computed as </p><p class="formulaDsp">
+\[ {\boldsymbol R} = {\boldsymbol {\hat{X}}} - {\boldsymbol X}&#39; {\boldsymbol P}^T. \]
+</p>
+<p> A residual matrix with entries mostly close to zero indicates a good representation.</p>
+<p>The residual norm \( r \) is simply </p><p class="formulaDsp">
+\[ r = \|{\boldsymbol R}\|_F \]
+</p>
+<p> where \( \|\cdot\|_F \) is the Frobenius norm. The relative residual norm \( r&#39; \) is </p><p class="formulaDsp">
+\[ r&#39; = \frac{ \|{\boldsymbol R}\|_F }{\|{\boldsymbol X}\|_F } \]
+</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="pca__project_8sql__in.html" title="Principal Component Analysis Projection. ">pca_project.sql_in</a> documenting the SQL functions</dd></dl>
+<p><a class="el" href="group__grp__pca__train.html">Principal Component Analysis</a> </p>
+</div><!-- contents -->
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+<title>MADlib: Principal Component Analysis</title>
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+  <td style="padding-left: 0.5em;">
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+  <div class="headertitle">
+<div class="title">Principal Component Analysis<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> &raquo; <a class="el" href="group__grp__pca.html">Dimensionality Reduction</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#notes">Notes</a> </li>
+<li class="level1">
+<a href="#background_pca">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance (i.e., accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (i.e., uncorrelated with) the preceding components.</p>
+<p>See the <a class="el" href="group__grp__pca__train.html#background_pca">Technical Background</a> for an introduction to principal component analysis and the implementation notes.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The training functions have the following formats: <pre class="syntax">
+pca_train( source_table,
+           out_table,
+           row_id,
+           k,
+           grouping_cols,
+           lanczos_iter,
+           use_correlation,
+           result_summary_table
+         )
+</pre> and <pre class="syntax">
+pca_sparse_train( source_table,
+                  out_table,
+                  row_id,
+                  col_id,
+                  val_id,
+                  row_dim,
+                  col_dim,
+                  k,
+                  grouping_cols,
+                  lanczos_iter,
+                  use_correlation,
+                  result_summary_table
+                )
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. Name of the input table containing the data for PCA training. The input data matrix should have \( N \) rows and \( M \) columns, where \( N \) is the number of data points, and \( M \) is the number of features for each data point.</p>
+<p>A dense input table is expected to be in the one of the two standard MADlib dense matrix formats, and a sparse input table should be in the standard MADlib sparse matrix format.</p>
+<p>The two standard MADlib dense matrix formats are </p><pre>{TABLE|VIEW} <em>source_table</em> (
+    <em>row_id</em> INTEGER,
+    <em>row_vec</em> FLOAT8[],
+)</pre><p> and </p><pre>{TABLE|VIEW} <em>source_table</em> (
+    <em>row_id</em> INTEGER,
+    <em>col1</em> FLOAT8,
+    <em>col2</em> FLOAT8,
+    ...
+)</pre><p>Note that the column name <em>row_id</em> is taken as an input parameter, and should be an INTEGER and contain a list of continguous row indices from 0 to N-1, where N = number of rows.</p>
+<p>The input table for sparse PCA is expected to be in the form:</p>
+<pre>{TABLE|VIEW} <em>source_table</em> (
+    ...
+    <em>row_id</em> INTEGER,
+    <em>col_id</em> INTEGER,
+    <em>val_id</em> FLOAT8,
+    ...
+)</pre><p>The <em>row_id</em> and <em>col_id</em> columns specify which entries in the matrix are nonzero, and the <em>val_id</em> column defines the values of the nonzero entries. </p>
+<p class="enddd"></p>
+</dd>
+<dt>out_table </dt>
+<dd><p class="startdd">TEXT. The name of the table that will contain the output. The output is divided into three tables.</p>
+<p>The primary output table (<em>out_table</em>) encodes the principal components with the <em>k</em> highest eigenvalues. The table has the following columns: </p><table  class="output">
+<tr>
+<th>row_id </th><td>Eigenvalue rank in descending order of the eigenvalue size.  </td></tr>
+<tr>
+<th>principal_components </th><td>Vectors containing elements of the principal components.  </td></tr>
+<tr>
+<th>eigen_values </th><td>The eigenvalues associated with each principal component.  </td></tr>
+</table>
+<p>The table <em>out_table</em>_means contains the column means. This table has just one column: </p><table  class="output">
+<tr>
+<th>column_means </th><td>A vector containing the column means for the input matrix.  </td></tr>
+</table>
+<p>The optional table <em>result_summary_table</em> contains information about the performance of the PCA. The contents of this table are described under the <em>result_summary_table</em> argument. </p>
+<p class="enddd"></p>
+</dd>
+<dt>row_id </dt>
+<dd><p class="startdd">TEXT. Column name containing the row IDs in the input source table.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_id </dt>
+<dd><p class="startdd">TEXT. Name of 'col_id' column in sparse matrix representation (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>val_id </dt>
+<dd><p class="startdd">TEXT. Name of 'val_id' column in sparse matrix representation (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>row_dim </dt>
+<dd><p class="startdd">INTEGER. The number of rows in the sparse matrix (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>col_dim </dt>
+<dd><p class="startdd">INTEGER. The number of columns in the sparse matrix (sparse matrices only). </p>
+<p class="enddd"></p>
+</dd>
+<dt>k </dt>
+<dd><p class="startdd">INTEGER. The number of principal components to calculate from the input data. </p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_cols (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. Currently <em>grouping_cols</em> is present as a placeholder for forward compatibility. The parameter is planned to be implemented as a comma-separated list of column names, with the source data grouped using the combination of all the columns. An independent PCA model will be computed for each combination of the grouping columns.</p>
+<p class="enddd"></p>
+</dd>
+<dt>lanczos_iter (optional) </dt>
+<dd><p class="startdd">INTEGER, default: minimum of {k+40, smallest matrix dimension}. The number of Lanczos iterations for the SVD calculation. The Lanczos iteration number roughly corresponds to the accuracy of the SVD calculation, and a higher iteration number corresponds to greater accuracy but longer computation time. The number of iterations must be at least as large as the value of <em>k</em>, but no larger than the smallest dimension of the matrix. If the iteration number is given as zero, then the default number of iterations is used.</p>
+<p class="enddd"></p>
+</dd>
+<dt>use_correlation (optional) </dt>
+<dd><p class="startdd">BOOLEAN, default FALSE. Whether to use the correlation matrix for calculating the principal components instead of the covariance matrix. Currently <em>use_correlation</em> is a placeholder for forward compatibility, and this value must be set to false.</p>
+<p class="enddd"></p>
+</dd>
+<dt>result_summary_table (optional) </dt>
+<dd><p class="startdd">TEXT, default NULL. Name of the optional summary table. When NULL, no summary table is generated.</p>
+<p class="enddd">This sumary table has the following columns: </p><table  class="output">
+<tr>
+<th>rows_used </th><td>INTEGER. Number of data points in the input.  </td></tr>
+<tr>
+<th>exec_time (ms) </th><td>FLOAT8. Number of milliseconds for the PCA calculation to run.  </td></tr>
+<tr>
+<th>iter </th><td>INTEGER. Number of iterations used in the SVD calculation.  </td></tr>
+<tr>
+<th>recon_error </th><td>FLOAT8. The absolute error in the SVD approximation.  </td></tr>
+<tr>
+<th>relative_recon_error </th><td>FLOAT8. The relative error in the SVD approximation.  </td></tr>
+<tr>
+<th>use_correlation </th><td>BOOLEAN. Indicates if the correlation matrix was used.  </td></tr>
+</table>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the PCA training function. <pre class="example">
+SELECT madlib.pca_train();
+</pre></li>
+<li>Create the sample data. <pre class="example">
+DROP TABLE IF EXISTS mat;
+CREATE TABLE mat (
+    row_id integer,
+    row_vec double precision[]
+);
+COPY mat (row_id, row_vec) FROM stdin;
+0   {1,2,3}
+1   {2,1,2}
+2   {3,2,1}
+\.
+</pre></li>
+<li>Run the PCA function: <pre class="example">
+DROP TABLE result_table;
+SELECT pca_train( 'mat',
+                  'result_table',
+                  'row_id',
+                  3
+                );
+</pre></li>
+<li>View the PCA results: <pre class="example">
+SELECT * FROM result_table;
+</pre> Result <pre class="result">
+ row_id |                     principal_components                     |     eigen_values
+--------+--------------------------------------------------------------+----------------------
+      0 | {0.707106781186547,0.408248290459781,-0.577350269192513}     |                    2
+      2 | {-0.707106781186547,0.408248290459781,-0.577350269192512}    | 1.26294130828989e-08
+      1 | {2.08166817117217e-17,-0.816496580931809,-0.577350269183852} |    0.816496580927726
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<ul>
+<li>Table names can be optionally schema qualified (current_schemas() would be searched if a schema name is not provided) and all table and column names should follow case-sensitivity and quoting rules per the database. (For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"').</li>
+<li>Because of the centering step in PCA (see <a class="el" href="group__grp__pca__train.html#background_pca">Technical Background</a>), sparse matrices almost always become dense during the training process. Thus, this implementation automatically densifies sparse matrix input, and there should be no expected performance improvement in using sparse matrix input over dense matrix input.</li>
+</ul>
+<p><a class="anchor" id="background_pca"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>The PCA implemented here uses an SVD decomposition implementation to recover the principal components (as opposed to the directly computing the eigenvectors of the covariance matrix). Let \( \boldsymbol X \) be the data matrix, and let \( \hat{x} \) be a vector of the column averages of \( \boldsymbol{X}\). PCA computes the matrix \( \hat{\boldsymbol X} \) as </p><p class="formulaDsp">
+\[ \hat{\boldsymbol X} = {\boldsymbol X} - \vec{e} \hat{x}^T \]
+</p>
+<p> where \( \vec{e} \) is the vector of all ones.</p>
+<p>PCA then computes the SVD matrix factorization </p><p class="formulaDsp">
+\[ \hat{\boldsymbol X} = {\boldsymbol U}{\boldsymbol \Sigma}{\boldsymbol V}^T \]
+</p>
+<p> where \( {\boldsymbol \Sigma} \) is a diagonal matrix. The eigenvalues are recovered as the entries of \( {\boldsymbol \Sigma}/(\sqrt{N-1}) \), and the principal components are the rows of \( {\boldsymbol V} \).</p>
+<p>It is important to note that the PCA implementation assumes that the user will use only the principal components that have non-zero eigenvalues. The SVD calculation is done with the Lanczos method, with does not guarantee correctness for singular vectors with zero-valued eigenvalues. Consequently, principal components with zero-valued eigenvalues are not guaranteed to be correct. Generally, this will not be problem unless the user wants to use the principal components for the entire eigenspectrum.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Principal Component Analysis. <a href="http://en.wikipedia.org/wiki/Principal_component_analysis">http://en.wikipedia.org/wiki/Principal_component_analysis</a></p>
+<p>[2] Shlens, Jonathon (2009), A Tutorial on Principal Component Analysis</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="pca_8sql__in.html" title="Principal Component Analysis. ">pca.sql_in</a> documenting the SQL functions</p>
+<p><a class="el" href="group__grp__pca__project.html">Principal Component Projection</a> </p>
+</div><!-- contents -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+</html>

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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
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+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+$(document).ready(function(){initNavTree('group__grp__pmml.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">PMML Export<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#function">PMML Export Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Background</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p><a class="anchor" id="function"></a></p><dl class="section user"><dt>PMML Export Function</dt><dd>The PMML export function in MADlib has the following syntax: <pre class="syntax">
+pmml ( model_table,
+       name_spec
+     )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>model_table </dt>
+<dd><p class="startdd">VARCHAR. The name of the table containing the model.</p>
+<p class="enddd"></p>
+</dd>
+<dt>name_spec (optional) </dt>
+<dd>VARCHAR or VARCHAR[]. Names to be used in the Data Dictionary of the PMML. See <a class="el" href="table__to__pmml_8sql__in.html#a9635b6989d9f972497b6b4164b77aa0a" title="Given the model constructed from a data mining algorithm, this function converts the model into PMML ...">pmml()</a> for detailed explanation. </dd>
+</dl>
+</dd></dl>
+<p><b>Output</b> XML. The output of this function is a standard PMML document, some examples of which are covered in the next section. </p>
+<dl class="section note"><dt>Note</dt><dd>In PostgreSQL, users may be required to install their database with XML support in order to use this function.</dd></dl>
+<p>Usually the user wants to export the resulting PMML contents into a PMML file so that external softwares can use it. The following method can be used (Note: the user needs to use unaligned table output mode for psql with '-A' flag. And inside psql client, both '\t' and '\o' should be used):</p>
+<pre class="example">
+&gt; # under bash
+&gt; psql -A my_database
+# -- in psql now
+# \t
+# \o test.pmml -- export to a file
+# select madlib.pmml('tree_out');
+# \o
+# \t
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>Create the training data table. <pre class="example">
+CREATE TABLE patients( id integer NOT NULL,
+                       second_attack integer,
+                       treatment integer,
+                       trait_anxiety integer);
+INSERT INTO patients(id, second_attack, treatment, trait_anxiety) VALUES
+( 1, 1, 1, 70),
+( 3, 1, 1, 50),
+( 5, 1, 0, 40),
+( 7, 1, 0, 75),
+( 9, 1, 0, 70),
+(11, 0, 1, 65),
+(13, 0, 1, 45),
+(15, 0, 1, 40),
+(17, 0, 0, 55),
+(19, 0, 0, 50),
+( 2, 1, 1, 80),
+( 4, 1, 0, 60),
+( 6, 1, 0, 65),
+( 8, 1, 0, 80),
+(10, 1, 0, 60),
+(12, 0, 1, 50),
+(14, 0, 1, 35),
+(16, 0, 1, 50),
+(18, 0, 0, 45),
+(20, 0, 0, 60);
+</pre></li>
+<li>Train a regression model using <a class="el" href="logistic_8sql__in.html#a74210a7ef513dfcbdfdd9f3b37bfe428" title="Compute logistic-regression coefficients and diagnostic statistics. ">logregr_train()</a>. <pre class="example">
+SELECT madlib.logregr_train(
+        'patients',
+        'patients_logregr',
+        'second_attack',
+        'ARRAY[1, treatment, trait_anxiety]');
+</pre></li>
+<li>View the PMML export for this model. <pre class="example">
+SELECT madlib.pmml('patients_logregr');
+</pre> Result: <pre class="result">
+&lt;?xml version="1.0" standalone="yes"?&gt;
+&lt;PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html"&gt;
+  &lt;Header copyright="Copyright (c) 2014 gpadmin"&gt;
+    &lt;Extension extender="MADlib" name="user" value="gpadmin"&gt;
+    &lt;Application name="MADlib" version="1.7"&gt;
+    &lt;Timestamp&gt;
+      2014-06-13 17:30:14.527899 PDT
+    &lt;/Timestamp&gt;
+  &lt;/Header&gt;
+  &lt;DataDictionary numberoffields="4"&gt;
+    &lt;DataField datatype="boolean" name="second_attack_pmml_prediction" optype="categorical"&gt;
+    &lt;DataField datatype="double" name="1" optype="continuous"&gt;
+    &lt;DataField datatype="double" name="treatment" optype="continuous"&gt;
+    &lt;DataField datatype="double" name="trait_anxiety" optype="continuous"&gt;
+  &lt;/DataDictionary&gt;
+  &lt;RegressionModel functionname="classification" normalizationmethod="softmax"&gt;
+    &lt;MiningSchema&gt;
+      &lt;MiningField name="second_attack_pmml_prediction" usagetype="predicted"&gt;
+      &lt;MiningField name="1"&gt;
+      &lt;MiningField name="treatment"&gt;
+      &lt;MiningField name="trait_anxiety"&gt;
+    &lt;/MiningSchema&gt;
+    &lt;RegressionTable intercept="0.0" targetcategory="True"&gt;
+      &lt;NumericPredictor coefficient="-6.36346994178" name="1"&gt;
+      &lt;NumericPredictor coefficient="-1.02410605239" name="treatment"&gt;
+      &lt;NumericPredictor coefficient="0.119044916669" name="trait_anxiety"&gt;
+    &lt;/RegressionTable&gt;
+    &lt;RegressionTable intercept="0.0" targetcategory="False"&gt;
+  &lt;/RegressionModel&gt;
+&lt;/PMML&gt;
+</pre></li>
+</ol>
+</dd></dl>
+<p>Alternatively, the above can also be invoked as below if custom names are needed for fields in the Data Dictionary: </p><pre class="example">
+SELECT madlib.pmml('patients_logregr',
+                   'out_attack~1+in_trait_anxiety+in_treatment');
+</pre><p><b>Note:</b> If the second argument of 'pmml' function is not specified, a default suffix "_pmml_prediction" will be automatically append to the column name to be predicted. This can help avoid name conflicts.</p>
+<p>The following example demonstrates grouping columns in the model table for the same dataset as the previous example.</p>
+<ol type="1">
+<li>Train a different regression model with 'treatment' as the grouping column. <pre class="example">
+SELECT madlib.logregr_train(
+        'patients',
+        'patients_logregr_grouping',
+        'second_attack',
+        'ARRAY[1, trait_anxiety]',
+        'treatment');
+</pre></li>
+<li>View the PMML export for this model. <pre class="example">
+SELECT madlib.pmml('patients_logregr_grouping',
+                   ARRAY['second_attack','1','in_trait_anxiety']);
+</pre> Result: <pre class="result">
+&lt;?xml version="1.0" standalone="yes"?&gt;
+ &lt;PMML version="4.1" xmlns="http://www.dmg.org/pmml-v4-1.html"&gt;
+   &lt;Header copyright="Copyright (c) 2014 gpadmin"&gt;
+     &lt;Extension extender="MADlib" name="user" value="gpadmin"&gt;
+     &lt;Application name="MADlib" version="1.7"&gt;
+     &lt;Timestamp&gt;
+       2014-06-13 17:37:55.786307 PDT
+     &lt;/Timestamp&gt;
+   &lt;/Header&gt;
+   &lt;DataDictionary numberoffields="4"&gt;
+     &lt;DataField datatype="boolean" name="second_attack" optype="categorical"&gt;
+     &lt;DataField datatype="double" name="1" optype="continuous"&gt;
+     &lt;DataField datatype="double" name="in_trait_anxiety" optype="continuous"&gt;
+     &lt;DataField datatype="string" name="treatment" optype="categorical"&gt;
+   &lt;/DataDictionary&gt;
+   &lt;MiningModel functionname="classification"&gt;
+     &lt;MiningSchema&gt;
+       &lt;MiningField name="second_attack" usagetype="predicted"&gt;
+       &lt;MiningField name="1"&gt;
+       &lt;MiningField name="in_trait_anxiety"&gt;
+       &lt;MiningField name="treatment"&gt;
+     &lt;/MiningSchema&gt;
+     &lt;Segmentation multiplemodelmethod="selectFirst"&gt;
+       &lt;Segment&gt;
+         &lt;SimplePredicate field="treatment" operator="equal" value="1"&gt;
+         &lt;RegressionModel functionname="classification" normalizationmethod="softmax"&gt;
+           &lt;MiningSchema&gt;
+             &lt;MiningField name="second_attack" usagetype="predicted"&gt;
+             &lt;MiningField name="1"&gt;
+             &lt;MiningField name="in_trait_anxiety"&gt;
+           &lt;/MiningSchema&gt;
+           &lt;RegressionTable intercept="0.0" targetcategory="True"&gt;
+             &lt;NumericPredictor coefficient="-8.02068430057" name="1"&gt;
+             &lt;NumericPredictor coefficient="0.130090428526" name="in_trait_anxiety"&gt;
+           &lt;/RegressionTable&gt;
+           &lt;RegressionTable intercept="0.0" targetcategory="False"&gt;
+         &lt;/RegressionModel&gt;
+       &lt;/Segment&gt;
+       &lt;Segment&gt;
+         &lt;SimplePredicate field="treatment" operator="equal" value="0"&gt;
+         &lt;RegressionModel functionname="classification" normalizationmethod="softmax"&gt;
+           &lt;MiningSchema&gt;
+             &lt;MiningField name="second_attack" usagetype="predicted"&gt;
+             &lt;MiningField name="1"&gt;
+             &lt;MiningField name="in_trait_anxiety"&gt;
+           &lt;/MiningSchema&gt;
+           &lt;RegressionTable intercept="0.0" targetcategory="True"&gt;
+             &lt;NumericPredictor coefficient="-5.75043192191" name="1"&gt;
+             &lt;NumericPredictor coefficient="0.108282446319" name="in_trait_anxiety"&gt;
+           &lt;/RegressionTable&gt;
+           &lt;RegressionTable intercept="0.0" targetcategory="False"&gt;
+         &lt;/RegressionModel&gt;
+       &lt;/Segment&gt;
+     &lt;/Segmentation&gt;
+   &lt;/MiningModel&gt;
+ &lt;/PMML&gt;
+</pre></li>
+</ol>
+<p><b>Note:</b> MADlib currently supports PMML export for Linear Regression, Logistic Regression, Generalized Linear Regression Model, Multinomial Logistic Regression, Ordinal Linear Regression, Decision Tree and Random Forests.</p>
+<p>In Ordinal Regression, the signs of feature coefficients will be different in PMML export and in the default output model table from ordinal(). This is due to the difference of model settings.</p>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Background</dt><dd>The Predictive Model Markup Language (PMML) is an XML-based file format that provides a way for applications to describe and exchange models produced by data mining and machine learning algorithms. A PMML file comprises the following components:<ul>
+<li>Header: Contains general information of the model, such as copyright information and model description.</li>
+<li>Data Dictionary: Contains definitions of fields used in the model.</li>
+<li>Data Transformations: Contains transformations for mapping user data into a form that can be used by the mining model.</li>
+<li>Model: Contains definitions of the data mining model, which includes attributes such as the model name, function name, and algorithm name.</li>
+<li>Mining Schema: Contains specific information for the fields used in the model, which includes the name and usage type.</li>
+<li>Targets: Allows for post-processing of the predicted value.</li>
+<li>Output: Allows for naming of output fields expected from the model.</li>
+</ul>
+</dd></dl>
+<p>MADlib follows the PMML v4.1 standard. For more details about PMML, see <a href="http://www.dmg.org/v4-1/GeneralStructure.html">http://www.dmg.org/v4-1/GeneralStructure.html</a>.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="table__to__pmml_8sql__in.html">table_to_pmml.sql_in</a> documenting the PMML export functions.</p>
+<p><a class="el" href="group__grp__linreg.html">Linear Regression</a></p>
+<p><a class="el" href="group__grp__logreg.html">Logistic Regression</a></p>
+<p><a class="el" href="group__grp__glm.html">Generalized Linear Models</a></p>
+<p><a class="el" href="group__grp__ordinal.html">Ordinal Regression</a></p>
+<p><a class="el" href="group__grp__multinom.html">Multinomial Regression</a></p>
+<p><a class="el" href="group__grp__decision__tree.html">Decision Tree</a></p>
+<p><a class="el" href="group__grp__random__forest.html">Random Forest</a> </p>
+</div><!-- contents -->
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__prob.html
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+<title>MADlib: Probability Functions</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
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+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+       class="ui-resizable-handle">
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+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__prob.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
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+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Probability Functions<div class="ingroups"><a class="el" href="group__grp__stats.html">Statistics</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>The Probability Functions module provides cumulative distribution, density/mass, and quantile functions for a wide range of probability distributions.</p>
+<p>Unless otherwise documented, all of these functions are wrappers around functionality provided by the boost C++ library [1, “<a href="http://www.boost.org/doc/libs/1_49_0/libs/math/doc/sf_and_dist/html/math_toolkit/dist.html">Statistical Distributions and Functions</a>”].</p>
+<p>For convenience, all cumulative distribution and density/mass functions (CDFs and PDF/PMFs in short) are defined over the range of all floating-point numbers including infinity. Inputs that are <code>NULL</code> or <code>NaN</code> (not a number) will always produce a <code>NULL</code> or <code>NaN</code> result, respectively. Inputs that are plus or minus infinity will return the respective limits.</p>
+<p>A quantile function for a probability distrution with CDF \( F \) takes a probability argument \( p \in [0,1] \) and returns the value \( x \) so that \( F(x) = p \), provided such an \( x \) exists and it is unique. If it does not, the result will be \( \sup \{ x \in D \mid F(x) \leq p \} \) (interpreted as 0 if the supremum is over an empty set) if \( p &lt; 0.5 \), and \( \inf \{ x \in D \mid F(x) \geq p \} \) if \( p \geq 0.5 \). Here \( D \) denotes the domain of the distribution, which is the set of reals \( \mathbb R \) for continuous and the set of nonnegative integers \( \mathbb N_0 \) for discrete distributions.</p>
+<p>Intuitively, the formulas in the previous paragraph deal with the following special cases. The 0-quantile will always be the “left end” of the support, and the 1-quantile will be the “right end” of the support of the distribution. For discrete distributions, most values of \( p \in [0,1] \) do not admit an \( x \) with \( F(x) = p \). Instead, there is an \( x \in \mathbb N_0 \) so that \( F(x) &lt; p &lt; F(x + 1) \). The above formulas mean that the value returned as \( p \)-quantile is \( x \) if \( p &lt; 0.5 \), and it is \( x + 1 \) if \( p \geq 0.5 \). (As a special case, in order to ensure that quantiles are always within the support, the \( p \)-quantile will be 0 if \( p &lt; F(0) \)).</p>
+<p>The rationale for choosing this behavior is that \(p\)-quantiles for \( p &lt; 0.5 \) are typically requested when interested in the value \( x \) such that with confidence level <b>at least</b> \( 1 - p \) a random variable will be \( &gt; x \) (or equivalently, with probability <b>at most</b> \( p \), it will be \( \leq x \)). Likewise, \(p\)-quantiles for \( p \geq 0.5 \) are typically requested when interested in the value \( x \) such that with confidence level <b>at least</b> \( p \) a random variable will be \( \leq x \). See also [1, “<a href="http://www.boost.org/doc/libs/1_46_1/libs/math/doc/sf_and_dist/html/math_toolkit/policy/pol_tutorial/understand_dis_quant.html">Understanding Quantiles of Discrete Distributions</a>”].</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd></dd></dl>
+<p>Cumulative distribution functions:</p>
+<pre class="syntax"><em>distribution</em>_cdf(<em>random variate</em>[, <em>parameter1</em> [, <em>parameter2</em> [, <em>parameter3</em>] ] ])</pre><p>Probability density/mass functions: </p><pre class="syntax"><em>distribution</em>_{pdf|pmf}(<em>random variate</em>[, <em>parameter1</em> [, <em>parameter2</em> [, <em>parameter3</em>] ] ])</pre><p>Quantile functions: </p><pre class="syntax"><em>distribution</em>_quantile(<em>probability</em>[, <em>parameter1</em> [, <em>parameter2</em> [, <em>parameter3</em>] ] ])</pre><p>For concrete function signatures, see <a class="el" href="prob_8sql__in.html">prob.sql_in</a>.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<pre class="example">
+SELECT madlib.normal_cdf(0);
+</pre><p> Result: </p><pre class="result">
+ normal_cdf
+&#160;-----------
+        0.5
+</pre> <pre class="example">
+SELECT madlib.normal_quantile(0.5, 0, 1);
+</pre><p> Result: </p><pre class="result">
+ normal_quantile
+&#160;----------------
+               0
+(1 row)
+</pre><p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] John Maddock, Paul A. Bristow, Hubert Holin, Xiaogang Zhang, Bruno Lalande, Johan Råde, Gautam Sewani and Thijs van den Berg: <em>Boost Math Toolkit</em>, Version 1.49, available at: <a href="http://www.boost.org/doc/libs/1_49_0/libs/math/doc/sf_and_dist/html/index.html">http://www.boost.org/doc/libs/1_49_0/libs/math/doc/sf_and_dist/html/index.html</a></p>
+<dl class="section user"><dt>Related Topics</dt><dd><a class="anchor" id="related"></a>File <a class="el" href="prob_8sql__in.html" title="SQL functions for evaluating probability functions. ">prob.sql_in</a> documenting the SQL functions. </dd></dl>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[26/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_a.html
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diff --git a/docs/latest/globals_a.html b/docs/latest/globals_a.html
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+++ b/docs/latest/globals_a.html
@@ -0,0 +1,253 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
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+  ga('send', 'pageview');
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
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+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
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+      <div id="nav-sync" class="sync"></div>
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+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_a.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
+<li>angle()
+: <a class="el" href="svec__util_8sql__in.html#a7e395ee71f6cf05afa76d89a72d45bec">svec_util.sql_in</a>
+</li>
+<li>argmax()
+: <a class="el" href="bayes_8sql__in.html#ad47c68a2b8aeedf66d509f0c32a9732d">bayes.sql_in</a>
+</li>
+<li>argmax_combine()
+: <a class="el" href="bayes_8sql__in.html#ab150442ec577f4a0a3948176baf5ebd0">bayes.sql_in</a>
+</li>
+<li>argmax_final()
+: <a class="el" href="bayes_8sql__in.html#aa7017323fd6fad40c9acbbee5f1fec7e">bayes.sql_in</a>
+</li>
+<li>argmax_transition()
+: <a class="el" href="bayes_8sql__in.html#a0671e6ac35a1967f41d2efb9e8417e1b">bayes.sql_in</a>
+</li>
+<li>arima_forecast()
+: <a class="el" href="arima_8sql__in.html#a08645edc97c591bce174964ac309a3a0">arima.sql_in</a>
+</li>
+<li>arima_train()
+: <a class="el" href="arima_8sql__in.html#a9af899e0260745b75b1872159fea1281">arima.sql_in</a>
+</li>
+<li>array_add()
+: <a class="el" href="array__ops_8sql__in.html#a91c8d3715142927b3967f05a4fbf1575">array_ops.sql_in</a>
+</li>
+<li>array_avg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a5734f5796a403ebf2e765cba136f54c8">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_avg_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a1f5594f3a1199bfd91d7479292e25b5c">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_avg_merge()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a45bd6774621ad8fe0baa9427f7ce8fd4">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_avg_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a0ffc559f8f54d7ea49c8d1a865ef5a37">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_collapse()
+: <a class="el" href="profile_8sql__in.html#a8b4ddf1ad8fb4777936f441b873ac968">profile.sql_in</a>
+</li>
+<li>array_contains()
+: <a class="el" href="array__ops_8sql__in.html#aedf6cb13eb4803bcc12dc4d95ea8ff4e">array_ops.sql_in</a>
+</li>
+<li>array_div()
+: <a class="el" href="array__ops_8sql__in.html#a6cc05e7052495f8b64692faf40219576">array_ops.sql_in</a>
+</li>
+<li>array_dot()
+: <a class="el" href="array__ops_8sql__in.html#acde10964ed23b7c8da515fb84cb8d5e0">array_ops.sql_in</a>
+</li>
+<li>array_elem_corr_agg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a8928da50ddcf85de812ab058214324db">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_max()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a1de2b192c73ede707b42726e9469664d">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_min()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a2ec0ea5a3faba236a75d4d9cc16e40a4">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_fill()
+: <a class="el" href="array__ops_8sql__in.html#a065a5323f3b742be47e39ad8b4c90fc2">array_ops.sql_in</a>
+</li>
+<li>array_filter()
+: <a class="el" href="array__ops_8sql__in.html#acc295a568878940ffc3e2c9a75990efb">array_ops.sql_in</a>
+</li>
+<li>array_max()
+: <a class="el" href="array__ops_8sql__in.html#ae891429cc50705c530f3e5ca15541849">array_ops.sql_in</a>
+</li>
+<li>array_mean()
+: <a class="el" href="array__ops_8sql__in.html#a407598f9eb70637798b02fd731bfca2c">array_ops.sql_in</a>
+</li>
+<li>array_min()
+: <a class="el" href="array__ops_8sql__in.html#a6659bf9d9363eb179fab34f81f8ac59b">array_ops.sql_in</a>
+</li>
+<li>array_mult()
+: <a class="el" href="array__ops_8sql__in.html#a652d70c480d484c4a1a92ded384b0dd7">array_ops.sql_in</a>
+</li>
+<li>array_of_bigint()
+: <a class="el" href="array__ops_8sql__in.html#ab7d8550e66d2e0bd54b8f0997d93880c">array_ops.sql_in</a>
+</li>
+<li>array_of_float()
+: <a class="el" href="array__ops_8sql__in.html#ab066e65a41db78b00b4532996b2a6efc">array_ops.sql_in</a>
+</li>
+<li>array_scalar_add()
+: <a class="el" href="array__ops_8sql__in.html#a0b6ffe59b12c3dee076c3059f9ab363f">array_ops.sql_in</a>
+</li>
+<li>array_scalar_mult()
+: <a class="el" href="array__ops_8sql__in.html#ae6881cc5c86941b6ffca35d7f3cd5c12">array_ops.sql_in</a>
+</li>
+<li>array_stddev()
+: <a class="el" href="array__ops_8sql__in.html#a3b6c2d173a611e6d6b184d825c2b336d">array_ops.sql_in</a>
+</li>
+<li>array_sub()
+: <a class="el" href="array__ops_8sql__in.html#a2875a161a01c7dcdea9a4997b074eefc">array_ops.sql_in</a>
+</li>
+<li>array_sum()
+: <a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec">array_ops.sql_in</a>
+</li>
+<li>array_sum_big()
+: <a class="el" href="array__ops_8sql__in.html#a418de59800833aa95f9b7cbd6b12901c">array_ops.sql_in</a>
+</li>
+<li>array_to_1d()
+: <a class="el" href="linalg_8sql__in.html#a71304c333a999e69ac1430c285c35181">linalg.sql_in</a>
+</li>
+<li>array_to_2d()
+: <a class="el" href="linalg_8sql__in.html#a0799a4ccb93a699a280861126c3e1a74">linalg.sql_in</a>
+</li>
+<li>array_union()
+: <a class="el" href="crf_8sql__in.html#a73693c959db4ca3dbfd1b9debcca2c8f">crf.sql_in</a>
+</li>
+<li>assert()
+: <a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd">utilities.sql_in</a>
+</li>
+<li>assoc_rules()
+: <a class="el" href="assoc__rules_8sql__in.html#af9456adb6dad01e452415b9a0a5371dc">assoc_rules.sql_in</a>
+</li>
+<li>avg()
+: <a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4">linalg.sql_in</a>
+</li>
+<li>avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#ad468b52a7caa9b47a0650d39e32d9c50">linalg.sql_in</a>
+</li>
+<li>avg_vector_merge()
+: <a class="el" href="linalg_8sql__in.html#a5ac255c13e1f2d91274b829ff0383e44">linalg.sql_in</a>
+</li>
+<li>avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#aaea269f4746f952faadffed9d9b107d7">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_b.html
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_b.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
+<li>bernoulli_cdf()
+: <a class="el" href="prob_8sql__in.html#aea21a931dc5578a570e3370af3d8d43a">prob.sql_in</a>
+</li>
+<li>bernoulli_pmf()
+: <a class="el" href="prob_8sql__in.html#a434b3ad1f3964835834dc2a942b820ef">prob.sql_in</a>
+</li>
+<li>bernoulli_quantile()
+: <a class="el" href="prob_8sql__in.html#a7133c2e86fd2f6384416ee0e4fd3a60b">prob.sql_in</a>
+</li>
+<li>beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a72e1cca872da35592075dbcfb18aed3f">prob.sql_in</a>
+</li>
+<li>beta_pdf()
+: <a class="el" href="prob_8sql__in.html#aa105049e6e3bb9b3891b0ed1b343e28e">prob.sql_in</a>
+</li>
+<li>beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a32433aa742c0504d33e98e28a3e2f190">prob.sql_in</a>
+</li>
+<li>big_or()
+: <a class="el" href="sketch_8sql__in.html#a335fba86bbb3fb5a3284808cc64b8f16">sketch.sql_in</a>
+</li>
+<li>binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#aa5000bad6e2e4af1c8cbfec7ea884476">prob.sql_in</a>
+</li>
+<li>binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#aa0614475b8685bf8e37533d2ac5bb116">prob.sql_in</a>
+</li>
+<li>binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#a49f421c58d2e1abd63b83d71af9edf21">prob.sql_in</a>
+</li>
+<li>bool_to_text()
+: <a class="el" href="utilities_8sql__in.html#abc867482053e6692047d051b679953a5">utilities.sql_in</a>
+</li>
+<li>bytea8in()
+: <a class="el" href="utilities_8sql__in.html#a60c4765ce8dc639cbb0ad4c389f5f1ad">utilities.sql_in</a>
+</li>
+<li>bytea8out()
+: <a class="el" href="utilities_8sql__in.html#adff923d42d4bdb2b631d6cafd5f37a43">utilities.sql_in</a>
+</li>
+<li>bytea8recv()
+: <a class="el" href="utilities_8sql__in.html#a244a7ff30f2788a6f4816417982eb13b">utilities.sql_in</a>
+</li>
+<li>bytea8send()
+: <a class="el" href="utilities_8sql__in.html#aeb90c740e8e6b67aa60a11c430fa9050">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_c.html
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diff --git a/docs/latest/globals_c.html b/docs/latest/globals_c.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_c.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
+<li>c45_classify()
+: <a class="el" href="c45_8sql__in.html#afe136e52f498f2ff9e2b91e38e29d670">c45.sql_in</a>
+</li>
+<li>c45_clean()
+: <a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793">c45.sql_in</a>
+</li>
+<li>c45_display()
+: <a class="el" href="c45_8sql__in.html#a32d2bcbc016c990991d77b6f6148306d">c45.sql_in</a>
+</li>
+<li>c45_genrule()
+: <a class="el" href="c45_8sql__in.html#ac71787c47795b3b0b133cdbd37438242">c45.sql_in</a>
+</li>
+<li>c45_score()
+: <a class="el" href="c45_8sql__in.html#a1b634db47e9006d114da0987e80b9601">c45.sql_in</a>
+</li>
+<li>c45_train()
+: <a class="el" href="c45_8sql__in.html#a4fbee855d22101d15d195d573189eb98">c45.sql_in</a>
+</li>
+<li>cauchy_cdf()
+: <a class="el" href="prob_8sql__in.html#a2d8874c2a5679403a473bfedb14467a4">prob.sql_in</a>
+</li>
+<li>cauchy_pdf()
+: <a class="el" href="prob_8sql__in.html#aebfad9365a7fc7a553c3b5c7931f2450">prob.sql_in</a>
+</li>
+<li>cauchy_quantile()
+: <a class="el" href="prob_8sql__in.html#ae8aa9b741e89c8d9236a682d218006e0">prob.sql_in</a>
+</li>
+<li>check_if_col_exists()
+: <a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470">utilities.sql_in</a>
+</li>
+<li>check_if_raises_error()
+: <a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0">utilities.sql_in</a>
+</li>
+<li>chi2_gof_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4c912b329fb103a44253932a653d4e40">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a61c31dfde998db18afd6552239b872c4">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a2b8265718a16ec65e89d2ab512f6a3e1">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a7c580537666776f1bd4b9d4a0a6b6438">hypothesis_tests.sql_in</a>
+</li>
+<li>chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#a230513b6b549d5b445cbacbdbab42c15">prob.sql_in</a>
+</li>
+<li>chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#a90bccc717d7052e83bafd7f160a783b1">prob.sql_in</a>
+</li>
+<li>chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad125307fe65a33b60f6dd524037d4548">prob.sql_in</a>
+</li>
+<li>cleanup_madlib_temp_tables()
+: <a class="el" href="utilities_8sql__in.html#aab5ea2cd6201e38e24ca62b0fb91fb7a">utilities.sql_in</a>
+</li>
+<li>cleanup_madlib_temp_tables_script()
+: <a class="el" href="utilities_8sql__in.html#a156eb9d1c814592e59600980977a64eb">utilities.sql_in</a>
+</li>
+<li>closest_column()
+: <a class="el" href="linalg_8sql__in.html#a31c8d60f9a631c27f5f91964e0108da9">linalg.sql_in</a>
+</li>
+<li>closest_columns()
+: <a class="el" href="linalg_8sql__in.html#ad864339591086b635d12015db993b5bc">linalg.sql_in</a>
+</li>
+<li>clustered_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a91f46572f245f34c9fe92d0ec806a998">clustered_variance_coxph.sql_in</a>
+</li>
+<li>clustered_variance_linregr()
+: <a class="el" href="clustered__variance_8sql__in.html#abaa6702a3482f65199ce64b8a14316d7">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_logregr()
+: <a class="el" href="clustered__variance_8sql__in.html#a0debc923b0494f8259938d9c6f5cdbcd">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_mlogregr()
+: <a class="el" href="clustered__variance_8sql__in.html#aa72cfa6f9259cceea3e36b6db45a7624">clustered_variance.sql_in</a>
+</li>
+<li>cmsketch()
+: <a class="el" href="sketch_8sql__in.html#ae6671bfb7474db7d8b5c9dada29f36ab">sketch.sql_in</a>
+</li>
+<li>cmsketch_centile()
+: <a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">sketch.sql_in</a>
+</li>
+<li>cmsketch_count()
+: <a class="el" href="sketch_8sql__in.html#a3498d2c778d1289154f61d34e84c609e">sketch.sql_in</a>
+</li>
+<li>cmsketch_depth_histogram()
+: <a class="el" href="sketch_8sql__in.html#a9e6d30f20b724b96249cc4a0f67a279e">sketch.sql_in</a>
+</li>
+<li>cmsketch_median()
+: <a class="el" href="sketch_8sql__in.html#a0ef6428fa7ba4b7f7b1f633e6f8003ea">sketch.sql_in</a>
+</li>
+<li>cmsketch_rangecount()
+: <a class="el" href="sketch_8sql__in.html#aeff9e36cfb3338c4e405d4ac77d3968c">sketch.sql_in</a>
+</li>
+<li>cmsketch_width_histogram()
+: <a class="el" href="sketch_8sql__in.html#a8482f62849adf40a2c7df78c23ea33a4">sketch.sql_in</a>
+</li>
+<li>compute_coxph_result()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a74fe60f99c3808e38421c71286850ea8">cox_prop_hazards.sql_in</a>
+</li>
+<li>compute_lincrf()
+: <a class="el" href="crf_8sql__in.html#a20c096420d878cdd66dac6a232483737">crf.sql_in</a>
+</li>
+<li>conjugate_gradient()
+: <a class="el" href="conjugate__gradient_8sql__in.html#ab94e8a12cc9c2d0dac35168721ec8585">conjugate_gradient.sql_in</a>
+</li>
+<li>correlation()
+: <a class="el" href="correlation_8sql__in.html#a17cb16646fa7ebdf7ce7dd8c58349ce5">correlation.sql_in</a>
+</li>
+<li>correlation_agg()
+: <a class="el" href="correlation_8sql__in.html#a1c3081444f6bafc88a9b377f2af32968">correlation.sql_in</a>
+</li>
+<li>correlation_final()
+: <a class="el" href="correlation_8sql__in.html#a85a1a9b0ae5e608fd235f08ae01ff966">correlation.sql_in</a>
+</li>
+<li>correlation_merge()
+: <a class="el" href="correlation_8sql__in.html#ac53cf4dd28185715a1798176bce44b1b">correlation.sql_in</a>
+</li>
+<li>correlation_transition()
+: <a class="el" href="correlation_8sql__in.html#ae6223d0ba3e2b8a2d298ee78a293be6a">correlation.sql_in</a>
+</li>
+<li>cosine_similarity()
+: <a class="el" href="linalg_8sql__in.html#a1782f2ba00d9f9fab20894a576079f87">linalg.sql_in</a>
+</li>
+<li>cox_prop_hazards()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ad778b289eb19ae0bb2b7e02a89bab3bc">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_prop_hazards_regr()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3310cf98478b7c1e400e8fb1b3965d30">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_zph()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a682d95d5475ce33e47937067cadc2766">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_a_b()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a5f5ca89e3265e170c325125e61ef191d">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_final()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a2373d116ad1e2b2090f8c23a0c15edb5">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_merge()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a62bff141ced5972df28ceecdfe522888">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_transition()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a7081022015c00a14ecca27e017a4458b">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_clustered_stats()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a713f413fc571244b7cec6c0084a61eb9">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_w()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a523e61b6d56c8ff59fa8ae3121073a49">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a2743e0cecadd7f3fa2a16f9d7d40564b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#aeb95cacc36c5d6c8cc25c8d37418c71b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_merge()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ab122003102dd22274a76aa1a830c69dd">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a03a39bc1fee64cd9b8b6a373758819da">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_improved_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ac5ae827993064fd6e0ccf13a96262cdc">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_step_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a6a9ae1ea761b31e1522c47a9794cc233">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a41a415076894d3844475208b2163a267">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_outer()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abb21bacbff7362724d2698568d857555">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_predict()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#acd445fe632e93b2c8260b0455478e2e5">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_inner_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3c96655e41fd17c86dfc9ad37b0959d0">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_outer_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abaf93fc77a284f0b765a64ef827c1bed">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_train()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3fd1cd09fd5de41d6dc8aca0a18eec8b">cox_prop_hazards.sql_in</a>
+</li>
+<li>create_indicator_variables()
+: <a class="el" href="data__preparation_8sql__in.html#a617b67999ab8c9c07fb0d24101a1115d">data_preparation.sql_in</a>
+</li>
+<li>create_nb_classify_view()
+: <a class="el" href="bayes_8sql__in.html#a76a03f1d7ff2b1891cb310668077b386">bayes.sql_in</a>
+</li>
+<li>create_nb_prepared_data_tables()
+: <a class="el" href="bayes_8sql__in.html#aeb4eae7843dd789cc38d5fc57f4ccfb2">bayes.sql_in</a>
+</li>
+<li>create_nb_probs_view()
+: <a class="el" href="bayes_8sql__in.html#a6d1186aa04f4d4e3ac44035467818d3c">bayes.sql_in</a>
+</li>
+<li>create_schema_pg_temp()
+: <a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000">utilities.sql_in</a>
+</li>
+<li>crf_test_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#abf2a39a0b84a5e7ae215db28b3a87f81">crf_data_loader.sql_in</a>
+</li>
+<li>crf_test_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_feature_gen.sql_in</a>
+</li>
+<li>crf_train_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#aaa609ec1d1d4cff0f335fa358091d5c3">crf_data_loader.sql_in</a>
+</li>
+<li>crf_train_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_feature_gen.sql_in</a>
+</li>
+<li>cross_validation_general()
+: <a class="el" href="cross__validation_8sql__in.html#a5efbb27898e99f7685816d9d5dfd082c">cross_validation.sql_in</a>
+</li>
+<li>crossprod()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#a588d5855d1673f7bcbc2a1c983081f76">pivotalr_arrayops.sql_in</a>
+</li>
+<li>crossprod_sym()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#aa1e36bf44b3206314c186bba80179c9c">pivotalr_arrayops.sql_in</a>
+</li>
+<li>cv_linregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#aa572f1f57c0dd106b30948928161d8cc">cross_validation.sql_in</a>
+</li>
+<li>cv_linregr_train()
+: <a class="el" href="cross__validation_8sql__in.html#aa151eb3fa9acc7f4cc33236e22ad4362">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#ac1b5c57473ff672af45191c8d53f46ed">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#ac7dbc115f0c4624ccbf62b2d5494388d">cross_validation.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_d.html
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diff --git a/docs/latest/globals_d.html b/docs/latest/globals_d.html
new file mode 100644
index 0000000..f0fda35
--- /dev/null
+++ b/docs/latest/globals_d.html
@@ -0,0 +1,184 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_d.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
+<li>deconstruct_2d_array()
+: <a class="el" href="linalg_8sql__in.html#a21d0a134f1674f71b3e872aacc5f3789">linalg.sql_in</a>
+</li>
+<li>dense_direct_linear_system()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a379f260d8dfbf5c2be05c60776974188">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a63a3ed2c811216d3d88c120005a13875">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a607403f90e1921435183d031825e5e54">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#acbfc47693d0b50ccfc6b7f7c38209b18">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a52f2bcdd0d038f19e64a44d4bb07fefb">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a01935dd8c4aef8c92e77b7f0f905fa4b">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a73ebde08b8395fd7bddbe1dc37b9539f">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#ad34728809ab3121705342cf91b34c6dd">dense_linear_systems.sql_in</a>
+</li>
+<li>discrete_distribution_agg()
+: <a class="el" href="distribution_8sql__in.html#af9418a44cb502919d54c59e909e19606">distribution.sql_in</a>
+</li>
+<li>discrete_distribution_final()
+: <a class="el" href="distribution_8sql__in.html#a99a03822b9063a37df8641cfcf2e450b">distribution.sql_in</a>
+</li>
+<li>discrete_distribution_transition()
+: <a class="el" href="distribution_8sql__in.html#aa7e45a9ebd2b5ad656e61aa9276116d3">distribution.sql_in</a>
+</li>
+<li>dist_angle()
+: <a class="el" href="linalg_8sql__in.html#a8c7b9281a72ff22caf06161701b27e84">linalg.sql_in</a>
+</li>
+<li>dist_inf_norm()
+: <a class="el" href="linalg_8sql__in.html#a31fa9f2f5b45507c09f136464fdad1db">linalg.sql_in</a>
+</li>
+<li>dist_jaccard()
+: <a class="el" href="linalg_8sql__in.html#ac1397ac9f4a35b3b67c3be05b5e1a828">linalg.sql_in</a>
+</li>
+<li>dist_norm1()
+: <a class="el" href="linalg_8sql__in.html#aad193850e79c4b9d811ca9bc53e13476">linalg.sql_in</a>
+</li>
+<li>dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#aa58e51526edea6ea98db30b6f250adb4">linalg.sql_in</a>
+</li>
+<li>dist_pnorm()
+: <a class="el" href="linalg_8sql__in.html#ad9cc156ae57bf7c0a2fe90798259105a">linalg.sql_in</a>
+</li>
+<li>dist_tanimoto()
+: <a class="el" href="linalg_8sql__in.html#afa13b4c6122b99422d666dedea136c18">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_e.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_e.html b/docs/latest/globals_e.html
new file mode 100644
index 0000000..d589fb0
--- /dev/null
+++ b/docs/latest/globals_e.html
@@ -0,0 +1,160 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_e.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
+<li>elastic_net_binomial_predict()
+: <a class="el" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_binomial_prob()
+: <a class="el" href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_gaussian_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_train()
+: <a class="el" href="elastic__net_8sql__in.html#a63c8ed415005cc446a59bdc47a09791d">elastic_net.sql_in</a>
+</li>
+<li>exponential_cdf()
+: <a class="el" href="prob_8sql__in.html#a6d1bf6816f56b8e5ba6bf6ca94752f46">prob.sql_in</a>
+</li>
+<li>exponential_pdf()
+: <a class="el" href="prob_8sql__in.html#a18a5458c4bc85f0c4ea321317f90bdbb">prob.sql_in</a>
+</li>
+<li>exponential_quantile()
+: <a class="el" href="prob_8sql__in.html#ae3687b8e69a402154b829a6531b1b279">prob.sql_in</a>
+</li>
+<li>extreme_value_cdf()
+: <a class="el" href="prob_8sql__in.html#acffffe04c15eccd2e88cdac250bccc68">prob.sql_in</a>
+</li>
+<li>extreme_value_pdf()
+: <a class="el" href="prob_8sql__in.html#a03a3494462f4cb8c9fb6212e72b0b2e9">prob.sql_in</a>
+</li>
+<li>extreme_value_quantile()
+: <a class="el" href="prob_8sql__in.html#aeb5a7d295b83a891774a4fb0ef27c458">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_f.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_f.html b/docs/latest/globals_f.html
new file mode 100644
index 0000000..56fef50
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+++ b/docs/latest/globals_f.html
@@ -0,0 +1,196 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7dev</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_f.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
+<li>f_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a5a946aa469ff6ddf8d276db16fa26ad4">hypothesis_tests.sql_in</a>
+</li>
+<li>f_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abc6006e8da028dd93ac48b8fd9ae8786">hypothesis_tests.sql_in</a>
+</li>
+<li>fisher_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a6c5b3e35531e44098f9d0cbef14cb8a6">prob.sql_in</a>
+</li>
+<li>fisher_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a1c7937426379a8913519a6abc5a38ac2">prob.sql_in</a>
+</li>
+<li>fisher_f_quantile()
+: <a class="el" href="prob_8sql__in.html#ab6ed888a5338a0bee9c55edf4d33847f">prob.sql_in</a>
+</li>
+<li>float8arr_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a2a6e17fd9dafc349c7cfa070486e6ad1">svec.sql_in</a>
+</li>
+<li>float8arr_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af8db439cf1a73749052f06fc30c7d282">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int2()
+: <a class="el" href="svec_8sql__in.html#a5e38a27f556a6ac88549efec37da786f">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int4()
+: <a class="el" href="svec_8sql__in.html#a9c05d02233680a2e96dc6058155e4483">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a4862b4e281323db16e9be4dfd681db2e">svec.sql_in</a>
+</li>
+<li>float8arr_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a0bc8ef7e7b8e37b784c81a940bd817f0">svec.sql_in</a>
+</li>
+<li>float8arr_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a20d4a7b907e967351f6439640482e0f9">svec.sql_in</a>
+</li>
+<li>float8arr_div_svec()
+: <a class="el" href="svec_8sql__in.html#a5c4ac1cc9a2ee3057958070b8e7bffcc">svec.sql_in</a>
+</li>
+<li>float8arr_eq()
+: <a class="el" href="svec_8sql__in.html#a0684988340d533c00523951f9a95107e">svec.sql_in</a>
+</li>
+<li>float8arr_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a4c82ac5de8e026c85e83aae4e0f06220">svec.sql_in</a>
+</li>
+<li>float8arr_minus_svec()
+: <a class="el" href="svec_8sql__in.html#a457f2f9210baba1c13820801c42cd06a">svec.sql_in</a>
+</li>
+<li>float8arr_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#abb0b3f155c45bb370ef7adac49096835">svec.sql_in</a>
+</li>
+<li>float8arr_mult_svec()
+: <a class="el" href="svec_8sql__in.html#a0e7dd7a82b41f4b8344efa21208e1637">svec.sql_in</a>
+</li>
+<li>float8arr_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6bb068ab6ba775619fe787ed845f5a0d">svec.sql_in</a>
+</li>
+<li>float8arr_plus_svec()
+: <a class="el" href="svec_8sql__in.html#a98aa60ec0c58c828b0a72c75d604322e">svec.sql_in</a>
+</li>
+<li>fmsketch_dcount()
+: <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b">sketch.sql_in</a>
+</li>
+<li>forest_predict()
+: <a class="el" href="random__forest_8sql__in.html#aef268338ffa67f54ca1371326bb25fa0">random_forest.sql_in</a>
+</li>
+<li>forest_train()
+: <a class="el" href="random__forest_8sql__in.html#a04cbc760f3790bcf69fe48e008cfba3d">random_forest.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[10/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__logreg.html
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diff --git a/docs/latest/group__grp__logreg.html b/docs/latest/group__grp__logreg.html
new file mode 100644
index 0000000..bd862cf
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+++ b/docs/latest/group__grp__logreg.html
@@ -0,0 +1,425 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: Logistic Regression</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
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+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
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+</table>
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__logreg.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Logistic Regression<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Binomial logistic regression models the relationship between a dichotomous dependent variable and one or more predictor variables. The dependent variable may be a Boolean value or a categorial variable that can be represented with a Boolean expression. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the predictor variables, using a logistic function.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The logistic regression training function has the following format: <pre class="syntax">
+logregr_train( source_table,
+               out_table,
+               dependent_varname,
+               independent_varname,
+               grouping_cols,
+               max_iter,
+               optimizer,
+               tolerance,
+               verbose
+             )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>out_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the output model.</p>
+<p>The output table produced by the logistic regression training function contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td><p class="starttd">Text. Grouping columns, if provided in input. This could be multiple columns depending on the <code>grouping_col</code> input. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>coef </th><td><p class="starttd">FLOAT8. Vector of the coefficients of the regression. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>log_likelihood </th><td><p class="starttd">FLOAT8. The log-likelihood \( l(\boldsymbol c) \). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>std_err </th><td><p class="starttd">FLOAT8[]. Vector of the standard error of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>z_stats </th><td><p class="starttd">FLOAT8[]. Vector of the z-statistics of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>p_values </th><td><p class="starttd">FLOAT8[]. Vector of the p-values of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>odds_ratios </th><td><p class="starttd">FLOAT8[]. The odds ratio, \( \exp(c_i) \). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>condition_no </th><td><p class="starttd">FLOAT8[]. The condition number of the \(X^{*}X\) matrix. A high condition number is usually an indication that there may be some numeric instability in the result yielding a less reliable model. A high condition number often results when there is a significant amount of colinearity in the underlying design matrix, in which case other regression techniques may be more appropriate. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The number of iterations actually completed. This would be different from the <code>nIterations</code> argument if a <code>tolerance</code> parameter is provided and the algorithm converges before all iterations are completed.  </td></tr>
+<tr>
+<th>num_rows_processed </th><td>INTEGER. The number of rows actually processed, which is equal to the total number of rows in the source table minus the number of skipped rows.  </td></tr>
+<tr>
+<th>num_missing_rows_skipped </th><td>INTEGER. The number of rows skipped during the training. A row will be skipped if the independent_varname is NULL or contains NULL values.  </td></tr>
+</table>
+<p>A summary table named &lt;out_table&gt;_summary is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>source_table </th><td><p class="starttd">The data source table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>out_table </th><td><p class="starttd">The output table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">The dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">The independent variables </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>optimizer_params </th><td><p class="starttd">A string that contains all the optimizer parameters, and has the form of 'optimizer=..., max_iter=..., tolerance=...' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">How many groups of data were fit by the logistic model. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">How many groups' fitting processes failed. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_processed </th><td><p class="starttd">The total number of rows usd in the computation. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_missing_rows_skipped </th><td>The total number of rows skipped.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">TEXT. Name of the dependent variable column (of type BOOLEAN) in the training data or an expression evaluating to a BOOLEAN.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">TEXT. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant <code>1</code> term in the independent variable list.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_cols (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>max_iter (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 20. The maximum number of iterations that are allowed.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer (optional) </dt>
+<dd><p class="startdd">TEXT, default: 'irls'. The name of the optimizer to use: </p><table  class="output">
+<tr>
+<th>'newton' or 'irls' </th><td>Iteratively reweighted least squares  </td></tr>
+<tr>
+<th>'cg' </th><td>conjugate gradient  </td></tr>
+<tr>
+<th>'igd' </th><td>incremental gradient descent.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>tolerance (optional) </dt>
+<dd><p class="startdd">FLOAT8, default: 0.0001. The difference between log-likelihood values in successive iterations that should indicate convergence. A zero disables the convergence criterion, so that execution stops after <code>n</code> iterations have completed.</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Provides verbose output of the results of training. </dd>
+</dl>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>For p-values, we just return the computation result directly. Other statistical packages, like 'R', produce the same result, but on printing the result to screen, another format function is used and any p-value that is smaller than the machine epsilon (the smallest positive floating-point number 'x' such that '1 + x != 1') will be printed on screen as "&lt; xxx" (xxx is the value of the machine epsilon). Although the result may look different, they are in fact the same.</dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>Two prediction functions are provided to either predict the boolean value of the dependent variable or the probability of the value of dependent variable being 'True', both functions using the same syntax.</dd></dl>
+<p>The function to predict the boolean value (True/False) of the dependent variable has the following syntax: </p><pre class="syntax">
+logregr_predict(coefficients,
+                ind_var
+               )
+</pre><p>The function to predict the probability of the dependent variable being True has the following syntax: </p><pre class="syntax">
+logregr_predict_prob(coefficients,
+                     ind_var
+                    )
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>coefficients </dt>
+<dd><p class="startdd">DOUBLE PRECISION[]. Model coefficients obtained from <a class="el" href="logistic_8sql__in.html#a74210a7ef513dfcbdfdd9f3b37bfe428">logregr_train()</a>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>ind_var </dt>
+<dd>Independent variables, as a DOUBLE array. This should be the same length as the array obtained by evaluation of the 'independent_varname' argument in <a class="el" href="logistic_8sql__in.html#a74210a7ef513dfcbdfdd9f3b37bfe428">logregr_train()</a>. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>Create the training data table. <pre class="example">
+CREATE TABLE patients( id INTEGER NOT NULL,
+                       second_attack INTEGER,
+                       treatment INTEGER,
+                       trait_anxiety INTEGER);
+COPY patients FROM STDIN WITH DELIMITER '|';
+  1 |             1 |         1 |            70
+  3 |             1 |         1 |            50
+  5 |             1 |         0 |            40
+  7 |             1 |         0 |            75
+  9 |             1 |         0 |            70
+ 11 |             0 |         1 |            65
+ 13 |             0 |         1 |            45
+ 15 |             0 |         1 |            40
+ 17 |             0 |         0 |            55
+ 19 |             0 |         0 |            50
+  2 |             1 |         1 |            80
+  4 |             1 |         0 |            60
+  6 |             1 |         0 |            65
+  8 |             1 |         0 |            80
+ 10 |             1 |         0 |            60
+ 12 |             0 |         1 |            50
+ 14 |             0 |         1 |            35
+ 16 |             0 |         1 |            50
+ 18 |             0 |         0 |            45
+ 20 |             0 |         0 |            60
+\.
+</pre></li>
+<li>Train a regression model. <pre class="example">
+SELECT madlib.logregr_train( 'patients',
+                             'patients_logregr',
+                             'second_attack',
+                             'ARRAY[1, treatment, trait_anxiety]',
+                             NULL,
+                             20,
+                             'irls'
+                           );
+</pre></li>
+<li>View the regression results. <pre class="example">
+-- Set extended display on for easier reading of output
+\x on
+SELECT * from patients_logregr;
+</pre> Result: <pre class="result">
+coef           | {5.59049410898112,2.11077546770772,-0.237276684606453}
+log_likelihood | -467.214718489873
+std_err        | {0.318943457652178,0.101518723785383,0.294509929481773}
+z_stats        | {17.5281667482197,20.7919819024719,-0.805666162169712}
+p_values       | {8.73403463417837e-69,5.11539430631541e-96,0.420435365338518}
+odds_ratios    | {267.867942976278,8.2546400100702,0.788773016471171}
+condition_no   | 179.186118573205
+num_iterations | 9
+</pre></li>
+<li>Alternatively, unnest the arrays in the results for easier reading of output: <pre class="example">
+\x off
+SELECT unnest(array['intercept', 'treatment', 'trait_anxiety']) as attribute,
+       unnest(coef) as coefficient,
+       unnest(std_err) as standard_error,
+       unnest(z_stats) as z_stat,
+       unnest(p_values) as pvalue,
+       unnest(odds_ratios) as odds_ratio
+    FROM patients_logregr;
+</pre></li>
+<li>Predicting dependent variable using the logistic regression model. (This example uses the original data table to perform the prediction. Typically a different test dataset with the same features as the original training dataset would be used for prediction.) <pre class="example">
+\x off
+-- Display prediction value along with the original value
+SELECT p.id, madlib.logregr_predict(coef, ARRAY[1, treatment, trait_anxiety]),
+       p.second_attack
+FROM patients p, patients_logregr m
+ORDER BY p.id;
+</pre></li>
+<li>Predicting the probability of the dependent variable being TRUE. <pre class="example">
+\x off
+-- Display prediction value along with the original value
+SELECT p.id, madlib.logregr_predict_prob(coef, ARRAY[1, treatment, trait_anxiety])
+FROM patients p, patients_logregr m
+ORDER BY p.id;
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd>All table names can be optionally schema qualified (current_schemas() would be searched if a schema name is not provided) and all table and column names should follow case-sensitivity and quoting rules per the database. (For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"').</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>(Binomial) logistic regression refers to a stochastic model in which the conditional mean of the dependent dichotomous variable (usually denoted \( Y \in \{ 0,1 \} \)) is the logistic function of an affine function of the vector of independent variables (usually denoted \( \boldsymbol x \)). That is, </p><p class="formulaDsp">
+\[ E[Y \mid \boldsymbol x] = \sigma(\boldsymbol c^T \boldsymbol x) \]
+</p>
+<p> for some unknown vector of coefficients \( \boldsymbol c \) and where \( \sigma(x) = \frac{1}{1 + \exp(-x)} \) is the logistic function. Logistic regression finds the vector of coefficients \( \boldsymbol c \) that maximizes the likelihood of the observations.</p>
+<p>Let</p><ul>
+<li>\( \boldsymbol y \in \{ 0,1 \}^n \) denote the vector of observed dependent variables, with \( n \) rows, containing the observed values of the dependent variable,</li>
+<li>\( X \in \mathbf R^{n \times k} \) denote the design matrix with \( k \) columns and \( n \) rows, containing all observed vectors of independent variables \( \boldsymbol x_i \) as rows.</li>
+</ul>
+<p>By definition, </p><p class="formulaDsp">
+\[ P[Y = y_i | \boldsymbol x_i] = \sigma((-1)^{(1 - y_i)} \cdot \boldsymbol c^T \boldsymbol x_i) \,. \]
+</p>
+<p> Maximizing the likelihood \( \prod_{i=1}^n \Pr(Y = y_i \mid \boldsymbol x_i) \) is equivalent to maximizing the log-likelihood \( \sum_{i=1}^n \log \Pr(Y = y_i \mid \boldsymbol x_i) \), which simplifies to </p><p class="formulaDsp">
+\[ l(\boldsymbol c) = -\sum_{i=1}^n \log(1 + \exp((-1)^{(1 - y_i)} \cdot \boldsymbol c^T \boldsymbol x_i)) \,. \]
+</p>
+<p> The Hessian of this objective is \( H = -X^T A X \) where \( A = \text{diag}(a_1, \dots, a_n) \) is the diagonal matrix with \( a_i = \sigma(\boldsymbol c^T \boldsymbol x) \cdot \sigma(-\boldsymbol c^T \boldsymbol x) \,. \) Since \( H \) is non-positive definite, \( l(\boldsymbol c) \) is convex. There are many techniques for solving convex optimization problems. Currently, logistic regression in MADlib can use one of three algorithms:</p><ul>
+<li>Iteratively Reweighted Least Squares</li>
+<li>A conjugate-gradient approach, also known as Fletcher-Reeves method in the literature, where we use the Hestenes-Stiefel rule for calculating the step size.</li>
+<li>Incremental gradient descent, also known as incremental gradient methods or stochastic gradient descent in the literature.</li>
+</ul>
+<p>We estimate the standard error for coefficient \( i \) as </p><p class="formulaDsp">
+\[ \mathit{se}(c_i) = \left( (X^T A X)^{-1} \right)_{ii} \,. \]
+</p>
+<p> The Wald z-statistic is </p><p class="formulaDsp">
+\[ z_i = \frac{c_i}{\mathit{se}(c_i)} \,. \]
+</p>
+<p>The Wald \( p \)-value for coefficient \( i \) gives the probability (under the assumptions inherent in the Wald test) of seeing a value at least as extreme as the one observed, provided that the null hypothesis ( \( c_i = 0 \)) is true. Letting \( F \) denote the cumulative density function of a standard normal distribution, the Wald \( p \)-value for coefficient \( i \) is therefore </p><p class="formulaDsp">
+\[ p_i = \Pr(|Z| \geq |z_i|) = 2 \cdot (1 - F( |z_i| )) \]
+</p>
+<p> where \( Z \) is a standard normally distributed random variable.</p>
+<p>The odds ratio for coefficient \( i \) is estimated as \( \exp(c_i) \).</p>
+<p>The condition number is computed as \( \kappa(X^T A X) \) during the iteration immediately <em>preceding</em> convergence (i.e., \( A \) is computed using the coefficients of the previous iteration). A large condition number (say, more than 1000) indicates the presence of significant multicollinearity.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>A somewhat random selection of nice write-ups, with valuable pointers into further literature.</p>
+<p>[1] Cosma Shalizi: Statistics 36-350: Data Mining, Lecture Notes, 18 November 2009, <a href="http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf">http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf</a></p>
+<p>[2] Thomas P. Minka: A comparison of numerical optimizers for logistic regression, 2003 (revised Mar 26, 2007), <a href="http://research.microsoft.com/en-us/um/people/minka/papers/logreg/minka-logreg.pdf">http://research.microsoft.com/en-us/um/people/minka/papers/logreg/minka-logreg.pdf</a></p>
+<p>[3] Paul Komarek, Andrew W. Moore: Making Logistic Regression A Core Data Mining Tool With TR-IRLS, IEEE International Conference on Data Mining 2005, pp. 685-688, <a href="http://komarix.org/ac/papers/tr-irls.short.pdf">http://komarix.org/ac/papers/tr-irls.short.pdf</a></p>
+<p>[4] D. P. Bertsekas: Incremental gradient, subgradient, and proximal methods for convex optimization: a survey, Technical report, Laboratory for Information and Decision Systems, 2010, <a href="http://web.mit.edu/dimitrib/www/Incremental_Survey_LIDS.pdf">http://web.mit.edu/dimitrib/www/Incremental_Survey_LIDS.pdf</a></p>
+<p>[5] A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro: Robust stochastic approximation approach to stochastic programming, SIAM Journal on Optimization, 19(4), 2009, <a href="http://www2.isye.gatech.edu/~nemirovs/SIOPT_RSA_2009.pdf">http://www2.isye.gatech.edu/~nemirovs/SIOPT_RSA_2009.pdf</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="logistic_8sql__in.html" title="SQL functions for logistic regression. ">logistic.sql_in</a> documenting the training function</p>
+<p><a class="el" href="logistic_8sql__in.html#a74210a7ef513dfcbdfdd9f3b37bfe428" title="Compute logistic-regression coefficients and diagnostic statistics. ">logregr_train()</a></p>
+<p><a class="el" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" title="Interface for elastic net. ">elastic_net_train()</a></p>
+<p><a class="el" href="group__grp__linreg.html">Linear Regression</a></p>
+<p><a class="el" href="group__grp__multinom.html">Multinomial Regression</a></p>
+<p><a class="el" href="group__grp__ordinal.html">Ordinal Regression</a></p>
+<p><a class="el" href="group__grp__robust.html">Robust Variance</a></p>
+<p><a class="el" href="group__grp__clustered__errors.html">Clustered Variance</a></p>
+<p><a class="el" href="group__grp__validation.html">Cross Validation</a></p>
+<p><a class="el" href="group__grp__marginal.html">Marginal Effects</a></p>
+</div><!-- contents -->
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+<title>MADlib: Marginal Effects</title>
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+  <td style="padding-left: 0.5em;">
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+<div class="title">Marginal Effects<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#margins">Marginal Effects with Interaction Terms</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#background">Technical Background</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>A marginal effect (ME) or partial effect measures the effect on the conditional mean of \( y \) for a change in one of the regressors, say \(X_k\). In the linear regression model, the ME equals the relevant slope coefficient, greatly simplifying analysis. For nonlinear models, specialized algorithms are required for calculating ME. The marginal effect computed is the average of the marginal effect at every data point present in the source table.</p>
+<p>MADlib provides marginal effects regression functions for linear, logistic and multinomial logistic regressions.</p>
+<dl class="section warning"><dt>Warning</dt><dd>The <a class="el" href="marginal_8sql__in.html#a9517d679ee4209126895445cbed51fe3">margins_logregr()</a> and <a class="el" href="marginal_8sql__in.html#ae39ad0e1beca060fd153dba35901a4e7">margins_mlogregr()</a> functions have been deprecated in favor of the <a class="el" href="marginal_8sql__in.html#a36fcae5245ca31517723fce38b183c90" title="Marginal effects with default variable_names. ">margins()</a> function.</dd></dl>
+<p><a class="anchor" id="margins"></a></p><dl class="section user"><dt>Marginal Effects with Interaction Terms</dt><dd><pre class="syntax">
+margins( model_table,
+         output_table,
+         x_design,
+         source_table,
+         marginal_vars
+       )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>model_table </dt>
+<dd>VARCHAR. The name of the model table, which is the output of <a class="el" href="logistic_8sql__in.html#a74210a7ef513dfcbdfdd9f3b37bfe428" title="Compute logistic-regression coefficients and diagnostic statistics. ">logregr_train()</a> or <a class="el" href="multilogistic_8sql__in.html#aedc13474e6abbc88451d120ad97e44d4" title="Compute multinomial logistic regression coefficients. ">mlogregr_train()</a>. </dd>
+<dt>output_table </dt>
+<dd>VARCHAR. The name of the result table. The output table has the following columns. <table  class="output">
+<tr>
+<th>variables </th><td>INTEGER[]. The indices of the basis variables.  </td></tr>
+<tr>
+<th>margins </th><td>DOUBLE PRECISION[]. The marginal effects.  </td></tr>
+<tr>
+<th>std_err </th><td>DOUBLE PRECISION[]. An array of the standard errors, computed using the delta method.  </td></tr>
+<tr>
+<th>z_stats </th><td>DOUBLE PRECISION[]. An array of the z-stats of the marginal effects.  </td></tr>
+<tr>
+<th>p_values </th><td>DOUBLE PRECISION[]. An array of the Wald p-values of the marginal effects.  </td></tr>
+</table>
+</dd>
+<dt>x_design (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: NULL. The design of independent variables, necessary only if interaction term or indicator (categorical) terms are present. This parameter is necessary since the independent variables in the underlying regression is not parsed to extract the relationship between variables.</p>
+<p>Example: The <em>independent_varname</em> in the regression method can be specified in either of the following ways:</p><ul>
+<li><code> ‘array[1, color_blue, color_green, gender_female, gpa, gpa^2, gender_female*gpa, gender_female*gpa^2, weight]’ </code></li>
+<li><code> ‘x’ </code></li>
+</ul>
+<p>In the second version, the column <em>x</em> is an array containing data identical to that expressed in the first version, computed in a prior data preparation step. Supply an <em>x_design argument</em> to the <a class="el" href="marginal_8sql__in.html#a36fcae5245ca31517723fce38b183c90" title="Marginal effects with default variable_names. ">margins()</a> function in the following way:</p><ul>
+<li><code> ‘1, i.color_blue.color, i.color_green.color, i.gender_female, gpa, gpa^2, gender_female*gpa, gender_female*gpa^2, weight’</code></li>
+</ul>
+<p>The variable names (<em>'gpa', 'weight', ...</em>), referred to here as <em>identifiers</em>, should be unique for each basis variable and need not be the same as the original variable name in <em>independent_varname</em>. They should, however, be in the same order as the corresponding variables in <em>independent_varname</em>. The length of <em>x_design</em> is expected to be the same as the length of <em>independent_varname</em>. Each <em>identifier</em> name can contain only alphanumeric characters and the underscore.</p>
+<p>Indicator (dummy) variables are prefixed with an 'i.' (This is only necessary for the basis term; it is not needed in the interaction terms.) Indicator variables that are obtained from the same categorical variable (for example, 'color_blue' and 'color_green') need to have a common and unique suffix (for example, '.color'). The '.' is used to add the prefix and suffix. If a reference indicator variable is present, it should contain the prefix 'ir.'.</p>
+<p>An identifier may contain alphanumeric characters and underscores. To include other characters, the string must be double-quoted. Escape-characters are not currently supported. </p>
+<p class="enddd"></p>
+</dd>
+<dt>source_table (optional) </dt>
+<dd><p class="startdd">VARCHAR, default: NULL. Name of the data table to apply marginal effects on. If not provided or NULL then the marginal effects are computed on the training table.</p>
+<p class="enddd"></p>
+</dd>
+<dt>marginal_vars (optional) </dt>
+<dd>VARCHAR, default: NULL. Comma-separated string containing specific variable identifiers to calculate marginal effects on. When NULL, marginal effects for all variables are returned. </dd>
+</dl>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>No output will be provided for the reference indicator variable, since the marginal effect for that variable is undefined. If a reference variable is included in the independent variables and <em>marginal_vars</em>, the <a class="el" href="marginal_8sql__in.html#a36fcae5245ca31517723fce38b183c90" title="Marginal effects with default variable_names. ">margins()</a> function will ignore that variable for the output. The variable can still be included in the regression and margins, since it will affect the values for other related indicator variables.</dd></dl>
+<p><a class="anchor" id="logregr_train"></a></p><dl class="section user"><dt>Marginal Effects for Logistic Regression</dt><dd></dd></dl>
+<dl class="section warning"><dt>Warning</dt><dd>This function has been deprecated in favor of the <a class="el" href="marginal_8sql__in.html#a36fcae5245ca31517723fce38b183c90" title="Marginal effects with default variable_names. ">margins()</a> function.</dd></dl>
+<pre class="syntax">
+margins_logregr( source_table,
+                 output_table,
+                 dependent_variable,
+                 independent_variable,
+                 grouping_cols,
+                 marginal_vars,
+                 max_iter,
+                 optimizer,
+                 tolerance,
+                 verbose_mode
+               )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>VARCHAR. The name of the data table. </dd>
+<dt>output_table </dt>
+<dd><p class="startdd">VARCHAR. The name of the result table. The output table has the following columns. </p><table  class="output">
+<tr>
+<th>margins </th><td>DOUBLE PRECISION[]. The marginal effects.  </td></tr>
+<tr>
+<th>std_err </th><td>DOUBLE PRECISION[]. An array of the standard errors, using the delta method.  </td></tr>
+<tr>
+<th>z_stats </th><td>DOUBLE PRECISION[]. An array of the z-stats of the marginal effects.  </td></tr>
+<tr>
+<th>p_values </th><td>DOUBLE PRECISION[]. An array of the Wald p-values of the marginal effects.  </td></tr>
+</table>
+<p>A summary table named &lt;output_table&gt;_summary is also created, which is the same as the summary table created by <a class="el" href="logistic_8sql__in.html#a74210a7ef513dfcbdfdd9f3b37bfe428" title="Compute logistic-regression coefficients and diagnostic statistics. ">logregr_train()</a> function. Refer to the documentation for logistic regression for details.</p>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_variable </dt>
+<dd>VARCHAR. The name of the column for dependent variables. </dd>
+<dt>independent_variable </dt>
+<dd>VARCHAR. The name of the column for independent variables. Can be any SQL expression that evaluates to an array. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>VARCHAR, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd>
+<dt>marginal_vars (optional) </dt>
+<dd>INTEGER[], default: NULL. An index list (base 1) representing the independent variables to compute marginal effects on. When NULL, computes marginal effects on all variables. </dd>
+<dt>max_iter (optional) </dt>
+<dd>INTEGER, default: 20. The maximum number of iterations for the logistic regression. </dd>
+<dt>optimizer (optional) </dt>
+<dd>VARCHAR, default: 'irls'. The optimizer to use for the logistic regression: newton/irls, cg, or igd. </dd>
+<dt>tolerance (optional) </dt>
+<dd>DOUBLE PRECISION, default: 1e-4. Termination criterion for logistic regression (relative). </dd>
+<dt>verbose_mode (optional) </dt>
+<dd>BOOLEAN, default FALSE. When TRUE, provides verbose output of the results of training.  </dd>
+</dl>
+<p><a class="anchor" id="mlogregr_train"></a></p><dl class="section user"><dt>Marginal Effects for Multinomial Logistic Regression</dt><dd></dd></dl>
+<dl class="section warning"><dt>Warning</dt><dd>This function has been deprecated in favor of the <a class="el" href="marginal_8sql__in.html#a36fcae5245ca31517723fce38b183c90" title="Marginal effects with default variable_names. ">margins()</a> function.</dd></dl>
+<pre class="syntax">
+margins_mlogregr( source_table,
+                  out_table,
+                  dependent_varname,
+                  independent_varname,
+                  ref_category,
+                  grouping_cols,
+                  marginal_vars,
+                  optimizer_params,
+                  verbose_mode
+                )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>VARCHAR. The name of data table. </dd>
+<dt>out_table </dt>
+<dd><p class="startdd">VARCHAR. The name of result table. The output table has the following columns. </p><table  class="output">
+<tr>
+<th>category </th><td>The category.  </td></tr>
+<tr>
+<th>ref_category </th><td>The refererence category used for modeling.  </td></tr>
+<tr>
+<th>margins </th><td>DOUBLE PRECISION[]. The marginal effects.  </td></tr>
+<tr>
+<th>std_err </th><td>DOUBLE PRECISION[]. An array of the standard errors, using the delta method.  </td></tr>
+<tr>
+<th>z_stats </th><td>DOUBLE PRECISION[]. An array of the z-stats of the marginal effects.  </td></tr>
+<tr>
+<th>p_values </th><td>DOUBLE PRECISION[]. An array of the Wald p-values of the marginal effects.  </td></tr>
+</table>
+<p>A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by <a class="el" href="multilogistic_8sql__in.html#aedc13474e6abbc88451d120ad97e44d4" title="Compute multinomial logistic regression coefficients. ">mlogregr_train()</a> function. Refer to the documentation for multinomial logistic regression for details.</p>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>VARCHAR. The name of the column for dependent variables. </dd>
+<dt>independent_varname </dt>
+<dd>VARCHAR. The name of the column for independent variables. Can be any SQL expression that evaluates to an array. </dd>
+<dt>ref_category (optional) </dt>
+<dd>INTEGER, default: 0. Reference category for the multinomial logistic regression. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>VARCHAR, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd>
+<dt>marginal_vars(optional) </dt>
+<dd>INTEGER[], default: NULL. An index list (base 1) representing the independent variables to compute marginal effects on. When NULL, computes marginal effects on all variables. </dd>
+<dt>optimizer_params (optional) </dt>
+<dd>TEXT, default: NULL, which uses the default values of optimizer parameters: max_iter=20, optimizer='newton', tolerance=1e-4. It should be a string that contains 'key=value' pairs separated by commas. </dd>
+<dt>verbose_mode (optional) </dt>
+<dd>BOOLEAN, default FALSE. When TRUE, provides verbose output of the results of training.  </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the marginal effects function. <pre class="example">
+SELECT madlib.margins();
+</pre></li>
+<li>Create the sample data set. Use the <code>patients</code> dataset from the <a href="group__grp__logreg.html#examples">Logistic Regression examples</a>. <pre class="example">
+SELECT * FROM patients;
+</pre> Result: <pre class="result">
+ id | second_attack | treatment | trait_anxiety
+&#160;---+---------------+-----------+---------------
+  1 |             1 |         1 |            70
+  3 |             1 |         1 |            50
+  5 |             1 |         0 |            40
+  7 |             1 |         0 |            75
+  9 |             1 |         0 |            70
+ 11 |             0 |         1 |            65
+ 13 |             0 |         1 |            45
+ 15 |             0 |         1 |            40
+ 17 |             0 |         0 |            55
+ 19 |             0 |         0 |            50
+  2 |             1 |         1 |            80
+  4 |             1 |         0 |            60
+  6 |             1 |         0 |            65
+  8 |             1 |         0 |            80
+ 10 |             1 |         0 |            60
+ 12 |             0 |         1 |            50
+ 14 |             0 |         1 |            35
+ 16 |             0 |         1 |            50
+ 18 |             0 |         0 |            45
+ 20 |             0 |         0 |            60
+</pre></li>
+<li>Run logistic regression to get the model, compute the marginal effects of all variables, and view the results. <pre class="example">
+DROP TABLE IF EXISTS model_table;
+DROP TABLE IF EXISTS model_table_summary;
+DROP TABLE IF EXISTS margins_table;
+SELECT madlib.logregr_train( 'patients',
+                             'model_table',
+                             'second_attack',
+                             'ARRAY[1, treatment, trait_anxiety, treatment^2, treatment * trait_anxiety]'
+                           );
+SELECT madlib.margins( 'model_table',
+                       'margins_table',
+                       'intercept, treatment, trait_anxiety, treatment^2, treatment*trait_anxiety',
+                       NULL,
+                       NULL
+                     );
+\x ON
+SELECT * FROM margins_table;
+</pre> Result: <pre class="result">
+variables | {intercept, treatment, trait_anxiety}
+margins   | {-0.876046514609573,-0.0648833521465306,0.0177196513589633}
+std_err   | {0.551714275062467,0.373592457067442,0.00458001207971933}
+z_stats   | {-1.58786269307674,-0.173674149247659,3.86890930646828}
+p_values  | {0.112317391159946,0.862121554662231,0.000109323294026272}
+</pre></li>
+<li>Compute the marginal effects of the first variable using the previous model and view the results (using different names in 'x_design'). <pre class="example">
+DROP TABLE IF EXISTS result_table;
+SELECT madlib.margins( 'model_table',
+                       'result_table',
+                       'i, tre, tra, tre^2, tre*tra',
+                       NULL,
+                       'tre'
+                     );
+SELECT * FROM result_table;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-------------------
+variables | {tre}
+margins   | {-0.110453283517281}
+std_err   | {0.228981529064089}
+z_stats   | {-0.482367656329023}
+p_values  | {0.629544793219806}
+</pre></li>
+<li>Create a sample data set for multinomial logistic regression. (The full dataset has three categories.) Use the dataset from the <a href="group__grp__mlogreg.html#examples">Multinomial Regression example</a>. <pre class="example">
+\x OFF
+SELECT * FROM test3;
+</pre> Result: <pre class="result">
+ feat1 | feat2 | cat
+-------+-------+-----
+     2 |    33 |   0
+     2 |    31 |   1
+     2 |    36 |   1
+     2 |    31 |   1
+     2 |    41 |   1
+     2 |    37 |   1
+     2 |    44 |   1
+     2 |    46 |   1
+     2 |    46 |   2
+     2 |    39 |   0
+     2 |    44 |   1
+     2 |    44 |   0
+     2 |    67 |   2
+     2 |    59 |   2
+     2 |    59 |   0
+...
+</pre></li>
+<li>Run the regression function and then compute the marginal effects of all variables in the regression. <pre class="example">
+DROP TABLE IF EXISTS model_table;
+DROP TABLE IF EXISTS model_table_summary;
+DROP TABLE IF EXISTS result_table;
+SELECT madlib.mlogregr_train('test3', 'model_table', 'cat',
+                             'ARRAY[1, feat1, feat2, feat1*feat2]',
+                             0);
+SELECT madlib.margins('model_table',
+                      'result_table',
+                      'intercept, feat1, feat2, feat1*feat2');
+\x ON
+SELECT * FROM result_table;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]+-------------------------------------------------------------
+category     | 1
+ref_category | 0
+variables    | {intercept,feat1,feat2}
+margins      | {2.38176571752675,-0.0545733108729351,-0.0147264917310351}
+std_err      | {0.851299967007829,0.0697049196489632,0.00374946341567828}
+z_stats      | {2.79779843748643,-0.782919070099622,-3.92762646235104}
+p_values     | {0.00514522099923651,0.43367463815468,8.57883141882439e-05}
+-[ RECORD 2 ]+-------------------------------------------------------------
+category     | 2
+ref_category | 0
+variables    | {intercept,feat1,feat2}
+margins      | {-1.99279068434949,0.0922540608068343,0.0168049205501686}
+std_err      | {0.742790306495022,0.0690712705200096,0.00202015384479213}
+z_stats      | {-2.68284422524683,1.33563578767686,8.31863404536785}
+p_values     | {0.00729989838349161,0.181668346802398,8.89828265128986e-17}
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a> </p><dl class="section note"><dt>Note</dt><dd>The <em>marginal_vars</em> argument is a list with the names matching those in 'x_design'. If no 'x_design' is present (i.e. no interaction and no indicator variables), then <em>marginal_vars</em> must be the indices (base 1) of variables in 'independent_varname'. Use <em>NULL</em> to use all independent variables. It is important to note that the <em>independent_varname</em> array in the underlying regression is assumed to start with a lower bound index of 1. Arrays that don't follow this would result in an incorrect solution.</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>The standard approach to modeling dichotomous/binary variables (so \(y \in \{0, 1\} \)) is to estimate a generalized linear model under the assumption that \( y \) follows some form of Bernoulli distribution. Thus the expected value of \( y \) becomes, </p><p class="formulaDsp">
+\[ y = G(X&#39; \beta), \]
+</p>
+<p>where G is the specified binomial distribution. For logistic regression, the function \( G \) represents the inverse logit function.</p>
+<p>In logistic regression: </p><p class="formulaDsp">
+\[ P = \frac{1}{1 + e^{-(\beta_0 + \beta_1 x_1 + \dots \beta_j x_j)}} = \frac{1}{1 + e^{-z}} \implies \frac{\partial P}{\partial X_k} = \beta_k \cdot \frac{1}{1 + e^{-z}} \cdot \frac{e^{-z}}{1 + e^{-z}} \\ = \beta_k \cdot P \cdot (1-P) \]
+</p>
+<p>There are several methods for calculating the marginal effects for dichotomous dependent variables. This package uses the average of the marginal effects at every sample observation.</p>
+<p>This is calculated as follows: </p><p class="formulaDsp">
+\[ \frac{\partial y}{\partial x_k} = \beta_k \frac{\sum_{i=1}^n P(y_i = 1)(1-P(y_i = 1))}{n}, \\ \text{where}, P(y_i=1) = g(X^{(i)}\beta) \]
+</p>
+<p>We use the delta method for calculating standard errors on the marginal effects.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] mfx function in STATA: <a href="http://www.stata.com/help.cgi?mfx_option">http://www.stata.com/help.cgi?mfx_option</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="marginal_8sql__in.html" title="SQL functions for linear regression. ">marginal.sql_in</a> documenting the SQL functions.</p>
+</div><!-- contents -->
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+<div class="title">Matrix Operations<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#description">Description</a> </li>
+<li class="level1">
+<a href="#operations">Matrix Operations</a> </li>
+<li class="level1">
+<a href="#glossary">Glossary of arguments</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p><a class="anchor" id="description"></a>This module provides a set of basic matrix operations for matrices that are too big to fit in memory. We provide two storage formats for a matrix:</p>
+<ul>
+<li>Dense: The matrix is represented as a distributed collection of 1-D arrays. An example 3x10 matrix would be the below table: <pre>
+ row_id |         row_vec
+--------+-------------------------
+   1    | {9,6,5,8,5,6,6,3,10,8}
+   2    | {8,2,2,6,6,10,2,1,9,9}
+   3    | {3,9,9,9,8,6,3,9,5,6}
+</pre></li>
+</ul>
+<p>A '<em>row</em>' column (called as <em>row_id</em> above) provides the row number of each row and a '<em>val</em>' column (called as <em>row_vec</em> above) provides each row as an array. <b>The <em>row</em> column should contain a series of integers from 1 to <em>N</em> with no duplicates, where <em>N</em> is the row dimensionality</b>.</p>
+<ul>
+<li>Sparse: The matrix is represented using the row and column indices for each non-zero entry of the matrix. This representation is useful for sparse matrices, containing multiple zero elements. Given below is an example of a sparse 4x7 matrix with just 6 out of 28 entries being non-zero. The dimensionality of the matrix is inferred using the max value in <em>row</em> and <em>col</em> columns. Note the last entry is included (even though it is 0) to provide the dimensionality of the matrix (indicating that the 4th row and 7th column contain all zeros). <pre>
+ row_id | col_id | value
+--------+--------+-------
+      1 |      1 |     9
+      1 |      5 |     6
+      1 |      6 |     6
+      2 |      1 |     8
+      3 |      1 |     3
+      3 |      2 |     9
+      4 |      7 |     0
+(6 rows)
+</pre></li>
+</ul>
+<p><b>For sparse matrices, the <em>row</em> and <em>col</em> columns together should not contain a duplicate entry and the <em>val</em> column should be of scalar (non-array) data type</b>. <br />
+ For comparison, the dense representation of this matrix is shown below. Note the dimensionality of the dense matrix is 4 x 7 since the max value of <em>row</em> and <em>col</em> is 4 and 7 respectively, leading to all zeros in the last row and last column. &#160; </p><pre>
+ row_id |         row_vec
+--------+-------------------------
+   1    | {9,0,0,0,6,6,0}
+   2    | {8,0,0,0,0,0,0}
+   3    | {3,9,0,0,0,0,0}
+   4    | {0,0,0,0,0,0,0}
+</pre><dl class="section note"><dt>Note</dt><dd>The functions below support several numeric types (unless otherwise noted) including SMALLINT, INTEGER, BIGINT, DOUBLE PRECISION (FLOAT8), NUMERIC (internally casted into FLOAT8, loss of precision can happen).</dd></dl>
+<p><a class="anchor" id="operations"></a></p><dl class="section user"><dt>Matrix Operations</dt><dd></dd></dl>
+<p>Given below are the supported matrix operations. The meaning of the arguments and other terms are common to all functions and provided at the end of the list as a glossary.</p>
+<ul>
+<li><b>Representation</b> <pre class="syntax">
+-- Convert to sparse representation
+&#160; <b>matrix_sparsify</b>( matrix_in, in_args, matrix_out, out_args)
+&#160;
+-- Convert to dense representation
+&#160; <b>matrix_densify</b>( matrix_in, in_args, matrix_out, out_args)
+</pre></li>
+<li><b>Mathematical operations</b> <pre class="syntax">
+-- Matrix transposition
+&#160; <b>matrix_trans</b>( matrix_in, in_args, matrix_out, out_args)
+&#160;
+-- Matrix addition
+&#160; <b>matrix_add</b>( matrix_a, a_args, matrix_b, b_args, matrix_out, out_args)
+&#160;
+-- Matrix subtraction
+&#160; <b>matrix_sub</b>( matrix_a, a_args, matrix_b, b_args, matrix_out, out_args)
+&#160;
+-- Matrix multiplication
+&#160; <b>matrix_mult</b>( matrix_a, a_args, matrix_b, b_args, matrix_out, out_args)
+&#160;
+-- Element-wise matrix multiplication
+&#160; <b>matrix_elem_mult</b>( matrix_a, a_args, matrix_b, b_args, matrix_out, out_args)
+&#160;
+-- Multiply matrix with scalar.
+&#160; <b>matrix_scalar_mult</b>( matrix_in, in_args, scalar, matrix_out, out_args)
+&#160;
+-- Multiply matrix with vector.
+&#160; <b>matrix_vec_mult</b>( matrix_in, in_args, vector)
+</pre></li>
+<li><b>Extraction/visitor methods</b> <pre class="syntax">
+-- Extract row from matrix given row index
+&#160; <b>matrix_extract_row</b>( matrix_in, in_args, index)
+&#160;
+-- Extract column from matrix given column index
+&#160; <b>matrix_extract_col</b>( matrix_in, in_args, index)
+</pre></li>
+<li><b>Reduction operations (aggregate across specific dimension)</b> <pre class="syntax">
+-- Get max value along dim. Also returns corresponding index if <em>fetch_index</em> = True
+&#160; <b>matrix_max</b>( matrix_in, in_args, dim, matrix_out, fetch_index)
+&#160;
+-- Get min value along dim. Also returns corresponding index if <em>fetch_index</em> = True
+&#160; <b>matrix_min</b>( matrix_in, in_args, dim, matrix_out, fetch_index)
+&#160;
+-- Get sum value along dimension from matrix.
+&#160; <b>matrix_sum</b>( matrix_in, in_args, dim)
+&#160;
+-- Get mean value along dimension from matrix.
+&#160; <b>matrix_mean</b>( matrix_in, in_args, dim)
+</pre></li>
+</ul>
+<p><a class="anchor" id="glossary"></a><b>Glossary</b> </p>
+<p>The table below provides a glossary of the terms used in the matrix operations.</p>
+<dl class="arglist">
+<dt>matrix_in, matrix_a, matrix_b </dt>
+<dd><p class="startdd">TEXT. Name of the table containing the input matrix.</p><ul>
+<li>For functions accepting one matrix, <em>matrix_in</em> denotes the input matrix.</li>
+<li>For functions accepting two matrices, <em>matrix_a</em> denotes the first matrix and <em>matrix_b</em> denotes the second matrix. These two matrices can <b>independently</b> be in either dense or sparse format. </li>
+</ul>
+<p class="enddd"></p>
+</dd>
+<dt>in_args, a_args, b_args </dt>
+<dd><p class="startdd">TEXT. A comma-delimited string containing multiple named arguments of the form "name=value". This argument is used as a container for multiple parameters related to a single matrix.</p>
+<p>The following parameters are supported for this string argument: </p><table  class="output">
+<tr>
+<th>row </th><td>(Default: 'row_num') Name of the column containing row index of the matrix.   </td></tr>
+<tr>
+<th>col </th><td>(Default: 'col_num') Name of the column containing column index of the matrix.  </td></tr>
+<tr>
+<th>val </th><td>(Default: 'val') Name of the column containing the entries of the matrix.  </td></tr>
+<tr>
+<th>trans </th><td>(Default: False) Boolean flag to indicate if the matrix should be transposed before the operation. This is currently functional only for <em>matrix_mult</em>.  </td></tr>
+</table>
+<p>For example, the string argument with default values will be 'row=row_num, col=col_num, val=val, trans=False'. Alternatively, the string argument can be set to <em>NULL</em> or be blank ('') if default values are to be used. </p>
+<p class="enddd"></p>
+</dd>
+<dt>matrix_out </dt>
+<dd><p class="startdd">TEXT. Name of the table to store the result matrix. </p>
+<p class="enddd"></p>
+</dd>
+<dt>out_args </dt>
+<dd><p class="startdd">TEXT. A comma-delimited string containing named arguments of the form "name=value". This is an <b>optional parameter</b> and the default value is set as follows:</p><ul>
+<li>For functions with one input matrix, default = <em>in_args</em></li>
+<li>For functions with two input matrices, default = <em>a_args</em>.</li>
+</ul>
+<p>The following parameters are supported for this string argument: </p><table  class="output">
+<tr>
+<th>row </th><td>Name of the column containing row index of the matrix.   </td></tr>
+<tr>
+<th>col </th><td>Name of the column containing column index of the matrix.  </td></tr>
+<tr>
+<th>val </th><td>Name of the column containing the entries of the matrix.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>index </dt>
+<dd><p class="startdd">INTEGER. An integer representing a row or column index of the matrix. Should be a number from 1 to <em>N</em>, where <em>N</em> is the maximum size of the dimension.</p>
+<p class="enddd"></p>
+</dd>
+<dt>dim </dt>
+<dd><p class="startdd">INTEGER. Should either be 1 or 2. This value indicates the dimension to operate along for the reduction/aggregation operations. <b>The value of <em>dim</em> should be interpreted as the dimension to be flattened i.e. whose length reduces to 1 in the result.</b> <br />
+ For <em>dim=1</em>, a reduction function on an <em>NxM</em> matrix operates on successive elements in each column and returns a single vector with <em>M</em> elements (i.e. matrix with just 1 row and <em>M</em> columns). <br />
+ For <em>dim=2</em>, a single vector is returned with <em>N</em> elements (i.e. matrix with just 1 column and <em>N</em> rows). </p>
+<p class="enddd"></p>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ul>
+<li>Create some random data tables in dense format. <pre class="syntax">
+CREATE TABLE "mat_A" (
+    row_id integer,
+    row_vec integer[]
+);
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (1, '{9,6,5,8,5,6,6,3,10,8}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (2, '{8,2,2,6,6,10,2,1,9,9}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (3, '{3,9,9,9,8,6,3,9,5,6}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (4, '{6,4,2,2,2,7,8,8,0,7}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (5, '{6,8,9,9,4,6,9,5,7,7}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (6, '{4,10,7,3,9,5,9,2,3,4}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (7, '{8,10,7,10,1,9,7,9,8,7}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (8, '{7,4,5,6,2,8,1,1,4,8}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (9, '{8,8,8,5,2,6,9,1,8,3}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (10, '{4,6,3,2,6,4,1,2,3,8}');
+
+CREATE TABLE "mat_B" (
+    row_id integer,
+    vector integer[]
+);
+INSERT INTO "mat_B" (row_id, vector) VALUES (1, '{9,10,2,4,6,5,3,7,5,6}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (2, '{5,3,5,2,8,6,9,7,7,6}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (3, '{0,1,2,3,2,7,7,3,10,1}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (4, '{2,9,0,4,3,6,8,6,3,4}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (5, '{3,8,7,7,0,5,3,9,2,10}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (6, '{5,3,1,7,6,3,5,3,6,4}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (7, '{4,8,4,4,2,7,10,0,3,3}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (8, '{4,6,0,1,3,1,6,6,9,8}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (9, '{6,5,1,7,2,7,10,6,0,6}');
+INSERT INTO "mat_B" (row_id, vector) VALUES (10, '{1,4,4,4,8,5,2,8,5,5}');
+</pre></li>
+<li>Transpose a matrix <pre class="syntax">
+SELECT madlib.matrix_trans('"mat_B"', 'row=row_id, val=vector',
+                           'mat_r');
+SELECT * FROM mat_r ORDER BY row_id;
+</pre> <pre class="result">
+-- Note the result matrix has inherited 'vector' as the name of value column
+ row_id |         vector
+--------+-------------------------
+      1 | {9,5,0,2,3,5,4,4,6,1}
+      2 | {10,3,1,9,8,3,8,6,5,4}
+      3 | {2,5,2,0,7,1,4,0,1,4}
+      4 | {4,2,3,4,7,7,4,1,7,4}
+      5 | {6,8,2,3,0,6,2,3,2,8}
+      6 | {5,6,7,6,5,3,7,1,7,5}
+      7 | {3,9,7,8,3,5,10,6,10,2}
+      8 | {7,7,3,6,9,3,0,6,6,8}
+      9 | {5,7,10,3,2,6,3,9,0,5}
+      10 | {6,6,1,4,10,4,3,8,6,5}
+(10 rows)
+</pre></li>
+<li>Add the two matrices <pre class="syntax">
+SELECT madlib.matrix_add('"mat_A"', 'row=row_id, val=row_vec',
+                         '"mat_B"', 'row=row_id, val=vector',
+                         'mat_r', 'val=vector');
+SELECT * FROM mat_r ORDER BY row_id;
+</pre> <pre class="result">
+ row_id |            vector
+--------+-------------------------------
+      1 | {18,16,7,12,11,11,9,10,15,14}
+      2 | {13,5,7,8,14,16,11,8,16,15}
+      3 | {3,10,11,12,10,13,10,12,15,7}
+      4 | {8,13,2,6,5,13,16,14,3,11}
+      5 | {9,16,16,16,4,11,12,14,9,17}
+      6 | {9,13,8,10,15,8,14,5,9,8}
+      7 | {12,18,11,14,3,16,17,9,11,10}
+      8 | {11,10,5,7,5,9,7,7,13,16}
+      9 | {14,13,9,12,4,13,19,7,8,9}
+      10 | {5,10,7,6,14,9,3,10,8,13}
+(10 rows)
+</pre></li>
+<li>Multiply the two matrices <pre class="syntax">
+DROP TABLE IF EXISTS mat_r;
+SELECT madlib.matrix_mult('"mat_A"', 'row=row_id, val=row_vec',
+                          '"mat_B"', 'row=row_id, val=vector, trans=true',
+                          'mat_r');
+SELECT * FROM mat_r ORDER BY row_id;
+</pre> <pre class="result">
+ row_id |                  row_vec
+--------+-------------------------------------------
+     1  | {380,373,251,283,341,303,302,309,323,281}
+     2  | {318,318,222,221,269,259,236,249,264,248}
+     3  | {382,366,216,300,397,276,277,270,313,338}
+     4  | {275,284,154,244,279,183,226,215,295,204}
+     5  | {381,392,258,319,394,298,342,302,360,300}
+     6  | {321,333,189,276,278,232,300,236,281,250}
+     7  | {443,411,282,365,456,318,360,338,406,330}
+     8  | {267,240,150,186,270,194,210,184,233,193}
+     9  | {322,328,234,264,291,245,317,253,291,219}
+     10 | {246,221,109,173,222,164,167,185,181,189}
+(10 rows)
+</pre></li>
+<li>Extract row and column from matrix given index <pre class="syntax">
+SELECT madlib.matrix_extract_row('"mat_A"', 'row=row_id, val=row_vec', 2) as row,
+       madlib.matrix_extract_col('"mat_A"', 'row=row_id, val=row_vec', 3) as col;
+</pre> <pre class="result">
+          row           |          col
+------------------------+-----------------------
+ {8,2,2,6,6,10,2,1,9,9} | {5,2,9,2,9,7,7,5,8,3}
+(1 rows)
+</pre></li>
+<li>Get min and max values along a specific dimension as well as corresponding indicies. Note that <em>dim=2</em> implies that the min and max is computed on each row returning a column vector i.e. the column (dim=2) is flattened. <pre class="syntax">
+SELECT madlib.matrix_max('"mat_A"', 'row=row_id, val=row_vec', 2, 'mat_max_r', true),
+       madlib.matrix_min('"mat_A"', 'row=row_id, val=row_vec', 2, 'mat_min_r', true);
+SELECT * from mat_max_r;
+SELECT * from mat_min_r;
+</pre> <pre class="result">
+         index         |            max
+-----------------------+---------------------------
+ {8,5,1,6,2,1,1,5,6,9} | {10,10,9,8,9,10,10,8,9,8}
+(1 rows)
+&#160;
+         index         |          min
+-----------------------+-----------------------
+ {7,7,0,8,4,7,4,6,7,6} | {3,1,3,0,4,2,1,1,1,1}
+(1 rows)
+</pre></li>
+<li>Element-wise multiplication between two matrices <pre class="syntax">
+SELECT madlib.matrix_elem_mult('"mat_A"', 'row=row_id, val=row_vec',
+                               '"mat_B"', 'val=vector',
+                               'mat_r', 'val=vector');
+SELECT * FROM mat_r ORDER BY row_id;
+</pre> <pre class="result">
+ row_id |             vector
+--------+---------------------------------
+     1  | {81,60,10,32,30,30,18,21,50,48}
+     2  | {40,6,10,12,48,60,18,7,63,54}
+     3  | {0,9,18,27,16,42,21,27,50,6}
+     4  | {12,36,0,8,6,42,64,48,0,28}
+     5  | {18,64,63,63,0,30,27,45,14,70}
+     6  | {20,30,7,21,54,15,45,6,18,16}
+     7  | {32,80,28,40,2,63,70,0,24,21}
+     8  | {28,24,0,6,6,8,6,6,36,64}
+     9  | {48,40,8,35,4,42,90,6,0,18}
+     10 | {4,24,12,8,48,20,2,16,15,40}
+</pre></li>
+<li>Get sum values along dimension. Sum is computed for each row (i.e. column is flattened since dim=2) <pre class="syntax">
+SELECT madlib.matrix_sum('"mat_A"', 'row=row_id, val=row_vec', 2);
+</pre> <pre class="result">
+           matrix_sum
+---------------------------------
+ {66,55,67,46,70,56,76,46,58,39}
+(1 rows)
+</pre></li>
+<li>Multiply matrix with a scalar <pre class="syntax">
+DROP TABLE IF EXISTS mat_r;
+SELECT madlib.matrix_scalar_mult('"mat_A"', 'row=row_id, val=row_vec', 3, 'mat_r');
+SELECT * FROM mat_r ORDER BY row_id;
+</pre> <pre class="result">
+ row_id |             row_vec
+--------+---------------------------------
+      0 | {27,18,15,24,15,18,18,9,30,24}
+      1 | {24,6,6,18,18,30,6,3,27,27}
+      2 | {9,27,27,27,24,18,9,27,15,18}
+      3 | {18,12,6,6,6,21,24,24,0,21}
+      4 | {18,24,27,27,12,18,27,15,21,21}
+      5 | {12,30,21,9,27,15,27,6,9,12}
+      6 | {24,30,21,30,3,27,21,27,24,21}
+      7 | {21,12,15,18,6,24,3,3,12,24}
+      8 | {24,24,24,15,6,18,27,3,24,9}
+      9 | {12,18,9,6,18,12,3,6,9,24}
+(10 rows)
+</pre></li>
+<li>Multiply matrix with a vector <pre class="syntax">
+SELECT madlib.matrix_vec_mult('"mat_A"', 'row=row_id, val=row_vec',
+                              array[1,2,3,4,5,6,7,8,9,10]);
+</pre> <pre class="result">
+              matrix_vec_mult
+-------------------------------------------
+ {365,325,358,270,377,278,411,243,287,217}
+(10 rows)
+</pre></li>
+<li><b>Examples with sparse representation</b>. The function calls are the same as before. <pre class="syntax">
+SELECT madlib.matrix_sparsify('"mat_B"', 'row=row_id, val=vector',
+                              '"mat_B_sparse"', 'col=col_id, val=val');
+</pre></li>
+<li>Create a matrix in sparse format. <pre class="syntax">
+CREATE TABLE "mat_A_sparse" (
+    "rowNum" integer,
+    col_num integer,
+    entry integer
+);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (1, 1, 9);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (1, 2, 6);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (1, 7, 3);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (1, 8, 10);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (1, 9, 8);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (2, 1, 8);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (2, 2, 2);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (2, 3, 6);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (3, 5, 6);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (3, 6, 3);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (7, 1, 7);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (8, 2, 8);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (8, 3, 5);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (9, 1, 6);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (9, 2, 3);
+INSERT INTO "mat_A_sparse" ("rowNum", col_num, entry) VALUES (10, 10, 0);
+</pre></li>
+<li>Transpose a matrix in sparse format <pre class="syntax">
+-- Note the double quotes for '"rowNum"' required per PostgreSQL rules
+SELECT madlib.matrix_trans('"mat_A_sparse"', 'row="rowNum", val=entry',
+                           'matrix_r_sparse');
+SELECT "rowNum", col_num, entry FROM matrix_r_sparse ORDER BY col_num;
+</pre> <pre class="result">
+ rowNum | col_num | entry
+--------+---------+-------
+      1 |       1 |     9
+      2 |       1 |     6
+      7 |       1 |     3
+      8 |       1 |    10
+      9 |       1 |     8
+      1 |       2 |     8
+      2 |       2 |     2
+      3 |       2 |     6
+      5 |       3 |     6
+      6 |       3 |     3
+      1 |       7 |     7
+      2 |       8 |     8
+      3 |       8 |     5
+      1 |       9 |     6
+      2 |       9 |     3
+     10 |      10 |     0
+(16 rows)
+</pre></li>
+<li>Add two sparse matrices <pre class="syntax">
+SELECT madlib.matrix_add('"mat_A_sparse"', 'row="rowNum", val=entry',
+                         '"mat_B_sparse"', 'row=row_id, col=col_id, val=val',
+                         'matrix_r_sparse', 'col=col_out');
+SELECT madlib.matrix_densify('matrix_r_sparse', 'row="rowNum", col=col_out, val=entry',
+                             'matrix_r');
+SELECT * FROM matrix_r ORDER BY "rowNum";
+</pre> <pre class="result">
+ rowNum |           entry
+--------+---------------------------
+      1 | {18,16,2,4,6,5,6,17,13,6}
+      2 | {13,5,11,2,8,6,9,7,7,6}
+      3 | {0,1,2,3,8,10,7,3,10,1}
+      4 | {2,9,0,4,3,6,8,6,3,4}
+      5 | {3,8,7,7,0,5,3,9,2,10}
+      6 | {5,3,1,7,6,3,5,3,6,4}
+      7 | {11,8,4,4,2,7,10,0,3,3}
+      8 | {4,14,5,1,3,1,6,6,9,8}
+      9 | {12,8,1,7,2,7,10,6,0,6}
+     10 | {1,4,4,4,8,5,2,8,5,5}
+(10 rows)
+</pre></li>
+<li>Multiply two sparse matrices <pre class="syntax">
+SELECT madlib.matrix_mult('"mat_A_sparse"', 'row="rowNum", col=col_num, val=entry',
+                          '"mat_B_sparse"', 'row=row_id, col=col_id, val=val, trans=true',
+                          'matrix_r');
+SELECT * FROM matrix_r ORDER BY "rowNum";
+</pre> <pre class="result">
+ rowNum |                   entry
+--------+-------------------------------------------
+      1 | {260,216,137,180,190,156,138,222,174,159}
+      2 | {104,76,14,34,82,52,72,44,64,40}
+      3 | {51,66,33,36,15,45,33,21,33,63}
+      4 | {0,0,0,0,0,0,0,0,0,0}
+      5 | {0,0,0,0,0,0,0,0,0,0}
+      6 | {0,0,0,0,0,0,0,0,0,0}
+      7 | {63,35,0,14,21,35,28,28,42,7}
+      8 | {90,49,18,72,99,29,84,48,45,52}
+      9 | {84,39,3,39,42,39,48,42,51,18}
+     10 | {0,0,0,0,0,0,0,0,0,0}
+(10 rows)
+</pre></li>
+</ul>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="array__ops_8sql__in.html" title="implementation of array operations in SQL ">array_ops.sql_in</a> documenting the array operations <a class="el" href="group__grp__array.html">Array Operations</a></p>
+<p>File <a class="el" href="matrix__ops_8sql__in.html" title="Implementation of matrix operations in SQL. ">matrix_ops.sql_in</a> for list of functions and usage. </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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+<div class="title">Matrix Factorization<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__lmf"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__lmf.html">Low-rank Matrix Factorization</a></td></tr>
+<tr class="memdesc:group__grp__lmf"><td class="mdescLeft">&#160;</td><td class="mdescRight">Performs low-rank matrix factorization for an incomplete matrix. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__svd"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__svd.html">Singular Value Decomposition</a></td></tr>
+<tr class="memdesc:group__grp__svd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Performs factorization of dense, sparse, and block matrices. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
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+    [ "Singular Value Decomposition", "group__grp__svd.html", null ]
+];
\ No newline at end of file

[02/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+  <div class="headertitle">
+<div class="title">hypothesis_tests.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for statistical hypothesis tests.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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+<tr class="memitem:a3bd1bcc335a2da73d01b40e06f7d2eea"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a3bd1bcc335a2da73d01b40e06f7d2eea">t_test_one_transition</a> (float8[] state, float8 value)</td></tr>
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+<tr class="memitem:a14fdcfa276fd1a7ea2e3adb41ebe7db4"><td class="memItemLeft" align="right" valign="top">aggregate float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a14fdcfa276fd1a7ea2e3adb41ebe7db4">t_test_one</a> (float8 value)</td></tr>
+<tr class="memdesc:a14fdcfa276fd1a7ea2e3adb41ebe7db4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform one-sample or dependent paired Student t-test.  <a href="#a14fdcfa276fd1a7ea2e3adb41ebe7db4">More...</a><br /></td></tr>
+<tr class="separator:a14fdcfa276fd1a7ea2e3adb41ebe7db4"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a1a835d80d1846a10a2c25b91ce81c6d2"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a5c306ba8380ce6567831fef4610e515b"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="memdesc:ac50750a0e0797ce24af1dc479b9699e1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform unpooled (i.e., unequal variances) t-test (also known as Welch's t-test)  <a href="#ac50750a0e0797ce24af1dc479b9699e1">More...</a><br /></td></tr>
+<tr class="separator:ac50750a0e0797ce24af1dc479b9699e1"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="memdesc:a5a946aa469ff6ddf8d276db16fa26ad4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform Fisher F-test.  <a href="#a5a946aa469ff6ddf8d276db16fa26ad4">More...</a><br /></td></tr>
+<tr class="separator:a5a946aa469ff6ddf8d276db16fa26ad4"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a7c580537666776f1bd4b9d4a0a6b6438"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:ab4b83895c48dd1c1ca2e106b15741868"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a66d03891a6add6d67f944df5344ed40e"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a2b8265718a16ec65e89d2ab512f6a3e1"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="memitem:a4c912b329fb103a44253932a653d4e40"><td class="memItemLeft" align="right" valign="top">aggregate float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a4c912b329fb103a44253932a653d4e40">chi2_gof_test</a> (bigint observed, float8 expected=1, bigint df=0)</td></tr>
+<tr class="memdesc:a4c912b329fb103a44253932a653d4e40"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform Pearson's chi-squared goodness-of-fit test.  <a href="#a4c912b329fb103a44253932a653d4e40">More...</a><br /></td></tr>
+<tr class="separator:a4c912b329fb103a44253932a653d4e40"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a09a8ed9d073f8c43d9ade3cf2defb2b0"><td class="memItemLeft" align="right" valign="top">aggregate float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a09a8ed9d073f8c43d9ade3cf2defb2b0">chi2_gof_test</a> (bigint observed, float8 expected)</td></tr>
+<tr class="separator:a09a8ed9d073f8c43d9ade3cf2defb2b0"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a13730efbff97aa5624a350135a9b83ff"><td class="memItemLeft" align="right" valign="top">aggregate float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a13730efbff97aa5624a350135a9b83ff">chi2_gof_test</a> (bigint observed)</td></tr>
+<tr class="separator:a13730efbff97aa5624a350135a9b83ff"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a58ffb5b2b8392e005f4f3e21560df93f"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a58ffb5b2b8392e005f4f3e21560df93f">ks_test_transition</a> (float8[] state, boolean first, float8 value, bigint numFirst, bigint numSecond)</td></tr>
+<tr class="separator:a58ffb5b2b8392e005f4f3e21560df93f"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:abd3f6d1d6dc4203cab3bcc980ec8ed8d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a4e324c82b069ebf7b498012aa83931c5"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a4e324c82b069ebf7b498012aa83931c5">mw_test_transition</a> (float8[] state, boolean first, float8 value)</td></tr>
+<tr class="memdesc:a4e324c82b069ebf7b498012aa83931c5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform Kolmogorov-Smirnov test.  <a href="#a4e324c82b069ebf7b498012aa83931c5">More...</a><br /></td></tr>
+<tr class="separator:a4e324c82b069ebf7b498012aa83931c5"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ac18e666088117997da2d22236e982f5e"><td class="memItemLeft" align="right" valign="top">mw_test_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#ac18e666088117997da2d22236e982f5e">mw_test_final</a> (float8[] state)</td></tr>
+<tr class="separator:ac18e666088117997da2d22236e982f5e"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="memdesc:a0d85654832dfa961cd13526c052642f3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform Mann-Whitney test.  <a href="#a0d85654832dfa961cd13526c052642f3">More...</a><br /></td></tr>
+<tr class="separator:a0d85654832dfa961cd13526c052642f3"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a4093de00ab033f4900ce186d481fa012"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a4093de00ab033f4900ce186d481fa012">wsr_test_transition</a> (float8[] state, float8 value)</td></tr>
+<tr class="separator:a4093de00ab033f4900ce186d481fa012"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a8f0431ace27ac78e9e1df9906f1f1c33"><td class="memItemLeft" align="right" valign="top">wsr_test_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a8f0431ace27ac78e9e1df9906f1f1c33">wsr_test_final</a> (float8[] state)</td></tr>
+<tr class="separator:a8f0431ace27ac78e9e1df9906f1f1c33"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa694f4ce95280210a3887773bb3f417b"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#aa694f4ce95280210a3887773bb3f417b">one_way_anova_transition</a> (float8[] state, integer group, float8 value)</td></tr>
+<tr class="memdesc:aa694f4ce95280210a3887773bb3f417b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform Wilcoxon-Signed-Rank test.  <a href="#aa694f4ce95280210a3887773bb3f417b">More...</a><br /></td></tr>
+<tr class="separator:aa694f4ce95280210a3887773bb3f417b"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad6c05d07183c961961f171b0a925ad93"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#ad6c05d07183c961961f171b0a925ad93">one_way_anova_merge_states</a> (float8[] state1, float8[] state2)</td></tr>
+<tr class="separator:ad6c05d07183c961961f171b0a925ad93"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a5ac28bec7ff670a7da8b9eae4a8ed4cb"><td class="memItemLeft" align="right" valign="top">one_way_anova_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#a5ac28bec7ff670a7da8b9eae4a8ed4cb">one_way_anova_final</a> (float8[] state)</td></tr>
+<tr class="separator:a5ac28bec7ff670a7da8b9eae4a8ed4cb"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:abd5c80afd954294de268030ee73e88cc"><td class="memItemLeft" align="right" valign="top">aggregate float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="hypothesis__tests_8sql__in.html#abd5c80afd954294de268030ee73e88cc">one_way_anova</a> (integer group, float8 value)</td></tr>
+<tr class="memdesc:abd5c80afd954294de268030ee73e88cc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Perform one-way analysis of variance.  <a href="#abd5c80afd954294de268030ee73e88cc">More...</a><br /></td></tr>
+<tr class="separator:abd5c80afd954294de268030ee73e88cc"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section see"><dt>See also</dt><dd>For an overview of hypthesis-test functions, see the module description <a class="el" href="group__grp__stats__tests.html">Hypothesis Tests</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a4c912b329fb103a44253932a653d4e40"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] chi2_gof_test </td>
+          <td>(</td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>observed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>expected</em> = <code>1</code>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>df</em> = <code>0</code>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Let \( n_1, \dots, n_k \) be a realization of a (vector) random variable \( N = (N_1, \dots, N_k) \) that follows the multinomial distribution with parameters \( k \) and \( p = (p_1, \dots, p_k) \). Test the null hypothesis \( H_0 : p = p^0 \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">observed</td><td>Number \( n_i \) of observations of the current event/row </td></tr>
+    <tr><td class="paramname">expected</td><td>Expected number of observations of current event/row. This number is not required to be normalized. That is, \( p^0_i \) will be taken as <code>expected</code> divided by <code>sum(expected)</code>. Hence, if this parameter is not specified, chi2_test() will by default use \( p^0 = (\frac 1k, \dots, \frac 1k) \), i.e., test that \( p \) is a discrete uniform distribution. </td></tr>
+    <tr><td class="paramname">df</td><td>Degrees of freedom. This is the number of events reduced by the degree of freedom lost by using the observed numbers for defining the expected number of observations. If this parameter is 0, the degree of freedom is taken as \( (k - 1) \).</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value as follows. Let \( n = \sum_{i=1}^n n_i \).<ul>
+<li><code>statistic FLOAT8</code> - Statistic <p class="formulaDsp">
+\[ \chi^2 = \sum_{i=1}^k \frac{(n_i - np_i)^2}{np_i} \]
+</p>
+ The corresponding random variable is approximately chi-squared distributed with <code>df</code> degrees of freedom.</li>
+<li><code>df BIGINT</code> - Degrees of freedom</li>
+<li><code>p_value FLOAT8</code> - Approximate p-value, i.e., \( \Pr[X^2 \geq \chi^2 \mid p = p^0] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a230513b6b549d5b445cbacbdbab42c15">chi_squared_cdf</a>(statistic))</code>.</li>
+<li><code>phi FLOAT8</code> - Phi coefficient, i.e., \( \phi = \sqrt{\frac{\chi^2}{n}} \)</li>
+<li><code>contingency_coef FLOAT8</code> - Contingency coefficient, i.e., \( \sqrt{\frac{\chi^2}{n + \chi^2}} \)</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Test null hypothesis that all possible outcomes of a categorical variable are equally likely: <pre>SELECT (chi2_gof_test(<em>observed</em>, 1, NULL)).* FROM <em>source</em></pre></li>
+<li>Test null hypothesis that two categorical variables are independent. Such data is often shown in a <em>contingency table</em> (also known as <em>crosstab</em>). A crosstab is a matrix where possible values for the first variable correspond to rows and values for the second variable to columns. The matrix elements are the observation frequencies of the joint occurrence of the respective values. <a class="el" href="hypothesis__tests_8sql__in.html#a4c912b329fb103a44253932a653d4e40" title="Perform Pearson&#39;s chi-squared goodness-of-fit test. ">chi2_gof_test()</a> assumes that the crosstab is stored in normalized form, i.e., there are three columns <code><em>var1</em></code>, <code><em>var2</em></code>, <code><em>observed</em></code>. <pre>SELECT (chi2_gof_test(<em>observed</em>, expected, deg_freedom)).*
+FROM (
+    SELECT
+        <em>observed</em>,
+        sum(<em>observed</em>) OVER (PARTITION BY var1)::DOUBLE PRECISION
+            * sum(<em>observed</em>) OVER (PARTITION BY var2) AS expected
+    FROM <em>source</em>
+) p, (
+   SELECT
+        (count(DISTINCT <em>var1</em>) - 1) * (count(DISTINCT <em>var2</em>) - 1) AS deg_freedom
+    FROM <em>source</em>
+) q;</pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a09a8ed9d073f8c43d9ade3cf2defb2b0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] chi2_gof_test </td>
+          <td>(</td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>observed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>expected</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a13730efbff97aa5624a350135a9b83ff"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] chi2_gof_test </td>
+          <td>(</td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>observed</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a61c31dfde998db18afd6552239b872c4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">chi2_test_result chi2_gof_test_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2b8265718a16ec65e89d2ab512f6a3e1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] chi2_gof_test_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7c580537666776f1bd4b9d4a0a6b6438"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] chi2_gof_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>observed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>expected</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>df</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab4b83895c48dd1c1ca2e106b15741868"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] chi2_gof_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>observed</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>expected</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a66d03891a6add6d67f944df5344ed40e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] chi2_gof_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>observed</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5a946aa469ff6ddf8d276db16fa26ad4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] f_test </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>first</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_m \) and \( y_1, \dots, y_n \) of i.i.d. random variables \( X_1, \dots, X_m \sim N(\mu_X, \sigma^2) \) and \( Y_1, \dots, Y_n \sim N(\mu_Y, \sigma^2) \) with unknown parameters \( \mu_X, \mu_Y, \) and \( \sigma^2 \), test the null hypotheses \( H_0 : \sigma_X &lt; \sigma_Y \) and \( H_0 : \sigma_X = \sigma_Y \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">first</td><td>Indicator whether <code>value</code> is from first sample \( x_1, \dots, x_m \) (if <code>TRUE</code>) or from second sample \( y_1, \dots, y_n \) (if <code>FALSE</code>) </td></tr>
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \) or \( y_i \)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value as follows. We denote by \( \bar x, \bar y \) the sample means and by \( s_X^2, s_Y^2 \) the sample variances.<ul>
+<li><code>statistic FLOAT8</code> - Statistic <p class="formulaDsp">
+\[ f = \frac{s_Y^2}{s_X^2} \]
+</p>
+ The corresponding random variable is F-distributed with \( (n - 1) \) degrees of freedom in the numerator and \( (m - 1) \) degrees of freedom in the denominator.</li>
+<li><code>df1 BIGINT</code> - Degrees of freedom in the numerator \( (n - 1) \)</li>
+<li><code>df2 BIGINT</code> - Degrees of freedom in the denominator \( (m - 1) \)</li>
+<li><code>p_value_one_sided FLOAT8</code> - Lower bound on one-sided p-value. In detail, the result is \( \Pr[F \geq f \mid \sigma_X = \sigma_Y] \), which is a lower bound on \( \Pr[F \geq f \mid \sigma_X \leq \sigma_Y] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a6c5b3e35531e44098f9d0cbef14cb8a6">fisher_f_cdf</a>(statistic))</code>.</li>
+<li><code>p_value_two_sided FLOAT8</code> - Two-sided p-value, i.e., \( 2 \cdot \min \{ p, 1 - p \} \) where \( p = \Pr[ F \geq f \mid \sigma_X = \sigma_Y] \). Computed as <code>(min(p_value_one_sided, 1. - p_value_one_sided))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Test null hypothesis that the variance of the first sample is at most (or equal to, respectively) the variance of the second sample: <pre>SELECT (f_test(<em>first</em>, <em>value</em>)).* FROM <em>source</em></pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="abc6006e8da028dd93ac48b8fd9ae8786"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">f_test_result f_test_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abd3f6d1d6dc4203cab3bcc980ec8ed8d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">ks_test_result ks_test_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a58ffb5b2b8392e005f4f3e21560df93f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] ks_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>first</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>numFirst</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bigint&#160;</td>
+          <td class="paramname"><em>numSecond</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac18e666088117997da2d22236e982f5e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">mw_test_result mw_test_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4e324c82b069ebf7b498012aa83931c5"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] mw_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>first</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_m \) and \( y_1, \dots, y_m \) of i.i.d. random variables \( X_1, \dots, X_m \) and i.i.d. \( Y_1, \dots, Y_n \), respectively, test the null hypothesis that the underlying distributions function \( F_X, F_Y \) are identical, i.e., \( H_0 : F_X = F_Y \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">first</td><td>Determines whether the value belongs to the first (if <code>TRUE</code>) or the second sample (if <code>FALSE</code>) </td></tr>
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \) or \( y_i \) </td></tr>
+    <tr><td class="paramname">m</td><td>Size \( m \) of the first sample. See usage instructions below. </td></tr>
+    <tr><td class="paramname">n</td><td>Size of the second sample. See usage instructions below.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value.<ul>
+<li><code>statistic FLOAT8</code> - Kolmogorov–Smirnov statistic <p class="formulaDsp">
+\[ d = \max_{t \in \mathbb R} |F_x(t) - F_y(t)| \]
+</p>
+ where \( F_x(t) := \frac 1m |\{ i \mid x_i \leq t \}| \) and \( F_y \) (defined likewise) are the empirical distribution functions.</li>
+<li><code>k_statistic FLOAT8</code> - Kolmogorov statistic \( k = (r + 0.12 + \frac{0.11}{r}) \cdot d \) where \( r = \sqrt{\frac{m n}{m+n}}. \) and \( d \) is the statistic. Then \( k \) is approximately Kolmogorov distributed.</li>
+<li><code>p_value FLOAT8</code> - Approximate p-value, i.e., an approximate value for \( \Pr[D \geq d \mid F_X = F_Y] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#aeef43f74f583bdff17bd074d9c0d9607">kolmogorov_cdf</a>(k_statistic))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Test null hypothesis that two samples stem from the same distribution: <pre>SELECT (ks_test(<em>first</em>, <em>value</em>,
+    (SELECT count(<em>value</em>) FROM <em>source</em> WHERE <em>first</em>),
+    (SELECT count(<em>value</em>) FROM <em>source</em> WHERE NOT <em>first</em>)
+    ORDER BY <em>value</em>
+)).* FROM <em>source</em></pre></li>
+</ul>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This aggregate must be used as an ordered aggregate (<code>ORDER BY <em>value</code></em>) and will raise an exception if values are not ordered. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="abd5c80afd954294de268030ee73e88cc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] one_way_anova </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>group</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_{1,1}, \dots, x_{1, n_1}, x_{2,1}, \dots, x_{2,n_2}, \dots, x_{k,n_k} \) of i.i.d. random variables \( X_{i,j} \sim N(\mu_i, \sigma^2) \) with unknown parameters \( \mu_1, \dots, \mu_k \) and \( \sigma^2 \), test the null hypotheses \( H_0 : \mu_1 = \dots = \mu_k \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">group</td><td>Group which <code>value</code> is from. Note that <code>group</code> can assume arbitary value not limited to a continguous range of integers. </td></tr>
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_{i,j} \)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value as follows. Let \( n := \sum_{i=1}^k n_i \) be the total size of all samples. Denote by \( \bar x \) the grand mean, by \( \overline{x_i} \) the group sample means, and by \( s_i^2 \) the group sample variances.<ul>
+<li><code>sum_squares_between DOUBLE PRECISION</code> - sum of squares between the group means, i.e., \( \mathit{SS}_b = \sum_{i=1}^k n_i (\overline{x_i} - \bar x)^2. \)</li>
+<li><code>sum_squares_within DOUBLE PRECISION</code> - sum of squares within the groups, i.e., \( \mathit{SS}_w = \sum_{i=1}^k (n_i - 1) s_i^2. \)</li>
+<li><code>df_between BIGINT</code> - degree of freedom for between-group variation \( (k-1) \)</li>
+<li><code>df_within BIGINT</code> - degree of freedom for within-group variation \( (n-k) \)</li>
+<li><code>mean_squares_between DOUBLE PRECISION</code> - mean square between groups, i.e., \( s_b^2 := \frac{\mathit{SS}_b}{k-1} \)</li>
+<li><code>mean_squares_within DOUBLE PRECISION</code> - mean square within groups, i.e., \( s_w^2 := \frac{\mathit{SS}_w}{n-k} \)</li>
+<li><code>statistic DOUBLE PRECISION</code> - Statistic computed as <p class="formulaDsp">
+\[ f = \frac{s_b^2}{s_w^2}. \]
+</p>
+ This statistic is Fisher F-distributed with \( (k-1) \) degrees of freedom in the numerator and \( (n-k) \) degrees of freedom in the denominator.</li>
+<li><code>p_value DOUBLE PRECISION</code> - p-value, i.e., \( \Pr[ F \geq f \mid H_0] \).</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Test null hypothesis that the mean of the all samples is equal: <pre>SELECT (one_way_anova(<em>group</em>, <em>value</em>)).* FROM <em>source</em></pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a5ac28bec7ff670a7da8b9eae4a8ed4cb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">one_way_anova_result one_way_anova_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad6c05d07183c961961f171b0a925ad93"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] one_way_anova_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa694f4ce95280210a3887773bb3f417b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] one_way_anova_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>group</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_n \) of i.i.d. random variables \( X_1, \dots, X_n \) with unknown mean \( \mu \), test the null hypotheses \( H_0 : \mu \leq 0 \) and \( H_0 : \mu = 0 \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \) or \( y_i \). Values of 0 are ignored (i.e., they do not count towards \( n \)). </td></tr>
+    <tr><td class="paramname">precision</td><td>The precision \( \epsilon_i \) with which value is known. The precision determines the handling of ties. The current value \( v_i \) is regarded a tie with the previous value \( v_{i-1} \) if \( v_i - \epsilon_i \leq \max_{j=1, \dots, i-1} v_j + \epsilon_j \). If <code>precision</code> is negative, then it will be treated as <code>value * 2^(-52)</code>. (Note that \( 2^{-52} \) is the machine epsilon for type <code>DOUBLE PRECISION</code>.)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value:<ul>
+<li><code>statistic FLOAT8</code> - statistic computed as follows. Let \( w^+ = \sum_{i \mid x_i &gt; 0} r_i \) and \( w^- = \sum_{i \mid x_i &lt; 0} r_i \) be the <em>signed rank sums</em> where <p class="formulaDsp">
+\[ r_i = \{ j \mid |x_j| &lt; |x_i| \} + \frac{\{ j \mid |x_j| = |x_i| \} + 1}{2}. \]
+</p>
+ The Wilcoxon signed-rank statistic is \( w = \min \{ w^+, w^- \} \).</li>
+<li><code>rank_sum_pos FLOAT8</code> - rank sum of all positive values, i.e., \( w^+ \)</li>
+<li><code>rank_sum_neg FLOAT8</code> - rank sum of all negative values, i.e., \( w^- \)</li>
+<li><code>num BIGINT</code> - number \( n \) of non-zero values</li>
+<li><code>z_statistic FLOAT8</code> - z-statistic <p class="formulaDsp">
+\[ z = \frac{w^+ - \frac{n(n+1)}{4}} {\sqrt{\frac{n(n+1)(2n+1)}{24} - \sum_{i=1}^n \frac{t_i^2 - 1}{48}}} \]
+</p>
+ where \( t_i \) is the number of values with absolute value equal to \( |x_i| \). The corresponding random variable is approximately standard normally distributed.</li>
+<li><code>p_value_one_sided FLOAT8</code> - One-sided p-value i.e., \( \Pr[Z \geq z \mid \mu \leq 0] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a6c0a499faa80db26c0178f1e69cf7a50">normal_cdf</a>(z_statistic))</code>.</li>
+<li><code>p_value_two_sided FLOAT8</code> - Two-sided p-value, i.e., \( \Pr[ |Z| \geq |z| \mid \mu = 0] \). Computed as <code>(2 * <a class="el" href="prob_8sql__in.html#a6c0a499faa80db26c0178f1e69cf7a50">normal_cdf</a>(-abs(z_statistic)))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>One-sample test: Test null hypothesis that the mean of a sample is at most (or equal to, respectively) \( \mu_0 \): <pre>SELECT (wsr_test(<em>value</em> - <em>mu_0</em> ORDER BY abs(<em>value</em>))).* FROM <em>source</em></pre></li>
+<li>Dependent paired test: Test null hypothesis that the mean difference between the first and second value in a pair is at most (or equal to, respectively) \( \mu_0 \): <pre>SELECT (wsr_test(<em>first</em> - <em>second</em> - <em>mu_0</em> ORDER BY abs(<em>first</em> - <em>second</em>))).* FROM <em>source</em></pre> If correctly determining ties is important (e.g., you may want to do so when comparing to software products that take <code>first</code>, <code>second</code>, and <code>mu_0</code> as individual parameters), supply the precision parameter. This can be done as follows: <pre>SELECT (wsr_test(
+    <em>first</em> - <em>second</em> - <em>mu_0</em>,
+    3 * 2^(-52) * greatest(first, second, mu_0)
+    ORDER BY abs(<em>first</em> - <em>second</em>)
+)).* FROM <em>source</em></pre> Here \( 2^{-52} \) is the machine epsilon, which we scale to the magnitude of the input data and multiply with 3 because we have a sum with three terms.</li>
+</ul>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This aggregate must be used as an ordered aggregate (<code>ORDER BY abs(<em>value</code></em>)) and will raise an exception if the absolute values are not ordered. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ac97c5f5015790b59645d69858e127645"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] t_test_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a14fdcfa276fd1a7ea2e3adb41ebe7db4"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] t_test_one </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_n \) of i.i.d. random variables \( X_1, \dots, X_n \sim N(\mu, \sigma^2) \) with unknown parameters \( \mu \) and \( \sigma^2 \), test the null hypotheses \( H_0 : \mu \leq 0 \) and \( H_0 : \mu = 0 \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value as follows. We denote by \( \bar x \) the sample mean and by \( s^2 \) the sample variance.<ul>
+<li><code>statistic FLOAT8</code> - Statistic <p class="formulaDsp">
+\[ t = \frac{\sqrt n \cdot \bar x}{s} \]
+</p>
+ The corresponding random variable is Student-t distributed with \( (n - 1) \) degrees of freedom.</li>
+<li><code>df FLOAT8</code> - Degrees of freedom \( (n - 1) \)</li>
+<li><code>p_value_one_sided FLOAT8</code> - Lower bound on one-sided p-value. In detail, the result is \( \Pr[\bar X \geq \bar x \mid \mu = 0] \), which is a lower bound on \( \Pr[\bar X \geq \bar x \mid \mu \leq 0] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">students_t_cdf</a>(statistic))</code>.</li>
+<li><code>p_value_two_sided FLOAT8</code> - Two-sided p-value, i.e., \( \Pr[ |\bar X| \geq |\bar x| \mid \mu = 0] \). Computed as <code>(2 * <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">students_t_cdf</a>(-abs(statistic)))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>One-sample t-test: Test null hypothesis that the mean of a sample is at most (or equal to, respectively) \( \mu_0 \): <pre>SELECT (t_test_one(<em>value</em> - <em>mu_0</em>)).* FROM <em>source</em></pre></li>
+<li>Dependent paired t-test: Test null hypothesis that the mean difference between the first and second value in each pair is at most (or equal to, respectively) \( \mu_0 \): <pre>SELECT (t_test_one(<em>first</em> - <em>second</em> - <em>mu_0</em>)).*
+              FROM <em>source</em></pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ad570d893565618bcbbcbb01b3bb0a9b9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">t_test_result t_test_one_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3bd1bcc335a2da73d01b40e06f7d2eea"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] t_test_one_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5c306ba8380ce6567831fef4610e515b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] t_test_two_pooled </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>first</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_n \) and \( y_1, \dots, y_m \) of i.i.d. random variables \( X_1, \dots, X_n \sim N(\mu_X, \sigma^2) \) and \( Y_1, \dots, Y_m \sim N(\mu_Y, \sigma^2) \) with unknown parameters \( \mu_X, \mu_Y, \) and \( \sigma^2 \), test the null hypotheses \( H_0 : \mu_X \leq \mu_Y \) and \( H_0 : \mu_X = \mu_Y \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">first</td><td>Indicator whether <code>value</code> is from first sample \( x_1, \dots, x_n \) (if <code>TRUE</code>) or from second sample \( y_1, \dots, y_m \) (if <code>FALSE</code>) </td></tr>
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \) or \( y_i \)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value as follows. We denote by \( \bar x, \bar y \) the sample means and by \( s_X^2, s_Y^2 \) the sample variances.<ul>
+<li><code>statistic FLOAT8</code> - Statistic <p class="formulaDsp">
+\[ t = \frac{\bar x - \bar y}{s_p \sqrt{1/n + 1/m}} \]
+</p>
+ where <p class="formulaDsp">
+\[ s_p^2 = \frac{\sum_{i=1}^n (x_i - \bar x)^2 + \sum_{i=1}^m (y_i - \bar y)^2} {n + m - 2} \]
+</p>
+ is the <em>pooled variance</em>. The corresponding random variable is Student-t distributed with \( (n + m - 2) \) degrees of freedom.</li>
+<li><code>df FLOAT8</code> - Degrees of freedom \( (n + m - 2) \)</li>
+<li><code>p_value_one_sided FLOAT8</code> - Lower bound on one-sided p-value. In detail, the result is \( \Pr[\bar X - \bar Y \geq \bar x - \bar y \mid \mu_X = \mu_Y] \), which is a lower bound on \( \Pr[\bar X - \bar Y \geq \bar x - \bar y \mid \mu_X \leq \mu_Y] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">students_t_cdf</a>(statistic))</code>.</li>
+<li><code>p_value_two_sided FLOAT8</code> - Two-sided p-value, i.e., \( \Pr[ |\bar X - \bar Y| \geq |\bar x - \bar y| \mid \mu_X = \mu_Y] \). Computed as <code>(2 * <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">students_t_cdf</a>(-abs(statistic)))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Two-sample pooled t-test: Test null hypothesis that the mean of the first sample is at most (or equal to, respectively) the mean of the second sample: <pre>SELECT (t_test_pooled(<em>first</em>, <em>value</em>)).* FROM <em>source</em></pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a0a0a8a579bbf0f0d1efcbf62223e3431"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">t_test_result t_test_two_pooled_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a1a835d80d1846a10a2c25b91ce81c6d2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] t_test_two_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>first</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac50750a0e0797ce24af1dc479b9699e1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] t_test_two_unpooled </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>first</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_n \) and \( y_1, \dots, y_m \) of i.i.d. random variables \( X_1, \dots, X_n \sim N(\mu_X, \sigma_X^2) \) and \( Y_1, \dots, Y_m \sim N(\mu_Y, \sigma_Y^2) \) with unknown parameters \( \mu_X, \mu_Y, \sigma_X^2, \) and \( \sigma_Y^2 \), test the null hypotheses \( H_0 : \mu_X \leq \mu_Y \) and \( H_0 : \mu_X = \mu_Y \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">first</td><td>Indicator whether <code>value</code> is from first sample \( x_1, \dots, x_n \) (if <code>TRUE</code>) or from second sample \( y_1, \dots, y_m \) (if <code>FALSE</code>) </td></tr>
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \) or \( y_i \)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value as follows. We denote by \( \bar x, \bar y \) the sample means and by \( s_X^2, s_Y^2 \) the sample variances.<ul>
+<li><code>statistic FLOAT8</code> - Statistic <p class="formulaDsp">
+\[ t = \frac{\bar x - \bar y}{\sqrt{s_X^2/n + s_Y^2/m}} \]
+</p>
+ The corresponding random variable is approximately Student-t distributed with <p class="formulaDsp">
+\[ \frac{(s_X^2 / n + s_Y^2 / m)^2}{(s_X^2 / n)^2/(n-1) + (s_Y^2 / m)^2/(m-1)} \]
+</p>
+ degrees of freedom (Welch–Satterthwaite formula).</li>
+<li><code>df FLOAT8</code> - Degrees of freedom (as above)</li>
+<li><code>p_value_one_sided FLOAT8</code> - Lower bound on one-sided p-value. In detail, the result is \( \Pr[\bar X - \bar Y \geq \bar x - \bar y \mid \mu_X = \mu_Y] \), which is a lower bound on \( \Pr[\bar X - \bar Y \geq \bar x - \bar y \mid \mu_X \leq \mu_Y] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">students_t_cdf</a>(statistic))</code>.</li>
+<li><code>p_value_two_sided FLOAT8</code> - Two-sided p-value, i.e., \( \Pr[ |\bar X - \bar Y| \geq |\bar x - \bar y| \mid \mu_X = \mu_Y] \). Computed as <code>(2 * <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">students_t_cdf</a>(-abs(statistic)))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Two-sample unpooled t-test: Test null hypothesis that the mean of the first sample is at most (or equal to, respectively) the mean of the second sample: <pre>SELECT (t_test_unpooled(<em>first</em>, <em>value</em>)).* FROM <em>source</em></pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a8fe7f38d29bf835718adca811e36f15a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">t_test_result t_test_two_unpooled_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a8f0431ace27ac78e9e1df9906f1f1c33"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">wsr_test_result wsr_test_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0d85654832dfa961cd13526c052642f3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] wsr_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>precision</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>Given realizations \( x_1, \dots, x_m \) and \( y_1, \dots, y_m \) of i.i.d. random variables \( X_1, \dots, X_m \) and i.i.d. \( Y_1, \dots, Y_n \), respectively, test the null hypothesis that the underlying distributions are equal, i.e., \( H_0 : \forall i,j: \Pr[X_i &gt; Y_j] + \frac{\Pr[X_i = Y_j]}{2} = \frac 12 \).</p>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">first</td><td>Determines whether the value belongs to the first (if <code>TRUE</code>) or the second sample (if <code>FALSE</code>) </td></tr>
+    <tr><td class="paramname">value</td><td>Value of random variate \( x_i \) or \( y_i \)</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value.<ul>
+<li><code>statistic FLOAT8</code> - Statistic <p class="formulaDsp">
+\[ z = \frac{u - \bar x}{\sqrt{\frac{mn(m+n+1)}{12}}} \]
+</p>
+ where \( u \) is the u-statistic computed as follows. The z-statistic is approximately standard normally distributed.</li>
+<li><code>u_statistic FLOAT8</code> - Statistic \( u = \min \{ u_x, u_y \} \) where <p class="formulaDsp">
+\[ u_x = mn + \binom{m+1}{2} - \sum_{i=1}^m r_{x,i} \]
+</p>
+ where <p class="formulaDsp">
+\[ r_{x,i} = \{ j \mid x_j &lt; x_i \} + \{ j \mid y_j &lt; x_i \} + \frac{\{ j \mid x_j = x_i \} + \{ j \mid y_j = x_i \} + 1}{2} \]
+</p>
+ is defined as the rank of \( x_i \) in the combined list of all \( m+n \) observations. For ties, the average rank of all equal values is used.</li>
+<li><code>p_value_one_sided FLOAT8</code> - Approximate one-sided p-value, i.e., an approximate value for \( \Pr[Z \geq z \mid H_0] \). Computed as <code>(1.0 - <a class="el" href="prob_8sql__in.html#a6c0a499faa80db26c0178f1e69cf7a50">normal_cdf</a>(z_statistic))</code>.</li>
+<li><code>p_value_two_sided FLOAT8</code> - Approximate two-sided p-value, i.e., an approximate value for \( \Pr[|Z| \geq |z| \mid H_0] \). Computed as <code>(2 * <a class="el" href="prob_8sql__in.html#a6c0a499faa80db26c0178f1e69cf7a50">normal_cdf</a>(-abs(z_statistic)))</code>.</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Test null hypothesis that two samples stem from the same distribution: <pre>SELECT (mw_test(<em>first</em>, <em>value</em> ORDER BY <em>value</em>)).* FROM <em>source</em></pre></li>
+</ul>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This aggregate must be used as an ordered aggregate (<code>ORDER BY <em>value</code></em>) and will raise an exception if values are not ordered. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a4093de00ab033f4900ce186d481fa012"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] wsr_test_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_505cd743a8a717435eca324f49291a46.html">stats</a></li><li class="navelem"><a class="el" href="hypothesis__tests_8sql__in.html">hypothesis_tests.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/index.html
----------------------------------------------------------------------
diff --git a/docs/latest/index.html b/docs/latest/index.html
new file mode 100644
index 0000000..c2dff64
--- /dev/null
+++ b/docs/latest/index.html
@@ -0,0 +1,140 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: Main Page</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script src="../mathjax/MathJax.js"></script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="navtree_hack.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('index.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+<!-- iframe showing the search results (closed by default) -->
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">MADlib Documentation</div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="textblock"><p>MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data.</p>
+<p>The MADlib mission: to foster widespread development of scalable analytic skills, by harnessing efforts from commercial practice, academic research, and open-source development.</p>
+<p>Useful links: </p><ul>
+<li>
+MADlib project site <a href="http://madlib.incubator.apache.org/">http://madlib.incubator.apache.org/</a> </li>
+<li>
+MADlib JIRAs <a href="https://issues.apache.org/jira/browse/MADLIB/">https://issues.apache.org/jira/browse/MADLIB/</a> and wiki <a href="https://cwiki.apache.org/confluence/display/MADLIB">https://cwiki.apache.org/confluence/display/MADLIB</a> </li>
+<li>
+User documentation for earlier releases: <a href="../v1.7.1/index.html">v1.7.1</a>, <a href="../v1.7/index.html">v1.7</a>, <a href="../v1.6/index.html">v1.6</a>, <a href="../v1.5/index.html">v1.5</a>, <a href="../v1.4/index.html">v1.4</a>, <a href="../v1.3/index.html">v1.3</a>, <a href="../v1.2/index.html">v1.2</a>  </li>
+</ul>
+<p>Please refer to the <a href="https://github.com/madlib/madlib/blob/v1.8/ReadMe.txt">Read-Me</a> file for information about incorporated third-party material. License information regarding MADlib and included third-party libraries can be found inside the <a href="https://github.com/madlib/madlib/tree/v1.8/license">license</a> directory. </p>
+</div></div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:46 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[24/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x63.html
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diff --git a/docs/latest/globals_func_0x63.html b/docs/latest/globals_func_0x63.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="navtree.js"></script>
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+<script type="text/javascript" src="search/search.js"></script>
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+  $(document).ready(function() { searchBox.OnSelectItem(0); });
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+  MathJax.Hub.Config({
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+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
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+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_0x63.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
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+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
+<li>c45_classify()
+: <a class="el" href="c45_8sql__in.html#afe136e52f498f2ff9e2b91e38e29d670">c45.sql_in</a>
+</li>
+<li>c45_clean()
+: <a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793">c45.sql_in</a>
+</li>
+<li>c45_display()
+: <a class="el" href="c45_8sql__in.html#a32d2bcbc016c990991d77b6f6148306d">c45.sql_in</a>
+</li>
+<li>c45_genrule()
+: <a class="el" href="c45_8sql__in.html#ac71787c47795b3b0b133cdbd37438242">c45.sql_in</a>
+</li>
+<li>c45_score()
+: <a class="el" href="c45_8sql__in.html#a1b634db47e9006d114da0987e80b9601">c45.sql_in</a>
+</li>
+<li>c45_train()
+: <a class="el" href="c45_8sql__in.html#a4fbee855d22101d15d195d573189eb98">c45.sql_in</a>
+</li>
+<li>cauchy_cdf()
+: <a class="el" href="prob_8sql__in.html#a2d8874c2a5679403a473bfedb14467a4">prob.sql_in</a>
+</li>
+<li>cauchy_pdf()
+: <a class="el" href="prob_8sql__in.html#aebfad9365a7fc7a553c3b5c7931f2450">prob.sql_in</a>
+</li>
+<li>cauchy_quantile()
+: <a class="el" href="prob_8sql__in.html#ae8aa9b741e89c8d9236a682d218006e0">prob.sql_in</a>
+</li>
+<li>check_if_col_exists()
+: <a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470">utilities.sql_in</a>
+</li>
+<li>check_if_raises_error()
+: <a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0">utilities.sql_in</a>
+</li>
+<li>chi2_gof_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4c912b329fb103a44253932a653d4e40">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a61c31dfde998db18afd6552239b872c4">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a2b8265718a16ec65e89d2ab512f6a3e1">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a7c580537666776f1bd4b9d4a0a6b6438">hypothesis_tests.sql_in</a>
+</li>
+<li>chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#a230513b6b549d5b445cbacbdbab42c15">prob.sql_in</a>
+</li>
+<li>chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#a90bccc717d7052e83bafd7f160a783b1">prob.sql_in</a>
+</li>
+<li>chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad125307fe65a33b60f6dd524037d4548">prob.sql_in</a>
+</li>
+<li>closest_column()
+: <a class="el" href="linalg_8sql__in.html#aa2856d10fa6cb12a5b168bab217197ed">linalg.sql_in</a>
+</li>
+<li>closest_columns()
+: <a class="el" href="linalg_8sql__in.html#a23e86c595619142c790b31292ebdd810">linalg.sql_in</a>
+</li>
+<li>clustered_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a91f46572f245f34c9fe92d0ec806a998">clustered_variance_coxph.sql_in</a>
+</li>
+<li>clustered_variance_linregr()
+: <a class="el" href="clustered__variance_8sql__in.html#abaa6702a3482f65199ce64b8a14316d7">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_logregr()
+: <a class="el" href="clustered__variance_8sql__in.html#a0debc923b0494f8259938d9c6f5cdbcd">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_mlogregr()
+: <a class="el" href="clustered__variance_8sql__in.html#aa72cfa6f9259cceea3e36b6db45a7624">clustered_variance.sql_in</a>
+</li>
+<li>cmsketch()
+: <a class="el" href="sketch_8sql__in.html#ae6671bfb7474db7d8b5c9dada29f36ab">sketch.sql_in</a>
+</li>
+<li>cmsketch_centile()
+: <a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">sketch.sql_in</a>
+</li>
+<li>cmsketch_count()
+: <a class="el" href="sketch_8sql__in.html#a3498d2c778d1289154f61d34e84c609e">sketch.sql_in</a>
+</li>
+<li>cmsketch_depth_histogram()
+: <a class="el" href="sketch_8sql__in.html#a9e6d30f20b724b96249cc4a0f67a279e">sketch.sql_in</a>
+</li>
+<li>cmsketch_median()
+: <a class="el" href="sketch_8sql__in.html#a0ef6428fa7ba4b7f7b1f633e6f8003ea">sketch.sql_in</a>
+</li>
+<li>cmsketch_rangecount()
+: <a class="el" href="sketch_8sql__in.html#aeff9e36cfb3338c4e405d4ac77d3968c">sketch.sql_in</a>
+</li>
+<li>cmsketch_width_histogram()
+: <a class="el" href="sketch_8sql__in.html#a8482f62849adf40a2c7df78c23ea33a4">sketch.sql_in</a>
+</li>
+<li>compute_coxph_result()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a74fe60f99c3808e38421c71286850ea8">cox_prop_hazards.sql_in</a>
+</li>
+<li>compute_lincrf()
+: <a class="el" href="crf_8sql__in.html#a20c096420d878cdd66dac6a232483737">crf.sql_in</a>
+</li>
+<li>conjugate_gradient()
+: <a class="el" href="conjugate__gradient_8sql__in.html#ab94e8a12cc9c2d0dac35168721ec8585">conjugate_gradient.sql_in</a>
+</li>
+<li>correlation()
+: <a class="el" href="correlation_8sql__in.html#a17cb16646fa7ebdf7ce7dd8c58349ce5">correlation.sql_in</a>
+</li>
+<li>correlation_agg()
+: <a class="el" href="correlation_8sql__in.html#a1c3081444f6bafc88a9b377f2af32968">correlation.sql_in</a>
+</li>
+<li>correlation_final()
+: <a class="el" href="correlation_8sql__in.html#a85a1a9b0ae5e608fd235f08ae01ff966">correlation.sql_in</a>
+</li>
+<li>correlation_merge()
+: <a class="el" href="correlation_8sql__in.html#ac53cf4dd28185715a1798176bce44b1b">correlation.sql_in</a>
+</li>
+<li>correlation_transition()
+: <a class="el" href="correlation_8sql__in.html#ae6223d0ba3e2b8a2d298ee78a293be6a">correlation.sql_in</a>
+</li>
+<li>cox_prop_hazards()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ad778b289eb19ae0bb2b7e02a89bab3bc">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_prop_hazards_regr()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3310cf98478b7c1e400e8fb1b3965d30">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_zph()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#aad77f665f0030202444e99f5a0b895d3">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_a_b()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a5f5ca89e3265e170c325125e61ef191d">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_final()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a2373d116ad1e2b2090f8c23a0c15edb5">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_merge()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a62bff141ced5972df28ceecdfe522888">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_transition()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a7081022015c00a14ecca27e017a4458b">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_clustered_stats()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a713f413fc571244b7cec6c0084a61eb9">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_w()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a523e61b6d56c8ff59fa8ae3121073a49">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a2743e0cecadd7f3fa2a16f9d7d40564b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#aeb95cacc36c5d6c8cc25c8d37418c71b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_merge()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ab122003102dd22274a76aa1a830c69dd">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a03a39bc1fee64cd9b8b6a373758819da">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_improved_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ac5ae827993064fd6e0ccf13a96262cdc">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_step_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a6a9ae1ea761b31e1522c47a9794cc233">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a41a415076894d3844475208b2163a267">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_outer()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abb21bacbff7362724d2698568d857555">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_predict()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a815961d52fb8ce998071eb92056ee1dd">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_inner_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3c96655e41fd17c86dfc9ad37b0959d0">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_outer_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abaf93fc77a284f0b765a64ef827c1bed">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_train()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#afd5fb08d419aab4a8b8eb103afa30f29">cox_prop_hazards.sql_in</a>
+</li>
+<li>create_indicator_variables()
+: <a class="el" href="data__preparation_8sql__in.html#a5d0ef27bc38be2b4b5f063cf7b560d1f">data_preparation.sql_in</a>
+</li>
+<li>create_nb_classify_view()
+: <a class="el" href="bayes_8sql__in.html#a6472f620099368cb8c334e0badb12133">bayes.sql_in</a>
+</li>
+<li>create_nb_prepared_data_tables()
+: <a class="el" href="bayes_8sql__in.html#a3ed5ddbc4cab1013db8604be58a6da3d">bayes.sql_in</a>
+</li>
+<li>create_nb_probs_view()
+: <a class="el" href="bayes_8sql__in.html#a163afffd0c845d325f060f74bcf02243">bayes.sql_in</a>
+</li>
+<li>create_schema_pg_temp()
+: <a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000">utilities.sql_in</a>
+</li>
+<li>crf_test_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#abf2a39a0b84a5e7ae215db28b3a87f81">crf_data_loader.sql_in</a>
+</li>
+<li>crf_test_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_feature_gen.sql_in</a>
+</li>
+<li>crf_train_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#aaa609ec1d1d4cff0f335fa358091d5c3">crf_data_loader.sql_in</a>
+</li>
+<li>crf_train_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_feature_gen.sql_in</a>
+</li>
+<li>cross_validation_general()
+: <a class="el" href="cross__validation_8sql__in.html#a5efbb27898e99f7685816d9d5dfd082c">cross_validation.sql_in</a>
+</li>
+<li>crossprod()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#a588d5855d1673f7bcbc2a1c983081f76">pivotalr_arrayops.sql_in</a>
+</li>
+<li>crossprod_sym()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#aa1e36bf44b3206314c186bba80179c9c">pivotalr_arrayops.sql_in</a>
+</li>
+<li>cv_linregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#aa572f1f57c0dd106b30948928161d8cc">cross_validation.sql_in</a>
+</li>
+<li>cv_linregr_train()
+: <a class="el" href="cross__validation_8sql__in.html#aa151eb3fa9acc7f4cc33236e22ad4362">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#ac1b5c57473ff672af45191c8d53f46ed">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#ac7dbc115f0c4624ccbf62b2d5494388d">cross_validation.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x64.html
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
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+<!-- iframe showing the search results (closed by default) -->
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+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
+<li>deconstruct_2d_array()
+: <a class="el" href="linalg_8sql__in.html#a61003b10cda05541f0d1d32a47ba23fe">linalg.sql_in</a>
+</li>
+<li>dense_direct_linear_system()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a379f260d8dfbf5c2be05c60776974188">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a63a3ed2c811216d3d88c120005a13875">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a607403f90e1921435183d031825e5e54">dense_linear_systems.sql_in</a>
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+<li>dense_direct_linear_system_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#acbfc47693d0b50ccfc6b7f7c38209b18">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a52f2bcdd0d038f19e64a44d4bb07fefb">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a01935dd8c4aef8c92e77b7f0f905fa4b">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a73ebde08b8395fd7bddbe1dc37b9539f">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#ad34728809ab3121705342cf91b34c6dd">dense_linear_systems.sql_in</a>
+</li>
+<li>dist_angle()
+: <a class="el" href="linalg_8sql__in.html#a8c7b9281a72ff22caf06161701b27e84">linalg.sql_in</a>
+</li>
+<li>dist_norm1()
+: <a class="el" href="linalg_8sql__in.html#aad193850e79c4b9d811ca9bc53e13476">linalg.sql_in</a>
+</li>
+<li>dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#aa58e51526edea6ea98db30b6f250adb4">linalg.sql_in</a>
+</li>
+<li>dist_tanimoto()
+: <a class="el" href="linalg_8sql__in.html#afa13b4c6122b99422d666dedea136c18">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x65.html
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diff --git a/docs/latest/globals_func_0x65.html b/docs/latest/globals_func_0x65.html
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_e"></a>- e -</h3><ul>
+<li>elastic_net_binomial_predict()
+: <a class="el" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_binomial_prob()
+: <a class="el" href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_gaussian_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_predict()
+: <a class="el" href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b">elastic_net.sql_in</a>
+</li>
+<li>elastic_net_train()
+: <a class="el" href="elastic__net_8sql__in.html#a63c8ed415005cc446a59bdc47a09791d">elastic_net.sql_in</a>
+</li>
+<li>exponential_cdf()
+: <a class="el" href="prob_8sql__in.html#a6d1bf6816f56b8e5ba6bf6ca94752f46">prob.sql_in</a>
+</li>
+<li>exponential_pdf()
+: <a class="el" href="prob_8sql__in.html#a18a5458c4bc85f0c4ea321317f90bdbb">prob.sql_in</a>
+</li>
+<li>exponential_quantile()
+: <a class="el" href="prob_8sql__in.html#ae3687b8e69a402154b829a6531b1b279">prob.sql_in</a>
+</li>
+<li>extreme_value_cdf()
+: <a class="el" href="prob_8sql__in.html#acffffe04c15eccd2e88cdac250bccc68">prob.sql_in</a>
+</li>
+<li>extreme_value_pdf()
+: <a class="el" href="prob_8sql__in.html#a03a3494462f4cb8c9fb6212e72b0b2e9">prob.sql_in</a>
+</li>
+<li>extreme_value_quantile()
+: <a class="el" href="prob_8sql__in.html#aeb5a7d295b83a891774a4fb0ef27c458">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x66.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_0x66.html b/docs/latest/globals_func_0x66.html
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
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+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+<!-- iframe showing the search results (closed by default) -->
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+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_f"></a>- f -</h3><ul>
+<li>f_test()
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+</li>
+<li>f_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abc6006e8da028dd93ac48b8fd9ae8786">hypothesis_tests.sql_in</a>
+</li>
+<li>fisher_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a6c5b3e35531e44098f9d0cbef14cb8a6">prob.sql_in</a>
+</li>
+<li>fisher_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a1c7937426379a8913519a6abc5a38ac2">prob.sql_in</a>
+</li>
+<li>fisher_f_quantile()
+: <a class="el" href="prob_8sql__in.html#ab6ed888a5338a0bee9c55edf4d33847f">prob.sql_in</a>
+</li>
+<li>float8arr_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a2a6e17fd9dafc349c7cfa070486e6ad1">svec.sql_in</a>
+</li>
+<li>float8arr_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af8db439cf1a73749052f06fc30c7d282">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int2()
+: <a class="el" href="svec_8sql__in.html#a5e38a27f556a6ac88549efec37da786f">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int4()
+: <a class="el" href="svec_8sql__in.html#a9c05d02233680a2e96dc6058155e4483">svec.sql_in</a>
+</li>
+<li>float8arr_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a4862b4e281323db16e9be4dfd681db2e">svec.sql_in</a>
+</li>
+<li>float8arr_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a0bc8ef7e7b8e37b784c81a940bd817f0">svec.sql_in</a>
+</li>
+<li>float8arr_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a20d4a7b907e967351f6439640482e0f9">svec.sql_in</a>
+</li>
+<li>float8arr_div_svec()
+: <a class="el" href="svec_8sql__in.html#a5c4ac1cc9a2ee3057958070b8e7bffcc">svec.sql_in</a>
+</li>
+<li>float8arr_eq()
+: <a class="el" href="svec_8sql__in.html#a0684988340d533c00523951f9a95107e">svec.sql_in</a>
+</li>
+<li>float8arr_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a4c82ac5de8e026c85e83aae4e0f06220">svec.sql_in</a>
+</li>
+<li>float8arr_minus_svec()
+: <a class="el" href="svec_8sql__in.html#a457f2f9210baba1c13820801c42cd06a">svec.sql_in</a>
+</li>
+<li>float8arr_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#abb0b3f155c45bb370ef7adac49096835">svec.sql_in</a>
+</li>
+<li>float8arr_mult_svec()
+: <a class="el" href="svec_8sql__in.html#a0e7dd7a82b41f4b8344efa21208e1637">svec.sql_in</a>
+</li>
+<li>float8arr_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6bb068ab6ba775619fe787ed845f5a0d">svec.sql_in</a>
+</li>
+<li>float8arr_plus_svec()
+: <a class="el" href="svec_8sql__in.html#a98aa60ec0c58c828b0a72c75d604322e">svec.sql_in</a>
+</li>
+<li>fmsketch_dcount()
+: <a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b">sketch.sql_in</a>
+</li>
+<li>forest_predict()
+: <a class="el" href="random__forest_8sql__in.html#aef268338ffa67f54ca1371326bb25fa0">random_forest.sql_in</a>
+</li>
+<li>forest_train()
+: <a class="el" href="random__forest_8sql__in.html#a04cbc760f3790bcf69fe48e008cfba3d">random_forest.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x67.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_0x67.html b/docs/latest/globals_func_0x67.html
new file mode 100644
index 0000000..7ff2566
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
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+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
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+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_0x67.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_g"></a>- g -</h3><ul>
+<li>gamma_cdf()
+: <a class="el" href="prob_8sql__in.html#ab6760b0486bad2f1ab0635eb59404e7c">prob.sql_in</a>
+</li>
+<li>gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a6c37e3bda2596accbb46525321a328c4">prob.sql_in</a>
+</li>
+<li>gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#ac48bbd491bd34831415705c3a0b7bf29">prob.sql_in</a>
+</li>
+<li>gamma_random()
+: <a class="el" href="sample_8sql__in.html#afc523f72e64820329acb8874c2a759d3">sample.sql_in</a>
+</li>
+<li>gen_doc_svecs()
+: <a class="el" href="svec__util_8sql__in.html#a6136c83e9c419d783d1ec6db061f8d6e">svec_util.sql_in</a>
+</li>
+<li>gen_rules_from_cfp()
+: <a class="el" href="assoc__rules_8sql__in.html#a9a872579615586ce478d32ba145cc279">assoc_rules.sql_in</a>
+</li>
+<li>geometric_cdf()
+: <a class="el" href="prob_8sql__in.html#a00879bdf7d48ceddedb3b4cc33511497">prob.sql_in</a>
+</li>
+<li>geometric_pmf()
+: <a class="el" href="prob_8sql__in.html#a5e08db93bd448a1e2164e106ce5781a4">prob.sql_in</a>
+</li>
+<li>geometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a62674ca958aec0533cdf0a74a1dadea9">prob.sql_in</a>
+</li>
+<li>get_tree()
+: <a class="el" href="random__forest_8sql__in.html#af22b14c3da4f64b544b6c333a8e97070">random_forest.sql_in</a>
+</li>
+<li>get_tree_surr()
+: <a class="el" href="random__forest_8sql__in.html#a3ba1911b340a431982189e3e1f8c0111">random_forest.sql_in</a>
+</li>
+<li>glm()
+: <a class="el" href="glm_8sql__in.html#a4938524efbef7684ce8a8e2646f126ef">glm.sql_in</a>
+</li>
+<li>glm_predict()
+: <a class="el" href="glm_8sql__in.html#aa42b3ee4e9c39132154dfa81f96ed4ff">glm.sql_in</a>
+</li>
+<li>glm_predict_binomial()
+: <a class="el" href="glm_8sql__in.html#a9a468186034a936c3997cad22b03cafe">glm.sql_in</a>
+</li>
+<li>glm_predict_poisson()
+: <a class="el" href="glm_8sql__in.html#a941d49196e573048852def804c5ce4c4">glm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x68.html
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diff --git a/docs/latest/globals_func_0x68.html b/docs/latest/globals_func_0x68.html
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+: <a class="el" href="linear_8sql__in.html#ad10649c8460c337a285cf20f2b87d0c5">linear.sql_in</a>
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+: <a class="el" href="prob_8sql__in.html#a5c48e7fa2fc7bcbc69c7f4da663d457f">prob.sql_in</a>
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+<li>hypergeometric_pmf()
+: <a class="el" href="prob_8sql__in.html#afbd2f8d9fb30fb179f59cc14f1fd8d6d">prob.sql_in</a>
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+<li>hypergeometric_quantile()
+: <a class="el" href="prob_8sql__in.html#a813cc27fe097e797ed0fb6022c7bb79a">prob.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x69.html
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+<title>MADlib: File Members</title>
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+<li>internal_compute_lmf_igd()
+: <a class="el" href="lmf_8sql__in.html#aae221f5f5b2f670a93c00202b251857d">lmf.sql_in</a>
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+<li>internal_execute_using_kmeans_args()
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+<li>internal_lincrf_lbfgs_converge()
+: <a class="el" href="crf_8sql__in.html#a47ef6b30d54ac4ef89cb664a681d1067">crf.sql_in</a>
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+<li>internal_lincrf_lbfgs_result()
+: <a class="el" href="crf_8sql__in.html#aff63c9a56bebf501a2f40971271605c2">crf.sql_in</a>
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+<li>internal_linear_svm_igd_distance()
+: <a class="el" href="lsvm_8sql__in.html#a81b1bdf8278bf77439ac9e101ae2ae7a">lsvm.sql_in</a>
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+<li>internal_linear_svm_igd_result()
+: <a class="el" href="lsvm_8sql__in.html#a21dc69a5bcf6e08f6c0d6cb9c4461091">lsvm.sql_in</a>
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+</li>
+<li>inverse_gamma_pdf()
+: <a class="el" href="prob_8sql__in.html#a126211c2172a43a654288fa72a2349f9">prob.sql_in</a>
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+<li>inverse_gamma_quantile()
+: <a class="el" href="prob_8sql__in.html#a5876aae01f14729866d4fd52918a65ba">prob.sql_in</a>
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+<li>isnan()
+: <a class="el" href="utilities_8sql__in.html#a48bed63cab78a5446fa4c122eaae3a40">utilities.sql_in</a>
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+</ul>
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
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+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_k"></a>- k -</h3><ul>
+<li>kolmogorov_cdf()
+: <a class="el" href="prob_8sql__in.html#aeef43f74f583bdff17bd074d9c0d9607">prob.sql_in</a>
+</li>
+<li>ks_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#abd3f6d1d6dc4203cab3bcc980ec8ed8d">hypothesis_tests.sql_in</a>
+</li>
+<li>ks_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a58ffb5b2b8392e005f4f3e21560df93f">hypothesis_tests.sql_in</a>
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x6c.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_func_0x6c.html b/docs/latest/globals_func_0x6c.html
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<!-- google analytics -->
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+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
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+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_l"></a>- l -</h3><ul>
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+</li>
+<li>l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a1b6bb42a5176b020aed12734df23aca9">svec_util.sql_in</a>
+</li>
+<li>laplace_cdf()
+: <a class="el" href="prob_8sql__in.html#a64e197de8da3761acdeec9db7e211703">prob.sql_in</a>
+</li>
+<li>laplace_pdf()
+: <a class="el" href="prob_8sql__in.html#a750278ad29d514793f76e159b773f410">prob.sql_in</a>
+</li>
+<li>laplace_quantile()
+: <a class="el" href="prob_8sql__in.html#a77f94fc43d4777fc4f68d18e29454a81">prob.sql_in</a>
+</li>
+<li>lda_get_perplexity()
+: <a class="el" href="lda_8sql__in.html#a25c3ef12d9808d8a38c5fd2630f3b5a9">lda.sql_in</a>
+</li>
+<li>lda_get_topic_desc()
+: <a class="el" href="lda_8sql__in.html#a4ac16d87d50821aadcab0d75d65b3f1b">lda.sql_in</a>
+</li>
+<li>lda_get_topic_word_count()
+: <a class="el" href="lda_8sql__in.html#ace20b6314e5b4e98929a98a6f05d925d">lda.sql_in</a>
+</li>
+<li>lda_get_word_topic_count()
+: <a class="el" href="lda_8sql__in.html#a455699e72328d6b105c61a3c9e7ae5dc">lda.sql_in</a>
+</li>
+<li>lda_predict()
+: <a class="el" href="lda_8sql__in.html#af1fde06c39dd12bb9e5544997f815323">lda.sql_in</a>
+</li>
+<li>lda_train()
+: <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80">lda.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step()
+: <a class="el" href="crf_8sql__in.html#a590cd748d8e18055d851e7d4f4a3ca00">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_final()
+: <a class="el" href="crf_8sql__in.html#a5bf4b2738003096f4eeeccd0eb5e98f0">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_merge_states()
+: <a class="el" href="crf_8sql__in.html#a45f3eaf0c80cdfa99872cd071020d5c7">crf.sql_in</a>
+</li>
+<li>lincrf_lbfgs_step_transition()
+: <a class="el" href="crf_8sql__in.html#a2e7104fea95ca76587e7fae9bcb0d7ed">crf.sql_in</a>
+</li>
+<li>lincrf_train()
+: <a class="el" href="crf_8sql__in.html#a336ac57ae3d07e331646d397c7651d3d">crf.sql_in</a>
+</li>
+<li>linear_solver_dense()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a885d6a36c07dc2c048948cb5287a3b92">dense_linear_systems.sql_in</a>
+</li>
+<li>linear_solver_sparse()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3b3b7eea8b91c0a16ede3e7a9c6b17ef">sparse_linear_systems.sql_in</a>
+</li>
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+: <a class="el" href="lsvm_8sql__in.html#a614936d27848149eead4581db57a9236">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_merge()
+: <a class="el" href="lsvm_8sql__in.html#a241eea31c78737ecbb5da6639debec3b">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step()
+: <a class="el" href="lsvm_8sql__in.html#a8cd061b3cd2b9ef62e018c33dde27a4d">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_step_serial()
+: <a class="el" href="lsvm_8sql__in.html#a027f40b148849a721001d20f098a4a0e">lsvm.sql_in</a>
+</li>
+<li>linear_svm_igd_transition()
+: <a class="el" href="lsvm_8sql__in.html#a6f26e0007d7d95b4bb4b72db4f702b25">lsvm.sql_in</a>
+</li>
+<li>linregr()
+: <a class="el" href="linear_8sql__in.html#a5fcd1daa9796dccf5430929c37ea0ee9">linear.sql_in</a>
+</li>
+<li>linregr_final()
+: <a class="el" href="linear_8sql__in.html#a28b69254ba5cd513b35ba6b17efd2c35">linear.sql_in</a>
+</li>
+<li>linregr_merge_states()
+: <a class="el" href="linear_8sql__in.html#a042536820ffbad498a4e1c410f949e35">linear.sql_in</a>
+</li>
+<li>linregr_predict()
+: <a class="el" href="linear_8sql__in.html#a8da0030683d5931b93e5f2bee3fd0544">linear.sql_in</a>
+</li>
+<li>linregr_train()
+: <a class="el" href="linear_8sql__in.html#ad0175a9ecc455d9679f7d251af264848">linear.sql_in</a>
+</li>
+<li>linregr_transition()
+: <a class="el" href="linear_8sql__in.html#aa2b87a3e60272176bfa2fb859f99461a">linear.sql_in</a>
+</li>
+<li>lmf_igd_final()
+: <a class="el" href="lmf_8sql__in.html#a298f46c944cd6813b0360514bf8c9ed1">lmf.sql_in</a>
+</li>
+<li>lmf_igd_merge()
+: <a class="el" href="lmf_8sql__in.html#ae3bb16a76a927e1e59420d4a8e13012f">lmf.sql_in</a>
+</li>
+<li>lmf_igd_run()
+: <a class="el" href="lmf_8sql__in.html#ac1acb1f0e1f7008118f21c83546a4602">lmf.sql_in</a>
+</li>
+<li>lmf_igd_step()
+: <a class="el" href="lmf_8sql__in.html#a54d2ca2048d9a0c29d5593530b932a23">lmf.sql_in</a>
+</li>
+<li>lmf_igd_transition()
+: <a class="el" href="lmf_8sql__in.html#a187574cfe85888b29d6b1658554e7507">lmf.sql_in</a>
+</li>
+<li>logistic()
+: <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e">logistic.sql_in</a>
+</li>
+<li>logistic_cdf()
+: <a class="el" href="prob_8sql__in.html#a140f674876813d5e786a4d8ba8d75c87">prob.sql_in</a>
+</li>
+<li>logistic_pdf()
+: <a class="el" href="prob_8sql__in.html#afa38eb6c61d3c9825d5c172e6c17dbf7">prob.sql_in</a>
+</li>
+<li>logistic_quantile()
+: <a class="el" href="prob_8sql__in.html#a5a77a0bc5884af2a914a955174892ae2">prob.sql_in</a>
+</li>
+<li>lognormal_cdf()
+: <a class="el" href="prob_8sql__in.html#a4c05b347f8feb64e1236d21b850af61e">prob.sql_in</a>
+</li>
+<li>lognormal_pdf()
+: <a class="el" href="prob_8sql__in.html#a7370b797bf450f9aa54d4fea4d64d611">prob.sql_in</a>
+</li>
+<li>lognormal_quantile()
+: <a class="el" href="prob_8sql__in.html#aab3a6de990ae5a81834274a1cf9cad8f">prob.sql_in</a>
+</li>
+<li>logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#a2d1571ffa794176a5dfed9d35e70fed7">cross_validation.sql_in</a>
+</li>
+<li>logregr_predict()
+: <a class="el" href="logistic_8sql__in.html#acc8af7a39e7b0d604c2760d9d6bd0b5e">logistic.sql_in</a>
+</li>
+<li>logregr_predict_prob()
+: <a class="el" href="logistic_8sql__in.html#a75dba5248d75cd6c5d2fb69f944ce479">logistic.sql_in</a>
+</li>
+<li>logregr_train()
+: <a class="el" href="logistic_8sql__in.html#a3c217840f2fbe1dcd53ba15c4111b86a">logistic.sql_in</a>
+</li>
+<li>lsvm_classification()
+: <a class="el" href="lsvm_8sql__in.html#a75d126981ae4bf2e6641627501f0a2a5">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict()
+: <a class="el" href="lsvm_8sql__in.html#a0dee8d0cd54582f06de2935b25b7ecb0">lsvm.sql_in</a>
+</li>
+<li>lsvm_predict_batch()
+: <a class="el" href="lsvm_8sql__in.html#a1c0a002f50250133c0ef1d3c43c6d338">lsvm.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

[06/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__rf.html
----------------------------------------------------------------------
diff --git a/docs/latest/group__grp__rf.html b/docs/latest/group__grp__rf.html
new file mode 100644
index 0000000..12dba0e
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@@ -0,0 +1,544 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: Random Forest (old implementation)</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
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+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
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+  $(document).ready(function() { init_search(); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="navtree_hack.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Random Forest (old implementation)<div class="ingroups"><a class="el" href="group__grp__deprecated.html">Deprecated Modules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#train">Training Function</a> </li>
+<li>
+<a href="#classify">Classification Function</a> </li>
+<li>
+<a href="#score">Scoring Function</a> </li>
+<li>
+<a href="#display">Display Function</a> </li>
+<li>
+<a href="#clean">Cleaning Function</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This is an old implementation of random forests. For a newer implementation, please see <a class="el" href="group__grp__random__forest.html">Random Forest</a></em></dd></dl>
+<p>A random forest (RF) is an ensemble classifier that consists of many decision trees and outputs the class that is voted by the majority of the individual trees.</p>
+<p>It has the following well-known advantages:</p><ul>
+<li>Overall, RF produces better accuracy.</li>
+<li>It can be very efficient for large data sets. Trees of an RF can be trained in parallel.</li>
+<li>It can handle thousands of input attributes without attribute deletion.</li>
+</ul>
+<p>This module provides an implementation of the random forest algorithm described in [1].</p>
+<p>The implementation supports:</p><ul>
+<li>Building random forests</li>
+<li>Multiple split critera, including: . Information Gain . Gini Coefficient . Gain Ratio</li>
+<li>Random forest Classification/Scoring</li>
+<li>Random forest Display</li>
+<li>Continuous and Discrete features</li>
+<li>Equal frequency discretization for continuous features</li>
+<li>Missing value handling</li>
+<li>Sampling with replacement</li>
+</ul>
+<dl class="section user"><dt>Input</dt><dd></dd></dl>
+<p>The <b>data to classify</b> is expected to be of the same form as <b>training data</b>, except that it does not need a class column.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd></dd></dl>
+<p>Run the training algorithm on the source data. </p><pre class="syntax">
+rf_train( split_criterion,
+          training_table_name,
+          result_rf_table_name,
+          num_trees,
+          features_per_node,
+          sampling_percentage,
+          continuous_feature_names,
+          feature_col_names,
+          id_col_name,
+          class_col_name,
+          how2handle_missing_value,
+          max_tree_depth,
+          node_prune_threshold,
+          node_split_threshold,
+          verbosity
+        )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>split_criterion </dt>
+<dd><p class="startdd">The name of the split criterion that should be used for tree construction. The valid values are ‘infogain’, ‘gainratio’, and ‘gini’. It can't be NULL. Information gain(infogain) and gini index(gini) are biased toward multivalued attributes. Gain ratio(gainratio) adjusts for this bias. However, it tends to prefer unbalanced splits in which one partition is much smaller than the others.</p>
+<p class="enddd"></p>
+</dd>
+<dt>training_table_name </dt>
+<dd><p class="startdd">The name of the table/view with the training data. It can't be NULL and must exist.</p>
+<p>The <b>training data</b> is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>trainingSource</em> (
+    ...
+    <em>id</em> INT|BIGINT,
+    <em>feature1</em> SUPPORTED_DATA_TYPE,
+    <em>feature2</em> SUPPORTED_DATA_TYPE,
+    <em>feature3</em> SUPPORTED_DATA_TYPE,
+    ....................
+    <em>featureN</em> SUPPORTED_DATA_TYPE,
+    <em>class</em>    SUPPORTED_DATA_TYPE,
+    ...
+)</pre><p>SUPPORTED_DATA_TYPE can be any of the following: SMALLINT, INT, BIGINT, FLOAT8, REAL, DECIMAL, INET, CIDR, MACADDR, BOOLEAN, CHAR, VARCHAR, TEXT, "char", DATE, TIME, TIMETZ, TIMESTAMP, TIMESTAMPTZ, and INTERVAL. </p>
+<p class="enddd"></p>
+</dd>
+<dt>result_rf_table_name </dt>
+<dd><p class="startdd">The name of the table where the resulting trees are stored. It can not be NULL and must not exist.</p>
+<p class="enddd">The output table stores an abstract object (representing the model) used for further classification. The table has the following columns: </p><table  class="output">
+<tr>
+<th>id</th><td></td></tr>
+<tr>
+<th>tree_location</th><td></td></tr>
+<tr>
+<th>feature</th><td></td></tr>
+<tr>
+<th>probability</th><td></td></tr>
+<tr>
+<th>ebp_coeff</th><td></td></tr>
+<tr>
+<th>maxclass</th><td></td></tr>
+<tr>
+<th>split_gain</th><td></td></tr>
+<tr>
+<th>live</th><td></td></tr>
+<tr>
+<th>cat_size</th><td></td></tr>
+<tr>
+<th>parent_id</th><td></td></tr>
+<tr>
+<th>lmc_nid</th><td></td></tr>
+<tr>
+<th>lmc_fval</th><td></td></tr>
+<tr>
+<th>is_feature_cont</th><td></td></tr>
+<tr>
+<th>split_value</th><td></td></tr>
+<tr>
+<th>tid</th><td></td></tr>
+<tr>
+<th>dp_ids </th><td></td></tr>
+</table>
+</dd>
+<dt>num_trees </dt>
+<dd>The number of trees to be trained. If it's NULL, 10 will be used.  </dd>
+<dt>features_per_node </dt>
+<dd>The number of features to be considered when finding a best split. If it's NULL, sqrt(p), where p is the number of features, will be used. </dd>
+<dt>sampling_percentage </dt>
+<dd>The percentage of records sampled to train a tree. If it's NULL, 0.632 bootstrap will be used continuous_feature_names A comma-separated list of the names of the features whose values are continuous. NULL means there are no continuous features. </dd>
+<dt>feature_col_names </dt>
+<dd>A comma-separated list of names of the table columns, each of which defines a feature. NULL means all the columns except the ID and Class columns will be treated as features.  </dd>
+<dt>id_col_name </dt>
+<dd>The name of the column containing id of each record. It can't be NULL. </dd>
+<dt>class_col_name </dt>
+<dd>The name of the column containing correct class of each record. It can't be NULL. </dd>
+<dt>how2handle_missing_value </dt>
+<dd>The way to handle missing value. The valid values are 'explicit' and 'ignore'. It can't be NULL. </dd>
+<dt>max_tree_depth</dt>
+<dd>The maximum tree depth. It can't be NULL. </dd>
+<dt>node_prune_threshold </dt>
+<dd>The minimum percentage of the number of records required in a child node. It can't be NULL. The range of it is in [0.0, 1.0]. This threshold only applies to the non-root nodes. Therefore, if the percentage(p) between the sampled training set size of a tree (the number of rows) and the total training set size is less than or equal to the value of this parameter, then the tree only has one node (the root node); if its value is 1, then the percentage p is less than or equal to 1 definitely. Therefore, the tree only has one node (the root node). if its value is 0, then no nodes will be pruned by this parameter. </dd>
+<dt>node_split_threshold </dt>
+<dd>The minimum percentage of the number of records required in a node in order for a further split to be possible. It can't be NULL. The range of it is in [0.0, 1.0]. If the percentage(p) between the sampled training set size of a tree (the number of rows) and the total training set size is less than the value of this parameter, then the root node will be a leaf one. Therefore, the trained tree only has one node. If the percentage p is equal to the value of this parameter, then the trained tree only has two levels, since only the root node will grow. (the root node); if its value is 0, then trees can grow extensively. </dd>
+<dt>verbosity </dt>
+<dd>Greater than 0 means this function runs in verbose mode. It can't be NULL. </dd>
+</dl>
+<p><a class="anchor" id="classify"></a></p><dl class="section user"><dt>Classification Function</dt><dd></dd></dl>
+<p>The classification function creates the result_table with the classification results. </p><pre class="syntax">
+rf_classify( rf_table_name,
+             classification_table_name,
+             result_table_name)
+</pre><p><a class="anchor" id="score"></a></p><dl class="section user"><dt>Scoring Function</dt><dd></dd></dl>
+<p>The scoring function gives a ratio of correctly classified items in the validation data set. </p><pre class="syntax">
+rf_score( rf_table_name,
+          validation_table_name,
+          verbosity)
+</pre><p><a class="anchor" id="display"></a></p><dl class="section user"><dt>Display Function</dt><dd></dd></dl>
+<p>The display tree function displays the trained trees in a human-readable format. </p><pre class="syntax">
+rf_display( rf_table_name
+          )
+</pre><p><a class="anchor" id="clean"></a></p><dl class="section user"><dt>Cleaning Function</dt><dd></dd></dl>
+<p>The clean tree function cleans up the learned model and metadata. </p><pre class="syntax">
+rf_clean( rf_table_name
+        )
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare an input table. <pre class="example">
+SELECT * FROM golf_data ORDER BY id;
+</pre> Result: <pre class="result">
+ id | outlook  | temperature | humidity | windy  |    class
+&#160;---+----------+-------------+----------+--------+--------------
+  1 | sunny    |          85 |       85 |  false |  Do not Play
+  2 | sunny    |          80 |       90 |  true  |  Do not Play
+  3 | overcast |          83 |       78 |  false |  Play
+  4 | rain     |          70 |       96 |  false |  Play
+  5 | rain     |          68 |       80 |  false |  Play
+  6 | rain     |          65 |       70 |  true  |  Do not Play
+  7 | overcast |          64 |       65 |  true  |  Play
+  8 | sunny    |          72 |       95 |  false |  Do not Play
+  9 | sunny    |          69 |       70 |  false |  Play
+ 10 | rain     |          75 |       80 |  false |  Play
+ 11 | sunny    |          75 |       70 |  true  |  Play
+ 12 | overcast |          72 |       90 |  true  |  Play
+ 13 | overcast |          81 |       75 |  false |  Play
+ 14 | rain     |          71 |       80 |  true  |  Do not Play
+(14 rows)
+</pre></li>
+<li>Train the random forest. <pre class="example">
+SELECT * FROM madlib.rf_clean('trained_tree_infogain');
+SELECT * FROM madlib.rf_train(
+    'infogain',
+    'golf_data',
+    'trained_tree_infogain',
+    10,
+    NULL,
+    0.632,
+    'temperature,humidity',
+    'outlook,temperature,humidity,windy',
+    'id',
+    'class',
+    'explicit',
+    10,
+    0.0,
+    0.0,
+    0);
+</pre> Result: <pre class="result">
+ training_time  | num_of_samples | num_trees | features_per_node | num_tree_nodes | max_tree_depth | split_criterion |    acs_time     |    acc_time     |    olap_time    |   update_time   |    best_time
+&#160;---------------+--------------+-----------+-------------------+----------------+----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------
+ 00:00:03.60498 |           14 |        10 |                 3 |             71 |              6 | infogain        | 00:00:00.154991 | 00:00:00.404411 | 00:00:00.736876 | 00:00:00.374084 | 00:00:01.722658
+(1 row)
+</pre></li>
+<li>Check the table records that hold the random forest. <pre class="example">
+SELECT * FROM trained_tree_infogain ORDER BY tid, id;
+</pre> <pre class="result">
+ id | tree_location | feature |    probability    | ebp_coeff | maxclass |     split_gain     | live | cat_size | parent_id | lmc_nid | lmc_fval | is_feature_cont | split_value | tid | dp_ids
+&#160;---+---------------+---------+-------------------+-----------+----------+--------------------+------+----------+-----------+---------+----------+-----------------+-------------+-----+--------
+  1 | {0}           |       3 | 0.777777777777778 |         1 |        2 |  0.197530864197531 |    0 |        9 |         0 |      24 |        1 | f               |             |   1 |
+ 24 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        4 |         1 |         |          | f               |             |   1 | {3}
+ 25 | {0,2}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        2 |         1 |         |          | f               |             |   1 | {3}
+ 26 | {0,3}         |       2 | 0.666666666666667 |         1 |        1 |  0.444444444444444 |    0 |        3 |         1 |      42 |        1 | t               |          70 |   1 | {3}
+ 42 | {0,3,1}       |       4 |                 1 |         1 |        2 |                  0 |    0 |        1 |        26 |         |          | f               |             |   1 |
+ 43 | {0,3,2}       |       4 |                 1 |         1 |        1 |                  0 |    0 |        2 |        26 |         |          | f               |             |   1 |
+  2 | {0}           |       2 | 0.555555555555556 |         1 |        1 |   0.17636684303351 |    0 |        9 |         0 |      11 |        1 | t               |          65 |   2 |
+ 11 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        2 |         2 |         |          | f               |             |   2 |
+ 12 | {0,2}         |       4 | 0.714285714285714 |         1 |        1 |  0.217687074829932 |    0 |        7 |         2 |      44 |        1 | f               |             |   2 |
+ 44 | {0,2,1}       |       3 | 0.666666666666667 |         1 |        2 |  0.444444444444444 |    0 |        3 |        12 |      57 |        1 | f               |             |   2 | {4}
+ 45 | {0,2,2}       |       3 |                 1 |         1 |        1 |                  0 |    0 |        4 |        12 |         |          | f               |             |   2 | {4}
+ 57 | {0,2,1,1}     |       2 |                 1 |         1 |        2 |                  0 |    0 |        1 |        44 |         |          | t               |          78 |   2 | {4,3}
+ 58 | {0,2,1,2}     |       2 |                 1 |         1 |        2 |                  0 |    0 |        1 |        44 |         |          | t               |          96 |   2 | {4,3}
+ 59 | {0,2,1,3}     |       2 |                 1 |         1 |        1 |                  0 |    0 |        1 |        44 |         |          | t               |          85 |   2 | {4,3}
+  3 | {0}           |       2 | 0.777777777777778 |         1 |        2 |  0.197530864197531 |    0 |        9 |         0 |      27 |        1 | t               |          80 |   3 |
+ 27 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        6 |         3 |         |          | f               |             |   3 |
+ 28 | {0,2}         |       2 | 0.666666666666667 |         1 |        1 |  0.444444444444444 |    0 |        3 |         3 |      46 |        1 | t               |          90 |   3 |
+ 46 | {0,2,1}       |       4 |                 1 |         1 |        1 |                  0 |    0 |        2 |        28 |         |          | f               |             |   3 |
+ 47 | {0,2,2}       |       4 |                 1 |         1 |        2 |                  0 |    0 |        1 |        28 |         |          | f               |             |   3 |
+  4 | {0}           |       4 | 0.888888888888889 |         1 |        2 | 0.0493827160493827 |    0 |        9 |         0 |      13 |        1 | f               |             |   4 |
+ 13 | {0,1}         |       3 |                 1 |         1 |        2 |                  0 |    0 |        6 |         4 |         |          | f               |             |   4 | {4}
+ 14 | {0,2}         |       3 | 0.666666666666667 |         1 |        2 |  0.444444444444444 |    0 |        3 |         4 |      48 |        1 | f               |             |   4 | {4}
+ 48 | {0,2,1}       |       2 |                 1 |         1 |        2 |                  0 |    0 |        2 |        14 |         |          | t               |          90 |   4 | {4,3}
+ 49 | {0,2,2}       |       2 |                 1 |         1 |        1 |                  0 |    0 |        1 |        14 |         |          | t               |          80 |   4 | {4,3}
+  5 | {0}           |       2 | 0.888888888888889 |         1 |        2 |  0.197530864197531 |    0 |        9 |         0 |      29 |        1 | t               |          90 |   5 |
+ 29 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        8 |         5 |         |          | f               |             |   5 |
+ 30 | {0,2}         |       3 |                 1 |         1 |        1 |                  0 |    0 |        1 |         5 |         |          | f               |             |   5 |
+  6 | {0}           |       3 | 0.555555555555556 |         1 |        2 |  0.345679012345679 |    0 |        9 |         0 |      15 |        1 | f               |             |   6 |
+ 15 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        3 |         6 |         |          | f               |             |   6 | {3}
+ 16 | {0,2}         |       4 | 0.666666666666667 |         1 |        2 |  0.444444444444444 |    0 |        3 |         6 |      51 |        1 | f               |             |   6 | {3}
+ 17 | {0,3}         |       4 |                 1 |         1 |        1 |                  0 |    0 |        3 |         6 |         |          | f               |             |   6 | {3}
+ 51 | {0,2,1}       |       2 |                 1 |         1 |        2 |                  0 |    0 |        2 |        16 |         |          | t               |          96 |   6 | {3,4}
+ 52 | {0,2,2}       |       2 |                 1 |         1 |        1 |                  0 |    0 |        1 |        16 |         |          | t               |          70 |   6 | {3,4}
+  7 | {0}           |       4 | 0.666666666666667 |         1 |        2 |  0.253968253968254 |    0 |        9 |         0 |      31 |        1 | f               |             |   7 |
+ 31 | {0,1}         |       2 | 0.857142857142857 |         1 |        2 |  0.102040816326531 |    0 |        7 |         7 |      36 |        1 | t               |          80 |   7 | {4}
+ 32 | {0,2}         |       3 |                 1 |         1 |        1 |                  0 |    0 |        2 |         7 |         |          | f               |             |   7 | {4}
+ 36 | {0,1,1}       |       4 |                 1 |         1 |        2 |                  0 |    0 |        5 |        31 |         |          | f               |             |   7 |
+ 37 | {0,1,2}       |       2 |               0.5 |         1 |        2 |                0.5 |    0 |        2 |        31 |      60 |        1 | t               |          95 |   7 |
+ 60 | {0,1,2,1}     |       4 |                 1 |         1 |        1 |                  0 |    0 |        1 |        37 |         |          | f               |             |   7 |
+ 61 | {0,1,2,2}     |       4 |                 1 |         1 |        2 |                  0 |    0 |        1 |        37 |         |          | f               |             |   7 |
+  8 | {0}           |       3 | 0.777777777777778 |         1 |        2 | 0.0864197530864197 |    0 |        9 |         0 |      18 |        1 | f               |             |   8 |
+ 18 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        4 |         8 |         |          | f               |             |   8 | {3}
+ 19 | {0,2}         |       4 | 0.666666666666667 |         1 |        2 |  0.444444444444444 |    0 |        3 |         8 |      38 |        1 | f               |             |   8 | {3}
+ 20 | {0,3}         |       2 |               0.5 |         1 |        2 |                0.5 |    0 |        2 |         8 |      53 |        1 | t               |          70 |   8 | {3}
+ 38 | {0,2,1}       |       2 |                 1 |         1 |        2 |                  0 |    0 |        2 |        19 |         |          | t               |          80 |   8 | {3,4}
+ 39 | {0,2,2}       |       2 |                 1 |         1 |        1 |                  0 |    0 |        1 |        19 |         |          | t               |          80 |   8 | {3,4}
+ 53 | {0,3,1}       |       4 |                 1 |         1 |        2 |                  0 |    0 |        1 |        20 |         |          | f               |             |   8 |
+ 54 | {0,3,2}       |       4 |                 1 |         1 |        1 |                  0 |    0 |        1 |        20 |         |          | f               |             |   8 |
+  9 | {0}           |       3 | 0.555555555555556 |         1 |        2 |  0.327160493827161 |    0 |        9 |         0 |      33 |        1 | f               |             |   9 |
+ 33 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        2 |         9 |         |          | f               |             |   9 | {3}
+ 34 | {0,2}         |       4 |              0.75 |         1 |        2 |              0.375 |    0 |        4 |         9 |      55 |        1 | f               |             |   9 | {3}
+ 35 | {0,3}         |       4 |                 1 |         1 |        1 |                  0 |    0 |        3 |         9 |         |          | f               |             |   9 | {3}
+ 55 | {0,2,1}       |       2 |                 1 |         1 |        2 |                  0 |    0 |        3 |        34 |         |          | t               |          96 |   9 | {3,4}
+ 56 | {0,2,2}       |       2 |                 1 |         1 |        1 |                  0 |    0 |        1 |        34 |         |          | t               |          70 |   9 | {3,4}
+ 10 | {0}           |       3 | 0.666666666666667 |         1 |        2 |  0.277777777777778 |    0 |        9 |         0 |      21 |        1 | f               |             |  10 |
+ 21 | {0,1}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        1 |        10 |         |          | f               |             |  10 | {3}
+ 22 | {0,2}         |       4 |                 1 |         1 |        2 |                  0 |    0 |        4 |        10 |         |          | f               |             |  10 | {3}
+ 23 | {0,3}         |       2 |              0.75 |         1 |        1 |              0.375 |    0 |        4 |        10 |      40 |        1 | t               |          70 |  10 | {3}
+ 40 | {0,3,1}       |       4 |                 1 |         1 |        2 |                  0 |    0 |        1 |        23 |         |          | f               |             |  10 |
+ 41 | {0,3,2}       |       4 |                 1 |         1 |        1 |                  0 |    0 |        3 |        23 |         |          | f               |             |  10 |
+(60 rows)
+</pre></li>
+<li>Display the random forest in a human readable format. <pre class="example">
+SELECT * FROM madlib.rf_display( 'trained_tree_infogain'
+                               );
+</pre> Result: <pre class="result">
+                                             rf_display
+&#160;----------------------------------------------------------------------------------------------------
+&#160;
+ Tree 1
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.777777777777778)
+         outlook:  = overcast  : class( Play)   num_elements(4)  predict_prob(1)
+         outlook:  = rain  : class( Play)   num_elements(2)  predict_prob(1)
+         outlook:  = sunny  : class( Do not Play)   num_elements(3)  predict_prob(0.666666666666667)
+             humidity:  &lt;= 70  : class( Play)   num_elements(1)  predict_prob(1)
+             humidity:  &gt; 70  : class( Do not Play)   num_elements(2)  predict_prob(1)
+&#160;
+ Tree 2
+     Root Node  : class( Do not Play)   num_elements(9)  predict_prob(0.555555555555556)
+         humidity:  &lt;= 65  : class( Play)   num_elements(2)  predict_prob(1)
+         humidity:  &gt; 65  : class( Do not Play)   num_elements(7)  predict_prob(0.714285714285714)
+             windy:  =  false  : class( Play)   num_elements(3)  predict_prob(0.666666666666667)
+                 outlook:  = overcast  : class( Play)   num_elements(1)  predict_prob(1)
+                 outlook:  = rain  : class( Play)   num_elements(1)  predict_prob(1)
+                 outlook:  = sunny  : class( Do not Play)   num_elements(1)  predict_prob(1)
+             windy:  =  true  : class( Do not Play)   num_elements(4)  predict_prob(1)
+&#160;
+ Tree 3
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.777777777777778)
+         humidity:  &lt;= 80  : class( Play)   num_elements(6)  predict_prob(1)
+         humidity:  &gt; 80  : class( Do not Play)   num_elements(3)  predict_prob(0.666666666666667)
+             humidity:  &lt;= 90  : class( Do not Play)   num_elements(2)  predict_prob(1)
+             humidity:  &gt; 90  : class( Play)   num_elements(1)  predict_prob(1)
+&#160;
+ Tree 4
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.888888888888889)
+         windy:  =  false  : class( Play)   num_elements(6)  predict_prob(1)
+         windy:  =  true  : class( Play)   num_elements(3)  predict_prob(0.666666666666667)
+             outlook:  = overcast  : class( Play)   num_elements(2)  predict_prob(1)
+             outlook:  = rain  : class( Do not Play)   num_elements(1)  predict_prob(1)
+&#160;
+ Tree 5
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.888888888888889)
+         humidity:  &lt;= 90  : class( Play)   num_elements(8)  predict_prob(1)
+         humidity:  &gt; 90  : class( Do not Play)   num_elements(1)  predict_prob(1)
+&#160;
+ Tree 6
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.555555555555556)
+         outlook:  = overcast  : class( Play)   num_elements(3)  predict_prob(1)
+         outlook:  = rain  : class( Play)   num_elements(3)  predict_prob(0.666666666666667)
+             windy:  =  false  : class( Play)   num_elements(2)  predict_prob(1)
+             windy:  =  true  : class( Do not Play)   num_elements(1)  predict_prob(1)
+         outlook:  = sunny  : class( Do not Play)   num_elements(3)  predict_prob(1)
+&#160;
+ Tree 7
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.666666666666667)
+         windy:  =  false  : class( Play)   num_elements(7)  predict_prob(0.857142857142857)
+             humidity:  &lt;= 80  : class( Play)   num_elements(5)  predict_prob(1)
+             humidity:  &gt; 80  : class( Play)   num_elements(2)  predict_prob(0.5)
+                 humidity:  &lt;= 95  : class( Do not Play)   num_elements(1)  predict_prob(1)
+                 humidity:  &gt; 95  : class( Play)   num_elements(1)  predict_prob(1)
+         windy:  =  true  : class( Do not Play)   num_elements(2)  predict_prob(1)
+&#160;
+ Tree 8
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.777777777777778)
+         outlook:  = overcast  : class( Play)   num_elements(4)  predict_prob(1)
+         outlook:  = rain  : class( Play)   num_elements(3)  predict_prob(0.666666666666667)
+             windy:  =  false  : class( Play)   num_elements(2)  predict_prob(1)
+             windy:  =  true  : class( Do not Play)   num_elements(1)  predict_prob(1)
+         outlook:  = sunny  : class( Play)   num_elements(2)  predict_prob(0.5)
+             humidity:  &lt;= 70  : class( Play)   num_elements(1)  predict_prob(1)
+             humidity:  &gt; 70  : class( Do not Play)   num_elements(1)  predict_prob(1)
+&#160;
+ Tree 9
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.555555555555556)
+         outlook:  = overcast  : class( Play)   num_elements(2)  predict_prob(1)
+         outlook:  = rain  : class( Play)   num_elements(4)  predict_prob(0.75)
+             windy:  =  false  : class( Play)   num_elements(3)  predict_prob(1)
+             windy:  =  true  : class( Do not Play)   num_elements(1)  predict_prob(1)
+         outlook:  = sunny  : class( Do not Play)   num_elements(3)  predict_prob(1)
+&#160;
+ Tree 10
+     Root Node  : class( Play)   num_elements(9)  predict_prob(0.666666666666667)
+         outlook:  = overcast  : class( Play)   num_elements(1)  predict_prob(1)
+         outlook:  = rain  : class( Play)   num_elements(4)  predict_prob(1)
+         outlook:  = sunny  : class( Do not Play)   num_elements(4)  predict_prob(0.75)
+             humidity:  &lt;= 70  : class( Play)   num_elements(1)  predict_prob(1)
+             humidity:  &gt; 70  : class( Do not Play)   num_elements(3)  predict_prob(1)
+(10 rows)
+</pre></li>
+<li>Classify data with the learned model. <pre class="example">
+SELECT * FROM madlib.rf_classify( 'trained_tree_infogain',
+                                  'golf_data',
+                                  'classification_result'
+                                );
+</pre> Result: <pre class="result">
+ input_set_size | classification_time
+&#160;---------------+---------------------
+             14 | 00:00:02.215017
+(1 row)
+</pre></li>
+<li>Check the classification results. <pre class="example">
+SELECT t.id, t.outlook, t.temperature, t.humidity, t.windy, c.class
+FROM classification_result c, golf_data t
+WHERE t.id=c.id ORDER BY id;
+</pre> Result: <pre class="result">
+ id | outlook  | temperature | humidity | windy  |    class
+&#160;---+----------+-------------+----------+--------+--------------
+  1 | sunny    |          85 |       85 |  false |  Do not Play
+  2 | sunny    |          80 |       90 |  true  |  Do not Play
+  3 | overcast |          83 |       78 |  false |  Play
+  4 | rain     |          70 |       96 |  false |  Play
+  5 | rain     |          68 |       80 |  false |  Play
+  6 | rain     |          65 |       70 |  true  |  Do not Play
+  7 | overcast |          64 |       65 |  true  |  Play
+  8 | sunny    |          72 |       95 |  false |  Do not Play
+  9 | sunny    |          69 |       70 |  false |  Play
+ 10 | rain     |          75 |       80 |  false |  Play
+ 11 | sunny    |          75 |       70 |  true  |  Do not Play
+ 12 | overcast |          72 |       90 |  true  |  Play
+ 13 | overcast |          81 |       75 |  false |  Play
+ 14 | rain     |          71 |       80 |  true  |  Do not Play
+(14 rows)
+</pre></li>
+<li>Score the data against a validation set. <pre class="example">
+SELECT * FROM madlib.rf_score( 'trained_tree_infogain',
+                               'golf_data',
+                               0
+                             );
+</pre> Result: <pre class="result">
+     rf_score
+&#160;------------------
+ 0.928571428571429
+(1 row)
+</pre></li>
+<li>Clean up the random forest and other auxiliary information: <pre class="example">
+SELECT madlib.rf_clean( 'trained_tree_infogain'
+                      );
+</pre> Result: <pre class="result">
+ rf_clean
+&#160;---------
+ t
+(1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] <a href="http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm">http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm</a></p>
+<p>[2] <a href="http://en.wikipedia.org/wiki/Discretization_of_continuous_features">http://en.wikipedia.org/wiki/Discretization_of_continuous_features</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="rf_8sql__in.html" title="random forest APIs and main control logic written in PL/PGSQL ">rf.sql_in</a> documenting the SQL functions. </dd></dl>
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+  <div class="headertitle">
+<div class="title">Robust Variance<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train_linregr">Robust Linear Regression Training Function</a> </li>
+<li class="level1">
+<a href="#train_logregr">Robust Logistic Regression Training Function</a> </li>
+<li class="level1">
+<a href="#train_mlogregr">Robust Multinomial Logistic Regression Training Function</a> </li>
+<li class="level1">
+<a href="#robust_variance_coxph">Robust Variance Function For Cox Proportional Hazards</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>The functions in this module calculate robust variance (Huber-White estimates) for linear regression, logistic regression, multinomial logistic regression, and Cox proportional hazards. They are useful in calculating variances in a dataset with potentially noisy outliers. The Huber-White implemented here is identical to the "HC0" sandwich operator in the R module "sandwich".</p>
+<p>The interfaces for robust linear, logistic, and multinomial logistic regression are similar. Each regression type has its own training function. The regression results are saved in an output table with small differences, depending on the regression type.</p>
+<dl class="section warning"><dt>Warning</dt><dd>Please note that the interface for Cox proportional hazards, unlike the interface of other regression methods, accepts an output model table produced by <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef">coxph_train()</a> function.</dd></dl>
+<p><a class="anchor" id="train_linregr"></a></p><dl class="section user"><dt>Robust Linear Regression Training Function</dt><dd></dd></dl>
+<p>The <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust_variance_linregr()</a> function has the following syntax: </p><pre class="syntax">
+robust_variance_linregr( source_table,
+                         out_table,
+                         dependent_varname,
+                         independent_varname,
+                         grouping_cols
+                       )
+</pre> <dl class="arglist">
+<dt>source_table </dt>
+<dd>VARCHAR. The name of the table containing the training data. </dd>
+<dt>out_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table contains the following columns. </p><table  class="output">
+<tr>
+<th>coef </th><td>DOUBLE PRECISION[]. Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>std_err </th><td>DOUBLE PRECISION[]. Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>t_stats </th><td>DOUBLE PRECISION[]. Vector of the t-stats of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>DOUBLE PRECISION[]. Vector of the p-values of the coefficients.  </td></tr>
+</table>
+<p class="enddd">A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by linregr_train function. Please refer to the documentation for linear regression for details.  </p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>VARCHAR. The name of the column containing the dependent variable. </dd>
+<dt>independent_varname </dt>
+<dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list.  </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. Default value: NULL.  </dd>
+</dl>
+<p><a class="anchor" id="train_logregr"></a></p><dl class="section user"><dt>Robust Logistic Regression Training Function</dt><dd></dd></dl>
+<p>The <a class="el" href="robust_8sql__in.html#abc20ec2c5e74f268e7727c33a4bb9054">robust_variance_logregr()</a> function has the following syntax: </p><pre class="syntax">
+robust_variance_logregr( source_table,
+                         out_table,
+                         dependent_varname,
+                         independent_varname,
+                         grouping_cols,
+                         max_iter,
+                         optimizer,
+                         tolerance,
+                         verbose_mode
+                       )
+</pre> <dl class="arglist">
+<dt>source_table </dt>
+<dd>VARCHAR. The name of the table containing the training data. </dd>
+<dt>out_table </dt>
+<dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table has the following columns: </p><table  class="output">
+<tr>
+<th>coef </th><td>Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>std_err </th><td>Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>z_stats </th><td>Vector of the z-stats of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>Vector of the p-values of the coefficients.  </td></tr>
+</table>
+<p class="enddd">A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by logregr_train function. Please refer to the documentation for logistic regression for details.  </p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>VARCHAR. The name of the column containing the independent variable. </dd>
+<dt>independent_varname </dt>
+<dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated.  </dd>
+<dt>max_iter (optional) </dt>
+<dd>INTEGER, default: 20. The maximum number of iterations that are allowed. </dd>
+<dt>optimizer </dt>
+<dd>VARCHAR, default: 'fista'. Name of optimizer, either 'fista' or 'igd'. </dd>
+<dt>tolerance (optional) </dt>
+<dd>DOUBLE PRECISION, default: 1e-6. The criteria to end iterations. Both the 'fista' and 'igd' optimizers compute the average difference between the coefficients of two consecutive iterations, and when the difference is smaller than tolerance or the iteration number is larger than max_iter, the computation stops.  </dd>
+<dt>verbose_mode (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Whether the regression fit should print any warning messages.  </dd>
+</dl>
+<p><a class="anchor" id="train_mlogregr"></a></p><dl class="section user"><dt>Robust Multinomial Logistic Regression Function</dt><dd></dd></dl>
+<p>The <a class="el" href="robust_8sql__in.html#a1f27c072a4ef885a55825f75d12b3bd8">robust_variance_mlogregr()</a> function has the following syntax: </p><pre class="syntax">
+robust_variance_mlogregr( source_table,
+                          out_table,
+                          dependent_varname,
+                          independent_varname,
+                          ref_category,
+                          grouping_cols,
+                          optimizer_params,
+                          verbose_mode
+                        )
+</pre> <dl class="arglist">
+<dt>source_table </dt>
+<dd>VARCHAR. The name of the table containing training data, properly qualified. </dd>
+<dt>out_table </dt>
+<dd><p class="startdd">VARCHAR. The name of the table where the regression model will be stored. The output table has the following columns: </p><table  class="output">
+<tr>
+<th>category </th><td>The category.  </td></tr>
+<tr>
+<th>ref_category </th><td>The refererence category used for modeling.  </td></tr>
+<tr>
+<th>coef </th><td>Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>std_err </th><td>Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>z_stats </th><td>Vector of the z-stats of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>Vector of the p-values of the coefficients.  </td></tr>
+</table>
+<p class="enddd">A summary table named &lt;out_table&gt;_summary is also created, which is the same as the summary table created by mlogregr_train function. Please refer to the documentation for multinomial logistic regression for details.  </p>
+</dd>
+<dt>dependent_varname </dt>
+<dd>VARCHAR. The name of the column containing the dependent variable. </dd>
+<dt>independent_varname </dt>
+<dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. The <em>independent_varname</em> can be the name of a column that contains an array of numeric values. It can also be a string with the format 'ARRAY[1, x1, x2, x3]', where <em>x1</em>, <em>x2</em> and <em>x3</em> are each column names. </dd>
+<dt>ref_category (optional) </dt>
+<dd>INTEGER, default: 0. The reference category. </dd>
+<dt>grouping_cols (optional) </dt>
+<dd>VARCHAR, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd>
+<dt>optimizer_params (optional) </dt>
+<dd>TEXT, default: NULL, which uses the default values of optimizer parameters: max_iter=20, optimizer='newton', tolerance=1e-4. It should be a string that contains pairs of 'key=value' separated by commas. </dd>
+<dt>verbose_mode (optional) </dt>
+<dd>BOOLEAN, default FALSE. <em>Not currently implemented.</em> TRUE if the regression fit should print warning messages. </dd>
+</dl>
+<p><a class="anchor" id="robust_variance_coxph"></a></p><dl class="section user"><dt>Robust Variance Function For Cox Proportional Hazards</dt><dd></dd></dl>
+<p>The <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">robust_variance_coxph()</a> function has the following syntax: </p><pre class="syntax">
+robust_variance_coxph(model_table, output_table)
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>model_table </dt>
+<dd>TEXT. The name of the model table, which is exactaly the same as the 'output_table' parameter of <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> function. </dd>
+<dt>output_table </dt>
+<dd>TEXT. The name of the table where the output is saved. It has the following columns: <table  class="output">
+<tr>
+<th>coef </th><td>FLOAT8[]. Vector of the coefficients.  </td></tr>
+<tr>
+<th>loglikelihood </th><td>FLOAT8. Log-likelihood value of the MLE estimate.  </td></tr>
+<tr>
+<th>std_err </th><td>FLOAT8[]. Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>robust_se </th><td>FLOAT8[]. Vector of the robust standard errors of the coefficients.  </td></tr>
+<tr>
+<th>robust_z </th><td>FLOAT8[]. Vector of the robust z-stats of the coefficients.  </td></tr>
+<tr>
+<th>robust_p </th><td>FLOAT8[]. Vector of the robust p-values of the coefficients.  </td></tr>
+<tr>
+<th>hessian </th><td>FLOAT8[]. The Hessian matrix.  </td></tr>
+</table>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p><b> Logistic Regression Example </b></p><ol type="1">
+<li>View online help for the logistic regression training function. <pre class="example">
+SELECT madlib.robust_variance_logregr();
+</pre></li>
+<li>Create the training data table. <pre class="example">
+DROP TABLE IF EXISTS patients;
+CREATE TABLE patients (id INTEGER NOT NULL, second_attack INTEGER,
+    treatment INTEGER, trait_anxiety INTEGER);
+COPY patients FROM STDIN WITH DELIMITER '|';
+  1 |             1 |         1 |            70
+  3 |             1 |         1 |            50
+  5 |             1 |         0 |            40
+  7 |             1 |         0 |            75
+  9 |             1 |         0 |            70
+ 11 |             0 |         1 |            65
+ 13 |             0 |         1 |            45
+ 15 |             0 |         1 |            40
+ 17 |             0 |         0 |            55
+ 19 |             0 |         0 |            50
+  2 |             1 |         1 |            80
+  4 |             1 |         0 |            60
+  6 |             1 |         0 |            65
+  8 |             1 |         0 |            80
+ 10 |             1 |         0 |            60
+ 12 |             0 |         1 |            50
+ 14 |             0 |         1 |            35
+ 16 |             0 |         1 |            50
+ 18 |             0 |         0 |            45
+ 20 |             0 |         0 |            60
+\.
+</pre></li>
+<li>Run the logistic regression training function and compute the robust logistic variance of the regression: <pre class="example">
+DROP TABLE IF EXISTS patients_logregr;
+SELECT madlib.robust_variance_logregr( 'patients',
+                                       'patients_logregr',
+                                       'second_attack',
+                                       'ARRAY[1, treatment, trait_anxiety]'
+                                     );
+</pre></li>
+<li>View the regression results. <pre class="example">
+\x on
+Expanded display is on.
+SELECT * FROM patients_logregr;
+</pre> Result: <pre class="result">
+&#160;-[ RECORD 1 ]-------------------------------------------------------
+ coef     | {-6.36346994178179,-1.02410605239327,0.119044916668605}
+ std_err  | {3.45872062333648,1.1716192578234,0.0534328864185018}
+ z_stats  | {-1.83983346294192,-0.874094587943036,2.22793348156809}
+ p_values | {0.0657926909738889,0.382066744585541,0.0258849510757339}
+</pre> Alternatively, unnest the arrays in the results for easier reading of output. <pre class="example">
+\x off
+SELECT unnest(array['intercept', 'treatment', 'trait_anxiety' ]) as attribute,
+       unnest(coef) as coefficient,
+       unnest(std_err) as standard_error,
+       unnest(z_stats) as z_stat,
+       unnest(p_values) as pvalue
+FROM patients_logregr;
+</pre></li>
+</ol>
+<p><b> Cox Proportional Hazards Example </b></p><ol type="1">
+<li>View online help for the robust Cox Proportional hazards training method. <pre class="example">
+SELECT madlib.robust_variance_coxph();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+DROP TABLE IF EXISTS sample_data;
+CREATE TABLE sample_data (
+    id INTEGER NOT NULL,
+    grp DOUBLE PRECISION,
+    wbc DOUBLE PRECISION,
+    timedeath INTEGER,
+    status BOOLEAN
+);
+COPY sample_data FROM STDIN DELIMITER '|';
+  0 |   0 | 1.45 |        35 | t
+  1 |   0 | 1.47 |        34 | t
+  3 |   0 |  2.2 |        32 | t
+  4 |   0 | 1.78 |        25 | t
+  5 |   0 | 2.57 |        23 | t
+  6 |   0 | 2.32 |        22 | t
+  7 |   0 | 2.01 |        20 | t
+  8 |   0 | 2.05 |        19 | t
+  9 |   0 | 2.16 |        17 | t
+ 10 |   0 |  3.6 |        16 | t
+ 11 |   1 |  2.3 |        15 | t
+ 12 |   0 | 2.88 |        13 | t
+ 13 |   1 |  1.5 |        12 | t
+ 14 |   0 |  2.6 |        11 | t
+ 15 |   0 |  2.7 |        10 | t
+ 16 |   0 |  2.8 |         9 | t
+ 17 |   1 | 2.32 |         8 | t
+ 18 |   0 | 4.43 |         7 | t
+ 19 |   0 | 2.31 |         6 | t
+ 20 |   1 | 3.49 |         5 | t
+ 21 |   1 | 2.42 |         4 | t
+ 22 |   1 | 4.01 |         3 | t
+ 23 |   1 | 4.91 |         2 | t
+ 24 |   1 |    5 |         1 | t
+\.
+</pre></li>
+<li>Run the Cox regression function. <pre class="example">
+SELECT madlib.coxph_train( 'sample_data',
+                           'sample_cox',
+                           'timedeath',
+                           'ARRAY[grp,wbc]',
+                           'status'
+                         );
+</pre></li>
+<li>Run the Robust Cox regression function. <pre class="example">
+SELECT madlib.robust_variance_coxph( 'sample_cox',
+                           'sample_robust_cox'
+                         );
+</pre></li>
+<li>View the results of the robust Cox regression. <pre class="example">
+\x on
+SELECT * FROM sample_robust_cox;
+</pre> Results: <pre class="result">
+-[ RECORD 1 ]-+----------------------------------------------------------------------------
+coef          | {2.54407073265105,1.67172094780081}
+loglikelihood | -37.8532498733452
+std_err       | {0.677180599295459,0.387195514577754}
+robust_se     | {0.621095581073685,0.274773521439328}
+robust_z      | {4.09610180811965,6.08399579058399}
+robust_p      | {4.2016521208424e-05,1.17223683104729e-09}
+hessian       | {{2.78043065745405,-2.25848560642669},{-2.25848560642669,8.50472838284265}}
+</pre></li>
+</ol>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>When doing regression analysis, we are sometimes interested in the variance of the computed coefficients \( \boldsymbol c \). While the built-in regression functions provide variance estimates, we may prefer a <em>robust</em> variance estimate.</p>
+<p>The robust variance calculation can be expressed in a sandwich formation, which is the form </p><p class="formulaDsp">
+\[ S( \boldsymbol c) = B( \boldsymbol c) M( \boldsymbol c) B( \boldsymbol c) \]
+</p>
+<p> where \( B( \boldsymbol c)\) and \( M( \boldsymbol c)\) are matrices. The \( B( \boldsymbol c) \) matrix, also known as the bread, is relatively straight forward, and can be computed as </p><p class="formulaDsp">
+\[ B( \boldsymbol c) = n\left(\sum_i^n -H(y_i, x_i, \boldsymbol c) \right)^{-1} \]
+</p>
+<p> where \( H \) is the hessian matrix.</p>
+<p>The \( M( \boldsymbol c)\) matrix has several variations, each with different robustness properties. The form implemented here is the Huber-White sandwich operator, which takes the form </p><p class="formulaDsp">
+\[ M_{H} =\frac{1}{n} \sum_i^n \psi(y_i,x_i, \boldsymbol c)^T \psi(y_i,x_i, \boldsymbol c). \]
+</p>
+<p>The above method for calculating robust variance (Huber-White estimates) is implemented for linear regression, logistic regression, and multinomial logistic regression. It is useful in calculating variances in a dataset with potentially noisy outliers. The Huber-White implemented here is identical to the "HC0" sandwich operator in the R module "sandwich".</p>
+<p>When multinomial logistic regression is computed before the multinomial robust regression, it uses a default reference category of zero and the regression coefficients are included in the output table. The regression coefficients in the output are in the same order as the multinomial logistic regression function, which is described below. For a problem with \( K \) dependent variables \( (1, ..., K) \) and \( J \) categories \( (0, ..., J-1) \), let \( {m_{k,j}} \) denote the coefficient for dependent variable \( k \) and category \( j \) . The output is \( {m_{k_1, j_0}, m_{k_1, j_1} \ldots m_{k_1, j_{J-1}}, m_{k_2, j_0}, m_{k_2, j_1} \ldots m_{k_K, j_{J-1}}} \). The order is NOT CONSISTENT with the multinomial regression marginal effect calculation with function <em>marginal_mlogregr</em>. This is deliberate because the interfaces of all multinomial regressions (robust, clustered, ...) will be moved to match that used in marginal.</p>
+<p>The robust variance of Cox proportional hazards is more complex because coeeficients are trained by maximizing a partial log-likelihood. Therefore, one cannot directly use the formula for \( M( \boldsymbol c) \) as in Huber-White robust estimator. Extra terms are needed. See [4] for details.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] vce(cluster) function in STATA: <a href="http://www.stata.com/help.cgi?vce_option">http://www.stata.com/help.cgi?vce_option</a></p>
+<p>[2] clustered estimators in R: <a href="http://people.su.se/~ma/clustering.pdf">http://people.su.se/~ma/clustering.pdf</a></p>
+<p>[3] Achim Zeileis: Object-oriented Computation of Sandwich Estimators. Research Report Series / Department of Statistics and Mathematics, 37. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna. <a href="http://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf">http://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf</a></p>
+<p>[4] D. Y. Lin and L . J. Wei, <em>The Robust Inference for the Cox Proportional Hazards Model</em>, Journal of the American Statistical Association, Vol. 84, No. 408, p.1074 (1989).</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="robust_8sql__in.html" title="SQL functions for robust variance linear and logistic regression. ">robust.sql_in</a> documenting the SQL functions File <a class="el" href="robust__variance__coxph_8sql__in.html" title="SQL functions for robust cox proportional hazards regression. ">robust_variance_coxph.sql_in</a> documenting more the SQL functions</dd></dl>
+</div><!-- contents -->
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+  <div class="headertitle">
+<div class="title">Random Sampling<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#func_list">Functions</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>The random sampling module consists of useful utility functions for sampling operations. These functions can be used while implementing new algorithms.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Functions</dt><dd></dd></dl>
+<p>Sample a single row according to weights. </p><pre class="syntax">
+weighted_sample( value,
+                 weight
+               )
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>value </dt>
+<dd>BIGINT or FLOAT8[]. Value of row. Uniqueness is not enforced. If a value occurs multiple times, the probability of sampling this value is proportional to the sum of its weights.  </dd>
+<dt>weight </dt>
+<dd>FLOAT8. Weight for row. A negative value here is treated has zero weight.  </dd>
+</dl>
+<p>Refer to the file for documentation on each of the utility functions.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<dl class="section see"><dt>See also</dt><dd>File <a class="el" href="sample_8sql__in.html" title="SQL functions for random sampling. ">sample.sql_in</a> documenting the SQL functions. </dd></dl>
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+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="navtree_hack.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
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+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+<a href="#groups">Modules</a>  </div>
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+<div class="title">Cardinality Estimators<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div>  </div>
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+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<dl class="section warning"><dt>Warning</dt><dd><em> These MADlib methods are still in early stage development. There may be some issues that will be addressed in future versions. Interface and implementation is subject to change. </em></dd></dl>
+<p>Sketches (sometimes called "synopsis data structures") are small randomized in-memory data structures that capture statistical properties of a large set of values (e.g., a column of a table). Sketches can be formed in a single pass of the data, and used to approximate a variety of descriptive statistics.</p>
+<p>We implement sketches as SQL User-Defined Aggregates (UDAs). Because they are single-pass, small-space and parallelized, a single query can use many sketches to gather summary statistics on many columns of a table efficiently.</p>
+<p>This module currently implements user-defined aggregates based on three main sketch methods:</p><ul>
+<li><em>Count-Min (CM)</em> sketches, which can be used to approximate a number of descriptive statistics including<ul>
+<li><code>COUNT(*)</code> of rows whose column value matches a given value in a set</li>
+<li><code>COUNT(*)</code> of rows whose column value falls in a range (*)</li>
+<li>order statistics including <em>median</em> and <em>centiles</em> (*)</li>
+<li><em>histograms</em>: both <em>equi-width</em> and <em>equi-depth</em> (*)</li>
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+<p><em>Note:</em> Features marked with a star (*) only work for discrete types that can be cast to int8.</p>
+<p>The sketch methods consist of a number of SQL UDAs (user-defined aggregates) and UDFs (user-defined functions), to be used directly in SQL queries. </p>
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+<tr class="memdesc:group__grp__countmin"><td class="mdescLeft">&#160;</td><td class="mdescRight">Implements Cormode-Mathukrishnan <em>CountMin</em> sketches on integer values as a user-defined aggregate. <br /></td></tr>
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+<tr class="memdesc:group__grp__fmsketch"><td class="mdescLeft">&#160;</td><td class="mdescRight">Implements Flajolet-Martin's distinct count estimation as a user-defined aggregate. <br /></td></tr>
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+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[29/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x5f.html
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+    <li class="footer">Generated on Thu Jul 3 2014 14:09:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x61.html
----------------------------------------------------------------------
diff --git a/docs/latest/globals_0x61.html b/docs/latest/globals_0x61.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
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+<script type="text/javascript" src="navtree.js"></script>
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+  $(document).ready(function() { searchBox.OnSelectItem(0); });
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+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+               onfocus="searchBox.OnSearchFieldFocus(true)" 
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x61.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
+<li>angle()
+: <a class="el" href="svec__util_8sql__in.html#a7e395ee71f6cf05afa76d89a72d45bec">svec_util.sql_in</a>
+</li>
+<li>argmax()
+: <a class="el" href="bayes_8sql__in.html#ad47c68a2b8aeedf66d509f0c32a9732d">bayes.sql_in</a>
+</li>
+<li>argmax_combine()
+: <a class="el" href="bayes_8sql__in.html#ab150442ec577f4a0a3948176baf5ebd0">bayes.sql_in</a>
+</li>
+<li>argmax_final()
+: <a class="el" href="bayes_8sql__in.html#aa7017323fd6fad40c9acbbee5f1fec7e">bayes.sql_in</a>
+</li>
+<li>argmax_transition()
+: <a class="el" href="bayes_8sql__in.html#a0671e6ac35a1967f41d2efb9e8417e1b">bayes.sql_in</a>
+</li>
+<li>arima_forecast()
+: <a class="el" href="arima_8sql__in.html#a08645edc97c591bce174964ac309a3a0">arima.sql_in</a>
+</li>
+<li>arima_train()
+: <a class="el" href="arima_8sql__in.html#a9af899e0260745b75b1872159fea1281">arima.sql_in</a>
+</li>
+<li>array_add()
+: <a class="el" href="array__ops_8sql__in.html#a91c8d3715142927b3967f05a4fbf1575">array_ops.sql_in</a>
+</li>
+<li>array_avg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a5734f5796a403ebf2e765cba136f54c8">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_avg_final()
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+</li>
+<li>array_avg_merge()
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+</li>
+<li>array_avg_transition()
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+</li>
+<li>array_collapse()
+: <a class="el" href="profile_8sql__in.html#a8b4ddf1ad8fb4777936f441b873ac968">profile.sql_in</a>
+</li>
+<li>array_contains()
+: <a class="el" href="array__ops_8sql__in.html#aedf6cb13eb4803bcc12dc4d95ea8ff4e">array_ops.sql_in</a>
+</li>
+<li>array_div()
+: <a class="el" href="array__ops_8sql__in.html#a6cc05e7052495f8b64692faf40219576">array_ops.sql_in</a>
+</li>
+<li>array_dot()
+: <a class="el" href="array__ops_8sql__in.html#acde10964ed23b7c8da515fb84cb8d5e0">array_ops.sql_in</a>
+</li>
+<li>array_elem_corr_agg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a8928da50ddcf85de812ab058214324db">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_max()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a1de2b192c73ede707b42726e9469664d">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_min()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a2ec0ea5a3faba236a75d4d9cc16e40a4">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_fill()
+: <a class="el" href="array__ops_8sql__in.html#a065a5323f3b742be47e39ad8b4c90fc2">array_ops.sql_in</a>
+</li>
+<li>array_filter()
+: <a class="el" href="array__ops_8sql__in.html#acc295a568878940ffc3e2c9a75990efb">array_ops.sql_in</a>
+</li>
+<li>array_max()
+: <a class="el" href="array__ops_8sql__in.html#ae891429cc50705c530f3e5ca15541849">array_ops.sql_in</a>
+</li>
+<li>array_mean()
+: <a class="el" href="array__ops_8sql__in.html#a407598f9eb70637798b02fd731bfca2c">array_ops.sql_in</a>
+</li>
+<li>array_min()
+: <a class="el" href="array__ops_8sql__in.html#a6659bf9d9363eb179fab34f81f8ac59b">array_ops.sql_in</a>
+</li>
+<li>array_mult()
+: <a class="el" href="array__ops_8sql__in.html#a652d70c480d484c4a1a92ded384b0dd7">array_ops.sql_in</a>
+</li>
+<li>array_of_bigint()
+: <a class="el" href="array__ops_8sql__in.html#ab7d8550e66d2e0bd54b8f0997d93880c">array_ops.sql_in</a>
+</li>
+<li>array_of_float()
+: <a class="el" href="array__ops_8sql__in.html#ab066e65a41db78b00b4532996b2a6efc">array_ops.sql_in</a>
+</li>
+<li>array_scalar_add()
+: <a class="el" href="array__ops_8sql__in.html#a0b6ffe59b12c3dee076c3059f9ab363f">array_ops.sql_in</a>
+</li>
+<li>array_scalar_mult()
+: <a class="el" href="array__ops_8sql__in.html#ae6881cc5c86941b6ffca35d7f3cd5c12">array_ops.sql_in</a>
+</li>
+<li>array_stddev()
+: <a class="el" href="array__ops_8sql__in.html#a3b6c2d173a611e6d6b184d825c2b336d">array_ops.sql_in</a>
+</li>
+<li>array_sub()
+: <a class="el" href="array__ops_8sql__in.html#a2875a161a01c7dcdea9a4997b074eefc">array_ops.sql_in</a>
+</li>
+<li>array_sum()
+: <a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec">array_ops.sql_in</a>
+</li>
+<li>array_sum_big()
+: <a class="el" href="array__ops_8sql__in.html#a418de59800833aa95f9b7cbd6b12901c">array_ops.sql_in</a>
+</li>
+<li>array_to_1d()
+: <a class="el" href="linalg_8sql__in.html#a71304c333a999e69ac1430c285c35181">linalg.sql_in</a>
+</li>
+<li>array_to_2d()
+: <a class="el" href="linalg_8sql__in.html#ac3cb4979f60ce86579b7ba4a5dd8bb3e">linalg.sql_in</a>
+</li>
+<li>assert()
+: <a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd">utilities.sql_in</a>
+</li>
+<li>assoc_rules()
+: <a class="el" href="assoc__rules_8sql__in.html#a68a256d98b82ac15bac7df92e806f6f8">assoc_rules.sql_in</a>
+</li>
+<li>avg()
+: <a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4">linalg.sql_in</a>
+</li>
+<li>avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#ad468b52a7caa9b47a0650d39e32d9c50">linalg.sql_in</a>
+</li>
+<li>avg_vector_merge()
+: <a class="el" href="linalg_8sql__in.html#a5ac255c13e1f2d91274b829ff0383e44">linalg.sql_in</a>
+</li>
+<li>avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#aaea269f4746f952faadffed9d9b107d7">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x62.html
----------------------------------------------------------------------
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new file mode 100644
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { searchBox.OnSelectItem(0); });
+</script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x62.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
+<li>bernoulli_cdf()
+: <a class="el" href="prob_8sql__in.html#aea21a931dc5578a570e3370af3d8d43a">prob.sql_in</a>
+</li>
+<li>bernoulli_pmf()
+: <a class="el" href="prob_8sql__in.html#a434b3ad1f3964835834dc2a942b820ef">prob.sql_in</a>
+</li>
+<li>bernoulli_quantile()
+: <a class="el" href="prob_8sql__in.html#a7133c2e86fd2f6384416ee0e4fd3a60b">prob.sql_in</a>
+</li>
+<li>beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a72e1cca872da35592075dbcfb18aed3f">prob.sql_in</a>
+</li>
+<li>beta_pdf()
+: <a class="el" href="prob_8sql__in.html#aa105049e6e3bb9b3891b0ed1b343e28e">prob.sql_in</a>
+</li>
+<li>beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a32433aa742c0504d33e98e28a3e2f190">prob.sql_in</a>
+</li>
+<li>big_or()
+: <a class="el" href="sketch_8sql__in.html#a335fba86bbb3fb5a3284808cc64b8f16">sketch.sql_in</a>
+</li>
+<li>binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#aa5000bad6e2e4af1c8cbfec7ea884476">prob.sql_in</a>
+</li>
+<li>binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#aa0614475b8685bf8e37533d2ac5bb116">prob.sql_in</a>
+</li>
+<li>binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#a49f421c58d2e1abd63b83d71af9edf21">prob.sql_in</a>
+</li>
+<li>bool_to_text()
+: <a class="el" href="utilities_8sql__in.html#abc867482053e6692047d051b679953a5">utilities.sql_in</a>
+</li>
+<li>bytea8in()
+: <a class="el" href="utilities_8sql__in.html#a60c4765ce8dc639cbb0ad4c389f5f1ad">utilities.sql_in</a>
+</li>
+<li>bytea8out()
+: <a class="el" href="utilities_8sql__in.html#adff923d42d4bdb2b631d6cafd5f37a43">utilities.sql_in</a>
+</li>
+<li>bytea8recv()
+: <a class="el" href="utilities_8sql__in.html#a244a7ff30f2788a6f4816417982eb13b">utilities.sql_in</a>
+</li>
+<li>bytea8send()
+: <a class="el" href="utilities_8sql__in.html#aeb90c740e8e6b67aa60a11c430fa9050">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x63.html
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diff --git a/docs/latest/globals_0x63.html b/docs/latest/globals_0x63.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+  $(document).ready(function() { searchBox.OnSelectItem(0); });
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+    extensions: ["tex2jax.js", "TeX/AMSmath.js", "TeX/AMSsymbols.js"],
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+});
+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
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+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'auto');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x63.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_c"></a>- c -</h3><ul>
+<li>c45_classify()
+: <a class="el" href="c45_8sql__in.html#afe136e52f498f2ff9e2b91e38e29d670">c45.sql_in</a>
+</li>
+<li>c45_clean()
+: <a class="el" href="c45_8sql__in.html#ac25e17ecbc70149aa559018e718fc793">c45.sql_in</a>
+</li>
+<li>c45_display()
+: <a class="el" href="c45_8sql__in.html#a32d2bcbc016c990991d77b6f6148306d">c45.sql_in</a>
+</li>
+<li>c45_genrule()
+: <a class="el" href="c45_8sql__in.html#ac71787c47795b3b0b133cdbd37438242">c45.sql_in</a>
+</li>
+<li>c45_score()
+: <a class="el" href="c45_8sql__in.html#a1b634db47e9006d114da0987e80b9601">c45.sql_in</a>
+</li>
+<li>c45_train()
+: <a class="el" href="c45_8sql__in.html#a4fbee855d22101d15d195d573189eb98">c45.sql_in</a>
+</li>
+<li>cauchy_cdf()
+: <a class="el" href="prob_8sql__in.html#a2d8874c2a5679403a473bfedb14467a4">prob.sql_in</a>
+</li>
+<li>cauchy_pdf()
+: <a class="el" href="prob_8sql__in.html#aebfad9365a7fc7a553c3b5c7931f2450">prob.sql_in</a>
+</li>
+<li>cauchy_quantile()
+: <a class="el" href="prob_8sql__in.html#ae8aa9b741e89c8d9236a682d218006e0">prob.sql_in</a>
+</li>
+<li>check_if_col_exists()
+: <a class="el" href="utilities_8sql__in.html#ac1f509bd94c6309b59eb4d479fb2a470">utilities.sql_in</a>
+</li>
+<li>check_if_raises_error()
+: <a class="el" href="utilities_8sql__in.html#aa90ffaebb43a5cbcb5c48281cbadd3b0">utilities.sql_in</a>
+</li>
+<li>chi2_gof_test()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4c912b329fb103a44253932a653d4e40">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a61c31dfde998db18afd6552239b872c4">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_merge_states()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a2b8265718a16ec65e89d2ab512f6a3e1">hypothesis_tests.sql_in</a>
+</li>
+<li>chi2_gof_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a7c580537666776f1bd4b9d4a0a6b6438">hypothesis_tests.sql_in</a>
+</li>
+<li>chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#a230513b6b549d5b445cbacbdbab42c15">prob.sql_in</a>
+</li>
+<li>chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#a90bccc717d7052e83bafd7f160a783b1">prob.sql_in</a>
+</li>
+<li>chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad125307fe65a33b60f6dd524037d4548">prob.sql_in</a>
+</li>
+<li>closest_column()
+: <a class="el" href="linalg_8sql__in.html#aa2856d10fa6cb12a5b168bab217197ed">linalg.sql_in</a>
+</li>
+<li>closest_columns()
+: <a class="el" href="linalg_8sql__in.html#a23e86c595619142c790b31292ebdd810">linalg.sql_in</a>
+</li>
+<li>clustered_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a91f46572f245f34c9fe92d0ec806a998">clustered_variance_coxph.sql_in</a>
+</li>
+<li>clustered_variance_linregr()
+: <a class="el" href="clustered__variance_8sql__in.html#abaa6702a3482f65199ce64b8a14316d7">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_logregr()
+: <a class="el" href="clustered__variance_8sql__in.html#a0debc923b0494f8259938d9c6f5cdbcd">clustered_variance.sql_in</a>
+</li>
+<li>clustered_variance_mlogregr()
+: <a class="el" href="clustered__variance_8sql__in.html#aa72cfa6f9259cceea3e36b6db45a7624">clustered_variance.sql_in</a>
+</li>
+<li>cmsketch()
+: <a class="el" href="sketch_8sql__in.html#ae6671bfb7474db7d8b5c9dada29f36ab">sketch.sql_in</a>
+</li>
+<li>cmsketch_centile()
+: <a class="el" href="sketch_8sql__in.html#a2f2ab2fe3244515f5f73d49690e73b39">sketch.sql_in</a>
+</li>
+<li>cmsketch_count()
+: <a class="el" href="sketch_8sql__in.html#a3498d2c778d1289154f61d34e84c609e">sketch.sql_in</a>
+</li>
+<li>cmsketch_depth_histogram()
+: <a class="el" href="sketch_8sql__in.html#a9e6d30f20b724b96249cc4a0f67a279e">sketch.sql_in</a>
+</li>
+<li>cmsketch_median()
+: <a class="el" href="sketch_8sql__in.html#a0ef6428fa7ba4b7f7b1f633e6f8003ea">sketch.sql_in</a>
+</li>
+<li>cmsketch_rangecount()
+: <a class="el" href="sketch_8sql__in.html#aeff9e36cfb3338c4e405d4ac77d3968c">sketch.sql_in</a>
+</li>
+<li>cmsketch_width_histogram()
+: <a class="el" href="sketch_8sql__in.html#a8482f62849adf40a2c7df78c23ea33a4">sketch.sql_in</a>
+</li>
+<li>compute_coxph_result()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a74fe60f99c3808e38421c71286850ea8">cox_prop_hazards.sql_in</a>
+</li>
+<li>compute_lincrf()
+: <a class="el" href="crf_8sql__in.html#a20c096420d878cdd66dac6a232483737">crf.sql_in</a>
+</li>
+<li>conjugate_gradient()
+: <a class="el" href="conjugate__gradient_8sql__in.html#ab94e8a12cc9c2d0dac35168721ec8585">conjugate_gradient.sql_in</a>
+</li>
+<li>correlation()
+: <a class="el" href="correlation_8sql__in.html#a17cb16646fa7ebdf7ce7dd8c58349ce5">correlation.sql_in</a>
+</li>
+<li>correlation_agg()
+: <a class="el" href="correlation_8sql__in.html#a1c3081444f6bafc88a9b377f2af32968">correlation.sql_in</a>
+</li>
+<li>correlation_final()
+: <a class="el" href="correlation_8sql__in.html#a85a1a9b0ae5e608fd235f08ae01ff966">correlation.sql_in</a>
+</li>
+<li>correlation_merge()
+: <a class="el" href="correlation_8sql__in.html#ac53cf4dd28185715a1798176bce44b1b">correlation.sql_in</a>
+</li>
+<li>correlation_transition()
+: <a class="el" href="correlation_8sql__in.html#ae6223d0ba3e2b8a2d298ee78a293be6a">correlation.sql_in</a>
+</li>
+<li>cox_prop_hazards()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ad778b289eb19ae0bb2b7e02a89bab3bc">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_prop_hazards_regr()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3310cf98478b7c1e400e8fb1b3965d30">cox_prop_hazards.sql_in</a>
+</li>
+<li>cox_zph()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#aad77f665f0030202444e99f5a0b895d3">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_a_b()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a5f5ca89e3265e170c325125e61ef191d">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_final()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a2373d116ad1e2b2090f8c23a0c15edb5">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_merge()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a62bff141ced5972df28ceecdfe522888">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_a_b_transition()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a7081022015c00a14ecca27e017a4458b">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_clustered_stats()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a713f413fc571244b7cec6c0084a61eb9">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_compute_w()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#a523e61b6d56c8ff59fa8ae3121073a49">clustered_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a2743e0cecadd7f3fa2a16f9d7d40564b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#aeb95cacc36c5d6c8cc25c8d37418c71b">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_merge()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ab122003102dd22274a76aa1a830c69dd">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_h_s_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a03a39bc1fee64cd9b8b6a373758819da">robust_variance_coxph.sql_in</a>
+</li>
+<li>coxph_improved_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ac5ae827993064fd6e0ccf13a96262cdc">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_step_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a6a9ae1ea761b31e1522c47a9794cc233">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a41a415076894d3844475208b2163a267">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_improved_strata_step_outer()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abb21bacbff7362724d2698568d857555">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_predict()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a815961d52fb8ce998071eb92056ee1dd">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_inner_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a3c96655e41fd17c86dfc9ad37b0959d0">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_step_outer_transition()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#abaf93fc77a284f0b765a64ef827c1bed">cox_prop_hazards.sql_in</a>
+</li>
+<li>coxph_train()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#afd5fb08d419aab4a8b8eb103afa30f29">cox_prop_hazards.sql_in</a>
+</li>
+<li>create_indicator_variables()
+: <a class="el" href="data__preparation_8sql__in.html#a5d0ef27bc38be2b4b5f063cf7b560d1f">data_preparation.sql_in</a>
+</li>
+<li>create_nb_classify_view()
+: <a class="el" href="bayes_8sql__in.html#a6472f620099368cb8c334e0badb12133">bayes.sql_in</a>
+</li>
+<li>create_nb_prepared_data_tables()
+: <a class="el" href="bayes_8sql__in.html#a3ed5ddbc4cab1013db8604be58a6da3d">bayes.sql_in</a>
+</li>
+<li>create_nb_probs_view()
+: <a class="el" href="bayes_8sql__in.html#a163afffd0c845d325f060f74bcf02243">bayes.sql_in</a>
+</li>
+<li>create_schema_pg_temp()
+: <a class="el" href="utilities_8sql__in.html#a56501b6f9fabe65d7a6a6beb70a0e000">utilities.sql_in</a>
+</li>
+<li>crf_test_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#abf2a39a0b84a5e7ae215db28b3a87f81">crf_data_loader.sql_in</a>
+</li>
+<li>crf_test_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_feature_gen.sql_in</a>
+</li>
+<li>crf_train_data()
+: <a class="el" href="crf__data__loader_8sql__in.html#aaa609ec1d1d4cff0f335fa358091d5c3">crf_data_loader.sql_in</a>
+</li>
+<li>crf_train_fgen()
+: <a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_feature_gen.sql_in</a>
+</li>
+<li>cross_validation_general()
+: <a class="el" href="cross__validation_8sql__in.html#a5efbb27898e99f7685816d9d5dfd082c">cross_validation.sql_in</a>
+</li>
+<li>crossprod()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#a588d5855d1673f7bcbc2a1c983081f76">pivotalr_arrayops.sql_in</a>
+</li>
+<li>crossprod_sym()
+: <a class="el" href="pivotalr__arrayops_8sql__in.html#aa1e36bf44b3206314c186bba80179c9c">pivotalr_arrayops.sql_in</a>
+</li>
+<li>cv_linregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#aa572f1f57c0dd106b30948928161d8cc">cross_validation.sql_in</a>
+</li>
+<li>cv_linregr_train()
+: <a class="el" href="cross__validation_8sql__in.html#aa151eb3fa9acc7f4cc33236e22ad4362">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_accuracy()
+: <a class="el" href="cross__validation_8sql__in.html#ac1b5c57473ff672af45191c8d53f46ed">cross_validation.sql_in</a>
+</li>
+<li>cv_logregr_predict()
+: <a class="el" href="cross__validation_8sql__in.html#ac7dbc115f0c4624ccbf62b2d5494388d">cross_validation.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_0x64.html
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diff --git a/docs/latest/globals_0x64.html b/docs/latest/globals_0x64.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
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+</td>
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+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_0x64.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+<div class="textblock">Here is a list of all file members with links to the files they belong to:</div>
+
+<h3><a class="anchor" id="index_d"></a>- d -</h3><ul>
+<li>deconstruct_2d_array()
+: <a class="el" href="linalg_8sql__in.html#a61003b10cda05541f0d1d32a47ba23fe">linalg.sql_in</a>
+</li>
+<li>dense_direct_linear_system()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a379f260d8dfbf5c2be05c60776974188">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a63a3ed2c811216d3d88c120005a13875">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a607403f90e1921435183d031825e5e54">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_direct_linear_system_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#acbfc47693d0b50ccfc6b7f7c38209b18">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a52f2bcdd0d038f19e64a44d4bb07fefb">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_final()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a01935dd8c4aef8c92e77b7f0f905fa4b">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_merge_states()
+: <a class="el" href="dense__linear__systems_8sql__in.html#a73ebde08b8395fd7bddbe1dc37b9539f">dense_linear_systems.sql_in</a>
+</li>
+<li>dense_residual_norm_transition()
+: <a class="el" href="dense__linear__systems_8sql__in.html#ad34728809ab3121705342cf91b34c6dd">dense_linear_systems.sql_in</a>
+</li>
+<li>dist_angle()
+: <a class="el" href="linalg_8sql__in.html#a8c7b9281a72ff22caf06161701b27e84">linalg.sql_in</a>
+</li>
+<li>dist_norm1()
+: <a class="el" href="linalg_8sql__in.html#aad193850e79c4b9d811ca9bc53e13476">linalg.sql_in</a>
+</li>
+<li>dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#aa58e51526edea6ea98db30b6f250adb4">linalg.sql_in</a>
+</li>
+<li>dist_tanimoto()
+: <a class="el" href="linalg_8sql__in.html#afa13b4c6122b99422d666dedea136c18">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

[11/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__lda.html
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diff --git a/docs/latest/group__grp__lda.html b/docs/latest/group__grp__lda.html
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+++ b/docs/latest/group__grp__lda.html
@@ -0,0 +1,462 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: Latent Dirichlet Allocation</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__lda.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Latent Dirichlet Allocation<div class="ingroups"><a class="el" href="group__grp__unsupervised.html">Unsupervised Learning</a> &raquo; <a class="el" href="group__grp__topic__modelling.html">Topic Modelling</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#vocabulary">Vocabulary Format</a> </li>
+<li>
+<a href="#train">Training Function</a> </li>
+<li>
+<a href="#predict">Prediction Function</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a></li>
+<li>
+</li>
+</ul>
+</div><p>Latent Dirichlet Allocation (LDA) is an interesting generative probabilistic model for natural texts and has received a lot of attention in recent years. The model is quite versatile, having found uses in problems like automated topic discovery, collaborative filtering, and document classification.</p>
+<p>The LDA model posits that each document is associated with a mixture of various topics (e.g. a document is related to Topic 1 with probability 0.7, and Topic 2 with probability 0.3), and that each word in the document is attributable to one of the document's topics. There is a (symmetric) Dirichlet prior with parameter \( \alpha \) on each document's topic mixture. In addition, there is another (symmetric) Dirichlet prior with parameter \( \beta \) on the distribution of words for each topic.</p>
+<p>The following generative process then defines a distribution over a corpus of documents.</p>
+<ul>
+<li>Sample for each topic \( i \), a per-topic word distribution \( \phi_i \) from the Dirichlet( \(\beta\)) prior.</li>
+<li>For each document:<ul>
+<li>Sample a document length N from a suitable distribution, say, Poisson.</li>
+<li>Sample a topic mixture \( \theta \) for the document from the Dirichlet( \(\alpha\)) distribution.</li>
+<li>For each of the N words:<ul>
+<li>Sample a topic \( z_n \) from the multinomial topic distribution \( \theta \).</li>
+<li>Sample a word \( w_n \) from the multinomial word distribution \( \phi_{z_n} \) associated with topic \( z_n \).</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<p>In practice, only the words in each document are observable. The topic mixture of each document and the topic for each word in each document are latent unobservable variables that need to be inferred from the observables, and this is the problem people refer to when they talk about the inference problem for LDA. Exact inference is intractable, but several approximate inference algorithms for LDA have been developed. The simple and effective Gibbs sampling algorithm described in Griffiths and Steyvers [2] appears to be the current algorithm of choice.</p>
+<p>This implementation provides a parallel and scalable in-database solution for LDA based on Gibbs sampling. Different with the implementations based on MPI or Hadoop Map/Reduce, this implementation builds upon the shared-nothing MPP databases and enables high-performance in-database analytics.</p>
+<p><a class="anchor" id="vocabulary"></a></p><dl class="section user"><dt>Vocabulary Format</dt><dd></dd></dl>
+<p>The vocabulary, or dictionary, indexes all the words found in the corpus and has the following format: </p><pre>{TABLE|VIEW} <em>vocab_table</em> (
+    <em>wordid</em> INTEGER,
+    <em>word</em> TEXT
+)</pre><p> where <code>wordid</code> refers the word ID (the index of a word in the vocabulary) and <code>word</code> is the actual word.</p>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li><p class="startli">The training (i.e. topic inference) can be done with the following function: </p><pre>
+        SELECT <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80">lda_train</a>(
+            <em>'data_table'</em>,
+            <em>'model_table'</em>,
+            <em>'output_data_table'</em>,
+            <em>voc_size</em>,
+            <em>topic_num</em>,
+            <em>iter_num</em>,
+            <em>alpha</em>,
+            <em>beta</em>)
+    </pre><p class="startli">This function stores the resulting model in <code><em>model_table</em></code>. The table has only 1 row and is in the following form: </p><pre>{TABLE} <em>model_table</em> (
+        <em>voc_size</em> INTEGER,
+        <em>topic_num</em> INTEGER,
+        <em>alpha</em> FLOAT,
+        <em>beta</em> FLOAT,
+        <em>model</em> BIGINT[])
+    </pre><p class="startli">This function also stores the topic counts and the topic assignments in each document in <code><em>output_data_table</em></code>. The table is in the following form: </p><pre>{TABLE} <em>output_data_table</em> (
+        <em>docid</em> INTEGER,
+        <em>wordcount</em> INTEGER,
+        <em>words</em> INTEGER[],
+        <em>counts</em> INTEGER[],
+        <em>topic_count</em> INTEGER[],
+        <em>topic_assignment</em> INTEGER[])
+    </pre></li>
+<li><p class="startli">The prediction (i.e. labelling of test documents using a learned LDA model) can be done with the following function: </p><pre>
+        SELECT <a class="el" href="lda_8sql__in.html#aaa89e30c8fd0ba41b6feee01ee195330">lda_predict</a>(
+            <em>'data_table'</em>,
+            <em>'model_table'</em>,
+            <em>'output_table'</em>);
+    </pre><p class="startli">This function stores the prediction results in <em>output_table</em>. Each row in the table stores the topic distribution and the topic assignments for a docuemnt in the dataset. The table is in the following form: </p><pre>{TABLE} <em>output_table</em> (
+        <em>docid</em> INTEGER,
+        <em>wordcount</em> INTEGER,
+        <em>words</em> INTEGER,
+        <em>counts</em> INTEGER,
+        <em>topic_count</em> INTEGER[],
+        <em>topic_assignment</em> INTEGER[])
+    </pre></li>
+<li>This module also provides a function for computing the perplexity: <pre>
+        SELECT <a class="el" href="lda_8sql__in.html#a25c3ef12d9808d8a38c5fd2630f3b5a9">lda_get_perplexity</a>(
+            <em>'model_table'</em>,
+            <em>'output_data_table'</em>);
+    </pre></li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Implementation Notes</dt><dd>The input format requires the user to tokenize each document into an array of words. This process involves tokenizing and filtering documents - a process out-of-scope for this module. Internally, the input data will be validated and then converted to the following format for efficiency: <pre>{TABLE} <em>__internal_data_table__</em> (
+    <em>docid</em> INTEGER,
+    <em>wordcount</em> INTEGER,
+    <em>words</em> INTEGER[],
+    <em>counts</em> INTEGER[])
+</pre> where <code>docid</code> is the document ID, <code>wordcount</code> is the number of words in the document, <code>words</code> is the list of unique words in the document, and <code>counts</code> is a list of the number of occurrences of each unique word in the document.</dd></dl>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The LDA training function has the following syntax. <pre class="syntax">
+lda_train( data_table,
+           model_table,
+           output_data_table,
+           voc_size,
+           topic_num,
+           iter_num,
+           alpha,
+           beta
+         )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>data_table </dt>
+<dd><p class="startdd">TEXT. The name of the table storing the training dataset. Each row is in the form <code>&lt;docid, wordid, count&gt;</code> where <code>docid</code>, <code>wordid</code>, and <code>count</code> are non-negative integers.</p>
+<p class="enddd">The <code>docid</code> column refers to the document ID, the <code>wordid</code> column is the word ID (the index of a word in the vocabulary), and <code>count</code> is the number of occurrences of the word in the document.  </p>
+</dd>
+<dt>model_table </dt>
+<dd>TEXT. The name of the table storing the learned models. This table has one row and the following columns. <table  class="output">
+<tr>
+<th>voc_size </th><td>INTEGER. Size of the vocabulary. Note that the <code>wordid</code> should be continous integers starting from 0 to <code>voc_size</code> &minus; <code>1</code>. A data validation routine is called to validate the dataset.  </td></tr>
+<tr>
+<th>topic_num </th><td>INTEGER. Number of topics.  </td></tr>
+<tr>
+<th>alpha </th><td>DOUBLE PRECISION. Dirichlet parameter for the per-doc topic multinomial (e.g. 50/topic_num).  </td></tr>
+<tr>
+<th>beta </th><td>DOUBLE PRECISION. Dirichlet parameter for the per-topic word multinomial (e.g. 0.01).  </td></tr>
+<tr>
+<th>model </th><td>BIGINT[].  </td></tr>
+</table>
+</dd>
+<dt>output_data_table </dt>
+<dd>TEXT. The name of the table to store the output data. It has the following columns: <table  class="output">
+<tr>
+<th>docid </th><td>INTEGER.  </td></tr>
+<tr>
+<th>wordcount </th><td>INTEGER.  </td></tr>
+<tr>
+<th>words </th><td>INTEGER[].  </td></tr>
+<tr>
+<th>counts </th><td>INTEGER[].  </td></tr>
+<tr>
+<th>topic_count </th><td>INTEGER[].  </td></tr>
+<tr>
+<th>topic_assignment </th><td>INTEGER[].  </td></tr>
+</table>
+</dd>
+<dt>voc_size </dt>
+<dd>INTEGER. Size of the vocabulary. Note that the <code>wordid</code> should be continous integers starting from 0 to <code>voc_size</code> &minus; <code>1</code>. A data validation routine is called to validate the dataset. </dd>
+<dt>topic_num </dt>
+<dd>INTEGER. Number of topics. </dd>
+<dt>iter_num </dt>
+<dd>INTEGER. Number of iterations (e.g. 60). </dd>
+<dt>alpha </dt>
+<dd>DOUBLE PRECISION. Dirichlet parameter for the per-doc topic multinomial (e.g. 50/topic_num). </dd>
+<dt>beta </dt>
+<dd>DOUBLE PRECISION. Dirichlet parameter for the per-topic word multinomial (e.g. 0.01). </dd>
+</dl>
+</dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd></dd></dl>
+<p>Prediction&mdash;labelling test documents using a learned LDA model&mdash;is accomplished with the following function: </p><pre class="syntax">
+lda_predict( data_table,
+             model_table,
+             output_table
+           );
+</pre><p>This function stores the prediction results in <code><em>output_table</em></code>. Each row in the table stores the topic distribution and the topic assignments for a document in the dataset. The table has the following columns: </p><table  class="output">
+<tr>
+<th>docid </th><td>INTEGER.  </td></tr>
+<tr>
+<th>wordcount </th><td>INTEGER.  </td></tr>
+<tr>
+<th>words </th><td>INTEGER[]. List of word IDs in this document.  </td></tr>
+<tr>
+<th>counts </th><td>INTEGER[]. List of word counts in this document.  </td></tr>
+<tr>
+<th>topic_count </th><td>INTEGER[]. Of length topic_num, list of topic counts in this document.  </td></tr>
+<tr>
+<th>topic_assignment </th><td>INTEGER[]. Of length wordcount, list of topic index for each word.  </td></tr>
+</table>
+<p><a class="anchor" id="perplexity"></a></p><dl class="section user"><dt>Perplexity Function</dt><dd>This module provides a function for computing the perplexity. <pre class="syntax">
+lda_get_perplexity( model_table,
+                    output_data_table
+                  );
+</pre></dd></dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare a training dataset for LDA. The examples below are small strings extracted from various Wikipedia documents . <pre class="example">
+CREATE TABLE documents(docid INT4, contents TEXT);
+INSERT INTO documents VALUES
+(0, 'Statistical topic models are a class of Bayesian latent variable models, originally developed for analyzing the semantic content of large document corpora.'),
+(1, 'By the late 1960s, the balance between pitching and hitting had swung in favor of the pitchers. In 1968 Carl Yastrzemski won the American League batting title with an average of just .301, the lowest in history.'),
+(2, 'Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field.'),
+(3, 'California''s diverse geography ranges from the Sierra Nevada in the east to the Pacific Coast in the west, from the Redwood–Douglas fir forests of the northwest, to the Mojave Desert areas in the southeast. The center of the state is dominated by the Central Valley, a major agricultural area. ')
+</pre></li>
+<li>Build a word count table by extracting the words and building a histogram for each document using the <code>term_frequency</code> function (<a class="el" href="group__grp__text__utilities.html">Term Frequency</a>). <pre class="example">
+-- Convert a string to a list of words
+ALTER TABLE documents ADD COLUMN words TEXT[];
+UPDATE documents SET words = regexp_split_to_array(lower(contents), E'[\\s+\\.\\,]');
+
+-- Create the term frequency table
+DROP TABLE IF EXISTS my_training;
+SELECT madlib.term_frequency('documents', 'docid', 'words', 'my_training', TRUE);
+SELECT * FROM my_training order by docid limit 20;
+</pre> <pre class="result">
+ docid | wordid | count
+-------+--------+-------
+     0 |     57 |     1
+     0 |     86 |     1
+     0 |      4 |     1
+     0 |     55 |     1
+     0 |     69 |     2
+     0 |     81 |     1
+     0 |     30 |     1
+     0 |     33 |     1
+     0 |     36 |     1
+     0 |     43 |     1
+     0 |     25 |     1
+     0 |     65 |     2
+     0 |     72 |     1
+     0 |      9 |     1
+     0 |      0 |     2
+     0 |     29 |     1
+     0 |     18 |     1
+     0 |     12 |     1
+     0 |     96 |     1
+     0 |     91 |     1
+(20 rows)
+</pre> <pre class="example">
+SELECT * FROM my_training_vocabulary order by wordid limit 20;
+</pre> <pre class="result">
+ wordid |     word
+--------+--------------
+      0 |
+      1 | 1960s
+      2 | 1968
+      3 | 301
+      4 | a
+      5 | agricultural
+      6 | also
+      7 | american
+      8 | an
+      9 | analyzing
+     10 | and
+     11 | application
+     12 | are
+     13 | area
+     14 | areas
+     15 | average
+     16 | balance
+     17 | batting
+     18 | bayesian
+     19 | between
+(20 rows)
+</pre></li>
+<li>Create an LDA model using the <code><a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80" title="This UDF provides an entry for the lda training process. ">lda_train()</a></code> function. <pre class="example">
+SELECT madlib.lda_train( 'my_training',
+                         'my_model',
+                         'my_outdata',
+                         104,
+                         5,
+                         10,
+                         5,
+                         0.01
+                       );
+</pre> After a successful run of the <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80" title="This UDF provides an entry for the lda training process. ">lda_train()</a> function two tables are generated, one for storing the learned model and the other for storing the output data table.</li>
+<li>To get the detailed information about the learned model, run these commands: <pre class="example">
+-- The topic description by top-k words
+SELECT madlib.lda_get_topic_desc( 'my_model',
+                                  'my_training_vocabulary',
+                                  'my_topic_desc',
+                                  15);
+select * from my_topic_desc order by topicid, prob DESC;
+</pre> <pre class="result">
+ topicid | wordid |        prob        |       word
+---------+--------+--------------------+-------------------
+       1 |     69 |  0.181900726392252 | of
+       1 |     52 | 0.0608353510895884 | is
+       1 |     65 | 0.0608353510895884 | models
+       1 |     30 | 0.0305690072639225 | corpora
+       1 |      1 | 0.0305690072639225 | 1960s
+       1 |     57 | 0.0305690072639225 | latent
+       1 |     35 | 0.0305690072639225 | diverse
+       1 |     81 | 0.0305690072639225 | semantic
+       1 |     19 | 0.0305690072639225 | between
+       1 |     75 | 0.0305690072639225 | pitchers
+       1 |     43 | 0.0305690072639225 | for
+       1 |      6 | 0.0305690072639225 | also
+       1 |     40 | 0.0305690072639225 | favor
+       1 |     47 | 0.0305690072639225 | had
+       1 |     28 | 0.0305690072639225 | computational
+       ....
+</pre>  <pre class="example">
+-- The per-word topic counts (sorted by topic id)
+SELECT madlib.lda_get_word_topic_count( 'my_model',
+                                        'my_word_topic_count');
+</pre>  <pre class="result">
+ wordid | topic_count
+--------+--------------
+      0 | {0,17,0,0,0}
+      1 | {1,0,0,0,0}
+      2 | {0,0,0,0,1}
+      3 | {0,0,0,0,1}
+      4 | {0,0,0,0,3}
+      5 | {0,1,0,0,0}
+      6 | {1,0,0,0,0}
+      7 | {1,0,0,0,0}
+      8 | {0,0,0,1,0}
+      9 | {1,0,0,0,0}
+     10 | {0,0,0,0,3}
+     11 | {0,0,1,0,0}
+     ....
+</pre></li>
+<li>To get the topic counts and the topic assignments for each doucment, run the following commands: <pre class="example">
+-- The per-document topic assignments and counts:
+SELECT docid, topic_assignment, topic_count FROM my_outdata;
+</pre> <pre class="result">
+ docid |                                                topic_assignment                                                 |  topic_count
+-------+-----------------------------------------------------------------------------------------------------------------+----------------
+     1 | {1,1,1,1,1,1,2,4,1,4,4,4,1,0,2,1,0,2,2,3,4,2,1,1,4,2,4,3,0,0,2,4,4,3,3,3,3,3,0,1,0,4}                           | {6,12,7,7,10}
+     3 | {1,1,1,1,1,1,4,0,2,3,1,2,0,0,0,1,2,2,1,3,3,2,2,1,2,2,2,0,3,0,4,1,0,0,1,4,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,3} | {8,12,10,21,4}
+     0 | {1,1,4,2,1,4,4,4,1,3,1,0,0,0,0,0,0,0,0,1,1,3,0,1}                                                               | {9,8,1,2,4}
+     2 | {1,1,1,1,4,1,4,4,2,0,2,4,1,1,4,1,2,0,1,3,1,2,4,3,2,4,4,3,1,2,0,3,3,1,4,3,3,3,2,1}                               | {3,13,7,8,9}
+(4 rows)
+</pre></li>
+<li>To use a learned LDA model for prediction (that is, to label new documents), use the following command: <pre class="example">
+SELECT madlib.lda_predict( 'my_testing',
+                           'my_model',
+                           'my_pred'
+                         );
+</pre> The test table (<em>my_testing</em>) is expected to be in the same form as the training table (<em>my_training</em>) and can be created with the same process. After a successful run of the <a class="el" href="lda_8sql__in.html#af1fde06c39dd12bb9e5544997f815323" title="This UDF provides an entry for the lda predicton process. ">lda_predict()</a> function, the prediction results are generated and stored in <em>my_pred</em>. This table has the same schema as the <em>my_outdata</em> table generated by the <a class="el" href="lda_8sql__in.html#aeb7593251a4dedb695494f65dc2d1f80" title="This UDF provides an entry for the lda training process. ">lda_train()</a> function.</li>
+<li>Use the following command to compute the perplexity of the result. <pre class="example">
+SELECT madlib.lda_get_perplexity( 'my_model',
+                                  'my_pred'
+                                );
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] D.M. Blei, A.Y. Ng, M.I. Jordan, <em>Latent Dirichlet Allocation</em>, Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.</p>
+<p>[2] T. Griffiths and M. Steyvers, <em>Finding scientific topics</em>, PNAS, vol. 101, pp. 5228-5235, 2004.</p>
+<p>[3] Y. Wang, H. Bai, M. Stanton, W-Y. Chen, and E.Y. Chang, <em>lda: Parallel Dirichlet Allocation for Large-scale Applications</em>, AAIM, 2009.</p>
+<p>[4] <a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation">http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation</a></p>
+<p>[5] J. Chang, Collapsed Gibbs sampling methods for topic models, R manual, 2010.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="lda_8sql__in.html" title="SQL functions for Latent Dirichlet Allocation. ">lda.sql_in</a> documenting the SQL functions. </dd></dl>
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+  <td style="padding-left: 0.5em;">
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+  <div class="headertitle">
+<div class="title">Norms and Distance functions<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#functions">Linear Algebra Utility Functions</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Functions</a> </li>
+</ul>
+</div><p>The linalg module consists of useful utility functions for basic linear algebra operations. Several of these functions can be used while implementing new algorithms. These functions operate on vectors (1-D FLOAT8 array) and matrices (2-D FLOAT8 array). Note that other array types may need to be casted into FLOAT8[] before calling the functions.</p>
+<p>Refer to the <a class="el" href="linalg_8sql__in.html" title="SQL functions for linear algebra. ">linalg.sql_in</a> file for documentation on each of the utility functions.</p>
+<p><a class="anchor" id="functions"></a></p><dl class="section user"><dt>Linear Algebra Utility Functions</dt><dd><table  class="output">
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a300300fe4b8576ba0b97b95d8dea3057" title="1-norm of a vector ">norm1()</a> </th><td><p class="starttd">1-norm of a vector, \(\|\vec{a}\|_1\).</p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a50fdfe30cc0edc6888a909dbb4b4c239" title="2-norm of a vector ">norm2()</a> </th><td><p class="starttd">2-norm of a vector, \(\|\vec{a}\|_2\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#aad193850e79c4b9d811ca9bc53e13476" title="1-norm of the difference between two vectors ">dist_norm1()</a> </th><td><p class="starttd">1-norm of the difference between two vectors, \(\|\vec{a} - \vec{b}\|_1\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#aa58e51526edea6ea98db30b6f250adb4" title="2-norm of the difference between two vectors ">dist_norm2()</a> </th><td><p class="starttd">2-norm of the difference between two vectors, \(\|\vec{a} - \vec{b}\|_2\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#ad9cc156ae57bf7c0a2fe90798259105a" title="p-norm of the difference between two vectors ">dist_pnorm()</a> </th><td><p class="starttd">Generic p-norm of the difference between two vectors, \(\|\vec{a} - \vec{b}\|_p, p &gt; 0\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a31fa9f2f5b45507c09f136464fdad1db" title="Infinity-norm of the difference between two vectors. ">dist_inf_norm()</a> </th><td><p class="starttd">Infinity-norm of the difference between two vectors, \(\|\vec{a} - \vec{b}\|_\infty\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a00a08e69f27524f2096032214e15b668" title="Squared 2-norm of the difference between two vectors. ">squared_dist_norm2()</a> </th><td><p class="starttd">Squared 2-norm of the difference between two vectors, \(\|\vec{a} - \vec{b}\|_2^2\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a1782f2ba00d9f9fab20894a576079f87" title="cosine similarity score between two vectors ">cosine_similarity()</a> </th><td><p class="starttd">Cosine score between two vectors, \(\frac{\vec{a} \cdot \vec{b}}{\|\vec{a}\|_2 \|\vec{b}\|_2}\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a8c7b9281a72ff22caf06161701b27e84" title="Angle between two vectors. ">dist_angle()</a> </th><td><p class="starttd">Angle between two vectors in an Euclidean space, \(\cos^{-1}(\frac{\vec{a} \cdot \vec{b}}{\|\vec{a}\|_2 \|\vec{b}\|_2})\). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#afa13b4c6122b99422d666dedea136c18" title="Tanimoto distance between two vectors. ">dist_tanimoto()</a> </th><td><p class="starttd">Tanimoto distance between two vectors. [1] </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#ac1397ac9f4a35b3b67c3be05b5e1a828" title="Jaccard distance between two vectors (treated as sets) ">dist_jaccard()</a> </th><td><p class="starttd">Jaccard distance between two varchar vectors treated as sets. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#af6b905fcac7746ef0ed0c36df4a1e070" title="Get an indexed row of the given matrix (2-D array) ">get_row()</a> </th><td><p class="starttd">Return the indexed row of a matrix (2-D array). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a20f34c9e661191e5225cca7bc23252c5" title="Get an indexed col of the given matrix (2-D array) ">get_col()</a> </th><td><p class="starttd">Return the indexed col of a matrix (2-D array). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4" title="Compute the average of vectors. ">avg()</a> </th><td><p class="starttd">Compute the average of vectors. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a0b04663ca206f03e66aed5ea2b4cc461" title="Compute the normalized average of vectors. ">normalized_avg()</a> </th><td><p class="starttd">Compute the normalized average of vectors (unit vector in an Euclidean space). </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th><a class="el" href="linalg_8sql__in.html#a9c439706f35d6cac89f151d553a5f111" title="Combine vectors to a matrix. ">matrix_agg()</a> </th><td><p class="starttd">Combine vectors to a matrix. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+</dd></dl>
+<p><a class="anchor" id="examples"></a></p>
+<p><b>Vector Norms and Distances</b></p>
+<ol type="1">
+<li>Create a database table with two vector columns and add some data. <pre class="example">
+CREATE TABLE two_vectors(
+    id  integer,
+    a   float8[],
+    b   float8[]);
+</pre> <pre class="example">
+INSERT INTO two_vectors VALUES
+(1, '{3,4}', '{4,5}'),
+(2, '{1,1,0,-4,5,3,4,106,14}', '{1,1,0,6,-3,1,2,92,2}');
+</pre></li>
+<li>Invoke norm functions. <pre class="example">
+SELECT
+    id,
+    madlib.norm1(a),
+    madlib.norm2(a)
+FROM two_vectors;
+</pre> Result: <pre class="result">
+ id | norm1 |      norm2
+----+-------+------------------
+  1 |     7 |                5
+  2 |   138 | 107.238052947636
+(2 rows)
+</pre></li>
+<li>Invoke distance functions. <pre class="example">
+SELECT
+    id,
+    madlib.dist_norm1(a, b),
+    madlib.dist_norm2(a, b),
+    madlib.dist_pnorm(a, b, 5) AS norm5,
+    madlib.dist_inf_norm(a, b),
+    madlib.squared_dist_norm2(a, b) AS sq_dist_norm2,
+    madlib.cosine_similarity(a, b),
+    madlib.dist_angle(a, b),
+    madlib.dist_tanimoto(a, b),
+    madlib.dist_jaccard(a::text[], b::text[])
+FROM two_vectors;
+</pre> Result: <pre class="result">
+ id | dist_norm1 |    dist_norm2    |      norm5       | dist_inf_norm | sq_dist_norm2 | cosine_similarity |     dist_angle     |   dist_tanimoto    |   dist_jaccard
+----+------------+------------------+------------------+---------------+---------------+-------------------+--------------------+--------------------+-------------------
+  1 |          2 |  1.4142135623731 | 1.14869835499704 |             1 |             2 | 0.999512076087079 | 0.0312398334302684 | 0.0588235294117647 | 0.666666666666667
+  2 |         48 | 22.6274169979695 |  15.585086360695 |            14 |           512 | 0.985403348449008 |   0.17106899659286 | 0.0498733684005455 | 0.833333333333333
+(2 rows)
+</pre></li>
+</ol>
+<p><b>Matrix Functions</b></p>
+<ol type="1">
+<li>Create a database table with a matrix column. <pre class="example">
+CREATE TABLE matrix(
+    id  integer,
+    m   float8[]);
+</pre> <pre class="example">
+INSERT INTO matrix VALUES
+(1, '{{4,5},{3,5},{9,0}}');
+</pre></li>
+<li>Invoke matrix functions. <pre class="example">
+SELECT
+    madlib.get_row(m, 1) AS row_1,
+    madlib.get_row(m, 2) AS row_2,
+    madlib.get_row(m, 3) AS row_3,
+    madlib.get_col(m, 1) AS col_1,
+    madlib.get_col(m, 2) AS col_2
+FROM matrix;
+</pre> Result: <pre class="result">
+ row_1 | row_2 | row_3 |  col_1  |  col_2
+-------+-------+-------+---------+---------
+ {4,5} | {3,5} | {9,0} | {4,3,9} | {5,5,0}
+(1 row)
+</pre></li>
+</ol>
+<p><b>Aggregate Functions</b></p>
+<ol type="1">
+<li>Create a database table with a vector column. <pre class="example">
+CREATE TABLE vector(
+    id  integer,
+    v   float8[]);
+</pre> <pre class="example">
+INSERT INTO vector VALUES
+(1, '{4,3}'),
+(2, '{8,6}'),
+(3, '{12,9}');
+</pre></li>
+<li>Invoke aggregate functions. <pre class="example">
+SELECT
+    madlib.avg(v),
+    madlib.normalized_avg(v),
+    madlib.matrix_agg(v)
+FROM vector;
+</pre> Result: <pre class="result">
+  avg  | normalized_avg |      matrix_agg
+-------+----------------+----------------------
+ {8,6} | {0.8,0.6}      | {{4,3},{8,6},{12,9}}
+(1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] <a href="http://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance">http://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="linalg_8sql__in.html" title="SQL functions for linear algebra. ">linalg.sql_in</a> documenting the SQL functions. </dd></dl>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__linear__solver.html
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+<title>MADlib: Linear Solvers</title>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+          </span>
+        </div>
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+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+</div><!-- top -->
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+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Linear Solvers<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods that implement solutions for systems of consistent linear equations. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__dense__linear__solver"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__dense__linear__solver.html">Dense Linear Systems</a></td></tr>
+<tr class="memdesc:group__grp__dense__linear__solver"><td class="mdescLeft">&#160;</td><td class="mdescRight">Implements solution methods for large dense linear systems. Currently, restricted to problems that fit in memory. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:group__grp__sparse__linear__solver"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__sparse__linear__solver.html">Sparse Linear Systems</a></td></tr>
+<tr class="memdesc:group__grp__sparse__linear__solver"><td class="mdescLeft">&#160;</td><td class="mdescRight">Implements solution methods for linear systems with sparse matrix input. Currently, restricted to problems that fit in memory. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+</html>

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+   <div id="projectname">
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+   <div id="projectbrief">User Documentation for MADlib</div>
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+  <div class="headertitle">
+<div class="title">Linear Regression<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Linear regression models a linear relationship of a scalar dependent variable \( y \) to one or more explanatory independent variables \( x \) to build a model of coefficients.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd></dd></dl>
+<p>The linear regression training function has the following syntax. </p><pre class="syntax">
+linregr_train( source_table,
+               out_table,
+               dependent_varname,
+               independent_varname,
+               grouping_cols,
+               heteroskedasticity_option
+             )
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>out_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the output model.</p>
+<p>The output table contains the following columns. </p><table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td>Any grouping columns provided during training. Present only if the grouping option is used.  </td></tr>
+<tr>
+<th>coef </th><td>FLOAT8[]. Vector of the coefficients of the regression.  </td></tr>
+<tr>
+<th>r2 </th><td>FLOAT8. R-squared coefficient of determination of the model.  </td></tr>
+<tr>
+<th>std_err </th><td>FLOAT8[]. Vector of the standard error of the coefficients.  </td></tr>
+<tr>
+<th>t_stats </th><td>FLOAT8[]. Vector of the t-statistics of the coefficients.  </td></tr>
+<tr>
+<th>p_values </th><td>FLOAT8[]. Vector of the p-values of the coefficients.  </td></tr>
+<tr>
+<th>condition_no </th><td>FLOAT8 array. The condition number of the \(X^{*}X\) matrix. A high condition number is usually an indication that there may be some numeric instability in the result yielding a less reliable model. A high condition number often results when there is a significant amount of colinearity in the underlying design matrix, in which case other regression techniques, such as elastic net regression, may be more appropriate.  </td></tr>
+<tr>
+<th>bp_stats </th><td>FLOAT8. The Breush-Pagan statistic of heteroskedacity. Present only if the heteroskedacity argument was set to True when the model was trained.  </td></tr>
+<tr>
+<th>bp_p_value </th><td>FLOAT8. The Breush-Pagan calculated p-value. Present only if the heteroskedacity parameter was set to True when the model was trained.  </td></tr>
+<tr>
+<th>num_rows_processed </th><td>INTEGER. The number of rows that are actually used in each group.  </td></tr>
+<tr>
+<th>num_missing_rows_skipped </th><td>INTEGER. The number of rows that have NULL values in the dependent and independent variables, and were skipped in the computation for each group. </td></tr>
+</table>
+<p class="enddd">A summary table named &lt;out_table&gt;_summary is created together with the output table. It has the following columns: </p><table  class="output">
+<tr>
+<th>source_table </th><td>The data source table name </td></tr>
+<tr>
+<th>out_table </th><td>The output table name </td></tr>
+<tr>
+<th>dependent_varname </th><td>The dependent variable </td></tr>
+<tr>
+<th>independent_varname </th><td>The independent variables </td></tr>
+<tr>
+<th>num_rows_processed </th><td>The total number of rows that were used in the computation. </td></tr>
+<tr>
+<th>num_missing_rows_skipped </th><td>The total number of rows that were skipped because of NULL values in them. </td></tr>
+</table>
+</dd>
+<dt></dt>
+<dd><dl class="section note"><dt>Note</dt><dd>For p-values, we just return the computation result directly. Other statistical packages, like 'R', produce the same result, but on printing the result to screen, another format function is used and any p-value that is smaller than the machine epsilon (the smallest positive floating-point number 'x' such that '1 + x != 1') will be printed on screen as "&lt; xxx" (xxx is the value of the machine epsilon). Although the result may look different, they are in fact the same. </dd></dl>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">TEXT. Expression to evaluate for the dependent variable.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">TEXT. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant <code>1</code> term in the independent variable list.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_cols (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL <code>GROUP BY</code> clause. When this value is null, no grouping is used and a single result model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>heteroskedasticity_option (optional) </dt>
+<dd>BOOLEAN, default: FALSE. When TRUE, the heteroskedasticity of the model is also calculated and returned with the results. </dd>
+</dl>
+<p><a class="anchor" id="warning"></a></p><dl class="section warning"><dt>Warning</dt><dd>The aggregate 'linregr' has been deprecated in favor of the function 'linregr_train'. If the aggregate 'linregr' is used to output the results of linear regression to a table, it is recommended to follow the general pattern shown below (replace text within '&lt;...&gt;' with the appropriate variable names). <pre class="syntax">
+CREATE TABLE &lt;output table&gt; AS
+SELECT (r).*
+FROM (
+    SELECT linregr(&lt;dependent variable&gt;, &lt;independent variable&gt;) as r
+    FROM &lt;source table&gt;
+    ) q;
+</pre></dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd><pre class="syntax">
+linregr_predict(coef, col_ind)
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>coef </dt>
+<dd><p class="startdd">FLOAT8[]. Vector of the coefficients of regression.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_ind </dt>
+<dd><p class="startdd">FLOAT8[]. An array containing the independent variable column names. </p>
+<p class="enddd"><a class="anchor" id="examples"></a></p>
+</dd>
+</dl>
+</dd></dl>
+<dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>Create an input data set. <pre class="example">
+CREATE TABLE houses (id INT, tax INT, bedroom INT, bath FLOAT, price INT,
+            size INT, lot INT);
+COPY houses FROM STDIN WITH DELIMITER '|';
+  1 |  590 |       2 |    1 |  50000 |  770 | 22100
+  2 | 1050 |       3 |    2 |  85000 | 1410 | 12000
+  3 |   20 |       3 |    1 |  22500 | 1060 |  3500
+  4 |  870 |       2 |    2 |  90000 | 1300 | 17500
+  5 | 1320 |       3 |    2 | 133000 | 1500 | 30000
+  6 | 1350 |       2 |    1 |  90500 |  820 | 25700
+  7 | 2790 |       3 |  2.5 | 260000 | 2130 | 25000
+  8 |  680 |       2 |    1 | 142500 | 1170 | 22000
+  9 | 1840 |       3 |    2 | 160000 | 1500 | 19000
+ 10 | 3680 |       4 |    2 | 240000 | 2790 | 20000
+ 11 | 1660 |       3 |    1 |  87000 | 1030 | 17500
+ 12 | 1620 |       3 |    2 | 118600 | 1250 | 20000
+ 13 | 3100 |       3 |    2 | 140000 | 1760 | 38000
+ 14 | 2070 |       2 |    3 | 148000 | 1550 | 14000
+ 15 |  650 |       3 |  1.5 |  65000 | 1450 | 12000
+\.
+</pre></li>
+<li>Train a regression model. First, a single regression for all the data. <pre class="example">
+SELECT madlib.linregr_train( 'houses',
+                             'houses_linregr',
+                             'price',
+                             'ARRAY[1, tax, bath, size]'
+                           );
+</pre></li>
+<li>Generate three output models, one for each value of "bedroom". <pre class="example">
+SELECT madlib.linregr_train( 'houses',
+                             'houses_linregr_bedroom',
+                             'price',
+                             'ARRAY[1, tax, bath, size]',
+                             'bedroom'
+                           );
+</pre></li>
+<li>Examine the resulting models. <pre class="example">
+-- Set extended display on for easier reading of output
+\x ON
+SELECT * FROM houses_linregr;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]+---------------------------------------------------------------------------
+coef         | {-12849.4168959872,28.9613922651765,10181.6290712648,50.516894915354}
+r2           | 0.768577580597443
+std_err      | {33453.0344331391,15.8992104963997,19437.7710925923,32.928023174087}
+t_stats      | {-0.38410317968819,1.82156166004184,0.523806408809133,1.53416118083605}
+p_values     | {0.708223134615422,0.0958005827189772,0.610804093526536,0.153235085548186}
+condition_no | 9002.50457085737
+</pre></li>
+<li>View the results grouped by bedroom. <pre class="example">
+SELECT * FROM houses_linregr_bedroom;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]+--------------------------------------------------------------------------
+bedroom      | 2
+coef         | {-84242.0345406597,55.4430144648696,-78966.9753675319,225.611910021192}
+r2           | 0.968809546465313
+std_err      | {35018.9991665742,19.5731125320686,23036.8071292552,49.0448678148784}
+t_stats      | {-2.40560942761235,2.83261103077151,-3.42786111480046,4.60011251070697}
+p_values     | {0.250804617665239,0.21605133377602,0.180704400437373,0.136272031474122}
+condition_no | 10086.1048721726
+-[ RECORD 2 ]+--------------------------------------------------------------------------
+bedroom      | 4
+coef         | {0.0112536020318378,41.4132554771633,0.0225072040636757,31.3975496688276}
+r2           | 1
+std_err      | {0,0,0,0}
+t_stats      | {Infinity,Infinity,Infinity,Infinity}
+p_values     |
+condition_no | Infinity
+-[ RECORD 3 ]+--------------------------------------------------------------------------
+bedroom      | 3
+coef         | {-88155.8292501601,27.1966436294429,41404.0293363612,62.637521075324}
+r2           | 0.841699901311252
+std_err      | {57867.9999702625,17.8272309154689,43643.1321511114,70.8506824863954}
+t_stats      | {-1.52339512849005,1.52556747362508,0.948695185143966,0.884077878676067}
+p_values     | {0.188161432894871,0.187636685729869,0.386340032374927,0.417132778705789}
+condition_no | 11722.6225642147
+</pre> Alternatively you can unnest the results for easier reading of output. <pre class="example">
+\x OFF
+SELECT unnest(ARRAY['intercept','tax','bath','size']) as attribute,
+       unnest(coef) as coefficient,
+       unnest(std_err) as standard_error,
+       unnest(t_stats) as t_stat,
+       unnest(p_values) as pvalue
+FROM houses_linregr;
+</pre></li>
+<li>Use the prediction function to evaluate residuals. <pre class="example">
+SELECT houses.*,
+       madlib.linregr_predict( ARRAY[1,tax,bath,size],
+                               m.coef
+                             ) as predict,
+        price -
+          madlib.linregr_predict( ARRAY[1,tax,bath,size],
+                                  m.coef
+                                ) as residual
+FROM houses, houses_linregr m;
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Note</dt><dd>All table names can be optionally schema qualified (current_schemas() would be searched if a schema name is not provided) and all table and column names should follow case-sensitivity and quoting rules per the database. (For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"').</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Ordinary least-squares (OLS) linear regression refers to a stochastic model in which the conditional mean of the dependent variable (usually denoted \( Y \)) is an affine function of the vector of independent variables (usually denoted \( \boldsymbol x \)). That is, </p><p class="formulaDsp">
+\[ E[Y \mid \boldsymbol x] = \boldsymbol c^T \boldsymbol x \]
+</p>
+<p> for some unknown vector of coefficients \( \boldsymbol c \). The assumption is that the residuals are i.i.d. distributed Gaussians. That is, the (conditional) probability density of \( Y \) is given by </p><p class="formulaDsp">
+\[ f(y \mid \boldsymbol x) = \frac{1}{\sqrt{2 \pi \sigma^2}} \cdot \exp\left(-\frac{1}{2 \sigma^2} \cdot (y - \boldsymbol x^T \boldsymbol c)^2 \right) \,. \]
+</p>
+<p> OLS linear regression finds the vector of coefficients \( \boldsymbol c \) that maximizes the likelihood of the observations.</p>
+<p>Let</p><ul>
+<li>\( \boldsymbol y \in \mathbf R^n \) denote the vector of observed dependent variables, with \( n \) rows, containing the observed values of the dependent variable,</li>
+<li>\( X \in \mathbf R^{n \times k} \) denote the design matrix with \( k \) columns and \( n \) rows, containing all observed vectors of independent variables. \( \boldsymbol x_i \) as rows,</li>
+<li>\( X^T \) denote the transpose of \( X \),</li>
+<li>\( X^+ \) denote the pseudo-inverse of \( X \).</li>
+</ul>
+<p>Maximizing the likelihood is equivalent to maximizing the log-likelihood \( \sum_{i=1}^n \log f(y_i \mid \boldsymbol x_i) \), which simplifies to minimizing the <b>residual sum of squares</b> \( RSS \) (also called sum of squared residuals or sum of squared errors of prediction), </p><p class="formulaDsp">
+\[ RSS = \sum_{i=1}^n ( y_i - \boldsymbol c^T \boldsymbol x_i )^2 = (\boldsymbol y - X \boldsymbol c)^T (\boldsymbol y - X \boldsymbol c) \,. \]
+</p>
+<p> The first-order conditions yield that the \( RSS \) is minimized at </p><p class="formulaDsp">
+\[ \boldsymbol c = (X^T X)^+ X^T \boldsymbol y \,. \]
+</p>
+<p>Computing the <b>total sum of squares</b> \( TSS \), the <b>explained sum of squares</b> \( ESS \) (also called the regression sum of squares), and the <b>coefficient of determination</b> \( R^2 \) is done according to the following formulas: </p><p class="formulaDsp">
+\begin{align*} ESS &amp; = \boldsymbol y^T X \boldsymbol c - \frac{ \| y \|_1^2 }{n} \\ TSS &amp; = \sum_{i=1}^n y_i^2 - \frac{ \| y \|_1^2 }{n} \\ R^2 &amp; = \frac{ESS}{TSS} \end{align*}
+</p>
+<p> Note: The last equality follows from the definition \( R^2 = 1 - \frac{RSS}{TSS} \) and the fact that for linear regression \( TSS = RSS + ESS \). A proof of the latter can be found, e.g., at: <a href="http://en.wikipedia.org/wiki/Sum_of_squares">http://en.wikipedia.org/wiki/Sum_of_squares</a></p>
+<p>We estimate the variance \( Var[Y - \boldsymbol c^T \boldsymbol x \mid \boldsymbol x] \) as </p><p class="formulaDsp">
+\[ \sigma^2 = \frac{RSS}{n - k} \]
+</p>
+<p> and compute the t-statistic for coefficient \( i \) as </p><p class="formulaDsp">
+\[ t_i = \frac{c_i}{\sqrt{\sigma^2 \cdot \left( (X^T X)^{-1} \right)_{ii} }} \,. \]
+</p>
+<p>The \( p \)-value for coefficient \( i \) gives the probability of seeing a value at least as extreme as the one observed, provided that the null hypothesis ( \( c_i = 0 \)) is true. Letting \( F_\nu \) denote the cumulative density function of student-t with \( \nu \) degrees of freedom, the \( p \)-value for coefficient \( i \) is therefore </p><p class="formulaDsp">
+\[ p_i = \Pr(|T| \geq |t_i|) = 2 \cdot (1 - F_{n - k}( |t_i| )) \]
+</p>
+<p> where \( T \) is a student-t distributed random variable with mean 0.</p>
+<p>The condition number [2] \( \kappa(X) = \|X\|_2\cdot\|X^{-1}\|_2\) is computed as the product of two spectral norms [3]. The spectral norm of a matrix \(X\) is the largest singular value of \(X\) i.e. the square root of the largest eigenvalue of the positive-semidefinite matrix \(X^{*}X\):</p>
+<p class="formulaDsp">
+\[ \|X\|_2 = \sqrt{\lambda_{\max}\left(X^{*}X\right)}\ , \]
+</p>
+<p> where \(X^{*}\) is the conjugate transpose of \(X\). The condition number of a linear regression problem is a worst-case measure of how sensitive the result is to small perturbations of the input. A large condition number (say, more than 1000) indicates the presence of significant multicollinearity.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Cosma Shalizi: Statistics 36-350: Data Mining, Lecture Notes, 21 October 2009, <a href="http://www.stat.cmu.edu/~cshalizi/350/lectures/17/lecture-17.pdf">http://www.stat.cmu.edu/~cshalizi/350/lectures/17/lecture-17.pdf</a></p>
+<p>[2] Wikipedia: Condition Number, <a href="http://en.wikipedia.org/wiki/Condition_number">http://en.wikipedia.org/wiki/Condition_number</a>.</p>
+<p>[3] Wikipedia: Spectral Norm, <a href="http://en.wikipedia.org/wiki/Spectral_norm#Spectral_norm">http://en.wikipedia.org/wiki/Spectral_norm#Spectral_norm</a></p>
+<p>[4] Wikipedia: Breusch–Pagan test, <a href="http://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test">http://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test</a></p>
+<p>[5] Wikipedia: Heteroscedasticity-consistent standard errors, <a href="http://en.wikipedia.org/wiki/Heteroscedasticity-consistent_standard_errors">http://en.wikipedia.org/wiki/Heteroscedasticity-consistent_standard_errors</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p><a class="el" href="group__grp__robust.html">Robust Variance</a></p>
+<p><a class="el" href="group__grp__clustered__errors.html">Clustered Variance</a></p>
+<p><a class="el" href="group__grp__validation.html">Cross Validation</a></p>
+<p>File <a class="el" href="linear_8sql__in.html" title="SQL functions for linear regression. ">linear.sql_in</a>, source file for the SQL functions</p>
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+       class="ui-resizable-handle">
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+</script>
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+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Low-rank Matrix Factorization<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a> &raquo; <a class="el" href="group__grp__matrix__factorization.html">Matrix Factorization</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Function Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+</ul>
+</div><p>This module implements "factor model" for representing an incomplete matrix using a low-rank approximation [1]. Mathematically, this model seeks to find matrices U and V (also referred as factors) that, for any given incomplete matrix A, minimizes:</p>
+<p class="formulaDsp">
+\[ \|\boldsymbol A - \boldsymbol UV^{T} \|_2 \]
+</p>
+<p>subject to \(rank(\boldsymbol UV^{T}) \leq r\), where \(\|\cdot\|_2\) denotes the Frobenius norm. Let \(A\) be a \(m \times n\) matrix, then \(U\) will be \(m \times r\) and \(V\) will be \(n \times r\), in dimension, and \(1 \leq r \ll \min(m, n)\). This model is not intended to do the full decomposition, or to be used as part of inverse procedure. This model has been widely used in recommendation systems (e.g., Netflix [2]) and feature selection (e.g., image processing [3]).</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd></dd></dl>
+<p>Low-rank matrix factorization of an incomplete matrix into two factors.</p>
+<pre class="syntax">
+lmf_igd_run( rel_output,
+             rel_source,
+             col_row,
+             col_column,
+             col_value,
+             row_dim,
+             column_dim,
+             max_rank,
+             stepsize,
+             scale_factor,
+             num_iterations,
+             tolerance
+           )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>rel_output </dt>
+<dd><p class="startdd">TEXT. The name of the table to receive the output.</p>
+<p>Output factors matrix U and V are in a flattened format. </p><pre>RESULT AS (
+        matrix_u    DOUBLE PRECISION[],
+        matrix_v    DOUBLE PRECISION[],
+        rmse        DOUBLE PRECISION
+);</pre><p class="enddd">Features correspond to row i is <code>matrix_u[i:i][1:r]</code>. Features correspond to column j is <code>matrix_v[j:j][1:r]</code>.  </p>
+</dd>
+<dt>rel_source </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the input data.</p>
+<p>The input matrix&gt; is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>input_table</em> (
+    <em>row</em>    INTEGER,
+    <em>col</em>    INTEGER,
+    <em>value</em>  DOUBLE PRECISION
+)</pre><p class="enddd">Input is contained in a table that describes an incomplete matrix, with available entries specified as (row, column, value). The input matrix should be 1-based, which means row &gt;= 1, and col &gt;= 1. NULL values are not expected.  </p>
+</dd>
+<dt>col_row </dt>
+<dd>TEXT. The name of the column containing the row number. </dd>
+<dt>col_column </dt>
+<dd>TEXT. The name of the column containing the column number. </dd>
+<dt>col_value </dt>
+<dd>DOUBLE PRECISION. The value at (row, col). </dd>
+<dt>row_dim (optional) </dt>
+<dd>INTEGER, default: "SELECT max(col_row) FROM rel_source". The number of columns in the matrix. </dd>
+<dt>column_dim (optional) </dt>
+<dd>INTEGER, default: "SELECT max(col_col) FROM rel_source". The number of rows in the matrix. </dd>
+<dt>max_rank </dt>
+<dd>INTEGER, default: 20. The rank of desired approximation. </dd>
+<dt>stepsize (optional) </dt>
+<dd>DOUBLE PRECISION, default: 0.01. Hyper-parameter that decides how aggressive the gradient steps are.  </dd>
+<dt>scale_factor (optional) </dt>
+<dd>DOUBLE PRECISION, default: 0.1. Hyper-parameter that decides scale of initial factors. </dd>
+<dt>num_iterations (optional) </dt>
+<dd>INTEGER, default: 10. Maximum number if iterations to perform regardless of convergence. </dd>
+<dt>tolerance (optional) </dt>
+<dd>DOUBLE PRECISION, default: 0.0001. Acceptable level of error in convergence. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare an input table/view: <pre class="example">
+DROP TABLE IF EXISTS lmf_data;
+CREATE TABLE lmf_data (
+ row INT,
+ col INT,
+ val FLOAT8
+);
+</pre></li>
+<li>Populate the input table with some data. <pre class="example">
+INSERT INTO lmf_data VALUES (1, 1, 5.0);
+INSERT INTO lmf_data VALUES (3, 100, 1.0);
+INSERT INTO lmf_data VALUES (999, 10000, 2.0);
+</pre></li>
+<li>Call the <a class="el" href="lmf_8sql__in.html#ac1acb1f0e1f7008118f21c83546a4602" title="Low-rank matrix factorization of a incomplete matrix into two factors. ">lmf_igd_run()</a> stored procedure. <pre class="example">
+DROP TABLE IF EXISTS lmf_model;
+SELECT madlib.lmf_igd_run( 'lmf_model',
+                           'lmf_data',
+                           'row',
+                           'col',
+                           'val',
+                           999,
+                           10000,
+                           3,
+                           0.1,
+                           2,
+                           10,
+                           1e-9
+                         );
+</pre> Example result (the exact result may not be the same). <pre class="result">
+NOTICE:
+Finished low-rank matrix factorization using incremental gradient
+DETAIL:
+   table : lmf_data (row, col, val)
+Results:
+   RMSE = 0.0145966345300041
+Output:
+   view : SELECT * FROM lmf_model WHERE id = 1
+ lmf_igd_run
+&#160;-----------
+           1
+ (1 row)
+</pre></li>
+<li>Sanity check of the result. You may need a model id returned and also indicated by the function <a class="el" href="lmf_8sql__in.html#ac1acb1f0e1f7008118f21c83546a4602" title="Low-rank matrix factorization of a incomplete matrix into two factors. ">lmf_igd_run()</a>, assuming 1 here: <pre class="example">
+SELECT array_dims(matrix_u) AS u_dims, array_dims(matrix_v) AS v_dims
+FROM lmf_model
+WHERE id = 1;
+</pre> Result: <pre class="result">
+     u_dims    |     v_dims
+ --------------+----------------
+  [1:999][1:3] | [1:10000][1:3]
+ (1 row)
+</pre></li>
+<li>Query the result value. <pre class="example">
+SELECT matrix_u[2:2][1:3] AS row_2_features
+FROM lmf_model
+WHERE id = 1;
+</pre> Example output (the exact result may not be the same): <pre class="result">
+                       row_2_features
+&#160;---------------------------------------------------------
+  {{1.12030523084104,0.522217971272767,0.0264869043603539}}
+ (1 row)
+</pre></li>
+<li>Make prediction of a missing entry (row=2, col=7654). <pre class="example">
+SELECT madlib.array_dot(
+    matrix_u[2:2][1:3],
+    matrix_v[7654:7654][1:3]
+    ) AS row_2_col_7654
+FROM lmf_model
+WHERE id = 1;
+</pre> Example output (the exact result may not be the same due the randomness of the algorithm): <pre class="result">
+   row_2_col_7654
+&#160;------------------
+  1.3201582940851
+ (1 row)
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] N. Srebro and T. Jaakkola. “Weighted Low-Rank Approximations.” In: ICML. Ed. by T. Fawcett and N. Mishra. AAAI Press, 2003, pp. 720–727. isbn: 1-57735-189-4.</p>
+<p>[2] Simon Funk, Netflix Update: Try This at Home, December 11 2006, <a href="http://sifter.org/~simon/journal/20061211.html">http://sifter.org/~simon/journal/20061211.html</a></p>
+<p>[3] J. Wright, A. Ganesh, S. Rao, Y. Peng, and Y. Ma. “Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization.” In: NIPS. Ed. by Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta. Curran Associates, Inc., 2009, pp. 2080–2088. isbn: 9781615679119. </p>
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+   <div id="projectbrief">User Documentation for MADlib</div>
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+</div><!--header-->
+<div class="contents">
+
+<p>Functions used in C4.5 and random forest for data preprocessing.  
+<a href="#details">More...</a></p>
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+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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+<tr class="separator:a203a0f45da5e6117d253d45b2b72ee4e"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a303140fc756d5e53a606a82276902e8d"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#a303140fc756d5e53a606a82276902e8d">__get_class_column_name</a> (text meta_tbl_name)</td></tr>
+<tr class="separator:a303140fc756d5e53a606a82276902e8d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a952d5d8db24ef2831cf290c6caec7e3d"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#a952d5d8db24ef2831cf290c6caec7e3d">__get_class_value</a> (int code, text meta_tbl_name)</td></tr>
+<tr class="separator:a952d5d8db24ef2831cf290c6caec7e3d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:afae4bac25d825c359f6fc50ef0735ecd"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#afae4bac25d825c359f6fc50ef0735ecd">__breakup_table</a> (text input_tbl_name, text breakup_tbl_name, text kv_cls_name, text id_col_name, text cls_col_name, text[] attr_col_names, bool[] is_conts, int h2hmv_routine_id, int verbosity)</td></tr>
+<tr class="separator:afae4bac25d825c359f6fc50ef0735ecd"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aedf7411a752ea4505e725ff8dc9c07ef"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#aedf7411a752ea4505e725ff8dc9c07ef">__gen_vertical_encoded_table</a> (text breakup_tbl_name, text enc_tbl_name, text kv_attr_name, bool is_tbl_tmp, int verbosity)</td></tr>
+<tr class="separator:aedf7411a752ea4505e725ff8dc9c07ef"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a669cfeef954d60e81bd86b1b8adff066"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#a669cfeef954d60e81bd86b1b8adff066">__gen_horizontal_encoded_table</a> (text hor_tbl_name, text ver_tbl_name, int attr_count, int verbosity)</td></tr>
+<tr class="separator:a669cfeef954d60e81bd86b1b8adff066"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad4ad4f439f9a24a9541548228fdcbd68"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#ad4ad4f439f9a24a9541548228fdcbd68">__encode_columns</a> (text kv_attr_name, text breakup_tbl_name, int h2hmv_routine_id, int verbosity)</td></tr>
+<tr class="separator:ad4ad4f439f9a24a9541548228fdcbd68"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad7f67f799628b62cc9bf9feb3fc9d7d1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#ad7f67f799628b62cc9bf9feb3fc9d7d1">__encode_table</a> (text input_tbl_name, text id_col_name, text[] feature_names, text cls_col_name, text[] cont_attr_names, text enc_table_name, text meta_tbl_name, int h2hmv_routine_id, int verbosity)</td></tr>
+<tr class="separator:ad7f67f799628b62cc9bf9feb3fc9d7d1"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a0f220ad406479dd08b61799ac5d3837d"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__preproc_8sql__in.html#a0f220ad406479dd08b61799ac5d3837d">__encode_table</a> (text input_tbl_name, text enc_tbl_name, text meta_tbl_name, int h2hmv_routine_id, int verbosity)</td></tr>
+<tr class="separator:a0f220ad406479dd08b61799ac5d3837d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>July 19, 2012 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="afae4bac25d825c359f6fc50ef0735ecd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __breakup_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>breakup_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>kv_cls_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cls_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>attr_col_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bool[]&#160;</td>
+          <td class="paramname"><em>is_conts</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>h2hmv_routine_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5101cb7fa622adb504779a691b1b9b01"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __create_metatable </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a436aa21412718984868087afca2c83ec"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">int4 __distinct_feature_value </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>feature_id</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae8236322b62a83dd2862aef0068b68d9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __drop_metatable </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad4ad4f439f9a24a9541548228fdcbd68"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __encode_columns </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>kv_attr_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>breakup_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>h2hmv_routine_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ad7f67f799628b62cc9bf9feb3fc9d7d1"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __encode_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>feature_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>cls_col_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>cont_attr_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>enc_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>h2hmv_routine_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a0f220ad406479dd08b61799ac5d3837d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __encode_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>enc_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>h2hmv_routine_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a669cfeef954d60e81bd86b1b8adff066"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __gen_horizontal_encoded_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>hor_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>ver_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>attr_count</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aedf7411a752ea4505e725ff8dc9c07ef"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __gen_vertical_encoded_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>breakup_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>enc_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>kv_attr_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">bool&#160;</td>
+          <td class="paramname"><em>is_tbl_tmp</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a303140fc756d5e53a606a82276902e8d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_class_column_name </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a952d5d8db24ef2831cf290c6caec7e3d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_class_value </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>code</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab033c418165fe11195006bd948bacd04"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_classtable_name </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a6f7558c509403b4d1693f9cf2ef35610"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_column_value </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>column_index</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>code</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">char&#160;</td>
+          <td class="paramname"><em>column_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a592394d16acfe6382f721b8efa37debc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_feature_name </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>feature_index</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ab671b52e3bcbf06ff854266b297a5c8f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_feature_value </td>
+          <td>(</td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>feature_index</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>code</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a203a0f45da5e6117d253d45b2b72ee4e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_id_column_name </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a45d6e5d9934d5f435c41f82b28dd5193"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __insert_into_metatable </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>col_index</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>column_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">char&#160;</td>
+          <td class="paramname"><em>column_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>is_cont</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>num_dist_value</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
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+      <table class="memname">
+        <tr>
+          <td class="memname">int4 __num_of_class </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
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+<a class="anchor" id="ac7914f60e80d98f1515ad3c0da0781ae"></a>
+<div class="memitem">
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+      <table class="memname">
+        <tr>
+          <td class="memname">int4 __num_of_feature </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
+        </tr>
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+</div><div class="memdoc">
+
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+</div>
+<a class="anchor" id="a490f7a4dc4fcf407a0177370ce877256"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __validate_input_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>feature_columns</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>id_column</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>class_column</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a09ad455902828bd5a474fdaf78615a0f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __validate_metatable </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>meta_tbl_name</em></td><td>)</td>
+          <td></td>
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/dt__utility_8sql__in.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">dt_utility.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>Utility functions widely used in C4.5 and random forest.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a3775e498d398d11af57ce69f716675cb"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__utility_8sql__in.html#a3775e498d398d11af57ce69f716675cb">__regclass_to_text</a> (regclass rc)</td></tr>
+<tr class="separator:a3775e498d398d11af57ce69f716675cb"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:ac0d671e18bdced796c139672e4cbfeea"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a73736314b7017bc048008d5d5db8b4b8"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a755012b4ca1cc28586148e60a3b25a0a"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:aa45a4dd4f12fbd9c1ca3bb918c080cb3"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a4c8c034f56f8c95ae1422fa0ea547f69"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a891cd53edfff0adb36603df41b3a1b71"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a292fab77e9ea1b22cf7fc8848571e244"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a754a692fb8a63e1cb82858699a2ceddd"><td class="memItemLeft" align="right" valign="top">text[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__utility_8sql__in.html#a754a692fb8a63e1cb82858699a2ceddd">__csvstr_to_array</a> (text csv_str)</td></tr>
+<tr class="separator:a754a692fb8a63e1cb82858699a2ceddd"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa1341c743f3dabd5a96f36cda5564679"><td class="memItemLeft" align="right" valign="top">boolean[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__utility_8sql__in.html#aa1341c743f3dabd5a96f36cda5564679">__array_elem_in</a> (anyarray src_arr, anyarray tst_arr)</td></tr>
+<tr class="separator:aa1341c743f3dabd5a96f36cda5564679"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a4d1e4d0afa6cf17b02795e9cdd05ea27"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="dt__utility_8sql__in.html#a4d1e4d0afa6cf17b02795e9cdd05ea27">__array_sort</a> (anyarray arr)</td></tr>
+<tr class="separator:a4d1e4d0afa6cf17b02795e9cdd05ea27"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>April 5, 2012 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="aa1341c743f3dabd5a96f36cda5564679"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean [] __array_elem_in </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>src_arr</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>tst_arr</em>&#160;</td>
+        </tr>
+        <tr>
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+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a213c48be09bf0227f2feba174761c354"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean __array_search </td>
+          <td>(</td>
+          <td class="paramtype">anyelement&#160;</td>
+          <td class="paramname"><em>find</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>arr</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4d1e4d0afa6cf17b02795e9cdd05ea27"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">anyarray __array_sort </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>arr</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a61821817abe0e95f52f6fac2315e566d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __assert </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>condition</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>reason</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a50de3c039d3e5457c79bccb9fd5cbfb0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __assert_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>existence</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a65d4c26a20c2f3905f724e075f054586"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean __column_exists </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>column_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="acf898fa38da07cb8bebd35225078d552"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean __columns_in_table </td>
+          <td>(</td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>column_names</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>table_name</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a754a692fb8a63e1cb82858699a2ceddd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text [] __csvstr_to_array </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>csv_str</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac0d671e18bdced796c139672e4cbfeea"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __format </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>fmt</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text[]&#160;</td>
+          <td class="paramname"><em>args</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a73736314b7017bc048008d5d5db8b4b8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __format </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>fmt</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg2</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg3</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg4</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a755012b4ca1cc28586148e60a3b25a0a"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __format </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>fmt</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg2</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg3</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa45a4dd4f12fbd9c1ca3bb918c080cb3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __format </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>fmt</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa7ee9374473fdd45e78442117454796d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __format </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>fmt</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>arg1</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a4b68d0889379f0ef8d9a8d40feb1596c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_schema_name </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae19c342c1130f1981891822cb56bb896"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __get_table_name </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a292fab77e9ea1b22cf7fc8848571e244"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">int __num_of_columns </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3775e498d398d11af57ce69f716675cb"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __regclass_to_text </td>
+          <td>(</td>
+          <td class="paramtype">regclass&#160;</td>
+          <td class="paramname"><em>rc</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a453f2af37f235e65a7d04d8f64f867a0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void __rename_table </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>from_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>to_table</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a891cd53edfff0adb36603df41b3a1b71"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text __strip_schema_name </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
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+<a class="anchor" id="a4c8c034f56f8c95ae1422fa0ea547f69"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">boolean __table_exists </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>full_table_name</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_704eb8350b43e1ca74c0f90ed1ba450e.html">methods</a></li><li class="navelem"><a class="el" href="dir_fbc4f2259ae1d6f6cc75298ebbd15532.html">cart</a></li><li class="navelem"><a class="el" href="dir_42a199e31e82b0c41cf7961a25e597db.html">src</a></li><li class="navelem"><a class="el" href="dir_64bd18b6b0e4b6a9c2cd2ca1d5a05b4c.html">pg_gp</a></li><li class="navelem"><a class="el" href="dt__utility_8sql__in.html">dt_utility.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/dynsections.js
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diff --git a/docs/latest/dynsections.js b/docs/latest/dynsections.js
new file mode 100644
index 0000000..85e1836
--- /dev/null
+++ b/docs/latest/dynsections.js
@@ -0,0 +1,97 @@
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+}
+
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+    currentRowSpans.filter(".iconfopen").removeClass("iconfopen").addClass("iconfclosed");
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+    currentRowSpans.filter(".iconfclosed").removeClass("iconfclosed").addClass("iconfopen");
+    currentRowSpans.filter(".arrow").html('&#9660;');
+    // replace down arrows by right arrows for child rows
+    var childRowsSpans = childRows.find("span");
+    childRowsSpans.filter(".iconfopen").removeClass("iconfopen").addClass("iconfclosed");
+    childRowsSpans.filter(".arrow").html('&#9658;');
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+}
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+    rows.css('display','none');
+    $(img).attr('src',src.substring(0,src.length-8)+'closed.png');
+  } else {
+    rows.css('display','table-row'); // using show() causes jump in firefox
+    $(img).attr('src',src.substring(0,src.length-10)+'open.png');
+  }
+}
+

[49/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/array__ops_8sql__in.html
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diff --git a/docs/latest/array__ops_8sql__in.html b/docs/latest/array__ops_8sql__in.html
new file mode 100644
index 0000000..5472f07
--- /dev/null
+++ b/docs/latest/array__ops_8sql__in.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
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+<title>MADlib: array_ops.sql_in File Reference</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">array_ops.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>implementation of array operations in SQL  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a91c8d3715142927b3967f05a4fbf1575"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a91c8d3715142927b3967f05a4fbf1575">array_add</a> (anyarray x, anyarray y)</td></tr>
+<tr class="memdesc:a91c8d3715142927b3967f05a4fbf1575"><td class="mdescLeft">&#160;</td><td class="mdescRight">Adds two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#a91c8d3715142927b3967f05a4fbf1575">More...</a><br /></td></tr>
+<tr class="separator:a91c8d3715142927b3967f05a4fbf1575"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a26e8508a2bae10a6574cec697a270eea"><td class="memItemLeft" align="right" valign="top">aggregate anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a26e8508a2bae10a6574cec697a270eea">sum</a> (anyarray)</td></tr>
+<tr class="memdesc:a26e8508a2bae10a6574cec697a270eea"><td class="mdescLeft">&#160;</td><td class="mdescRight">Aggregate, element-wise sum of arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#a26e8508a2bae10a6574cec697a270eea">More...</a><br /></td></tr>
+<tr class="separator:a26e8508a2bae10a6574cec697a270eea"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a2875a161a01c7dcdea9a4997b074eefc"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a2875a161a01c7dcdea9a4997b074eefc">array_sub</a> (anyarray x, anyarray y)</td></tr>
+<tr class="memdesc:a2875a161a01c7dcdea9a4997b074eefc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Subtracts two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#a2875a161a01c7dcdea9a4997b074eefc">More...</a><br /></td></tr>
+<tr class="separator:a2875a161a01c7dcdea9a4997b074eefc"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a652d70c480d484c4a1a92ded384b0dd7"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a652d70c480d484c4a1a92ded384b0dd7">array_mult</a> (anyarray x, anyarray y)</td></tr>
+<tr class="memdesc:a652d70c480d484c4a1a92ded384b0dd7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Element-wise product of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#a652d70c480d484c4a1a92ded384b0dd7">More...</a><br /></td></tr>
+<tr class="separator:a652d70c480d484c4a1a92ded384b0dd7"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a6cc05e7052495f8b64692faf40219576"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a6cc05e7052495f8b64692faf40219576">array_div</a> (anyarray x, anyarray y)</td></tr>
+<tr class="memdesc:a6cc05e7052495f8b64692faf40219576"><td class="mdescLeft">&#160;</td><td class="mdescRight">Element-wise division of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#a6cc05e7052495f8b64692faf40219576">More...</a><br /></td></tr>
+<tr class="separator:a6cc05e7052495f8b64692faf40219576"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:acde10964ed23b7c8da515fb84cb8d5e0"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#acde10964ed23b7c8da515fb84cb8d5e0">array_dot</a> (anyarray x, anyarray y)</td></tr>
+<tr class="memdesc:acde10964ed23b7c8da515fb84cb8d5e0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Dot-product of two arrays. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#acde10964ed23b7c8da515fb84cb8d5e0">More...</a><br /></td></tr>
+<tr class="separator:acde10964ed23b7c8da515fb84cb8d5e0"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aedf6cb13eb4803bcc12dc4d95ea8ff4e"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#aedf6cb13eb4803bcc12dc4d95ea8ff4e">array_contains</a> (anyarray x, anyarray y)</td></tr>
+<tr class="memdesc:aedf6cb13eb4803bcc12dc4d95ea8ff4e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Checks whether one array contains the other. This function returns TRUE if each non-zero element in the right array equals to the element with the same index in the left array.  <a href="#aedf6cb13eb4803bcc12dc4d95ea8ff4e">More...</a><br /></td></tr>
+<tr class="separator:aedf6cb13eb4803bcc12dc4d95ea8ff4e"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ae891429cc50705c530f3e5ca15541849"><td class="memItemLeft" align="right" valign="top">anyelement&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#ae891429cc50705c530f3e5ca15541849">array_max</a> (anyarray x)</td></tr>
+<tr class="memdesc:ae891429cc50705c530f3e5ca15541849"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the maximum value in the array. NULLs are ignored. Return type is the same as the input type.  <a href="#ae891429cc50705c530f3e5ca15541849">More...</a><br /></td></tr>
+<tr class="separator:ae891429cc50705c530f3e5ca15541849"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa415256a9064aecc600dfb5e377fb7b1"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#aa415256a9064aecc600dfb5e377fb7b1">array_max_index</a> (anyarray x)</td></tr>
+<tr class="memdesc:aa415256a9064aecc600dfb5e377fb7b1"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the maximum value and corresponding index in the array. NULLs are ignored. Return type is the same as the input type.  <a href="#aa415256a9064aecc600dfb5e377fb7b1">More...</a><br /></td></tr>
+<tr class="separator:aa415256a9064aecc600dfb5e377fb7b1"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a6659bf9d9363eb179fab34f81f8ac59b"><td class="memItemLeft" align="right" valign="top">anyelement&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a6659bf9d9363eb179fab34f81f8ac59b">array_min</a> (anyarray x)</td></tr>
+<tr class="memdesc:a6659bf9d9363eb179fab34f81f8ac59b"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the minimum value in the array. NULLs are ignored. Return type is the same as the input type.  <a href="#a6659bf9d9363eb179fab34f81f8ac59b">More...</a><br /></td></tr>
+<tr class="separator:a6659bf9d9363eb179fab34f81f8ac59b"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a813a4d9ffc1c18b1b3e18f6ecdb2051f"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a813a4d9ffc1c18b1b3e18f6ecdb2051f">array_min_index</a> (anyarray x)</td></tr>
+<tr class="memdesc:a813a4d9ffc1c18b1b3e18f6ecdb2051f"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the minimum value and corresponding index in the array. NULLs are ignored. Return type is the same as the input type.  <a href="#a813a4d9ffc1c18b1b3e18f6ecdb2051f">More...</a><br /></td></tr>
+<tr class="separator:a813a4d9ffc1c18b1b3e18f6ecdb2051f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a4c98f20e6a737358806f63318daea5ec"><td class="memItemLeft" align="right" valign="top">anyelement&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec">array_sum</a> (anyarray x)</td></tr>
+<tr class="memdesc:a4c98f20e6a737358806f63318daea5ec"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the sum of the values in the array. NULLs are ignored. Return type is the same as the input type.  <a href="#a4c98f20e6a737358806f63318daea5ec">More...</a><br /></td></tr>
+<tr class="separator:a4c98f20e6a737358806f63318daea5ec"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a418de59800833aa95f9b7cbd6b12901c"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a418de59800833aa95f9b7cbd6b12901c">array_sum_big</a> (anyarray x)</td></tr>
+<tr class="memdesc:a418de59800833aa95f9b7cbd6b12901c"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the sum of the values in the array. NULLs are ignored. Return type is always FLOAT8 regardless of input. This function is meant to replace <a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec" title="This function finds the sum of the values in the array. NULLs are ignored. Return type is the same as...">array_sum()</a> in the cases when sum may overflow the element type.  <a href="#a418de59800833aa95f9b7cbd6b12901c">More...</a><br /></td></tr>
+<tr class="separator:a418de59800833aa95f9b7cbd6b12901c"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a407598f9eb70637798b02fd731bfca2c"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a407598f9eb70637798b02fd731bfca2c">array_mean</a> (anyarray x)</td></tr>
+<tr class="memdesc:a407598f9eb70637798b02fd731bfca2c"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the mean of the values in the array. NULLs are ignored.  <a href="#a407598f9eb70637798b02fd731bfca2c">More...</a><br /></td></tr>
+<tr class="separator:a407598f9eb70637798b02fd731bfca2c"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a3b6c2d173a611e6d6b184d825c2b336d"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a3b6c2d173a611e6d6b184d825c2b336d">array_stddev</a> (anyarray x)</td></tr>
+<tr class="memdesc:a3b6c2d173a611e6d6b184d825c2b336d"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function finds the standard deviation of the values in the array. NULLs are ignored.  <a href="#a3b6c2d173a611e6d6b184d825c2b336d">More...</a><br /></td></tr>
+<tr class="separator:a3b6c2d173a611e6d6b184d825c2b336d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ab066e65a41db78b00b4532996b2a6efc"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#ab066e65a41db78b00b4532996b2a6efc">array_of_float</a> (integer k)</td></tr>
+<tr class="memdesc:ab066e65a41db78b00b4532996b2a6efc"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function creates an array of set size (the argument value) of FLOAT8, initializing the values to 0.0;.  <a href="#ab066e65a41db78b00b4532996b2a6efc">More...</a><br /></td></tr>
+<tr class="separator:ab066e65a41db78b00b4532996b2a6efc"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ab7d8550e66d2e0bd54b8f0997d93880c"><td class="memItemLeft" align="right" valign="top">bigint[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#ab7d8550e66d2e0bd54b8f0997d93880c">array_of_bigint</a> (integer k)</td></tr>
+<tr class="memdesc:ab7d8550e66d2e0bd54b8f0997d93880c"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function creates an array of set size (the argument value) of BIGINT, initializing the values to 0;.  <a href="#ab7d8550e66d2e0bd54b8f0997d93880c">More...</a><br /></td></tr>
+<tr class="separator:ab7d8550e66d2e0bd54b8f0997d93880c"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a065a5323f3b742be47e39ad8b4c90fc2"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#a065a5323f3b742be47e39ad8b4c90fc2">array_fill</a> (anyarray x, anyelement k)</td></tr>
+<tr class="memdesc:a065a5323f3b742be47e39ad8b4c90fc2"><td class="mdescLeft">&#160;</td><td class="mdescRight">This functions set every values in the array to some desired value (provided as the argument).  <a href="#a065a5323f3b742be47e39ad8b4c90fc2">More...</a><br /></td></tr>
+<tr class="separator:a065a5323f3b742be47e39ad8b4c90fc2"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ae6881cc5c86941b6ffca35d7f3cd5c12"><td class="memItemLeft" align="right" valign="top">anyarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="array__ops_8sql__in.html#ae6881cc5c86941b6ffca35d7f3cd5c12">array_scalar_mult</a> (anyarray x, anyelement k)</td></tr>
+<tr class="memdesc:ae6881cc5c86941b6ffca35d7f3cd5c12"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function takes an array as the input and executes element-wise multiplication by the scalar provided as the second argument, returning the resulting array. It requires that all the values are NON-NULL. Return type is the same as the input type.  <a href="#ae6881cc5c86941b6ffca35d7f3cd5c12">More...</a><br /></td></tr>
+<tr class="separator:ae6881cc5c86941b6ffca35d7f3cd5c12"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>April 2011 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a91c8d3715142927b3967f05a4fbf1575"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">anyarray array_add </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+    <tr><td class="paramname">y</td><td>Array y </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Sum of x and y. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="aedf6cb13eb4803bcc12dc4d95ea8ff4e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bool array_contains </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+    <tr><td class="paramname">y</td><td>Array y </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Returns true if x contains y. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a6cc05e7052495f8b64692faf40219576"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">anyarray array_div </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+    <tr><td class="paramname">y</td><td>Array y </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Element-wise division of x and y. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="acde10964ed23b7c8da515fb84cb8d5e0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 array_dot </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
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+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+    <tr><td class="paramname">y</td><td>Array y </td></tr>
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+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Dot-product of x and y. </dd></dl>
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+</div>
+</div>
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+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">anyarray array_fill </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyelement&#160;</td>
+          <td class="paramname"><em>k</em>&#160;</td>
+        </tr>
+        <tr>
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+          <td></td><td></td>
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+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Some array </td></tr>
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+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Fills array with desired value. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ae891429cc50705c530f3e5ca15541849"></a>
+<div class="memitem">
+<div class="memproto">
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+        <tr>
+          <td class="memname">anyelement array_max </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
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+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Max of x. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="aa415256a9064aecc600dfb5e377fb7b1"></a>
+<div class="memitem">
+<div class="memproto">
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+        <tr>
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+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
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+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Array as [max, index]. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a407598f9eb70637798b02fd731bfca2c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 array_mean </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em></td><td>)</td>
+          <td></td>
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+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Mean of x. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a6659bf9d9363eb179fab34f81f8ac59b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">anyelement array_min </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Min of x. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a813a4d9ffc1c18b1b3e18f6ecdb2051f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] array_min_index </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Array as [min, index]. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a652d70c480d484c4a1a92ded384b0dd7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">anyarray array_mult </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>y</em>&#160;</td>
+        </tr>
+        <tr>
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+          <td>)</td>
+          <td></td><td></td>
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+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
+    <tr><td class="paramname">y</td><td>Array y </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Element-wise product of x and y. </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="ab7d8550e66d2e0bd54b8f0997d93880c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">bigint [] array_of_bigint </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>k</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">k</td><td>Array size. </td></tr>
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+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Array of size k. </dd></dl>
+
+</div>
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+          <td class="memname">float8 [] array_of_float </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>k</em></td><td>)</td>
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+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
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+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Array of size k. </dd></dl>
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+<div class="memitem">
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+      <table class="memname">
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+          <td class="memname">anyarray array_scalar_mult </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">anyelement&#160;</td>
+          <td class="paramname"><em>k</em>&#160;</td>
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+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">x</td><td>Array x </td></tr>
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+        <tr>
+          <td class="paramkey"></td>
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+          <td class="memname">aggregate anyarray sum </td>
+          <td>(</td>
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+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Sum of x and y. </dd></dl>
+
+</div>
+</div>
+</div><!-- contents -->
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_704eb8350b43e1ca74c0f90ed1ba450e.html">methods</a></li><li class="navelem"><a class="el" href="dir_834e55cf992733df24fbe86b49356157.html">array_ops</a></li><li class="navelem"><a class="el" href="dir_c8a9890f716ab6621b63d44c36a168eb.html">src</a></li><li class="navelem"><a class="el" href="dir_fe647384fff178c2cff5ce75fb4044e5.html">pg_gp</a></li><li class="navelem"><a class="el" href="array__ops_8sql__in.html">array_ops.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">assoc_rules.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>The <a class="el" href="assoc__rules_8sql__in.html#a68a256d98b82ac15bac7df92e806f6f8">assoc_rules</a> function computes association rules for a given set of data. The data is assumed to have two dimensions; items (between which we are trying to discover associations), and a transaction id. This tranaction id groups the items by event and could also be a user id, date, etc. depending on the context of the data. This function assumes the data is stored in two columns with one transaction id and one item per row.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a9a872579615586ce478d32ba145cc279"><td class="memItemLeft" align="right" valign="top">set&lt; text[]&gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="assoc__rules_8sql__in.html#a9a872579615586ce478d32ba145cc279">gen_rules_from_cfp</a> (text, int)</td></tr>
+<tr class="separator:a9a872579615586ce478d32ba145cc279"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:af9456adb6dad01e452415b9a0a5371dc"><td class="memItemLeft" align="right" valign="top">assoc_rules_results&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="assoc__rules_8sql__in.html#af9456adb6dad01e452415b9a0a5371dc">assoc_rules</a> (float8 support, float8 confidence, text tid_col, text item_col, text input_table, text output_schema, boolean verbose)</td></tr>
+<tr class="separator:af9456adb6dad01e452415b9a0a5371dc"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a68a256d98b82ac15bac7df92e806f6f8"><td class="memItemLeft" align="right" valign="top">assoc_rules_results&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="assoc__rules_8sql__in.html#a68a256d98b82ac15bac7df92e806f6f8">assoc_rules</a> (float8 support, float8 confidence, text tid_col, text item_col, text input_table, text output_schema)</td></tr>
+<tr class="memdesc:a68a256d98b82ac15bac7df92e806f6f8"><td class="mdescLeft">&#160;</td><td class="mdescRight">The short form of the above function with vobose removed.  <a href="#a68a256d98b82ac15bac7df92e806f6f8">More...</a><br /></td></tr>
+<tr class="separator:a68a256d98b82ac15bac7df92e806f6f8"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>June 2011 </dd>
+<dd>
+August 2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to the association rules implementation, see the module description <a class="el" href="group__grp__assoc__rules.html">Apriori Algorithm</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="af9456adb6dad01e452415b9a0a5371dc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">assoc_rules_results assoc_rules </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>support</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>confidence</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tid_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>item_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_schema</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>verbose</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">support</td><td>minimum level of support needed for each itemset to be included in result </td></tr>
+    <tr><td class="paramname">confidence</td><td>minimum level of confidence needed for each rule to be included in result </td></tr>
+    <tr><td class="paramname">tid_col</td><td>name of the column storing the transaction ids </td></tr>
+    <tr><td class="paramname">item_col</td><td>name of the column storing the products </td></tr>
+    <tr><td class="paramname">input_table</td><td>name of the table where the data is stored </td></tr>
+    <tr><td class="paramname">output_schema</td><td>name of the schema where the final results will be stored </td></tr>
+    <tr><td class="paramname">verbose</td><td>determining if output contains comments</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>The schema and table name containing association rules, and total number of rules found.</dd></dl>
+<p>This function computes the association rules between products in a data set. It reads the name of the table, the column names of the product and ids, and computes ssociation rules using the Apriori algorithm, and subject to the support and confidence constraints as input by the user. This version of association rules has verbose functionality. When verbose is true, output of function includes iteration steps and comments on Apriori algorithm steps. </p>
+
+</div>
+</div>
+<a class="anchor" id="a68a256d98b82ac15bac7df92e806f6f8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">assoc_rules_results assoc_rules </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>support</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>confidence</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>tid_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>item_col</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_schema</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9a872579615586ce478d32ba145cc279"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">set&lt;text[]&gt; gen_rules_from_cfp </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
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+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_df86748cb94fb6c2fa09e991cce090c0.html">assoc_rules</a></li><li class="navelem"><a class="el" href="assoc__rules_8sql__in.html">assoc_rules.sql_in</a></li>
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[04/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__svd.html
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Singular Value Decomposition<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a> &raquo; <a class="el" href="group__grp__matrix__factorization.html">Matrix Factorization</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">SVD Functions</a> </li>
+<li>
+<a href="#output">Output Tables</a> </li>
+<li>
+<a href="#examples">Examples</a></li>
+<li>
+</li>
+<li>
+<a href="#background">Technical Background</a> </li>
+</ul>
+</div><p>In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics.</p>
+<p>Let \(A\) be a \(mxn\) matrix, where \(m \ge n\). Then \(A\) can be decomposed as follows: </p><p class="formulaDsp">
+\[ A = U \Sigma V^T, \]
+</p>
+<p> where \(U\) is a \(m \times n\) orthonormal matrix, \(\Sigma\) is a \(n \times n\) diagonal matrix, and \(V\) is an \(n \times n\) orthonormal matrix. The diagonal elements of \(\Sigma\) are called the <em>{singular</em> values}.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>SVD Functions</dt><dd></dd></dl>
+<p>SVD factorizations are provided for dense matrices, sparse matrices, and block matrices. In addition, a native implementation is provided for sparse matrices for improved performance.</p>
+<p><b>SVD Function for Dense Matrices</b></p>
+<pre class="syntax">
+svd( source_table,
+     output_table_prefix,
+     row_id,
+     k,
+     n_iterations,
+     result_summary_table
+);
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. Source table name (dense matrix).</p>
+<p class="enddd">The table contains a <code>row_id</code> column that identifies each row. Further, the other columns are assumed to be the data for the matrix represented in two possible forms, illustrated by the following 2x2 matrix example:</p><ol type="1">
+<li><pre class="example">
+            row_id     col1     col2
+row1         1           1         0
+row2         2           0         1
+    </pre></li>
+<li><pre class="example">
+        row_id     row_vec
+row1        1       {1, 0}
+row2        2       {0, 1}
+    </pre>  </li>
+</ol>
+</dd>
+<dt>output_table_prefix </dt>
+<dd>TEXT. Prefix for output tables. See <a href="#output">Output Tables</a>. </dd>
+<dt>row_id </dt>
+<dd>TEXT. ID for each row. </dd>
+<dt>k </dt>
+<dd>INTEGER. Number of singular vectors to compute. </dd>
+<dt>n_iterations (optional) </dt>
+<dd>INTEGER. Number of iterations to run. </dd>
+<dt>result_summary_table (optional) </dt>
+<dd>TEXT. The name of the table to store result summary. </dd>
+</dl>
+<hr/>
+<p> <b>SVD Function for Sparse Matrix input</b></p>
+<p>Use this function for matrices that are represented in the sparse-matrix format (example below). <b>The input matrix is converted to a dense matrix before the SVD operation.</b></p>
+<pre class="syntax">
+svd_sparse( source_table,
+            output_table_prefix,
+            row_id,
+            col_id,
+            value,
+            row_dim,
+            col_dim,
+            k,
+            n_iterations,
+            result_summary_table
+          );
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. Source table name (sparse matrix).</p>
+<p>An example sparse matrix representation is given below: </p><pre class="example">
+       row_id    col_id    value
+row1      1         1        2
+row2      2         1        1
+row3      3         2        1
+</pre><p> The <code>row_id</code> represents the row number, <code>col_id</code> represents the column number and the <code>value</code> represents the matrix value at [<code>row_id</code>, <code>col_id</code>]. The <code>row_id</code> and <code>col_id</code> values are indexed starting from 0. Thus the <code>row_id</code> ranges from 1 to <code>row_dim</code>, while the <code>col_id</code> ranges from 1 to <code>col_dim</code> </p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table_prefix </dt>
+<dd>TEXT. Prefix for output tables. See <a href="#output">Output Tables</a>. </dd>
+<dt>row_id </dt>
+<dd>TEXT. Name of the column containing the row index for each entry in sparse matrix. </dd>
+<dt>col_id </dt>
+<dd>TEXT. Name of the column containing the column index for each entry in sparse matrix. </dd>
+<dt>value </dt>
+<dd>TEXT. Name of column containing the non-zero values of the sparse matrix. </dd>
+<dt>row_dim </dt>
+<dd>INTEGER. Number of rows in matrix. </dd>
+<dt>col_dim </dt>
+<dd>INTEGER. Number of columns in matrix. </dd>
+<dt>k </dt>
+<dd>INTEGER. Number of singular vectors to compute. </dd>
+<dt>n_iterations (optional) </dt>
+<dd>INTEGER. Number of iterations to run. </dd>
+<dt>result_summary_table (optional) </dt>
+<dd>TEXT, default: NULL. The name of the table to store a summary of the results. </dd>
+</dl>
+<hr/>
+<p> <b>Native implementation for sparse matrix</b></p>
+<p>Use this function for matrices that are represented in the sparse-matrix format (example below). This function use the native sparse representation while computing the SVD. <b>This function should be favored if the matrix is highly sparse.</b></p>
+<pre class="syntax">
+svd_sparse_native( source_table,
+                   output_table_prefix,
+                   row_id,
+                   col_id,
+                   value,
+                   row_dim,
+                   col_dim,
+                   k,
+                   n_iterations,
+                   result_summary_table
+                 );
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>TEXT. Source table name (sparse matrix - see example above). </dd>
+<dt>output_table_prefix </dt>
+<dd>TEXT. Prefix for output tables. See <a href="#output">Output Tables</a>. </dd>
+<dt>row_id </dt>
+<dd>TEXT. ID for each row. </dd>
+<dt>col_id </dt>
+<dd>TEXT. ID for each column. </dd>
+<dt>value </dt>
+<dd>TEXT. Non-zero values of the sparse matrix. </dd>
+<dt>row_dim </dt>
+<dd>INTEGER. Row dimension of sparse matrix. </dd>
+<dt>col_dim </dt>
+<dd>INTEGER. Col dimension of sparse matrix. </dd>
+<dt>k </dt>
+<dd>INTEGER. Number of singular vectors to compute. </dd>
+<dt>n_iterations (optional) </dt>
+<dd>INTEGER. Number of iterations to run. </dd>
+<dt>result_summary_table (optional) </dt>
+<dd>TEXT. Table name to store result summary. </dd>
+</dl>
+<hr/>
+<p> <b>Block matrices</b></p>
+<pre class="syntax">
+svd_block( source_table,
+           output_table_prefix,
+           k,
+           n_iterations,
+           result_summary_table
+         );
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>source_table </dt>
+<dd>TEXT. Source table name (block matrix). </dd>
+<dt>output_table_prefix </dt>
+<dd>TEXT. Prefix for output tables. See <a href="#output">Output Tables</a>. </dd>
+<dt>k </dt>
+<dd>INTEGER. Number of singular vectors to compute. </dd>
+<dt>n_iterations (optional) </dt>
+<dd>INTEGER. Number of iterations to run. </dd>
+<dt>result_summary_table (optional) </dt>
+<dd>TEXT. Table name to store result summary. </dd>
+</dl>
+<p><a class="anchor" id="output"></a></p><dl class="section user"><dt>Output Tables</dt><dd></dd></dl>
+<p>Output for eigen vectors/values is in the following three tables:</p><ul>
+<li>Left singular matrix: Table named &lt;output_table_prefix&gt;_left (e.g. ‘netflix_u’)</li>
+<li>Right singular matrix: Table named &lt;output_table_prefix&gt;_right (e.g. ‘netflix_v’)</li>
+<li>Singular values: Table named &lt;output_table_prefix&gt;_s (e.g. ‘netflix_s’)</li>
+</ul>
+<p>The singular vector tables are of the format: </p><table  class="output">
+<tr>
+<th>row_id </th><td>INTEGER. The ID corresponding to each eigen value (in decreasing order).  </td></tr>
+<tr>
+<th>row_vec </th><td>FLOAT8[]. Singular vector elements for this row_id. Each array is of size k.  </td></tr>
+</table>
+<p>The singular values table is in a sparse table format, since only the diagonal elements of the matrix are non-zero: </p><table  class="output">
+<tr>
+<th>row_id </th><td>INTEGER. <em>i</em> for <em>ith</em> eigen value.  </td></tr>
+<tr>
+<th>col_id </th><td>INTEGER. <em>i</em> for <em>ith</em> eigen value (same as row_id).  </td></tr>
+<tr>
+<th>value </th><td>FLOAT8. Eigen Value.  </td></tr>
+</table>
+<p>All <code>row_id</code> (and <code>col_id</code>) in the above tables start from 0.</p>
+<p>The result summary table has the following columns: </p><table  class="output">
+<tr>
+<th>rows_used </th><td>INTEGER. Number of rows used for SVD calculation.  </td></tr>
+<tr>
+<th>exec_time </th><td>FLOAT8. Total time for executing SVD.  </td></tr>
+<tr>
+<th>iter </th><td>INTEGER. Total number of iterations run.  </td></tr>
+<tr>
+<th>recon_error </th><td>FLOAT8. Total quality score (i.e. approximation quality) for this set of orthonormal basis.  </td></tr>
+<tr>
+<th>relative_recon_error </th><td>FLOAT8. relative quality score.  </td></tr>
+</table>
+<p>In the result summary table, the reconstruction error is computed as \( \sqrt{mean((X - USV^T)_{ij}^2)} \), where the average is over all elements of the matrices. The relative reconstruction error is then computed as ratio of the reconstruction error and \( \sqrt{mean(X_{ij}^2)} \).</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>View online help for the SVD function. <pre class="example">
+SELECT madlib.svd();
+</pre></li>
+<li>Create an input dataset (dense matrix). <pre class="example">
+CREATE TABLE mat (
+    row_id integer,
+    row_vec double precision[]
+);
+COPY mat (row_id, row_vec) FROM stdin delimiter '|';
+1|{396,840,353,446,318,886,15,584,159,383}
+2|{691,58,899,163,159,533,604,582,269,390}
+3|{293,742,298,75,404,857,941,662,846,2}
+4|{462,532,787,265,982,306,600,608,212,885}
+5|{304,151,337,387,643,753,603,531,459,652}
+6|{327,946,368,943,7,516,272,24,591,204}
+7|{877,59,260,302,891,498,710,286,864,675}
+8|{458,959,774,376,228,354,300,669,718,565}
+9|{824,390,818,844,180,943,424,520,65,913}
+10|{882,761,398,688,761,405,125,484,222,873}
+11|{528,1,860,18,814,242,314,965,935,809}
+12|{492,220,576,289,321,261,173,1,44,241}
+13|{415,701,221,503,67,393,479,218,219,916}
+14|{350,192,211,633,53,783,30,444,176,932}
+15|{909,472,871,695,930,455,398,893,693,838}
+16|{739,651,678,577,273,935,661,47,373,618}
+\.
+</pre></li>
+<li>Run SVD function for a dense matrix. <pre class="example">
+DROP TABLE IF EXISTS svd_u;
+DROP TABLE IF EXISTS svd_v;
+DROP TABLE IF EXISTS svd_s;
+SELECT madlib.svd( 'mat',
+                   'svd',
+                   'row_id',
+                   10
+                 );
+</pre></li>
+<li>Create a sparse matrix by running the <a class="el" href="matrix__ops_8sql__in.html#a9ed8df5fc43740c00bfdfd3f934429ef">matrix_sparsify()</a> utility function on the dense matrix. <pre class="example">
+DROP TABLE IF EXISTS mat_sparse;
+SELECT madlib.matrix_sparsify( 'mat',
+                               'mat_sparse',
+                               FALSE
+                             );
+DROP TABLE IF EXISTS svd_u;
+DROP TABLE IF EXISTS svd_v;
+DROP TABLE IF EXISTS svd_s;
+</pre></li>
+<li>Run the SVD function for a sparse matrix. <pre class="example">
+SELECT madlib.svd_sparse( 'mat_sparse',
+                          'svd',
+                          'row_id',
+                          'col_id',
+                          'value',
+                          10
+                        );
+</pre></li>
+</ol>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd>In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix, with many useful applications in signal processing and statistics. Let \(A\) be a \(m \times n\) matrix, where \(m \ge n\). Then \(A\) can be decomposed as follows: <p class="formulaDsp">
+\[ A = U \Sigma V^T, \]
+</p>
+ where \(U\) is a \(m \times n\) orthonormal matrix, \(\Sigma\) is a \(n \times n\) diagonal matrix, and \(V\) is an \(n \times n\) orthonormal matrix. The diagonal elements of \(\Sigma\) are called the <em>{singular</em> values}. It is possible to formulate the problem of computing the singular triplets ( \(\sigma_i, u_i, v_i\)) of \(A\) as an eigenvalue problem involving a Hermitian matrix related to \(A\). There are two possible ways of achieving this:<ol type="1">
+<li>With the cross product matrix, \(A^TA\) and \(AA^T\)</li>
+<li>With the cyclic matrix <p class="formulaDsp">
+\[ H(A) = \begin{bmatrix} 0 &amp; A\\ A^* &amp; 0 \end{bmatrix} \]
+</p>
+ The singular values are the nonnegative square roots of the eigenvalues of the cross product matrix. This approach may imply a severe loss of accuracy in the smallest singular values. The cyclic matrix approach is an alternative that avoids this problem, but at the expense of significantly increasing the cost of the computation. Computing the cross product matrix explicitly is not recommended, especially in the case of sparse A. Bidiagonalization was proposed by Golub and Kahan [citation?] as a way of tridiagonalizing the cross product matrix without forming it explicitly. Consider the following decomposition <p class="formulaDsp">
+\[ A = P B Q^T, \]
+</p>
+ where \(P\) and \(Q\) are unitary matrices and \(B\) is an \(m \times n\) upper bidiagonal matrix. Then the tridiagonal matrix \(B*B\) is unitarily similar to \(A*A\). Additionally, specific methods exist that compute the singular values of \(B\) without forming \(B*B\). Therefore, after computing the SVD of B, <p class="formulaDsp">
+\[ B = X\Sigma Y^T, \]
+</p>
+ it only remains to compute the SVD of the original matrix with \(U = PX\) and \(V = QY\). </li>
+</ol>
+</dd></dl>
+</div><!-- contents -->
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+  <td style="padding-left: 0.5em;">
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+   </div>
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+<div class="header">
+  <div class="headertitle">
+<div class="title">Matrix Factorization<div class="ingroups"><a class="el" href="group__grp__deprecated.html">Deprecated Modules</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<dl class="section warning"><dt>Warning</dt><dd><em> This is an old implementation of Matrix Decomposition and has been deprecated. For SVD decomposition, please see <a class="el" href="group__grp__svd.html">Singular Value Decomposition</a>; for the latest version of low-rank approximation, please see <a class="el" href="group__grp__lmf.html">Low-rank Matrix Factorization</a></em></dd></dl>
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">SVD Function Syntax</a> </li>
+<li>
+<a href="#xamples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module implements "partial SVD decomposition" method for representing a sparse matrix using a low-rank approximation. Mathematically, this algorithm seeks to find matrices U and V that, for any given A, minimizes:<br />
+ </p><p class="formulaDsp">
+\[ ||\boldsymbol A - \boldsymbol UV ||_2 \]
+</p>
+<p> subject to \( rank(\boldsymbol UV) \leq k \), where \( ||\cdot||_2 \) denotes the Frobenius norm and \( k \leq rank(\boldsymbol A)\). If A is \( m \times n \), then U will be \( m \times k \) and V will be \( k \times n \).</p>
+<p>This algorithm is not intended to do the full decomposition, or to be used as part of inverse procedure. It effectively computes the SVD of a low-rank approximation of A (preferably sparse), with the singular values absorbed in U and V. Code is based on the write-up as appears at [1], with some modifications.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Function Syntax</dt><dd></dd></dl>
+<p>The SVD function is called as follows: </p><pre class="syntax">
+svdmf_run( input_table,
+           col_name,
+           row_name,
+           value, num_features)
+</pre><p>The <b>input matrix</b> is expected to be of the following form: </p><pre>{TABLE|VIEW} <em>input_table</em> (
+    <em>col_num</em> INTEGER,
+    <em>row_num</em> INTEGER,
+  <em>value</em> FLOAT
+)</pre><p>Input is contained in a table where column number and row number for each cell are sequential; that is to say that if the data was written as a matrix, those values would be the actual row and column numbers and not some random identifiers. All rows and columns must be associated with a value. There should not be any missing row, columns or values.</p>
+<p>The function returns two tables <code>matrix_u</code> and <code>matrix_v</code>, which represent the matrices U and V in table format.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare an input table/view. <pre class="example">
+CREATE TABLE svd_test ( col INT,
+                        row INT,
+                        val FLOAT
+                      );
+</pre></li>
+<li>Populate the input table with some data. <pre class="example">
+INSERT INTO svd_test SELECT ( g.a%1000)+1, g.a/1000+1, random()
+                     FROM generate_series(1,1000) AS g(a);
+</pre></li>
+<li><p class="startli">Call the <a class="el" href="svdmf_8sql__in.html#a6cff34415cca23aa0a826cc08a6283f5" title="Partial SVD decomposition of a sparse matrix into U and V components. ">svdmf_run()</a> stored procedure. </p><pre class="example">
+SELECT madlib.svdmf_run( 'svd_test',
+                         'col',
+                         'row',
+                         'val',
+                         3);
+</pre><p> Example result: </p><pre class="result">
+INFO:  ('Started <a class="el" href="svdmf_8sql__in.html#a6cff34415cca23aa0a826cc08a6283f5" title="Partial SVD decomposition of a sparse matrix into U and V components. ">svdmf_run()</a> with parameters:',)
+INFO:  (' * input_matrix = madlib_svdsparse_test.test',)
+INFO:  (' * col_name = col_num',)
+INFO:  (' * row_name = row_num',)
+INFO:  (' * value = val',)
+INFO:  (' * num_features = 3',)
+INFO:  ('Copying the source data into a temporary table...',)
+INFO:  ('Estimating feature: 1',)
+INFO:  ('...Iteration 1: residual_error = 33345014611.1, step_size = 4.9997500125e-10, min_improvement = 1.0',)
+INFO:  ('...Iteration 2: residual_error = 33345014557.6, step_size = 5.49972501375e-10, min_improvement = 1.0',)
+INFO:  ('...Iteration 3: residual_error = 33345014054.3, step_size = 6.04969751512e-10, min_improvement = 1.0',)
+...
+INFO:  ('...Iteration 78: residual_error = 2.02512133868, step_size = 5.78105354457e-10, min_improvement = 1.0',)
+INFO:  ('...Iteration 79: residual_error = 0.893810181282, step_size = 6.35915889903e-10, min_improvement = 1.0',)
+INFO:  ('...Iteration 80: residual_error = 0.34496773222, step_size = 6.99507478893e-10, min_improvement = 1.0',)
+INFO:  ('Swapping residual error matrix...',)
+                                         svdmf_run
+&#160;-------------------------------------------------------------------------------------------</pre><pre class="result"> Finished SVD matrix factorisation for madlib_svdsparse_test.test (row_num, col_num, val).
+ Results:
+    total error = 0.34496773222
+    number of estimated features = 1
+ Output:
+    table : madlib.matrix_u
+    table : madlib.matrix_v
+ Time elapsed: 4 minutes 47.86839 seconds.
+</pre></li>
+</ol>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Simon Funk, Netflix Update: Try This at Home, December 11 2006, <a href="http://sifter.org/~simon/journal/20061211.html">http://sifter.org/~simon/journal/20061211.html</a></p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="svdmf_8sql__in.html" title="SQL functions for SVD Matrix Factorization. ">svdmf.sql_in</a> documenting the SQL functions.</dd></dl>
+</div><!-- contents -->
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+  <div class="headertitle">
+<div class="title">Sparse Vectors<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transforms</a> &raquo; <a class="el" href="group__grp__arraysmatrix.html">Arrays and Matrices</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#usage">Using Sparse Vectors</a> </li>
+<li>
+<a href="#vectorization">Document Vectorization into Sparse Vectors</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module implements a sparse vector data type, named "svec", which provides compressed storage of vectors that have many duplicate elements.</p>
+<p>Arrays of floating point numbers for various calculations sometimes have long runs of zeros (or some other default value). This is common in applications like scientific computing, retail optimization, and text processing. Each floating point number takes 8 bytes of storage in memory and/or disk, so saving those zeros is often worthwhile. There are also many computations that can benefit from skipping over the zeros.</p>
+<p>Consider, for example, the following array of doubles stored as a Postgres/Greenplum "float8[]" data type:</p>
+<pre class="example">
+'{0, 33,...40,000 zeros..., 12, 22 }'::float8[]
+</pre><p>This array would occupy slightly more than 320KB of memory or disk, most of it zeros. Even if we were to exploit the null bitmap and store the zeros as nulls, we would still end up with a 5KB null bitmap, which is still not nearly as memory efficient as we'd like. Also, as we perform various operations on the array, we do work on 40,000 fields that turn out to be unimportant.</p>
+<p>To solve the problems associated with the processing of vectors discussed above, the svec type employs a simple Run Length Encoding (RLE) scheme to represent sparse vectors as pairs of count-value arrays. For example, the array above would be represented as</p>
+<pre class="example">
+'{1,1,40000,1,1}:{0,33,0,12,22}'::madlib.svec
+</pre><p>which says there is 1 occurrence of 0, followed by 1 occurrence of 33, followed by 40,000 occurrences of 0, etc. This uses just 5 integers and 5 floating point numbers to store the array. Further, it is easy to implement vector operations that can take advantage of the RLE representation to make computations faster. The SVEC module provides a library of such functions.</p>
+<p>The current version only supports sparse vectors of float8 values. Future versions will support other base types.</p>
+<p><a class="anchor" id="usage"></a></p><dl class="section user"><dt>Using Sparse Vectors</dt><dd></dd></dl>
+<p>An SVEC can be constructed directly with a constant expression, as follows: </p><pre class="example">
+SELECT '{n1,n2,...,nk}:{v1,v2,...vk}'::madlib.svec;
+</pre><p> where <code>n1,n2,...,nk</code> specifies the counts for the values <code>v1,v2,...,vk</code>.</p>
+<p>A float array can be cast to an SVEC: </p><pre class="example">
+SELECT ('{v1,v2,...vk}'::float[])::madlib.svec;
+</pre><p>An SVEC can be created with an aggregation: </p><pre class="example">
+SELECT madlib.svec_agg(v1) FROM generate_series(1,k);
+</pre><p>An SVEC can be created using the <code>madlib.svec_cast_positions_float8arr()</code> function by supplying an array of positions and an array of values at those positions: </p><pre class="example">
+SELECT madlib.svec_cast_positions_float8arr(
+    array[n1,n2,...nk],    -- positions of values in vector
+    array[v1,v2,...vk],    -- values at each position
+    length,                -- length of vector
+    base)                  -- value at unspecified positions
+</pre><p> For example, the following expression: </p><pre class="example">
+SELECT madlib.svec_cast_positions_float8arr(
+    array[1,3,5],
+    array[2,4,6],
+    10,
+    0.0)
+</pre><p> produces this SVEC: </p><pre class="result">
+ svec_cast_positions_float8arr
+ &#160;------------------------------
+ {1,1,1,1,1,5}:{2,0,4,0,6,0}
+</pre><p>Add MADlib to the search_path to use the svec operators defined in the module.</p>
+<p><a class="anchor" id="vectorization"></a></p><dl class="section user"><dt>Document Vectorization into Sparse Vectors</dt><dd>This module implements an efficient way for document vectorization, converting text documents into sparse vector representation (MADlib.svec), required by various machine learning algorithms in MADlib.</dd></dl>
+<p>The function accepts two tables as input, dictionary table and documents table, and produces the specified output table containing sparse vectors for the represented documents (in documents table).</p>
+<pre class="syntax">
+madlib.gen_doc_svecs(output_tbl,
+                     dictionary_tbl,
+                     dict_id_col,
+                     dict_term_col,
+                     documents_tbl,
+                     doc_id_col,
+                     doc_term_col,
+                     doc_term_info_col
+                    )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>output_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the output table to be created containing the sparse vector representation of the documents. It has the following columns: </p><table  class="output">
+<tr>
+<th>doc_id </th><td>__TYPE_DOC__. Document id. <br />
+ __TYPE_DOC__: Column type depends on the type of <code>doc_id_col</code> in <code>documents_tbl</code>.   </td></tr>
+<tr>
+<th>sparse_vector </th><td>MADlib.svec. Corresponding sparse vector representation.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dictionary_tbl </dt>
+<dd><p class="startdd">TEXT. Name of the dictionary table containing features. </p><table  class="output">
+<tr>
+<th>dict_id_col </th><td>TEXT. Name of the id column in the <code>dictionary_tbl</code>. <br />
+ Expected Type: INTEGER or BIGINT. <br />
+ NOTE: Values must be continuous ranging from 0 to total number of elements in the dictionary - 1.  </td></tr>
+<tr>
+<th>dict_term_col </th><td>TEXT. Name of the column containing term (features) in <code>dictionary_tbl</code>.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>documents_tbl </dt>
+<dd>TEXT. Name of the documents table representing documents. <table  class="output">
+<tr>
+<th>doc_id_col </th><td>TEXT. Name of the id column in the <code>documents_tbl</code>.  </td></tr>
+<tr>
+<th>doc_term_col </th><td>TEXT. Name of the term column in the <code>documents_tbl</code>.  </td></tr>
+<tr>
+<th>doc_term_info_col </th><td>TEXT. Name of the term info column in <code>documents_tbl</code>. The expected type of this column should be: <br />
+ - INTEGER, BIGINT or DOUBLE PRECISION: Values directly used to populate vector. <br />
+ - ARRAY: Length of the array used to populate the vector. <br />
+ ** For an example use case on using these types of column types, please refer to the example below.   </td></tr>
+</table>
+</dd>
+</dl>
+<p><b>Example:</b> <br />
+ Consider a corpus consisting of set of documents consisting of features (terms) along with doc ids: </p><pre class="example">
+1, {this,is,one,document,in,the,corpus}
+2, {i,am,the,second,document,in,the,corpus}
+3, {being,third,never,really,bothered,me,until,now}
+4, {the,document,before,me,is,the,third,document}
+</pre><ol type="1">
+<li>Prepare documents table in appropriate format. <br />
+ The corpus specified above can be represented by any of the following <code>documents_table:</code> <pre class="example">
+SELECT * FROM documents_table ORDER BY id;
+</pre> Result: <pre class="result">
+  id |   term   | count                 id |   term   | positions
+&#160;----+----------+-------               ----+----------+-----------
+   1 | is       |     1                  1 | is       | {1}
+   1 | in       |     1                  1 | in       | {4}
+   1 | one      |     1                  1 | one      | {2}
+   1 | this     |     1                  1 | this     | {0}
+   1 | the      |     1                  1 | the      | {5}
+   1 | document |     1                  1 | document | {3}
+   1 | corpus   |     1                  1 | corpus   | {6}
+   2 | second   |     1                  2 | second   | {3}
+   2 | document |     1                  2 | document | {4}
+   2 | corpus   |     1                  2 | corpus   | {7}
+   . | ...      |    ..                  . | ...      | ...
+   4 | document |     2                  4 | document | {1,7}
+...
+</pre></li>
+<li>Prepare dictionary table in appropriate format. <pre class="example">
+SELECT * FROM dictionary_table ORDER BY id;
+</pre> Result: <pre class="result">
+  id |   term
+&#160;----+----------
+   0 | am
+   1 | before
+   2 | being
+   3 | bothered
+   4 | corpus
+   5 | document
+   6 | i
+   7 | in
+   8 | is
+   9 | me
+...
+</pre></li>
+<li>Generate sparse vector for the documents using dictionary_table and documents_table. <br />
+ <code>doc_term_info_col</code> <code></code>(count) of type INTEGER: <pre class="example">
+SELECT * FROM madlib.gen_doc_svecs('svec_output', 'dictionary_table', 'id', 'term',
+                            'documents_table', 'id', 'term', 'count');
+</pre> <code>doc_term_info_col</code> <code></code>(positions) of type ARRAY: <pre class="example">
+SELECT * FROM madlib.gen_doc_svecs('svec_output', 'dictionary_table', 'id', 'term',
+                            'documents_table', 'id', 'term', 'positions');
+</pre> Result: <pre class="result">
+                                 gen_doc_svecs
+&#160;--------------------------------------------------------------------------------------
+ Created table svec_output (doc_id, sparse_vector) containing sparse vectors
+(1 row)
+</pre></li>
+<li>Analyze the sparse vectors created. <pre class="example">
+SELECT * FROM svec_output ORDER by doc_id;
+</pre> Result: <pre class="result">
+ doc_id |                  sparse_vector
+&#160;--------+-------------------------------------------------
+      1 | {4,2,1,2,3,1,2,1,1,1,1}:{0,1,0,1,0,1,0,1,0,1,0}
+      2 | {1,3,4,6,1,1,3}:{1,0,1,0,1,2,0}
+      3 | {2,2,5,3,1,1,2,1,1,1}:{0,1,0,1,0,1,0,1,0,1}
+      4 | {1,1,3,1,2,2,5,1,1,2}:{0,1,0,2,0,1,0,2,1,0}
+(4 rows)
+</pre></li>
+</ol>
+<p>See the file <a class="el" href="svec_8sql__in.html" title="SQL type definitions and functions for sparse vector data type svec ">svec.sql_in</a> for complete syntax.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p>We can use operations with svec type like &lt;, &gt;, *, **, /, =, +, SUM, etc, and they have meanings associated with typical vector operations. For example, the plus (+) operator adds each of the terms of two vectors having the same dimension together. </p><pre class="example">
+SELECT ('{0,1,5}'::float8[]::madlib.svec + '{4,3,2}'::float8[]::madlib.svec)::float8[];
+</pre><p> Result: </p><pre class="result">
+ float8
+&#160;--------
+ {4,4,7}
+</pre><p>Without the casting into float8[] at the end, we get: </p><pre class="example">
+SELECT '{0,1,5}'::float8[]::madlib.svec + '{4,3,2}'::float8[]::madlib.svec;
+</pre><p> Result: </p><pre class="result">
+ ?column?
+&#160;---------
+{2,1}:{4,7}
+</pre><p>A dot product (%*%) between the two vectors will result in a scalar result of type float8. The dot product should be (0*4 + 1*3 + 5*2) = 13, like this: </p><pre class="example">
+SELECT '{0,1,5}'::float8[]::madlib.svec %*% '{4,3,2}'::float8[]::madlib.svec;
+</pre> <pre class="result">
+ ?column?
+&#160;---------
+    13
+</pre><p>Special vector aggregate functions are also available. SUM is self explanatory. SVEC_COUNT_NONZERO evaluates the count of non-zero terms in each column found in a set of n-dimensional svecs and returns an svec with the counts. For instance, if we have the vectors {0,1,5}, {10,0,3},{0,0,3},{0,1,0}, then executing the SVEC_COUNT_NONZERO() aggregate function would result in {1,2,3}:</p>
+<pre class="example">
+CREATE TABLE list (a madlib.svec);
+INSERT INTO list VALUES ('{0,1,5}'::float8[]), ('{10,0,3}'::float8[]), ('{0,0,3}'::float8[]),('{0,1,0}'::float8[]);
+SELECT madlib.svec_count_nonzero(a)::float8[] FROM list;
+</pre><p> Result: </p><pre class="result">
+svec_count_nonzero
+&#160;----------------
+    {1,2,3}
+</pre><p>We do not use null bitmaps in the svec data type. A null value in an svec is represented explicitly as an NVP (No Value Present) value. For example, we have: </p><pre class="example">
+SELECT '{1,2,3}:{4,null,5}'::madlib.svec;
+</pre><p> Result: </p><pre class="result">
+      svec
+&#160;------------------
+ {1,2,3}:{4,NVP,5}
+</pre><p>Adding svecs with null values results in NVPs in the sum: </p><pre class="example">
+SELECT '{1,2,3}:{4,null,5}'::madlib.svec + '{2,2,2}:{8,9,10}'::madlib.svec;
+</pre><p> Result: </p><pre class="result">
+         ?column?
+ &#160;-------------------------
+  {1,2,1,2}:{12,NVP,14,15}
+</pre><p>An element of an svec can be accessed using the <a class="el" href="svec__util_8sql__in.html#a8787222aec691f94d9808d1369aa401c">svec_proj()</a> function, which takes an svec and the index of the element desired. </p><pre class="example">
+SELECT madlib.svec_proj('{1,2,3}:{4,5,6}'::madlib.svec, 1) + madlib.svec_proj('{4,5,6}:{1,2,3}'::madlib.svec, 15);
+</pre><p> Result: </p><pre class="result"> ?column?
+&#160;---------
+    7
+</pre><p>A subvector of an svec can be accessed using the <a class="el" href="svec__util_8sql__in.html#a5cb3446de5fc117befe88ccb1ebb0e4e">svec_subvec()</a> function, which takes an svec and the start and end index of the subvector desired. </p><pre class="example">
+SELECT madlib.svec_subvec('{2,4,6}:{1,3,5}'::madlib.svec, 2, 11);
+</pre><p> Result: </p><pre class="result">   svec_subvec
+&#160;----------------
+ {1,4,5}:{1,3,5}
+</pre><p>The elements/subvector of an svec can be changed using the function <a class="el" href="svec__util_8sql__in.html#a59407764a1cbf1937da39cf39a2f447c">svec_change()</a>. It takes three arguments: an m-dimensional svec sv1, a start index j, and an n-dimensional svec sv2 such that j + n - 1 &lt;= m, and returns an svec like sv1 but with the subvector sv1[j:j+n-1] replaced by sv2. An example follows: </p><pre class="example">
+SELECT madlib.svec_change('{1,2,3}:{4,5,6}'::madlib.svec,3,'{2}:{3}'::madlib.svec);
+</pre><p> Result: </p><pre class="result">     svec_change
+&#160;--------------------
+ {1,1,2,2}:{4,5,3,6}
+</pre><p>There are also higher-order functions for processing svecs. For example, the following is the corresponding function for lapply() in R. </p><pre class="example">
+SELECT madlib.svec_lapply('sqrt', '{1,2,3}:{4,5,6}'::madlib.svec);
+</pre><p> Result: </p><pre class="result">
+                  svec_lapply
+&#160;----------------------------------------------
+ {1,2,3}:{2,2.23606797749979,2.44948974278318}
+</pre><p>The full list of functions available for operating on svecs are available in svec.sql-in.</p>
+<p><b> A More Extensive Example</b></p>
+<p>For a text classification example, let's assume we have a dictionary composed of words in a sorted text array: </p><pre class="example">
+CREATE TABLE features (a text[]);
+INSERT INTO features VALUES
+            ('{am,before,being,bothered,corpus,document,i,in,is,me,
+               never,now,one,really,second,the,third,this,until}');
+</pre><p> We have a set of documents, each represented as an array of words: </p><pre class="example">
+CREATE TABLE documents(a int,b text[]);
+INSERT INTO documents VALUES
+            (1,'{this,is,one,document,in,the,corpus}'),
+            (2,'{i,am,the,second,document,in,the,corpus}'),
+            (3,'{being,third,never,really,bothered,me,until,now}'),
+            (4,'{the,document,before,me,is,the,third,document}');
+</pre><p>Now we have a dictionary and some documents, we would like to do some document categorization using vector arithmetic on word counts and proportions of dictionary words in each document.</p>
+<p>To start this process, we'll need to find the dictionary words in each document. We'll prepare what is called a Sparse Feature Vector or SFV for each document. An SFV is a vector of dimension N, where N is the number of dictionary words, and in each cell of an SFV is a count of each dictionary word in the document.</p>
+<p>Inside the sparse vector library, we have a function that will create an SFV from a document, so we can just do this (For a more efficient way for converting documents into sparse vectors, especially for larger datasets, please refer to <a href="#vectorization">Document Vectorization into Sparse Vectors</a>):</p>
+<pre class="example">
+SELECT madlib.svec_sfv((SELECT a FROM features LIMIT 1),b)::float8[]
+         FROM documents;
+</pre><p> Result: </p><pre class="result">
+                svec_sfv
+&#160;----------------------------------------
+ {0,0,0,0,1,1,0,1,1,0,0,0,1,0,0,1,0,1,0}
+ {0,0,1,1,0,0,0,0,0,1,1,1,0,1,0,0,1,0,1}
+ {1,0,0,0,1,1,1,1,0,0,0,0,0,0,1,2,0,0,0}
+ {0,1,0,0,0,2,0,0,1,1,0,0,0,0,0,2,1,0,0}
+</pre><p>Note that the output of madlib.svec_sfv() is an svec for each document containing the count of each of the dictionary words in the ordinal positions of the dictionary. This can more easily be understood by lining up the feature vector and text like this:</p>
+<pre class="example">
+SELECT madlib.svec_sfv((SELECT a FROM features LIMIT 1),b)::float8[]
+                , b
+         FROM documents;
+</pre><p> Result: </p><pre class="result">
+                svec_sfv                 |                        b
+&#160;----------------------------------------+--------------------------------------------------
+ {1,0,0,0,1,1,1,1,0,0,0,0,0,0,1,2,0,0,0} | {i,am,the,second,document,in,the,corpus}
+ {0,1,0,0,0,2,0,0,1,1,0,0,0,0,0,2,1,0,0} | {the,document,before,me,is,the,third,document}
+ {0,0,0,0,1,1,0,1,1,0,0,0,1,0,0,1,0,1,0} | {this,is,one,document,in,the,corpus}
+ {0,0,1,1,0,0,0,0,0,1,1,1,0,1,0,0,1,0,1} | {being,third,never,really,bothered,me,until,now}
+</pre> <pre class="example">
+SELECT * FROM features;
+</pre> <pre class="result">
+                                                a
+&#160;-------------------------------------------------------------------------------------------------------
+{am,before,being,bothered,corpus,document,i,in,is,me,never,now,one,really,second,the,third,this,until}
+</pre><p>Now when we look at the document "i am the second document in the corpus", its SFV is {1,3*0,1,1,1,1,6*0,1,2}. The word "am" is the first ordinate in the dictionary and there is 1 instance of it in the SFV. The word "before" has no instances in the document, so its value is "0" and so on.</p>
+<p>The function madlib.svec_sfv() can process large numbers of documents into their SFVs in parallel at high speed.</p>
+<p>The rest of the categorization process is all vector math. The actual count is hardly ever used. Instead, it's turned into a weight. The most common weight is called tf/idf for Term Frequency / Inverse Document Frequency. The calculation for a given term in a given document is</p>
+<pre class="example">
+{#Times in document} * log {#Documents / #Documents the term appears in}.
+</pre><p>For instance, the term "document" in document A would have weight 1 * log (4/3). In document D, it would have weight 2 * log (4/3). Terms that appear in every document would have tf/idf weight 0, since log (4/4) = log(1) = 0. (Our example has no term like that.) That usually sends a lot of values to 0.</p>
+<p>For this part of the processing, we'll need to have a sparse vector of the dictionary dimension (19) with the values </p><pre class="example">
+log(#documents/#Documents each term appears in).
+</pre><p> There will be one such vector for the whole list of documents (aka the "corpus"). The #documents is just a count of all of the documents, in this case 4, but there is one divisor for each dictionary word and its value is the count of all the times that word appears in the document. This single vector for the whole corpus can then be scalar product multiplied by each document SFV to produce the Term Frequency/Inverse Document Frequency weights.</p>
+<p>This can be done as follows: </p><pre class="example">
+CREATE TABLE corpus AS
+            (SELECT a, madlib.svec_sfv((SELECT a FROM features LIMIT 1),b) sfv
+         FROM documents);
+CREATE TABLE weights AS
+          (SELECT a docnum, madlib.svec_mult(sfv, logidf) tf_idf
+           FROM (SELECT madlib.svec_log(madlib.svec_div(count(sfv)::madlib.svec,madlib.svec_count_nonzero(sfv))) logidf
+                FROM corpus) foo, corpus ORDER BYdocnum);
+SELECT * FROM weights;
+</pre><p> Result </p><pre class="result">
+docnum |                tf_idf
+&#160;------+----------------------------------------------------------------------
+     1 | {4,1,1,1,2,3,1,2,1,1,1,1}:{0,0.69,0.28,0,0.69,0,1.38,0,0.28,0,1.38,0}
+     2 | {1,3,1,1,1,1,6,1,1,3}:{1.38,0,0.69,0.28,1.38,0.69,0,1.38,0.57,0}
+     3 | {2,2,5,1,2,1,1,2,1,1,1}:{0,1.38,0,0.69,1.38,0,1.38,0,0.69,0,1.38}
+     4 | {1,1,3,1,2,2,5,1,1,2}:{0,1.38,0,0.57,0,0.69,0,0.57,0.69,0}
+</pre><p>We can now get the "angular distance" between one document and the rest of the documents using the ACOS of the dot product of the document vectors: The following calculates the angular distance between the first document and each of the other documents: </p><pre class="example">
+SELECT docnum,
+                180. * ( ACOS( madlib.svec_dmin( 1., madlib.svec_dot(tf_idf, testdoc)
+                    / (madlib.svec_l2norm(tf_idf)*madlib.svec_l2norm(testdoc))))/3.141592654) angular_distance
+         FROM weights,(SELECT tf_idf testdoc FROM weights WHERE docnum = 1 LIMIT 1) foo
+         ORDER BY 1;
+</pre><p> Result: </p><pre class="result">
+docnum | angular_distance
+&#160;-------+------------------
+     1 |                0
+     2 | 78.8235846096986
+     3 | 89.9999999882484
+     4 | 80.0232034288617
+</pre><p>We can see that the angular distance between document 1 and itself is 0 degrees and between document 1 and 3 is 90 degrees because they share no features at all. The angular distance can now be plugged into machine learning algorithms that rely on a distance measure between data points.</p>
+<p>SVEC also provides functionality for declaring array given an array of positions and array of values, intermediate values betweens those are declared to be base value that user provides in the same function call. In the example below the fist array of integers represents the positions for the array two (array of floats). Positions do not need to come in the sorted order. Third value represents desired maximum size of the array. This assures that array is of that size even if last position is not. If max size &lt; 1 that value is ignored and array will end at the last position in the position vector. Final value is a float representing the base value to be used between the declared ones (0 would be a common candidate):</p>
+<pre class="example">
+SELECT madlib.svec_cast_positions_float8arr(ARRAY[1,2,7,5,87],ARRAY[.1,.2,.7,.5,.87],90,0.0);
+</pre><p> Result: </p><pre class="result">
+        svec_cast_positions_float8arr
+&#160;----------------------------------------------------
+{1,1,2,1,1,1,79,1,3}:{0.1,0.2,0,0.5,0,0.7,0,0.87,0}
+(1 row)
+</pre><p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>Other examples of svecs usage can be found in the k-means module, <a class="el" href="group__grp__kmeans.html">k-Means Clustering</a>.</p>
+<p>File <a class="el" href="svec_8sql__in.html" title="SQL type definitions and functions for sparse vector data type svec ">svec.sql_in</a> documenting the SQL functions.</p>
+</div><!-- contents -->
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+  <div class="headertitle">
+<div class="title">Text Analysis<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods to find patterns in textual data. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__text__utilities"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__text__utilities.html">Term Frequency</a></td></tr>
+<tr class="memdesc:group__grp__text__utilities"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides a collection of user-defined functions for performing common tasks related to text. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
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+<div class="title">Term Frequency<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> &raquo; <a class="el" href="group__grp__text__analysis.html">Text Analysis</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#term_frequency">Term Frequency</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#rel;ated">Related Topics</a> </li>
+</ul>
+</div><p><a class="anchor" id="term_frequency"></a></p><dl class="section user"><dt>Term frequency</dt><dd>Term frequency <code>tf(t,d)</code> is to the raw frequency of a word/term in a document, i.e. the number of times that word/term <code>t</code> occurs in document <code>d</code>. For this function, 'word' and 'term' are used interchangeably. <b>Note:</b> the term frequency is not normalized by the document length. <pre class="syntax">
+    term_frequency(input_table,
+                   doc_id_col,
+                   word_col,
+                   output_table,
+                   compute_vocab)
+</pre></dd></dl>
+<p><b>Arguments:</b> </p><dl class="arglist">
+<dt>input_table </dt>
+<dd><p class="startdd">TEXT. The name of the table storing the documents. Each row is in the form &lt;doc_id, word_vector&gt; where <code>doc_id</code> is an id, unique to each document, and <code>word_vector</code> is a text array containing the words in the document. The <code>word_vector</code> should contain multiple entries of a word if the document contains multiple occurrence of that word. </p>
+<p class="enddd"></p>
+</dd>
+<dt>id_col </dt>
+<dd><p class="startdd">TEXT. The name of the column containing the document id. </p>
+<p class="enddd"></p>
+</dd>
+<dt>word_col </dt>
+<dd><p class="startdd">TEXT. The name of the column containing the vector of words/terms in the document. This column should of type that can be cast to TEXT[].</p>
+<p class="enddd"></p>
+</dd>
+<dt>output_table </dt>
+<dd><p class="startdd">TEXT. The name of the table to store the term frequency output. The output table contains the following columns:</p><ul>
+<li><code>id_col:</code> This the document id column (same as the one provided as input).</li>
+<li><code>word:</code> A word/term present in a document. This is either the original word present in <code>word_col</code> or an id representing the word (depending on the value of compute_vocab below).</li>
+<li><code>count:</code> The number of times this word is found in the document. </li>
+</ul>
+<p class="enddd"></p>
+</dd>
+<dt>compute_vocab </dt>
+<dd>BOOLEAN. (Optional, Default=FALSE) Flag to indicate if a vocabulary is to be created. If TRUE, an additional output table is created containing the vocabulary of all words, with an id assigned to each word. The table is called <em>output_table</em>_vocabulary (suffix added to the <em>output_table</em> name) and contains the following columns:<ul>
+<li><code>wordid:</code> An id assignment for each word</li>
+<li><code>word:</code> The word/term  </li>
+</ul>
+</dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare datasets with some example documents <pre class="example">
+DROP TABLE IF EXISTS documents;
+CREATE TABLE documents(docid INTEGER, doc_contents TEXT);
+INSERT INTO documents VALUES
+(1, 'I like to eat broccoli and banana. I ate a banana and spinach smoothie for breakfast.'),
+(2, 'Chinchillas and kittens are cute.'),
+(3, 'My sister adopted two kittens yesterday'),
+(4, 'Look at this cute hamster munching on a piece of broccoli');
+</pre></li>
+<li>Add a new column containing the words (lower-cased) in a text array <pre class="example">
+ALTER TABLE documents DROP COLUMN words;
+ALTER TABLE documents ADD COLUMN words TEXT[];
+UPDATE documents SET words = regexp_split_to_array(lower(doc_contents), E'[\s+\.]');
+</pre></li>
+<li>Compute the frequency of each word in each document <pre class="example">
+DROP TABLE IF EXISTS documents_tf;
+SELECT madlib.term_frequency('documents', 'docid', 'words', 'documents_tf');
+SELECT * FROM documents_tf order by docid;
+</pre> <pre class="result">
+ docid |    word    | count
+-------+------------+-------
+     1 | ate        |     1
+     1 | like       |     1
+     1 | breakfast  |     1
+     1 | to         |     1
+     1 | broccoli   |     1
+     1 | spinach    |     1
+     1 | i          |     2
+     1 | and        |     2
+     1 | a          |     1
+     1 |            |     2
+     1 | smoothie   |     1
+     1 | eat        |     1
+     1 | banana     |     2
+     1 | for        |     1
+     2 | cute       |     1
+     2 | are        |     1
+     2 | kitten     |     1
+     2 | and        |     1
+     2 | chinchilla |     1
+     3 | kitten     |     1
+     3 | my         |     1
+     3 | a          |     1
+     3 | sister     |     1
+     3 | adopted    |     1
+     3 | yesterday  |     1
+     4 | at         |     1
+     4 | of         |     1
+     4 | piece      |     1
+     4 | this       |     1
+     4 | a          |     1
+     4 | broccoli   |     1
+     4 | hamster    |     1
+     4 | munching   |     1
+     4 | cute       |     1
+     4 | look       |     1
+(35 rows)
+</pre></li>
+<li>We also can create a vocabulary of the words and store a wordid in the output table instead of the actual word. <pre class="example">
+DROP TABLE IF EXISTS documents_tf;
+DROP TABLE IF EXISTS documents_tf_vocabulary;
+SELECT madlib.term_frequency('documents', 'docid', 'words', 'documents_tf', TRUE);
+-- Output with wordid instead of the actual words
+SELECT * FROM documents_tf order by docid;
+</pre>  <pre class="result">
+ docid | wordid | count
+-------+--------+-------
+     1 |      0 |     2
+     1 |      1 |     1
+     1 |      3 |     2
+     1 |      6 |     1
+     1 |      7 |     2
+     1 |      8 |     1
+     1 |      9 |     1
+     1 |     12 |     1
+     1 |     13 |     1
+     1 |     15 |     2
+     1 |     17 |     1
+     1 |     24 |     1
+     1 |     25 |     1
+     1 |     27 |     1
+     2 |     16 |     1
+     2 |      3 |     1
+     2 |      4 |     1
+     2 |     10 |     1
+     2 |     11 |     1
+     3 |      1 |     1
+     3 |     16 |     1
+     3 |     28 |     1
+     3 |     23 |     1
+     3 |      2 |     1
+     3 |     20 |     1
+     4 |      9 |     1
+     4 |     11 |     1
+     4 |     22 |     1
+     4 |     14 |     1
+     4 |     26 |     1
+     4 |      1 |     1
+     4 |      5 |     1
+     4 |     18 |     1
+     4 |     19 |     1
+     4 |     21 |     1
+(35 rows)
+</pre>  <pre class="example">
+-- Vocabulary
+SELECT * FROM documents_tf_vocabulary order by wordid;
+</pre> <pre class="result">
+ wordid |    word
+--------+------------
+      0 |
+      1 | a
+      2 | adopted
+      3 | and
+      4 | are
+      5 | at
+      6 | ate
+      7 | banana
+      8 | breakfast
+      9 | broccoli
+     10 | chinchilla
+     11 | cute
+     12 | eat
+     13 | for
+     14 | hamster
+     15 | i
+     16 | kitten
+     17 | like
+     18 | look
+     19 | munching
+     20 | my
+     21 | of
+     22 | piece
+     23 | sister
+     24 | smoothie
+     25 | spinach
+     26 | this
+     27 | to
+     28 | yesterday
+(29 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="text__utilities_8sql__in.html" title="SQL functions for carrying out routine text operations. ">text_utilities.sql_in</a> documenting the SQL functions. File <a class="el" href="utilities_8sql__in.html" title="SQL functions for carrying out routine tasks. ">utilities.sql_in</a> documenting the utility functions for DB administration. </p>
+</div><!-- contents -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
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+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__topic__modelling.html
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+  <div class="headertitle">
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+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods to uncover abstract topics in a document corpus </p>
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+<tr class="memitem:group__grp__lda"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__lda.html">Latent Dirichlet Allocation</a></td></tr>
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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[46/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+<div class="title">clustered_variance_coxph.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for clustered robust cox proportional hazards regression.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a91f46572f245f34c9fe92d0ec806a998"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="clustered__variance__coxph_8sql__in.html#a91f46572f245f34c9fe92d0ec806a998">clustered_variance_coxph</a> ()</td></tr>
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+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>Oct 2013</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to clustered robust cox regression, see the module description <a class="el" href="group__grp__clustered__errors.html">Clustered Variance</a> </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a91f46572f245f34c9fe92d0ec806a998"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">varchar clustered_variance_coxph </td>
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+<div class="memitem">
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+          <td class="memname">varchar clustered_variance_coxph </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>message</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="acc0a2119671931eea54e021a4db6168c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void clustered_variance_coxph </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>clustervar</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5f5ca89e3265e170c325125e61ef191d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate __coxph_a_b_result coxph_a_b </td>
+          <td>(</td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2373d116ad1e2b2090f8c23a0c15edb5"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__coxph_a_b_result coxph_a_b_final </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]</td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a62bff141ced5972df28ceecdfe522888"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] coxph_a_b_merge </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a7081022015c00a14ecca27e017a4458b"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] coxph_a_b_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a713f413fc571244b7cec6c0084a61eb9"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">__coxph_cl_var_result coxph_compute_clustered_stats </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>hessian</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>a</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a523e61b6d56c8ff59fa8ae3121073a49"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] coxph_compute_w </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>status</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>h</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>s</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>a</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>b</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="abaeae5d6cd30db4b06a49d24d714812e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void robust_variance_coxph </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>model_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>clustervar</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Variables</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(4)"><span class="SelectionMark">&#160;</span>Groups</a></div>
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+<div class="header">
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+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">compatibility.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>Compatibility SQL functions.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a62af9c8b5618f77beaee1b2e1c411714"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#a62af9c8b5618f77beaee1b2e1c411714">insert_into</a> (varchar inTableName, varchar inSQL)</td></tr>
+<tr class="memdesc:a62af9c8b5618f77beaee1b2e1c411714"><td class="mdescLeft">&#160;</td><td class="mdescRight">Mimick <code>INSERT INTO</code>. Create new rows in a table.  <a href="#a62af9c8b5618f77beaee1b2e1c411714">More...</a><br/></td></tr>
+<tr class="separator:a62af9c8b5618f77beaee1b2e1c411714"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:afd2582cdd89fdbcebb34fd6b095057a0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#afd2582cdd89fdbcebb34fd6b095057a0">internal_create_table_as</a> (boolean inTemporary, varchar inTableName, varchar inSQL, varchar inDistributed)</td></tr>
+<tr class="separator:afd2582cdd89fdbcebb34fd6b095057a0"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a7e4ff90d5d720d76651643eefa66cd14"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#a7e4ff90d5d720d76651643eefa66cd14">create_table_as</a> (varchar inTableName, varchar inSQL, varchar inDistributed=&quot;RANDOMLY&quot;)</td></tr>
+<tr class="memdesc:a7e4ff90d5d720d76651643eefa66cd14"><td class="mdescLeft">&#160;</td><td class="mdescRight">Mimick <code>CREATE TABLE AS</code>. Create a table and fill it with data computed by a <code>SELECT</code> command.  <a href="#a7e4ff90d5d720d76651643eefa66cd14">More...</a><br/></td></tr>
+<tr class="separator:a7e4ff90d5d720d76651643eefa66cd14"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa52660f116e2c29f93c185ed7d855509"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#aa52660f116e2c29f93c185ed7d855509">create_table_as</a> (varchar inTableName, varchar inSQL)</td></tr>
+<tr class="separator:aa52660f116e2c29f93c185ed7d855509"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a3f1c139a9b5dceb5312f730be2a5868d"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#a3f1c139a9b5dceb5312f730be2a5868d">create_temporary_table_as</a> (varchar inTableName, varchar inSQL, varchar inDistributed=&quot;RANDOMLY&quot;)</td></tr>
+<tr class="memdesc:a3f1c139a9b5dceb5312f730be2a5868d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Mimick <code>CREATE TEMPORARY TABLE AS</code>. Create a temporary table and fill it with data computed by a <code>SELECT</code> command.  <a href="#a3f1c139a9b5dceb5312f730be2a5868d">More...</a><br/></td></tr>
+<tr class="separator:a3f1c139a9b5dceb5312f730be2a5868d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a33a4cc09d748ef5d642d59d3294fe570"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#a33a4cc09d748ef5d642d59d3294fe570">create_temporary_table_as</a> (varchar inTableName, varchar inSQL)</td></tr>
+<tr class="separator:a33a4cc09d748ef5d642d59d3294fe570"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ae9433d283ed5f743dcbe39f2850503f8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#ae9433d283ed5f743dcbe39f2850503f8">create_temp_table_as</a> (varchar inTableName, varchar inSQL, varchar inDistributed=&quot;RANDOMLY&quot;)</td></tr>
+<tr class="separator:ae9433d283ed5f743dcbe39f2850503f8"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a9f6716bb6914530e1d46b65adbea7360"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="compatibility_8sql__in.html#a9f6716bb6914530e1d46b65adbea7360">create_temp_table_as</a> (varchar inTableName, varchar inSQL)</td></tr>
+<tr class="separator:a9f6716bb6914530e1d46b65adbea7360"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section see"><dt>See Also</dt><dd>For a brief overview of compatibility functions, see the module description <a class="el" href="group__grp__compatibility.html">Compatibility</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a7e4ff90d5d720d76651643eefa66cd14"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_table_as </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inDistributed</em> = <code>&quot;RANDOMLY&quot;</code>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">inTableName</td><td>The name (optionally schema-qualified) of the table to be created. </td></tr>
+    <tr><td class="paramname">inSQL</td><td>A SELECT command. </td></tr>
+    <tr><td class="paramname">inDistributed</td><td>The Greenplum Database distribution policy for the table. This can be either <code>RANDOMLY</code>, which is the default, or <code>BY (<em>column</em>, [...])</code>. This parameter is ignored on PostgreSQL.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section user"><dt>Usage</dt><dd>Use in the same way as the <code>CREATE TABLE AS</code> statement: <pre>SELECT create_table_as('<em>table_name</em>', $$
+    <em>query</em>
+$$, 'BY (<em>column</em>, [...]) | RANDOMLY');</pre></dd></dl>
+<dl class="section user"><dt>Examples</dt><dd><pre>SELECT create_table_as('public.test', $$
+    SELECT * FROM generate_series(1,10) AS id
+$$, 'BY (id)');<br/>
+
+SELECT create_table_as('logregr_result', $$
+    SELECT * FROM logregr('data', 'y', 'x')
+$$, 'RANDOMLY');</pre></dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This function is a workaround. For compliance with the SQL standard and for optimal performance, please use the normal <code>CREATE TABLE AS</code> statement whenever possible. Known caveats of this workaround:<ul>
+<li>For queries returning a large number of rows, this function will be significantly slower than the <code>CREATE TABLE AS</code> statement. </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="aa52660f116e2c29f93c185ed7d855509"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_table_as </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ae9433d283ed5f743dcbe39f2850503f8"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_temp_table_as </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inDistributed</em> = <code>&quot;RANDOMLY&quot;</code>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a9f6716bb6914530e1d46b65adbea7360"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_temp_table_as </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a3f1c139a9b5dceb5312f730be2a5868d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_temporary_table_as </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inDistributed</em> = <code>&quot;RANDOMLY&quot;</code>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="section see"><dt>See Also</dt><dd><a class="el" href="compatibility_8sql__in.html#a7e4ff90d5d720d76651643eefa66cd14" title="Mimick CREATE TABLE AS. Create a table and fill it with data computed by a SELECT command...">create_table_as()</a> </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a33a4cc09d748ef5d642d59d3294fe570"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void create_temporary_table_as </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a62af9c8b5618f77beaee1b2e1c411714"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void insert_into </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">inTableName</td><td>The name (optionally schema-qualified) of an existing table. </td></tr>
+    <tr><td class="paramname">inSQL</td><td>A SELECT command.</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section user"><dt>Usage</dt><dd>Use in the same way as the <code>INSERT INTO</code> statement: <pre>SELECT insert_into('<em>table_name</em>', $$
+    <em>query</em>
+$$);</pre></dd></dl>
+<dl class="section user"><dt>Examples</dt><dd><pre>SELECT insert_into('public.test', $$
+    SELECT * FROM generate_series(1,10) AS id
+$$);<br/>
+
+SELECT insert_into('logregr_results', $$
+    SELECT * FROM logregr('data', 'y', 'x')
+$$);</pre></dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This function is a workaround. For compliance with the SQL standard and for optimal performance, please use the normal <code>INSERT INTO</code> statement whenever possible. Known caveats of this workaround:<ul>
+<li>For queries returning a large number of rows, this function will be significantly slower than the <code>INSERT INTO</code> statement. </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="afd2582cdd89fdbcebb34fd6b095057a0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void internal_create_table_as </td>
+          <td>(</td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>inTemporary</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inTableName</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inSQL</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>inDistributed</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_7668ab45ab1521840e6e151e43539dd4.html">compatibility</a></li><li class="navelem"><a class="el" href="compatibility_8sql__in.html">compatibility.sql_in</a></li>
+    <li class="footer">Generated on Wed Mar 5 2014 14:20:31 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/conjugate__gradient_8sql__in.html
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+<!-- end header part -->
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">conjugate_gradient.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL function computing Conjugate Gradient.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:ab94e8a12cc9c2d0dac35168721ec8585"><td class="memItemLeft" align="right" valign="top">float[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="conjugate__gradient_8sql__in.html#ab94e8a12cc9c2d0dac35168721ec8585">conjugate_gradient</a> (text matrix, text val_id, text row_id, float[] b, float precision_limit, int verbosity)</td></tr>
+<tr class="memdesc:ab94e8a12cc9c2d0dac35168721ec8585"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute conjugate gradient.  <a href="#ab94e8a12cc9c2d0dac35168721ec8585">More...</a><br /></td></tr>
+<tr class="separator:ab94e8a12cc9c2d0dac35168721ec8585"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:abe53a640e2287e06587eb1541fb6b4a3"><td class="memItemLeft" align="right" valign="top">float[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="conjugate__gradient_8sql__in.html#abe53a640e2287e06587eb1541fb6b4a3">conjugate_gradient</a> (text matrix, text val_id, text row_id, float[] b, float precision_limit)</td></tr>
+<tr class="separator:abe53a640e2287e06587eb1541fb6b4a3"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>March 2011 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="ab94e8a12cc9c2d0dac35168721ec8585"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float [] conjugate_gradient </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>matrix</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>val_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>row_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float[]&#160;</td>
+          <td class="paramname"><em>b</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float&#160;</td>
+          <td class="paramname"><em>precision_limit</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">int&#160;</td>
+          <td class="paramname"><em>verbosity</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">matrix</td><td>Name of the table containing argument matrix A </td></tr>
+    <tr><td class="paramname">val_id</td><td>Name of the column contains row values </td></tr>
+    <tr><td class="paramname">row_id</td><td>Name of the column contains row number </td></tr>
+    <tr><td class="paramname">b</td><td>Array containing values of b </td></tr>
+    <tr><td class="paramname">precision_limit</td><td>Precision threshold after which process will terminate </td></tr>
+    <tr><td class="paramname">verbosity</td><td>Verbose flag (0 = false, 1 = true) </td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>Array containing values of x </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="abe53a640e2287e06587eb1541fb6b4a3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float [] conjugate_gradient </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>matrix</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>val_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>row_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float[]&#160;</td>
+          <td class="paramname"><em>b</em>, </td>
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/correlation_8sql__in.html
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">correlation.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for correlation computation.  
+<a href="#details">More...</a></p>
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+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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+          <td class="paramkey"></td>
+          <td></td>
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+          <td class="paramname"><em>output_table</em>, </td>
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+        <tr>
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+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>target_cols</em>, </td>
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+        <tr>
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+<div class="memitem">
+<div class="memproto">
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+          <td class="memname">correlation_result correlation </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>target_cols</em>&#160;</td>
+        </tr>
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+<div class="memitem">
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+          <td class="memname">correlation_result correlation </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>source_table</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>output_table</em>&#160;</td>
+        </tr>
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+          <td class="memname">text correlation </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>input_message</em></td><td>)</td>
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+          <td>(</td>
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+<div class="memitem">
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+          <td class="memname">aggregate float8 [] correlation_agg </td>
+          <td>(</td>
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+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
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+          <td class="paramtype">float8&#160;</td>
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+          <td class="memname">float8 [] correlation_final </td>
+          <td>(</td>
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+          <td class="paramname"><em>state</em></td><td>)</td>
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+          <td class="memname">float8 [] correlation_merge </td>
+          <td>(</td>
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+          <td class="paramname"><em>left_state</em>, </td>
+        </tr>
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+          <td class="paramkey"></td>
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+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>right_state</em>&#160;</td>
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+          <td class="memname">float8 [] correlation_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
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+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>x</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>mean</em>&#160;</td>
+        </tr>
+        <tr>
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+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_505cd743a8a717435eca324f49291a46.html">stats</a></li><li class="navelem"><a class="el" href="correlation_8sql__in.html">correlation.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[37/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/dir_dc596537ad427a4d866006d1a3e1fe29.html
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+          <td class="memname">aggregate float8 [] discrete_distribution_agg </td>
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+          <td class="paramtype">integer&#160;</td>
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+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
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+        <tr>
+          <td class="paramkey"></td>
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+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname">&#160;</td>
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+          <td class="memname">float8 [] discrete_distribution_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
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+          <td class="memname">float8 [] discrete_distribution_transition </td>
+          <td>(</td>
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+          <td class="paramname"><em>distribution</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
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+          <td class="paramtype">float8&#160;</td>
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+          <td class="memname">float8 [][] vectorized_distribution_final </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname"><em>state</em>[][]</td><td>)</td>
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+          <td class="memname">float8 [][] vectorized_distribution_transition </td>
+          <td>(</td>
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+          <td class="paramname"><em>distribution</em>[][], </td>
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">crf.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for conditional random field.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a2e7104fea95ca76587e7fae9bcb0d7ed"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf_8sql__in.html#a2e7104fea95ca76587e7fae9bcb0d7ed">lincrf_lbfgs_step_transition</a> (float8[], float8[], float8[], float8[], float8, float8, float8[])</td></tr>
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+<tr class="separator:aff63c9a56bebf501a2f40971271605c2"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="separator:a20c096420d878cdd66dac6a232483737"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<tr class="memdesc:a336ac57ae3d07e331646d397c7651d3d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute linear-chain crf coefficients and diagnostic statistics.  <a href="#a336ac57ae3d07e331646d397c7651d3d">More...</a><br /></td></tr>
+<tr class="separator:a336ac57ae3d07e331646d397c7651d3d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a6b7059a5e22e40eae252c718217bf574"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf_8sql__in.html#a6b7059a5e22e40eae252c718217bf574">lincrf_train</a> (text train_feature_tbl, text train_featureset_tbl, text label_tbl, text crf_stats_tbl, text crf_weights_tbl)</td></tr>
+<tr class="separator:a6b7059a5e22e40eae252c718217bf574"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>July 2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to conditional random field, see the module description <a class="el" href="group__grp__crf.html">Conditional Random Field</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a73693c959db4ca3dbfd1b9debcca2c8f"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate anyarray array_union </td>
+          <td>(</td>
+          <td class="paramtype">anyarray&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a20c096420d878cdd66dac6a232483737"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer compute_lincrf </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>sparse_R</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dense_M</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>sparse_M</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>featureSize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>tagSize</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>maxNumIterations</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a47ef6b30d54ac4ef89cb664a681d1067"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 internal_lincrf_lbfgs_converge </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aff63c9a56bebf501a2f40971271605c2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">lincrf_result internal_lincrf_lbfgs_result </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a590cd748d8e18055d851e7d4f4a3ca00"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">aggregate float8 [] lincrf_lbfgs_step </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5bf4b2738003096f4eeeccd0eb5e98f0"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] lincrf_lbfgs_step_final </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a45f3eaf0c80cdfa99872cd071020d5c7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] lincrf_lbfgs_step_merge_states </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state1</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>state2</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2e7104fea95ca76587e7fae9bcb0d7ed"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">float8 [] lincrf_lbfgs_step_transition </td>
+          <td>(</td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[], </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8&#160;</td>
+          <td class="paramname">[]&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a336ac57ae3d07e331646d397c7651d3d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text lincrf_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_feature_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_featureset_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>label_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>crf_stats_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>crf_weights_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>max_iterations</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">source</td><td>Name of the source relation containing the training data </td></tr>
+    <tr><td class="paramname">sparse_R</td><td>Name of the sparse single state feature column (of type DOUBLE PRECISION[]) </td></tr>
+    <tr><td class="paramname">dense_M</td><td>Name of the dense two state feature column (of type DOUBLE PRECISION[]) </td></tr>
+    <tr><td class="paramname">sparse_M</td><td>Name of the sparse two state feature column (of type DOUBLE PRECISION[]) </td></tr>
+    <tr><td class="paramname">featureSize</td><td>Name of feature size column (of type DOUBLE PRECISION) </td></tr>
+    <tr><td class="paramname">tagSize</td><td>The number of tags in the tag set </td></tr>
+    <tr><td class="paramname">featureset</td><td>The unique feature set </td></tr>
+    <tr><td class="paramname">crf_feature</td><td>The Name of output feature table </td></tr>
+    <tr><td class="paramname">maxNumIterations</td><td>The maximum number of iterations</td></tr>
+  </table>
+  </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>a composite value:<ul>
+<li><code>coef FLOAT8[]</code> - Array of coefficients, \( \boldsymbol c \)</li>
+<li><code>log_likelihood FLOAT8</code> - Log-likelihood \( l(\boldsymbol c) \)</li>
+<li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated <br />
+<br />
+A 'crf_feature' table is used to store all the features and corresponding weights</li>
+</ul>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>This function starts an iterative algorithm. It is not an aggregate function. Source and column names have to be passed as strings (due to limitations of the SQL syntax). </dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="a6b7059a5e22e40eae252c718217bf574"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">text lincrf_train </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_feature_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_featureset_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>label_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>crf_stats_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>crf_weights_tbl</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_70e9abe17564e15776adcab8550c7667.html">crf</a></li><li class="navelem"><a class="el" href="crf_8sql__in.html">crf.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/crf__data__loader_8sql__in.html
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">crf_data_loader.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>Create database tables and import POS/NER training/testing data to the database.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:aaa609ec1d1d4cff0f335fa358091d5c3"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__data__loader_8sql__in.html#aaa609ec1d1d4cff0f335fa358091d5c3">crf_train_data</a> (text datapath)</td></tr>
+<tr class="separator:aaa609ec1d1d4cff0f335fa358091d5c3"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:abf2a39a0b84a5e7ae215db28b3a87f81"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__data__loader_8sql__in.html#abf2a39a0b84a5e7ae215db28b3a87f81">crf_test_data</a> (text datapath)</td></tr>
+<tr class="separator:abf2a39a0b84a5e7ae215db28b3a87f81"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>Feb. 2012 </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="abf2a39a0b84a5e7ae215db28b3a87f81"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void crf_test_data </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>datapath</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="section user"><dt>Input</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare an input test data segment table, e.g.:</li>
+</ol>
+<ul>
+<li>CREATE TABLE test_segmenttbl (start_pos integer,doc_id integer,seg_text text, max_pos integer) <pre class="fragment">sql&gt; select * from test_segmenttbl order by doc_id, start_pos;
+start_pos | doc_id |   seg_text    | max_pos
+----------+---------+--------------+-------------
+     0    |    1   |          the  |    26
+     1    |    1   |       madlib  |    26
+     2    |    1   |      mission  |    26
+     3    |    1   |            :  |    26
+     4    |    1   |           to  |    26
+     5    |    1   |       foster  |    26
+     6    |    1   |   widespread  |    26
+     7    |    1   |  development  |    26
+     8    |    1   |           of  |    26
+     9    |    1   |     scalable  |    26
+     10   |    1   |     analytic  |    26
+     11   |    1   |       skills  |    26
+     12   |    1   |            ,  |    26
+     13   |    1   |           by  |    26
+     ...
+     24   |    1   |  open-source  |    26
+     25   |    1   |   development |    26
+     26   |    1   |             . |    26
+</pre></li>
+</ul>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>create tables and import data to the database SELECT madlib.crf_test_data('/path/to/modeldata') </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a class="anchor" id="aaa609ec1d1d4cff0f335fa358091d5c3"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void crf_train_data </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>datapath</em></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="section user"><dt>Input</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare an input train data segment table, e.g.:</li>
+</ol>
+<ul>
+<li>CREATE TABLE train_segmenttbl (start_pos integer,doc_id integer,seg_text text, max_pos integer) <pre class="fragment">sql&gt; select * from train_segmenttbl order by doc_id, start_pos;
+start_pos | doc_id | seg_text      | max_pos
+----------+---------+--------------+-------------
+     0    |    1   |       madlib  |    9
+     1    |    1   |           is  |    9
+     2    |    1   |           an  |    9
+     3    |    1   |  open-source  |    9
+     4    |    1   |      library  |    9
+     5    |    1   |          for  |    9
+     6    |    1   |     scalable  |    9
+     7    |    1   |  in-database  |    9
+     8    |    1   |    analytics  |    9
+     9    |    1   |            .  |    9
+     0    |    2   |           it  |   16
+     1    |    2   |     provides  |   16
+     2    |    2   |data-parallel  |   16
+     3    |    2   |implementations|   16
+     ...
+     14   |    2   |  unstructured |   16
+     15   |    2   |          data |   16
+     16   |    2   |             . |   16
+</pre></li>
+</ul>
+<ol type="1">
+<li>Prepare an input dictionary table, e.g.,:</li>
+</ol>
+<ul>
+<li>CREATE TABLE crf_dictionary (token text,token_id integer,label text,count integer,total integer) <pre class="fragment">sql&gt; select * from crf_dictionary;
+token       | label  | count | total
+------------+--------+--------------
+   freefall |   11   |   1   |  1
+     policy |   11   |   2   |  2
+   measures |   12   |   1   |  1
+ commitment |   11   |   1   |  1
+        new |    6   |   1   |  1
+     speech |   11   |   1   |  1
+         's |   16   |   2   |  2
+     reckon |   30   |   1   |  1
+ underlying |   28   |   1   |  1
+ ...
+</pre></li>
+</ul>
+<ol type="1">
+<li>Prepare an input label table, e.g.,:</li>
+</ol>
+<ul>
+<li>CREATE TABLE labeltbl (id integer,label character varying) <pre class="fragment">sql&gt; select * from labeltbl order by id;
+id          | label
+------------+--------
+      0     |   CC
+      1     |   CD
+      2     |   DT
+      3     |   EX
+      4     |   FW
+      5     |   IN
+      6     |   JJ
+...
+     42     |    ,
+     43     |    .
+     44     |    :
+</pre></li>
+</ul>
+<ol type="1">
+<li>Prepare an input regex table, e.g.,:</li>
+</ol>
+<ul>
+<li>CREATE TABLE crf_regex (pattern text,name text) <pre class="fragment">sql&gt; select * from crf_regex;
+pattern       |   name
+------------- +---------------
+^[A-Z][a-z]+$ |  InitCapital%
+     ^[A-Z]+$ |  isAllCapital%
+ ^.*[0-9]+.*$ |  containsDigit%
+      ^.+[.]$ |  endsWithDot%
+      ^.+[,]$ |  endsWithComma%
+       ^.+er$ |  endsWithER%
+      ^.+est$ |  endsWithEst%
+       ^.+ed$ |  endsWithED%
+...
+</pre></li>
+</ul>
+<ol type="1">
+<li>Prepare an input feature table, e.g.,:</li>
+</ol>
+<ul>
+<li>CREATE TABLE featuretbl (id integer,name text,prev_label_id integer,label_id integer,weight float) <pre class="fragment">sql&gt; select * from featuretbl order by id;
+id   |     name     | prev_label_id | label_id | weight
+-------------------------------------------------------
+1    | W_chancellor |       -1      |    13     | 2.2322
+2    |         E.13 |       13      |     5     | 2.3995
+3    |            U |       -1      |     5     | 1.2164
+4    |         W_of |       -1      |     5     | 2.8744
+5    |          E.5 |        5      |     2     | 3.7716
+6    |        W_the |       -1      |     2     | 4.1790
+7    |          E.2 |        2      |    13     | 0.8957
+...
+</pre></li>
+</ul>
+<ol type="1">
+<li>Prepare an crf feature set table, e.g.,:</li>
+</ol>
+<ul>
+<li>CREATE TABLE MADlib.crf_feature_dic(f_index integer, f_name text, feature integer[]) <pre class="fragment">sql&gt; select * from crf_feature_dic order by id;
+f_index|     f_name   | feature
+--------------------------------
+0      | W_chancellor |       -1
+1      |         E.13 |       13
+2      |            U |       -1
+3      |         W_of |       -1
+4      |          E.5 |        5
+5      |        W_the |       -1
+...
+</pre></li>
+</ul>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>create tables and import data to the database SELECT madlib.crf_train_data('/path/to/modeldata') </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">crf_feature_gen.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL function for POS/NER feature extraction.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a3548bd640c2af456f3755cb24b913ebd"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_train_fgen</a> (text train_segment_tbl, text regex_tbl, text label_tbl, text dictionary_tbl, text train_feature_tbl, text train_featureset_tbl)</td></tr>
+<tr class="memdesc:a3548bd640c2af456f3755cb24b913ebd"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function extracts POS/NER features from the training data.  <a href="#a3548bd640c2af456f3755cb24b913ebd">More...</a><br /></td></tr>
+<tr class="separator:a3548bd640c2af456f3755cb24b913ebd"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad9af2c3d1a4f1c937795634ca297dede"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_test_fgen</a> (text test_segment_tbl, text dictionary_tbl, text label_tbl, text regex_tbl, text crf_weights_tbl, text viterbi_mtbl, text viterbi_rtbl)</td></tr>
+<tr class="memdesc:ad9af2c3d1a4f1c937795634ca297dede"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function extracts POS/NER features from the testing data.  <a href="#ad9af2c3d1a4f1c937795634ca297dede">More...</a><br /></td></tr>
+<tr class="separator:ad9af2c3d1a4f1c937795634ca297dede"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>February 2012</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For an introduction to POS/NER feature extraction, see the module description <a class="el" href="group__grp__crf.html">Conditional Random Field</a> </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="ad9af2c3d1a4f1c937795634ca297dede"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void crf_test_fgen </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>test_segment_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dictionary_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>label_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regex_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>crf_weights_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>viterbi_mtbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>viterbi_rtbl</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>This feature extraction function will produce two factor tables, "m table" (<em>viterbi_mtbl</em>) and "r table" (<em>viterbi_rtbl</em>). The <em>viterbi_mtbl</em> table and <em>viterbi_rtbl</em> table are used to calculate the best label sequence for each sentence.</p>
+<ul>
+<li><em>viterbi_mtbl</em> table encodes the edge features which are solely dependent on upon current label and previous y value. The m table has three columns which are prev_label, label, and value respectively. If the number of labels in \( n \), then the m factor table will \( n^2 \) rows. Each row encodes the transition feature weight value from the previous label to the current label.</li>
+</ul>
+<p><em>startFeature</em> is considered as a special edge feature which is from the beginning to the first token. Likewise, <em>endFeature</em> can be considered as a special edge feature which is from the last token to the very end. So m table encodes the edgeFeature, startFeature, and endFeature. If the total number of labels in the label space is 45 from 0 to 44, then the m factor array is as follows: </p><pre>
+                 0  1  2  3  4  5...44
+startFeature -1  a  a  a  a  a  a...a
+edgeFeature   0  a  a  a  a  a  a...a
+edgeFeature   1  a  a  a  a  a  a...a
+...
+edgeFeature  44  a  a  a  a  a  a...a
+endFeature   45  a  a  a  a  a  a...a</pre><ul>
+<li>viterbi_r table is related to specific tokens. It encodes the single state features, e.g., wordFeature, RegexFeature for all tokens. The r table is represented in the following way. <pre>
+       0  1  2  3  4...44
+token1 a  a  a  a  a...a
+token2 a  a  a  a  a...a</pre></li>
+</ul>
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">test_segment_tbl</td><td>Name of table containing all the tokenized testing sentences. </td></tr>
+    <tr><td class="paramname">dictionary_tbl</td><td>Name of table containing the dictionary_tbl. </td></tr>
+    <tr><td class="paramname">label_tbl</td><td>Name of table containing the the label space used in POS or other NLP tasks. </td></tr>
+    <tr><td class="paramname">regex_tbl</td><td>Name of table containing all the regular expressions to capture regex features. </td></tr>
+    <tr><td class="paramname">crf_weights_tbl</td><td>Name of the table containing featureset weights. </td></tr>
+    <tr><td class="paramname">viterbi_mtbl</td><td>Name of table to store the m factors. </td></tr>
+    <tr><td class="paramname">viterbi_rtbl</td><td>Name of table to store the r factors. </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+<a class="anchor" id="a3548bd640c2af456f3755cb24b913ebd"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void crf_train_fgen </td>
+          <td>(</td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_segment_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>regex_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>label_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>dictionary_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_feature_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">text&#160;</td>
+          <td class="paramname"><em>train_featureset_tbl</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">train_segment_tbl</td><td>Name of table containing all the tokenized training sentences. </td></tr>
+    <tr><td class="paramname">regex_tbl</td><td>Name of table containing all the regular expressions to capture regex features. </td></tr>
+    <tr><td class="paramname">label_tbl</td><td>Name of the label table containing unique ids and label names. </td></tr>
+    <tr><td class="paramname">dictionary_tbl</td><td>Name of table containing the dictionary_tbl. </td></tr>
+    <tr><td class="paramname">train_feature_tbl</td><td>features generated from the traning dataset </td></tr>
+    <tr><td class="paramname">train_featureset_tbl</td><td>unique feature set generated from the training dataset </td></tr>
+  </table>
+  </dd>
+</dl>
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/cross__validation_8sql__in.html
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+  <td style="padding-left: 0.5em;">
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+   <div id="projectbrief">User Documentation for MADlib</div>
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+        name="MSearchResults" id="MSearchResults">
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+
+<div class="header">
+  <div class="summary">
+<a href="#func-members">Functions</a>  </div>
+  <div class="headertitle">
+<div class="title">cross_validation.sql_in File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for cross validation.  
+<a href="#details">More...</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
+Functions</h2></td></tr>
+<tr class="memitem:a2a7791b05f51e8748ab7b6ccf328a7e2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#a2a7791b05f51e8748ab7b6ccf328a7e2">cross_validation_general</a> (varchar modelling_func, varchar[] modelling_params, varchar[] modelling_params_type, varchar param_explored, varchar[] explore_values, varchar predict_func, varchar[] predict_params, varchar[] predict_params_type, varchar metric_func, varchar[] metric_params, varchar[] metric_params_type, varchar data_tbl, varchar data_id, boolean id_is_random, varchar validation_result, varchar[] data_cols, integer n_folds)</td></tr>
+<tr class="separator:a2a7791b05f51e8748ab7b6ccf328a7e2"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a5efbb27898e99f7685816d9d5dfd082c"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#a5efbb27898e99f7685816d9d5dfd082c">cross_validation_general</a> (varchar modelling_func, varchar[] modelling_params, varchar[] modelling_params_type, varchar param_explored, varchar[] explore_values, varchar predict_func, varchar[] predict_params, varchar[] predict_params_type, varchar metric_func, varchar[] metric_params, varchar[] metric_params_type, varchar data_tbl, varchar data_id, boolean id_is_random, varchar validation_result, varchar[] data_cols)</td></tr>
+<tr class="separator:a5efbb27898e99f7685816d9d5dfd082c"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa151eb3fa9acc7f4cc33236e22ad4362"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#aa151eb3fa9acc7f4cc33236e22ad4362">cv_linregr_train</a> (varchar tbl_source, varchar col_ind_var, varchar col_dep_var, varchar tbl_result)</td></tr>
+<tr class="memdesc:aa151eb3fa9acc7f4cc33236e22ad4362"><td class="mdescLeft">&#160;</td><td class="mdescRight">A wrapper for linear regression.  <a href="#aa151eb3fa9acc7f4cc33236e22ad4362">More...</a><br /></td></tr>
+<tr class="separator:aa151eb3fa9acc7f4cc33236e22ad4362"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:aa572f1f57c0dd106b30948928161d8cc"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#aa572f1f57c0dd106b30948928161d8cc">cv_linregr_predict</a> (varchar tbl_model, varchar tbl_newdata, varchar col_ind_var, varchar col_id, varchar tbl_predict)</td></tr>
+<tr class="memdesc:aa572f1f57c0dd106b30948928161d8cc"><td class="mdescLeft">&#160;</td><td class="mdescRight">A wrapper for linear regression prediction.  <a href="#aa572f1f57c0dd106b30948928161d8cc">More...</a><br /></td></tr>
+<tr class="separator:aa572f1f57c0dd106b30948928161d8cc"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a2cd71e756f90b0c4fa46f508a1ca261e"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#a2cd71e756f90b0c4fa46f508a1ca261e">mse_error</a> (varchar tbl_prediction, varchar tbl_actual, varchar id_actual, varchar values_actual, varchar tbl_error)</td></tr>
+<tr class="separator:a2cd71e756f90b0c4fa46f508a1ca261e"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad01cd1c97ae1be0ac7cd6c42abb75bb2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#ad01cd1c97ae1be0ac7cd6c42abb75bb2">misclassification_avg</a> (varchar tbl_prediction, varchar tbl_actual, varchar id_actual, varchar values_actual, varchar tbl_error)</td></tr>
+<tr class="separator:ad01cd1c97ae1be0ac7cd6c42abb75bb2"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ac7dbc115f0c4624ccbf62b2d5494388d"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#ac7dbc115f0c4624ccbf62b2d5494388d">cv_logregr_predict</a> (varchar tbl_model, varchar tbl_newdata, varchar col_ind_var, varchar col_id, varchar tbl_predict)</td></tr>
+<tr class="memdesc:ac7dbc115f0c4624ccbf62b2d5494388d"><td class="mdescLeft">&#160;</td><td class="mdescRight">A prediction function for logistic regression The result is stored in the table of tbl_predict.  <a href="#ac7dbc115f0c4624ccbf62b2d5494388d">More...</a><br /></td></tr>
+<tr class="separator:ac7dbc115f0c4624ccbf62b2d5494388d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a2d1571ffa794176a5dfed9d35e70fed7"><td class="memItemLeft" align="right" valign="top">integer&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#a2d1571ffa794176a5dfed9d35e70fed7">logregr_accuracy</a> (float8[] coef, float8[] col_ind, boolean col_dep)</td></tr>
+<tr class="memdesc:a2d1571ffa794176a5dfed9d35e70fed7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Metric function for logistic regression.  <a href="#a2d1571ffa794176a5dfed9d35e70fed7">More...</a><br /></td></tr>
+<tr class="separator:a2d1571ffa794176a5dfed9d35e70fed7"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ac1b5c57473ff672af45191c8d53f46ed"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="cross__validation_8sql__in.html#ac1b5c57473ff672af45191c8d53f46ed">cv_logregr_accuracy</a> (varchar tbl_predict, varchar tbl_source, varchar col_id, varchar col_dep_var, varchar tbl_accuracy)</td></tr>
+<tr class="memdesc:ac1b5c57473ff672af45191c8d53f46ed"><td class="mdescLeft">&#160;</td><td class="mdescRight">Metric function for logistic regression.  <a href="#ac1b5c57473ff672af45191c8d53f46ed">More...</a><br /></td></tr>
+<tr class="separator:ac1b5c57473ff672af45191c8d53f46ed"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<div class="textblock"><dl class="section date"><dt>Date</dt><dd>January 2011</dd></dl>
+<dl class="section see"><dt>See also</dt><dd>For a brief introduction to the usage of cross validation, see the module description <a class="el" href="group__grp__validation.html">Cross Validation</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a class="anchor" id="a2a7791b05f51e8748ab7b6ccf328a7e2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cross_validation_general </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>modelling_func</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>modelling_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>modelling_params_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>param_explored</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>explore_values</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>predict_func</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>predict_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>predict_params_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>metric_func</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>metric_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>metric_params_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>data_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>data_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>id_is_random</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>validation_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>data_cols</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">integer&#160;</td>
+          <td class="paramname"><em>n_folds</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a5efbb27898e99f7685816d9d5dfd082c"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cross_validation_general </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>modelling_func</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>modelling_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>modelling_params_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>param_explored</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>explore_values</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>predict_func</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>predict_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>predict_params_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>metric_func</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>metric_params</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>metric_params_type</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>data_tbl</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>data_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>id_is_random</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>validation_result</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar[]&#160;</td>
+          <td class="paramname"><em>data_cols</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa572f1f57c0dd106b30948928161d8cc"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cv_linregr_predict </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_model</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_newdata</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_predict</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="aa151eb3fa9acc7f4cc33236e22ad4362"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cv_linregr_train </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_result</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="ac1b5c57473ff672af45191c8d53f46ed"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cv_logregr_accuracy </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_predict</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_source</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_dep_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_accuracy</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>It computes the percentage of correct predictions. The result is stored in the table of tbl_accuracy </p>
+
+</div>
+</div>
+<a class="anchor" id="ac7dbc115f0c4624ccbf62b2d5494388d"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void cv_logregr_predict </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_model</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_newdata</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_ind_var</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>col_id</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_predict</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<p>This function can be used together with cross-validation </p>
+
+</div>
+</div>
+<a class="anchor" id="a2d1571ffa794176a5dfed9d35e70fed7"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">integer logregr_accuracy </td>
+          <td>(</td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>coef</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">float8[]&#160;</td>
+          <td class="paramname"><em>col_ind</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">boolean&#160;</td>
+          <td class="paramname"><em>col_dep</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+<dl class="params"><dt>Parameters</dt><dd>
+  <table class="params">
+    <tr><td class="paramname">coef</td><td>Logistic fitting coefficients. Note: MADlib logregr_train function, unlike elastic_net, does not produce a separate intercept term. </td></tr>
+    <tr><td class="paramname">col_ind</td><td>Independent variable, an array </td></tr>
+    <tr><td class="paramname">col_dep</td><td>Dependent variable</td></tr>
+  </table>
+  </dd>
+</dl>
+<p>returns 1 if the prediction is the same as col_dep, otherwise 0 </p>
+
+</div>
+</div>
+<a class="anchor" id="ad01cd1c97ae1be0ac7cd6c42abb75bb2"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void misclassification_avg </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_prediction</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_actual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>id_actual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>values_actual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_error</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a class="anchor" id="a2cd71e756f90b0c4fa46f508a1ca261e"></a>
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">void mse_error </td>
+          <td>(</td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_prediction</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_actual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>id_actual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>values_actual</em>, </td>
+        </tr>
+        <tr>
+          <td class="paramkey"></td>
+          <td></td>
+          <td class="paramtype">varchar&#160;</td>
+          <td class="paramname"><em>tbl_error</em>&#160;</td>
+        </tr>
+        <tr>
+          <td></td>
+          <td>)</td>
+          <td></td><td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_9f5e6edf0db58b0627c46b41dbbbb27f.html">validation</a></li><li class="navelem"><a class="el" href="cross__validation_8sql__in.html">cross_validation.sql_in</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[38/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/dir_9db0190ed89ab407304aec250d1ef64f.html
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diff --git a/docs/latest/dir_9db0190ed89ab407304aec250d1ef64f.html b/docs/latest/dir_9db0190ed89ab407304aec250d1ef64f.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: pca Directory Reference</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
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+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
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+<!-- hack in the navigation tree -->
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+<!-- google analytics -->
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+  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
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+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('dir_9db0190ed89ab407304aec250d1ef64f.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
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+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">pca Directory Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="files"></a>
+Files</h2></td></tr>
+<tr class="memitem:pca_8sql__in"><td class="memItemLeft" align="right" valign="top">file &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="pca_8sql__in.html">pca.sql_in</a></td></tr>
+<tr class="memdesc:pca_8sql__in"><td class="mdescLeft">&#160;</td><td class="mdescRight">Principal Component Analysis. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:pca__project_8sql__in"><td class="memItemLeft" align="right" valign="top">file &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="pca__project_8sql__in.html">pca_project.sql_in</a></td></tr>
+<tr class="memdesc:pca__project_8sql__in"><td class="mdescLeft">&#160;</td><td class="mdescRight">Principal Component Analysis Projection. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.html">src</a></li><li class="navelem"><a class="el" href="dir_efbcf68973d247bbf15f9eecae7f24e3.html">ports</a></li><li class="navelem"><a class="el" href="dir_a4a48839224ef8488facbffa8a397967.html">postgres</a></li><li class="navelem"><a class="el" href="dir_dc596537ad427a4d866006d1a3e1fe29.html">modules</a></li><li class="navelem"><a class="el" href="dir_9db0190ed89ab407304aec250d1ef64f.html">pca</a></li>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:46 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/dir_9e42ee0a0235722f482630aa6cc99334.html
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diff --git a/docs/latest/dir_9e42ee0a0235722f482630aa6cc99334.html b/docs/latest/dir_9e42ee0a0235722f482630aa6cc99334.html
new file mode 100644
index 0000000..01d97a2
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+<title>MADlib: linalg Directory Reference</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+});
+</script><script src="../mathjax/MathJax.js"></script>
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+<table cellspacing="0" cellpadding="0">
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[12/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+<title>MADlib: Generalized Linear Models</title>
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+<div class="title">Generalized Linear Models<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Generalized linear models extends ordinary linear regression by allowing the response variable to follow a more general set of distributions (rather than simply Gaussian distributions), and for a general family of functions of the response variable (the link function) to vary linearly with the predicted values (rather than assuming that the response itself must vary linearly).</p>
+<p>For example, data of counts would typically be modeled with a Poisson distribution and a log link, while binary outcomes would typically be modeled with a Bernoulli distribution (or binomial distribution, depending on exactly how the problem is phrased) and a log-odds (or logit) link function.</p>
+<p>Currently, the implemented distribution families are </p><center> <table class="doxtable">
+<tr>
+<th>Distribution Family </th><th>Link Functions  </th></tr>
+<tr>
+<td>Binomial </td><td>logit, probit  </td></tr>
+<tr>
+<td>Gamma </td><td>inverse, identity, log  </td></tr>
+<tr>
+<td>Gaussian </td><td>identity, inverse, log  </td></tr>
+<tr>
+<td>Inverse Gaussian </td><td>inverse of square, inverse, identity, log  </td></tr>
+<tr>
+<td>Poisson </td><td>log, identity, square-root<br />
+  </td></tr>
+</table>
+</center><p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>GLM training function has the following format: <pre class="syntax">
+glm(source_table,
+    model_table,
+    dependent_varname,
+    independent_varname,
+    family_params,
+    grouping_col,
+    optim_params,
+    verbose
+    )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the model.</p>
+<p>The model table produced by glm contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td><p class="starttd">Text. Grouping columns, if provided in input. This could be multiple columns depending on the <code>grouping_col</code> input. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>coef </th><td><p class="starttd">FLOAT8. Vector of the coefficients in linear predictor. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>log_likelihood </th><td><p class="starttd">FLOAT8. The log-likelihood \( l(\boldsymbol \beta) \). We use the maximum likelihood estimate of dispersion parameter to calculate the log-likelihood while R and Python use deviance estimate and Pearson estimate respectively. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>std_err </th><td><p class="starttd">FLOAT8[]. Vector of the standard error of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>z_stats or t_stats </th><td><p class="starttd">FLOAT8[]. Vector of the z-statistics (in Poisson distribtuion and Binomial distribution) or the t-statistics (in all other distributions) of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>p_values </th><td><p class="starttd">FLOAT8[]. Vector of the p-values of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dispersion </th><td><p class="starttd">FLOAT8. The dispersion value (Pearson estimate). When family=poisson or family=binomial, the dispersion is always 1. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_processed </th><td><p class="starttd">BIGINT. Numbers of rows processed. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_skipped </th><td><p class="starttd">BIGINT. Numbers of rows skipped due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The number of iterations actually completed. This would be different from the <code>nIterations</code> argument if a <code>tolerance</code> parameter is provided and the algorithm converges before all iterations are completed.  </td></tr>
+</table>
+<p>A summary table named &lt;model_table&gt;_summary is also created at the same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method </th><td><p class="starttd">'glm' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>source_table </th><td><p class="starttd">The data source table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>model_table </th><td><p class="starttd">The model table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">The dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">The independent variables </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>family_params </th><td><p class="starttd">A string that contains family parameters, and has the form of 'family=..., link=...' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>grouping_col </th><td><p class="starttd">Name of grouping columns. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>optimizer_params </th><td><p class="starttd">A string that contains optimizer parameters, and has the form of 'optimizer=..., max_iter=..., tolerance=...' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">Number of groups in glm training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">Number of failed groups in glm training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_processed </th><td><p class="starttd">BIGINT. Total numbers of rows processed in all groups. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_skipped </th><td><p class="starttd">BIGINT. Total numbers of rows skipped in all groups due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">TEXT. Name of the dependent variable column.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">TEXT. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant <code>1</code> term in the independent variable list.</p>
+<p class="enddd"></p>
+</dd>
+<dt>family_params (optional) </dt>
+<dd><p class="startdd">TEXT, Parameters for distribution family. Currently, we support</p>
+<p>(1) family=poisson and link=[log or identity or sqrt].</p>
+<p>(2) family=gaussian and link=[identity or log or inverse]. And when family=gaussian and link=identity, the GLM model is exactly the same as the linear regression.</p>
+<p>(3) family=gamma and link=[inverse or identity or log].</p>
+<p>(4) family=inverse_gaussian and link=[sqr_inverse or log or identity or inverse].</p>
+<p>(5) family=binomial and link=[probit or logit]. </p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optim_params (optional) </dt>
+<dd><p class="startdd">TEXT, default: 'max_iter=100,optimizer=irls,tolerance=1e-6'. Parameters for optimizer. Currently, we support tolerance=[tolerance for relative error between log-likelihoods], max_iter=[maximum iterations to run], optimizer=irls.</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Provides verbose output of the results of training. </dd>
+</dl>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>For p-values, we just return the computation result directly. Other statistical packages, like 'R', produce the same result, but on printing the result to screen, another format function is used and any p-value that is smaller than the machine epsilon (the smallest positive floating-point number 'x' such that '1 + x != 1') will be printed on screen as "&lt; xxx" (xxx is the value of the machine epsilon). Although the results may look different, they are in fact the same. </dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd>The prediction function is provided to estimate the conditional mean given a new predictor. It has the following syntax: <pre class="syntax">
+glm_predict(coef,
+            col_ind_var
+            link)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>coef </dt>
+<dd><p class="startdd">DOUBLE PRECISION[]. Model coefficients obtained from <a class="el" href="glm_8sql__in.html#a3f8eb219013e05675626acb8cf4612cc">glm()</a>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_ind_var </dt>
+<dd><p class="startdd">New predictor, as a DOUBLE array. This should be the same length as the array obtained by evaluation of the 'independent_varname' argument in <a class="el" href="glm_8sql__in.html#a3f8eb219013e05675626acb8cf4612cc">glm()</a>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>link </dt>
+<dd>link function, as a string. This should match the link function the user inputted in <a class="el" href="glm_8sql__in.html#a3f8eb219013e05675626acb8cf4612cc">glm()</a>. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>Create the training data table. <pre class="example">
+CREATE TABLE warpbreaks(
+    id      serial,
+    breaks  integer,
+    wool    char(1),
+    tension char(1)
+);
+INSERT INTO warpbreaks(breaks, wool, tension) VALUES
+(26, 'A', 'L'),
+(30, 'A', 'L'),
+(54, 'A', 'L'),
+(25, 'A', 'L'),
+(70, 'A', 'L'),
+(52, 'A', 'L'),
+(51, 'A', 'L'),
+(26, 'A', 'L'),
+(67, 'A', 'L'),
+(18, 'A', 'M'),
+(21, 'A', 'M'),
+(29, 'A', 'M'),
+(17, 'A', 'M'),
+(12, 'A', 'M'),
+(18, 'A', 'M'),
+(35, 'A', 'M'),
+(30, 'A', 'M'),
+(36, 'A', 'M'),
+(36, 'A', 'H'),
+(21, 'A', 'H'),
+(24, 'A', 'H'),
+(18, 'A', 'H'),
+(10, 'A', 'H'),
+(43, 'A', 'H'),
+(28, 'A', 'H'),
+(15, 'A', 'H'),
+(26, 'A', 'H'),
+(27, 'B', 'L'),
+(14, 'B', 'L'),
+(29, 'B', 'L'),
+(19, 'B', 'L'),
+(29, 'B', 'L'),
+(31, 'B', 'L'),
+(41, 'B', 'L'),
+(20, 'B', 'L'),
+(44, 'B', 'L'),
+(42, 'B', 'M'),
+(26, 'B', 'M'),
+(19, 'B', 'M'),
+(16, 'B', 'M'),
+(39, 'B', 'M'),
+(28, 'B', 'M'),
+(21, 'B', 'M'),
+(39, 'B', 'M'),
+(29, 'B', 'M'),
+(20, 'B', 'H'),
+(21, 'B', 'H'),
+(24, 'B', 'H'),
+(17, 'B', 'H'),
+(13, 'B', 'H'),
+(15, 'B', 'H'),
+(15, 'B', 'H'),
+(16, 'B', 'H'),
+(28, 'B', 'H');
+SELECT create_indicator_variables('warpbreaks', 'warpbreaks_dummy', 'wool,tension');
+</pre></li>
+<li>Train a GLM model. <pre class="example">
+SELECT glm('warpbreaks_dummy',
+           'glm_model',
+           'breaks',
+           'ARRAY[1.0,"wool_B","tension_M", "tension_H"]',
+           'family=poisson, link=log');
+</pre></li>
+<li>View the regression results. <pre class="example">
+-- Set extended display on for easier reading of output
+\x on
+SELECT * FROM glm_model;
+</pre> Result: <pre class="result">
+coef               | {3.69196314494079,-0.205988442638621,-0.321320431600611,-0.51848849651156}
+log_likelihood     | -242.527983208979
+std_err            | {0.04541079434248,0.0515712427835191,0.0602659166951256,0.0639595193956924}
+z_stats            | {81.3014438174473,-3.99425011926316,-5.3317106786264,-8.10651020224019}
+p_values           | {0,6.48993254938271e-05,9.72918600322907e-08,5.20943463005751e-16}
+num_rows_processed | 54
+num_rows_skipped   | 0
+iteration          | 5
+</pre> Alternatively, unnest the arrays in the results for easier reading of output: <pre class="example">
+\x off
+SELECT unnest(coef) as coefficient,
+       unnest(std_err) as standard_error,
+       unnest(z_stats) as z_stat,
+       unnest(p_values) as pvalue
+FROM glm_model;
+</pre></li>
+<li>Predicting dependent variable using GLM model. (This example uses the original data table to perform the prediction. Typically a different test dataset with the same features as the original training dataset would be used for prediction.) <pre class="example">
+\x off
+-- Display predicted mean value on the original dataset
+SELECT
+    w.id,
+    madlib.glm_predict(
+        coef,
+        ARRAY[1, "wool_B", "tension_M", "tension_H"]::float8[],
+        'log') AS mu
+FROM warpbreaks_dummy w, glm_model m
+ORDER BY w.id;
+</pre> <pre class="example">
+-- Display predicted counts (which are predicted mean values rounded to the nearest integral value) on the original dataset
+SELECT
+    w.id,
+    madlib.glm_predict_poisson(
+        coef,
+        ARRAY[1, "wool_B", "tension_M", "tension_H"]::float8[],
+        'log') AS poisson_count
+FROM warpbreaks_dummy w, glm_model m
+ORDER BY w.id;
+</pre></li>
+</ol>
+</dd></dl>
+<p><b>Example for Gaussian family:</b></p>
+<ol type="1">
+<li>Create a testing data table <pre class="example">
+CREATE TABLE abalone (
+    id integer,
+    sex text,
+    length double precision,
+    diameter double precision,
+    height double precision,
+    whole double precision,
+    shucked double precision,
+    viscera double precision,
+    shell double precision,
+    rings integer
+);
+INSERT INTO abalone VALUES
+(3151, 'F', 0.655000000000000027, 0.505000000000000004, 0.165000000000000008, 1.36699999999999999, 0.583500000000000019, 0.351499999999999979, 0.396000000000000019, 10),
+(2026, 'F', 0.550000000000000044, 0.469999999999999973, 0.149999999999999994, 0.920499999999999985, 0.381000000000000005, 0.243499999999999994, 0.267500000000000016, 10),
+(3751, 'I', 0.434999999999999998, 0.375, 0.110000000000000001, 0.41549999999999998, 0.170000000000000012, 0.0759999999999999981, 0.14499999999999999, 8),
+(720, 'I', 0.149999999999999994, 0.100000000000000006, 0.0250000000000000014, 0.0149999999999999994, 0.00449999999999999966, 0.00400000000000000008, 0.0050000000000000001, 2),
+(1635, 'F', 0.574999999999999956, 0.469999999999999973, 0.154999999999999999, 1.1160000000000001, 0.509000000000000008, 0.237999999999999989, 0.340000000000000024, 10),
+(2648, 'I', 0.5, 0.390000000000000013, 0.125, 0.582999999999999963, 0.293999999999999984, 0.132000000000000006, 0.160500000000000004, 8),
+(1796, 'F', 0.57999999999999996, 0.429999999999999993, 0.170000000000000012, 1.47999999999999998, 0.65349999999999997, 0.32400000000000001, 0.41549999999999998, 10),
+(209, 'F', 0.525000000000000022, 0.41499999999999998, 0.170000000000000012, 0.832500000000000018, 0.275500000000000023, 0.168500000000000011, 0.309999999999999998, 13),
+(1451, 'I', 0.455000000000000016, 0.33500000000000002, 0.135000000000000009, 0.501000000000000001, 0.274000000000000021, 0.0995000000000000051, 0.106499999999999997, 7),
+(1108, 'I', 0.510000000000000009, 0.380000000000000004, 0.115000000000000005, 0.515499999999999958, 0.214999999999999997, 0.113500000000000004, 0.166000000000000009, 8),
+(3675, 'F', 0.594999999999999973, 0.450000000000000011, 0.165000000000000008, 1.08099999999999996, 0.489999999999999991, 0.252500000000000002, 0.279000000000000026, 12),
+(2108, 'F', 0.675000000000000044, 0.550000000000000044, 0.179999999999999993, 1.68849999999999989, 0.562000000000000055, 0.370499999999999996, 0.599999999999999978, 15),
+(3312, 'F', 0.479999999999999982, 0.380000000000000004, 0.135000000000000009, 0.507000000000000006, 0.191500000000000004, 0.13650000000000001, 0.154999999999999999, 12),
+(882, 'M', 0.655000000000000027, 0.520000000000000018, 0.165000000000000008, 1.40949999999999998, 0.585999999999999965, 0.290999999999999981, 0.405000000000000027, 9),
+(3402, 'M', 0.479999999999999982, 0.395000000000000018, 0.149999999999999994, 0.681499999999999995, 0.214499999999999996, 0.140500000000000014, 0.2495, 18),
+(829, 'I', 0.409999999999999976, 0.325000000000000011, 0.100000000000000006, 0.394000000000000017, 0.20799999999999999, 0.0655000000000000027, 0.105999999999999997, 6),
+(1305, 'M', 0.535000000000000031, 0.434999999999999998, 0.149999999999999994, 0.716999999999999971, 0.347499999999999976, 0.14449999999999999, 0.194000000000000006, 9),
+(3613, 'M', 0.599999999999999978, 0.46000000000000002, 0.179999999999999993, 1.1399999999999999, 0.422999999999999987, 0.257500000000000007, 0.364999999999999991, 10),
+(1068, 'I', 0.340000000000000024, 0.265000000000000013, 0.0800000000000000017, 0.201500000000000012, 0.0899999999999999967, 0.0475000000000000006, 0.0550000000000000003, 5),
+(2446, 'M', 0.5, 0.380000000000000004, 0.135000000000000009, 0.583500000000000019, 0.22950000000000001, 0.126500000000000001, 0.179999999999999993, 12),
+(1393, 'M', 0.635000000000000009, 0.474999999999999978, 0.170000000000000012, 1.19350000000000001, 0.520499999999999963, 0.269500000000000017, 0.366499999999999992, 10),
+(359, 'M', 0.744999999999999996, 0.584999999999999964, 0.214999999999999997, 2.49900000000000011, 0.92649999999999999, 0.471999999999999975, 0.699999999999999956, 17),
+(549, 'F', 0.564999999999999947, 0.450000000000000011, 0.160000000000000003, 0.79500000000000004, 0.360499999999999987, 0.155499999999999999, 0.23000000000000001, 12),
+(1154, 'F', 0.599999999999999978, 0.474999999999999978, 0.160000000000000003, 1.02649999999999997, 0.484999999999999987, 0.2495, 0.256500000000000006, 9),
+(1790, 'F', 0.54500000000000004, 0.385000000000000009, 0.149999999999999994, 1.11850000000000005, 0.542499999999999982, 0.244499999999999995, 0.284499999999999975, 9),
+(3703, 'F', 0.665000000000000036, 0.540000000000000036, 0.195000000000000007, 1.76400000000000001, 0.850500000000000034, 0.361499999999999988, 0.469999999999999973, 11),
+(1962, 'F', 0.655000000000000027, 0.515000000000000013, 0.179999999999999993, 1.41199999999999992, 0.619500000000000051, 0.248499999999999999, 0.496999999999999997, 11),
+(1665, 'I', 0.604999999999999982, 0.469999999999999973, 0.14499999999999999, 0.802499999999999991, 0.379000000000000004, 0.226500000000000007, 0.220000000000000001, 9),
+(635, 'M', 0.359999999999999987, 0.294999999999999984, 0.100000000000000006, 0.210499999999999993, 0.0660000000000000031, 0.0524999999999999981, 0.0749999999999999972, 9),
+(3901, 'M', 0.445000000000000007, 0.344999999999999973, 0.140000000000000013, 0.475999999999999979, 0.205499999999999988, 0.101500000000000007, 0.108499999999999999, 15),
+(2734, 'I', 0.41499999999999998, 0.33500000000000002, 0.100000000000000006, 0.357999999999999985, 0.169000000000000011, 0.067000000000000004, 0.104999999999999996, 7),
+(3856, 'M', 0.409999999999999976, 0.33500000000000002, 0.115000000000000005, 0.440500000000000003, 0.190000000000000002, 0.0850000000000000061, 0.135000000000000009, 8),
+(827, 'I', 0.395000000000000018, 0.28999999999999998, 0.0950000000000000011, 0.303999999999999992, 0.127000000000000002, 0.0840000000000000052, 0.076999999999999999, 6),
+(3381, 'I', 0.190000000000000002, 0.130000000000000004, 0.0449999999999999983, 0.0264999999999999993, 0.00899999999999999932, 0.0050000000000000001, 0.00899999999999999932, 5),
+(3972, 'I', 0.400000000000000022, 0.294999999999999984, 0.0950000000000000011, 0.252000000000000002, 0.110500000000000001, 0.0575000000000000025, 0.0660000000000000031, 6),
+(1155, 'M', 0.599999999999999978, 0.455000000000000016, 0.170000000000000012, 1.1915, 0.695999999999999952, 0.239499999999999991, 0.239999999999999991, 8),
+(3467, 'M', 0.640000000000000013, 0.5, 0.170000000000000012, 1.4544999999999999, 0.642000000000000015, 0.357499999999999984, 0.353999999999999981, 9),
+(2433, 'F', 0.609999999999999987, 0.484999999999999987, 0.165000000000000008, 1.08699999999999997, 0.425499999999999989, 0.232000000000000012, 0.380000000000000004, 11),
+(552, 'I', 0.614999999999999991, 0.489999999999999991, 0.154999999999999999, 0.988500000000000045, 0.41449999999999998, 0.195000000000000007, 0.344999999999999973, 13),
+(1425, 'F', 0.729999999999999982, 0.57999999999999996, 0.190000000000000002, 1.73750000000000004, 0.678499999999999992, 0.434499999999999997, 0.520000000000000018, 11),
+(2402, 'F', 0.584999999999999964, 0.41499999999999998, 0.154999999999999999, 0.69850000000000001, 0.299999999999999989, 0.145999999999999991, 0.195000000000000007, 12),
+(1748, 'F', 0.699999999999999956, 0.535000000000000031, 0.174999999999999989, 1.77299999999999991, 0.680499999999999994, 0.479999999999999982, 0.512000000000000011, 15),
+(3983, 'I', 0.57999999999999996, 0.434999999999999998, 0.149999999999999994, 0.891499999999999959, 0.362999999999999989, 0.192500000000000004, 0.251500000000000001, 6),
+(335, 'F', 0.739999999999999991, 0.599999999999999978, 0.195000000000000007, 1.97399999999999998, 0.597999999999999976, 0.408499999999999974, 0.709999999999999964, 16),
+(1587, 'I', 0.515000000000000013, 0.349999999999999978, 0.104999999999999996, 0.474499999999999977, 0.212999999999999995, 0.122999999999999998, 0.127500000000000002, 10),
+(2448, 'I', 0.275000000000000022, 0.204999999999999988, 0.0800000000000000017, 0.096000000000000002, 0.0359999999999999973, 0.0184999999999999991, 0.0299999999999999989, 6),
+(1362, 'F', 0.604999999999999982, 0.474999999999999978, 0.174999999999999989, 1.07600000000000007, 0.463000000000000023, 0.219500000000000001, 0.33500000000000002, 9),
+(2799, 'M', 0.640000000000000013, 0.484999999999999987, 0.149999999999999994, 1.09800000000000009, 0.519499999999999962, 0.222000000000000003, 0.317500000000000004, 10),
+(1413, 'F', 0.67000000000000004, 0.505000000000000004, 0.174999999999999989, 1.01449999999999996, 0.4375, 0.271000000000000019, 0.3745, 10),
+(1739, 'F', 0.67000000000000004, 0.540000000000000036, 0.195000000000000007, 1.61899999999999999, 0.739999999999999991, 0.330500000000000016, 0.465000000000000024, 11),
+(1152, 'M', 0.584999999999999964, 0.465000000000000024, 0.160000000000000003, 0.955500000000000016, 0.45950000000000002, 0.235999999999999988, 0.265000000000000013, 7),
+(2427, 'I', 0.564999999999999947, 0.434999999999999998, 0.154999999999999999, 0.782000000000000028, 0.271500000000000019, 0.16800000000000001, 0.284999999999999976, 14),
+(1777, 'M', 0.484999999999999987, 0.369999999999999996, 0.154999999999999999, 0.967999999999999972, 0.418999999999999984, 0.245499999999999996, 0.236499999999999988, 9),
+(3294, 'M', 0.574999999999999956, 0.455000000000000016, 0.184999999999999998, 1.15599999999999992, 0.552499999999999991, 0.242999999999999994, 0.294999999999999984, 13),
+(1403, 'M', 0.650000000000000022, 0.510000000000000009, 0.190000000000000002, 1.54200000000000004, 0.715500000000000025, 0.373499999999999999, 0.375, 9),
+(2256, 'M', 0.510000000000000009, 0.395000000000000018, 0.14499999999999999, 0.61850000000000005, 0.215999999999999998, 0.138500000000000012, 0.239999999999999991, 12),
+(3984, 'F', 0.584999999999999964, 0.450000000000000011, 0.125, 0.873999999999999999, 0.354499999999999982, 0.20749999999999999, 0.225000000000000006, 6),
+(1116, 'M', 0.525000000000000022, 0.405000000000000027, 0.119999999999999996, 0.755499999999999949, 0.3755, 0.155499999999999999, 0.201000000000000012, 9),
+(1366, 'M', 0.609999999999999987, 0.474999999999999978, 0.170000000000000012, 1.02649999999999997, 0.434999999999999998, 0.233500000000000013, 0.303499999999999992, 10),
+(3759, 'I', 0.525000000000000022, 0.400000000000000022, 0.140000000000000013, 0.605500000000000038, 0.260500000000000009, 0.107999999999999999, 0.209999999999999992, 9);
+</pre></li>
+<li>Train a model with family=gaussian and link=identity <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=gaussian, link=identity');
+</pre></li>
+</ol>
+<p><b>Example for Gamma family:</b> (reuse the dataset in Gaussian case)</p>
+<ol type="1">
+<li>Reuse the test data set in Gaussian</li>
+<li>Train a model with family=gamma and link=inverse <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=gamma, link=inverse');
+</pre></li>
+</ol>
+<p><b>Example for Inverse Gaussian family:</b> (reuse the dataset in Gaussian case)</p>
+<ol type="1">
+<li>Reuse the test data set in Gaussian</li>
+<li>Train a model with family=inverse_gaussian and link=sqr_inverse <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=inverse_gaussian, link=sqr_inverse');
+</pre></li>
+</ol>
+<p><b>Example for Binomial family:</b> (reuse the dataset in Gaussian case)</p>
+<ol type="1">
+<li>Reuse the test data set in Gaussian</li>
+<li>Train a model with family=binomial and link=probit <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings &lt; 10',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=binomial, link=probit');
+</pre></li>
+<li>Predict output probabilities <pre class="example">
+SELECT madlib.glm_predict(
+    coef,
+    ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]::float8[],
+    'probit')
+FROM abalone_out, abalone;
+</pre></li>
+<li>Predict output categories <pre class="example">
+SELECT madlib.glm_predict(
+SELECT madlib.glm_predict_binomial(
+    coef,
+    ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]::float8[],
+    'probit')
+FROM abalone_out, abalone;
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd>All table names can be optionally schema qualified (current_schemas() would be searched if a schema name is not provided) and all table and column names should follow case-sensitivity and quoting rules per the database. (For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for entity names then the string should be double-quoted; in this case the input would be '"MyTable"').</dd></dl>
+<p>Currently implementation uses Newton's method and, according to performance tests, when number of features are over 1000, this GLM function could be running slowly.</p>
+<p>Functions in <a class="el" href="group__grp__linreg.html">Linear Regression</a> is prefered to GLM with family=gaussian,link=identity, as the former require only a single pass over the training data. In addition, if user expects to use robust variance, clustered variance, or marginal effects on top of the trained model, functions in <a class="el" href="group__grp__linreg.html">Linear Regression</a> and <a class="el" href="group__grp__logreg.html">Logistic Regression</a> should be used.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="glm_8sql__in.html" title="SQL functions for GLM (Poisson) ">glm.sql_in</a> documenting the training function</p>
+<p><a class="el" href="group__grp__linreg.html">Linear Regression</a></p>
+<p><a class="el" href="group__grp__logreg.html">Logistic Regression</a></p>
+<p><a class="el" href="group__grp__mlogreg.html">Multinomial Logistic Regression</a></p>
+<p><a class="el" href="group__grp__robust.html">Robust Variance</a></p>
+<p><a class="el" href="group__grp__clustered__errors.html">Clustered Variance</a></p>
+<p><a class="el" href="group__grp__validation.html">Cross Validation</a></p>
+<p><a class="el" href="group__grp__marginal.html">Marginal Effects</a></p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__glm.js
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diff --git a/docs/latest/group__grp__glm.js b/docs/latest/group__grp__glm.js
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@@ -0,0 +1,11 @@
+var group__grp__glm =
+[
+    [ "Linear Regression", "group__grp__linreg.html", null ],
+    [ "Logistic Regression", "group__grp__logreg.html", null ],
+    [ "Multinomial Logistic Regression", "group__grp__mlogreg.html", null ],
+    [ "Elastic Net Regularization", "group__grp__elasticnet.html", null ],
+    [ "Cox-Proportional Hazards Regression", "group__grp__cox__prop__hazards.html", null ],
+    [ "Robust Variance", "group__grp__robust.html", null ],
+    [ "Clustered Variance", "group__grp__clustered__errors.html", null ],
+    [ "Marginal Effects", "group__grp__marginal.html", null ]
+];
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/group__grp__inf__stats.html
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diff --git a/docs/latest/group__grp__inf__stats.html b/docs/latest/group__grp__inf__stats.html
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--- /dev/null
+++ b/docs/latest/group__grp__inf__stats.html
@@ -0,0 +1,140 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<title>MADlib: Inferential Statistics</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
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+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
+<p>A collection of methods to compute inferential statistics on a dataset. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__stats__tests"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__stats__tests.html">Hypothesis Tests</a></td></tr>
+<tr class="memdesc:group__grp__stats__tests"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provides functions to perform statistical hypothesis tests. <br /></td></tr>
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+<title>MADlib: Support Vector Machines</title>
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+<div class="title">Support Vector Machines<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#learn">Regression Learning Function</a> </li>
+<li>
+<a href="#classify">Classification Learning Function</a> </li>
+<li>
+<a href="#novelty">Novelty Detection Functions</a> </li>
+<li>
+<a href="#predict">Prediction Functions</a> </li>
+<li>
+<a href="#notes">Notes</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p>Support vector machines (SVMs) and related kernel methods have been among the most popular and well-studied machine learning techniques of the past 15 years, with an amazing number of innovations and applications.</p>
+<p>In a nutshell, an SVM model \(f(x)\) takes the form of </p><p class="formulaDsp">
+\[ f(x) = \sum_i \alpha_i k(x_i,x), \]
+</p>
+<p> where each \( \alpha_i \) is a real number, each \( \boldsymbol x_i \) is a data point from the training set (called a support vector), and \( k(\cdot, \cdot) \) is a kernel function that measures how "similar" two objects are. In regression, \( f(\boldsymbol x) \) is the regression function we seek. In classification, \( f(\boldsymbol x) \) serves as the decision boundary; so for example in binary classification, the predictor can output class 1 for object \(x\) if \( f(\boldsymbol x) \geq 0 \), and class 2 otherwise.</p>
+<p>In the case when the kernel function \( k(\cdot, \cdot) \) is the standard inner product on vectors, \( f(\boldsymbol x) \) is just an alternative way of writing a linear function </p><p class="formulaDsp">
+\[ f&#39;(\boldsymbol x) = \langle \boldsymbol w, \boldsymbol x \rangle, \]
+</p>
+<p> where \( \boldsymbol w \) is a weight vector having the same dimension as \( \boldsymbol x \). One of the key points of SVMs is that we can use more fancy kernel functions to efficiently learn linear models in high-dimensional feature spaces, since \( k(\boldsymbol x_i, \boldsymbol x_j) \) can be understood as an efficient way of computing an inner product in the feature space: </p><p class="formulaDsp">
+\[ k(\boldsymbol x_i, \boldsymbol x_j) = \langle \phi(\boldsymbol x_i), \phi(\boldsymbol x_j) \rangle, \]
+</p>
+<p> where \( \phi(\boldsymbol x) \) projects \( \boldsymbol x \) into a (possibly infinite-dimensional) feature space.</p>
+<p>There are many algorithms for learning kernel machines. This module implements the class of online learning with kernels algorithms described in Kivinen et al. [1]. It also includes the incremental gradient descent (IGD) method Feng et al. [3] for learning linear SVMs with the Hinge loss \(l(z) = \max(0, 1-z)\). See also the book Scholkopf and Smola [2] for more details of SVMs in general.</p>
+<p>The IGD implementation is based on Bismarck project in University of Wisconsin (<a href="http://hazy.cs.wisc.edu/hazy/victor/bismarck/">http://hazy.cs.wisc.edu/hazy/victor/bismarck/</a>). The methods introduced in [1] are implemented according to their original descriptions, except that we only update the support vector model when we make a significant error. The original algorithms in [1] update the support vector model at every step, even when no error was made, in the name of regularization. For practical purposes, and this is verified empirically to a certain degree, updating only when necessary is both faster and better from a learning-theoretic point of view, at least in the i.i.d. setting.</p>
+<p>Methods for classification, regression and novelty detection are available. Multiple instances of the algorithms can be executed in parallel on different subsets of the training data. The resultant support vector models can then be combined using standard techniques like averaging or majority voting.</p>
+<p>Training data points are accessed via a table or a view. The support vector models can also be stored in tables for fast execution.</p>
+<p><a class="anchor" id="learn"></a></p><dl class="section user"><dt>Regression Learning Function</dt><dd></dd></dl>
+<p>Regression learning is achieved through the following function: </p><pre class="syntax">
+svm_regression( input_table,
+                model_table,
+                parallel,
+                kernel_func,
+                verbose DEFAULT false,
+                eta DEFAULT 0.1,
+                nu DEFAULT 0.005,
+                slambda DEFAULT 0.05,
+                kernel_param DEFAULT 1.0
+              )</pre><p>For classification and regression, the training table/view is expected to be of the following form (the array size of <em>ind</em> must not be greater than 102,400.):<br />
+</p><pre>{TABLE|VIEW} input_table (
+    ...
+    id INT,
+    ind FLOAT8[],
+    label FLOAT8,
+    ...
+)</pre><p>For novelty detection, the label field is not required. Also note that the column names of input_table requires to be exactly the same as described above. This limitation will be removed when this module graduates from early development stage.</p>
+<p><a class="anchor" id="classify"></a></p><dl class="section user"><dt>Classification Learning Function</dt><dd></dd></dl>
+<p>Classification learning is achieved through the following two functions:</p>
+<ul>
+<li>Learn linear SVM(s) using IGD [3]. <pre class="syntax">
+lsvm_classification( input_table,
+                     model_table,
+                     parallel DEFAULT false,
+                     verbose DEFAULT false,
+                     eta DEFAULT 0.1,
+                     reg DEFAULT 0.001,
+                     max_iter DEFAULT 100
+                   )
+</pre> Note that, as any gradient descent methods, IGD will converge with a greater eta (stepsize), thus faster, if the input training data is well-conditioned. We highly recommend user to perform data preparation, such that the mean value of each feature column is 0 and standard error is 1, and append an extra feature with constant value 1 for intercept term. We plan to provide a function for this when this module graduates from early stage development.</li>
+<li>Learn linear or non-linear SVM(s) using the method described in [1]. <pre class="syntax">
+svm_classification( input_table,
+                    model_table,
+                    parallel,
+                    kernel_func,
+                    verbose DEFAULT false,
+                    eta DEFAULT 0.1,
+                    nu DEFAULT 0.005,
+                    kernel_param DEFAULT 1.0
+                  )
+</pre></li>
+</ul>
+<p><a class="anchor" id="novelty"></a></p><dl class="section user"><dt>Novelty Detection Function</dt><dd></dd></dl>
+<p>Novelty detection is achieved through the following function: </p><pre class="syntax">
+svm_novelty_detection( input_table,
+                       model_table,
+                       parallel,
+                       kernel_func,
+                       verbose DEFAULT false,
+                       eta DEFAULT 0.1,
+                       nu DEFAULT 0.005,
+                       kernel_param DEFAULT 1.0
+                     )
+</pre><p>Assuming the model_table parameter takes on value 'model', each learning function will produce two tables as output: 'model' and 'model_param'. The first contains the support vectors of the model(s) learned. The second contains the parameters of the model(s) learned, which include information like the kernel function used and the value of the intercept, if there is one.</p>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Functions</dt><dd></dd></dl>
+<ul>
+<li>To make predictions on a single data point x using a single model learned previously, we use the function <pre class="syntax">
+svm_predict_batch( input_table,
+                   data_col,
+                   id_col,
+                   model_table,
+                   output_table,
+                   parallel
+                 )
+</pre> If the <code>parallel</code> parameter is true, then each data point in the input table will have multiple predicted values corresponding to the number of models learned in</li>
+<li>If the model is produced by the <a class="el" href="lsvm_8sql__in.html#a6dcddc88d70523ddda32b46ab82dfbbf" title="This is the linear support vector classification function. ">lsvm_classification()</a> function, use the following prediction function instead. <pre class="syntax">
+lsvm_predict_batch( input_table,
+                    data_col,
+                    id_col,
+                    model_table,
+                    output_table,
+                  )
+</pre></li>
+<li>Note that, to make predictions on a subset of data points stored in a table, a separated view or table needs to be created ahead of time: <pre class="example">
+-- create subset as a view
+CREATE VIEW subset AS SELECT * FROM input_table WHERE id &lt;= 100;
+-- prediction on the subset
+SELECT svm_predict_batch('subset', 'ind', 'id',
+    'svm_model', 'subset_svm_predict', false);
+-- prediction using linear SVMs
+SELECT lsvm_predict_batch('subset', 'ind', 'id',
+    'lsvm_model', 'subset_lsvm_predict');
+</pre></li>
+</ul>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<p>The <code>kernel_func</code> argument of <code>svm_classification</code>, <code>svm_regression</code>, and <code>svm_novelty_detection</code> can only accept a kernel function in the following form:</p>
+<pre class="syntax">
+FLOAT8 kernel_function(FLOAT8[], FLOAT8[], FLOAT8)
+</pre><p>The first two parameters are feature vectors, and the third one is a control parameter for the kernel function. The value of the control parameter must be set throught the <code>kernel_param</code> argument of <code>svm_classification</code>, <code>svm_regression</code>, and <code>svm_novelty_detection</code>.</p>
+<p>Currently, three kernel functions have been implemented: linear or dot product (<a class="el" href="online__sv_8sql__in.html#ab53cac5790dafd7230359e08c72af4f1">svm_dot</a>), polynomial (<a class="el" href="online__sv_8sql__in.html#a1ac76fdf9623e0a4db47665f2a80be90">svm_polynomial</a>) and Gaussian (<a class="el" href="online__sv_8sql__in.html#a9f2a96e1a241ecc66386a78b110777d3">svm_gaussian</a>) kernels. Note that for the dot product kernel, it actually only requires two FLOAT8[] parameters. To be compliant with the requirements for the kernel function, we have an overloaded version of <code>svm_dot</code> which accepts two FLOAT8[] and one FLOAT8 and returns a FLOAT8, but the FLOAT8 parameter is simply a placeholder and will be ignored.</p>
+<p>With the HAWQ database, only the above pre-built kernel functions can be used. With the Greenplum database and PostgreSQL database, one can use any user-defined function as long as it conforms to the requirements for the kernel function.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<p>As a general first step, prepare and populate an input table/view with the following structure: </p><pre class="example">
+CREATE TABLE/VIEW my_schema.my_input_table(
+    id    INT,       -- point ID
+    ind   FLOAT8[],  -- data point
+    label FLOAT8     -- label of data point
+);
+</pre><p> The label field is not required for novelty detection.</p>
+<p><b>Example usage for regression</b>:</p><ol type="1">
+<li>Randomly generate 1000 5-dimensional data labelled using the simple target function. <pre class="example">
+t(x) = if x[5] = 10 then 50 else if x[5] = -10 then 50 else 0;
+</pre> and store that in the my_schema.my_train_data table as follows: <pre class="example">
+SELECT madlib.svm_generate_reg_data(
+    'my_schema.my_train_data', 1000, 5);
+</pre></li>
+<li>Learn a regression model and store the resultant model under the name 'myexp'. <pre class="example">
+SELECT madlib.svm_regression('my_schema.my_train_data',
+    'myexp1', false, 'madlib.svm_dot');
+</pre></li>
+<li>To learn multiple support vector models, we replace the learning step above by <pre class="example">
+SELECT madlib.svm_regression('my_schema.my_train_data',
+    'myexp2', true, 'madlib.svm_dot');
+</pre></li>
+<li>We can also predict the labels of data points stored in a table. For example, we can execute the following: <pre class="example">
+-- prepare test data
+CREATE TABLE madlib.svm_reg_test AS
+    SELECT id, ind
+    FROM my_schema.my_train_data
+    LIMIT 20;
+-- prediction using a single model
+SELECT madlib.svm_predict_batch('madlib.svm_reg_test', 'ind', 'id',
+    'myexp1', 'madlib.svm_reg_output1', false);
+SELECT * FROM madlib.svm_reg_output1;
+-- prediction using multiple models
+SELECT madlib.svm_predict_batch('madlib.svm_reg_test', 'ind', 'id',
+    'myexp2', 'madlib.svm_reg_output2', true);
+SELECT * FROM madlib.svm_reg_output2;
+</pre></li>
+</ol>
+<p><b>Example usage for classification:</b></p><ol type="1">
+<li>Randomly generate training and testing data labelled by the simple target function. <pre class="example">
+t(x) = if x[1] &gt; 0 and  x[2] &lt; 0 then 1 else -1;
+</pre> and store that in tables as follows: <pre class="example">
+SELECT madlib.svm_generate_cls_data(
+    'my_schema.my_train_data', 2000, 5);
+SELECT madlib.svm_generate_cls_data(
+    'my_schema.my_test_data', 3000, 5);
+</pre></li>
+<li>Learn a classification model and store the resultant model the table 'myexpc'. <pre class="example">
+SELECT madlib.svm_classification('my_schema.my_train_data',
+    'myexpc', false, 'madlib.svm_dot');
+</pre></li>
+<li>Start using the model to predict the labels of testing data points. <pre class="example">
+SELECT madlib.svm_predict_batch('my_schema.my_test_data', 'ind', 'id',
+    'myexpc', 'my_schema.svm_cls_output1', false);
+</pre></li>
+<li>To learn multiple support vector models, replace the model-building and prediction steps above. <pre class="example">
+-- training
+SELECT madlib.svm_classification('my_schema.my_train_data',
+    'myexpc', true, 'madlib.svm_dot');
+-- predicting
+SELECT madlib.svm_predict_batch('my_schema.my_test_data', 'ind', 'id',
+    'myexpc', 'my_schema.svm_cls_output1', true);
+</pre></li>
+<li>To learn a linear support vector model using IGD [3], replace the model-building and prediction steps. <pre class="example">
+-- training
+SELECT madlib.lsvm_classification(
+    'my_schema.my_train_data', 'my_lsvm');
+-- predicting
+SELECT madlib.lsvm_predict_batch('my_schema.my_test_data',
+    'ind', 'id', 'my_lsvm', 'my_lsvm_predict');
+</pre></li>
+</ol>
+<p><b>Example usage for novelty detection:</b></p>
+<ol type="1">
+<li>Randomly generate 100 2-dimensional data (the normal cases) and store that in the my_schema.my_train_data table. <pre class="example">
+SELECT madlib.svm_generate_nd_data(
+    'my_schema.my_train_data', 100, 2);
+</pre></li>
+<li>Learning and predicting using a single novelty detection model: <pre class="example">
+SELECT madlib.svm_novelty_detection( 'my_schema.my_train_data',
+                                    'myexpnd',
+                                    false,
+                                    'madlib.svm_dot'
+                                  );
+SELECT * FROM myexpnd;
+</pre></li>
+<li>Learning and predicting using multiple models can be done as follows: <pre class="example">
+SELECT madlib.svm_novelty_detection( 'my_schema.my_train_data',
+                                    'myexpnd',
+                                    true,
+                                    'madlib.svm_dot'
+                                  );
+SELECT * FROM myexpnd;
+</pre></li>
+</ol>
+<p><b>Model cleanup:</b> To drop all tables pertaining to the model, use: </p><pre class="example">
+SELECT svm_drop_model('model_table');
+</pre><p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Jyrki Kivinen, Alexander J. Smola, and Robert C. Williamson: <em>Online Learning with Kernels</em>, IEEE Transactions on Signal Processing, 52(8), 2165-2176, 2004.</p>
+<p>[2] Bernhard Scholkopf and Alexander J. Smola: <em>Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond</em>, MIT Press, 2002.</p>
+<p>[3] X. Feng, A. Kumar, B. Recht, and C. R&eacute;: <em>Towards a unified architecture for in-RDBMS analytics</em>, In SIGMOD Conference, 2012.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="online__sv_8sql__in.html" title="SQL functions for support vector machines. ">online_sv.sql_in</a> and <a class="el" href="lsvm_8sql__in.html" title="SQL functions for linear support vector machines. ">lsvm.sql_in</a> documenting the SQL functions.</p>
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+  <div class="headertitle">
+<div class="title">k-Means Clustering<div class="ingroups"><a class="el" href="group__grp__unsupervised.html">Unsupervised Learning</a> &raquo; <a class="el" href="group__grp__clustering.html">Clustering</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#output">Output Format</a> </li>
+<li class="level1">
+<a href="#assignment">Cluster Assignment</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#notes">Notes</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Clustering refers to the problem of partitioning a set of objects according to some problem-dependent measure of <em>similarity</em>. In the k-means variant, given \( n \) points \( x_1, \dots, x_n \in \mathbb R^d \), the goal is to position \( k \) centroids \( c_1, \dots, c_k \in \mathbb R^d \) so that the sum of <em>distances</em> between each point and its closest centroid is minimized. Each centroid represents a cluster that consists of all points to which this centroid is closest.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd></dd></dl>
+<p>The k-means algorithm can be invoked in four ways, depending on the source of the initial set of centroids:</p>
+<ul>
+<li>Use the random centroid seeding method. <pre class="syntax">
+kmeans_random( rel_source,
+               expr_point,
+               k,
+               fn_dist,
+               agg_centroid,
+               max_num_iterations,
+               min_frac_reassigned
+             )
+</pre></li>
+<li>Use the kmeans++ centroid seeding method. <pre class="syntax">
+kmeanspp( rel_source,
+          expr_point,
+          k,
+          fn_dist,
+          agg_centroid,
+          max_num_iterations,
+          min_frac_reassigned,
+          seeding_sample_ratio
+        )
+</pre></li>
+<li>Supply an initial centroid set in a relation identified by the <em>rel_initial_centroids</em> argument. <pre class="syntax">
+kmeans( rel_source,
+        expr_point,
+        rel_initial_centroids,
+        expr_centroid,
+        fn_dist,
+        agg_centroid,
+        max_num_iterations,
+        min_frac_reassigned
+      )
+</pre></li>
+<li>Provide an initial centroid set as an array expression in the <em>initial_centroids</em> argument. <pre class="syntax">
+kmeans( rel_source,
+        expr_point,
+        initial_centroids,
+        fn_dist,
+        agg_centroid,
+        max_num_iterations,
+        min_frac_reassigned
+      )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>rel_source </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the input data points.</p>
+<p>Data points and predefined centroids (if used) are expected to be stored row-wise, in a column of type <code><a class="el" href="group__grp__svec.html">SVEC</a></code> (or any type convertible to <code><a class="el" href="group__grp__svec.html">SVEC</a></code>, like <code>FLOAT[]</code> or <code>INTEGER[]</code>). Data points with non-finite values (NULL, NaN, infinity) in any component are skipped during analysis. </p>
+<p class="enddd"></p>
+</dd>
+<dt>expr_point </dt>
+<dd><p class="startdd">TEXT. The name of the column with point coordinates.</p>
+<p class="enddd"></p>
+</dd>
+<dt>k </dt>
+<dd><p class="startdd">INTEGER. The number of centroids to calculate.</p>
+<p class="enddd"></p>
+</dd>
+<dt>fn_dist (optional) </dt>
+<dd><p class="startdd">TEXT, default: squared_dist_norm2'. The name of the function to use to calculate the distance from a data point to a centroid.</p>
+<p>The following distance functions can be used (computation of barycenter/mean in parentheses): </p><ul>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#aad193850e79c4b9d811ca9bc53e13476">dist_norm1</a></b>: 1-norm/Manhattan (element-wise median [Note that MADlib does not provide a median aggregate function for support and performance reasons.]) </li>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#aa58e51526edea6ea98db30b6f250adb4">dist_norm2</a></b>: 2-norm/Euclidean (element-wise mean) </li>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#a00a08e69f27524f2096032214e15b668">squared_dist_norm2</a></b>: squared Euclidean distance (element-wise mean) </li>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#a8c7b9281a72ff22caf06161701b27e84">dist_angle</a></b>: angle (element-wise mean of normalized points) </li>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#afa13b4c6122b99422d666dedea136c18">dist_tanimoto</a></b>: tanimoto (element-wise mean of normalized points <a href="#kmeans-lit-5">[5]</a>) </li>
+<li>
+<b>user defined function</b> with signature <code>DOUBLE PRECISION[] x, DOUBLE PRECISION[] y -&gt; DOUBLE PRECISION</code></li>
+</ul>
+<p class="enddd"></p>
+</dd>
+<dt>agg_centroid (optional) </dt>
+<dd><p class="startdd">TEXT, default: 'avg'. The name of the aggregate function used to determine centroids.</p>
+<p>The following aggregate functions can be used:</p><ul>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4">avg</a></b>: average (Default) </li>
+<li>
+<b><a class="el" href="linalg_8sql__in.html#a0b04663ca206f03e66aed5ea2b4cc461">normalized_avg</a></b>: normalized average</li>
+</ul>
+<p class="enddd"></p>
+</dd>
+<dt>max_num_iterations (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 20. The maximum number of iterations to perform.</p>
+<p class="enddd"></p>
+</dd>
+<dt>min_frac_reassigned (optional) </dt>
+<dd><p class="startdd">DOUBLE PRECISION, default: 0.001. The minimum fraction of centroids reassigned to continue iterating. When fewer than this fraction of centroids are reassigned in an iteration, the calculation completes.</p>
+<p class="enddd"></p>
+</dd>
+<dt>seeding_sample_ratio (optional) </dt>
+<dd><p class="startdd">DOUBLE PRECISION, default: 1.0. The proportion of subsample of original dataset to use for kmeans++ centroid seeding method. Kmeans++ scans through the data sequentially 'k' times and can be too slow for big datasets. When 'seeding_sample_ratio' is greater than 0 (thresholded to be maximum value of 1.0), the seeding is run on an uniform random subsample of the data. Note: the final K-means algorithm is run on the complete dataset. This parameter only builds a subsample for the seeding and is only available for kmeans++.</p>
+<p class="enddd"></p>
+</dd>
+<dt>rel_initial_centroids </dt>
+<dd><p class="startdd">TEXT. The set of initial centroids. The centroid relation is expected to be of the following form: </p><pre>
+{TABLE|VIEW} rel_initial_centroids (
+    ...
+    expr_centroid DOUBLE PRECISION[],
+    ...
+)
+</pre><p> where <em>expr_centroid</em> is the name of a column with coordinates. </p>
+<p class="enddd"></p>
+</dd>
+<dt>expr_centroid </dt>
+<dd><p class="startdd">TEXT. The name of the column in the <em>rel_initial_centroids</em> relation that contains the centroid coordinates.</p>
+<p class="enddd"></p>
+</dd>
+<dt>initial_centroids </dt>
+<dd>TEXT. A string containing a DOUBLE PRECISION array expression with the initial centroid coordinates. </dd>
+</dl>
+</li>
+</ul>
+<p><a class="anchor" id="output"></a></p><dl class="section user"><dt>Output Format</dt><dd></dd></dl>
+<p>The output of the k-means module is a composite type with the following columns: </p><table  class="output">
+<tr>
+<th>centroids </th><td>DOUBLE PRECISION[][]. The final centroid positions.  </td></tr>
+<tr>
+<th>objective_fn </th><td>DOUBLE PRECISION. The value of the objective function.  </td></tr>
+<tr>
+<th>frac_reassigned </th><td>DOUBLE PRECISION. The fraction of points reassigned in the last iteration.  </td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The total number of iterations executed.  </td></tr>
+</table>
+<p><a class="anchor" id="assignment"></a></p><dl class="section user"><dt>Cluster Assignment</dt><dd></dd></dl>
+<p>After training, the cluster assignment for each data point can be computed with the help of the following function:</p>
+<pre class="syntax">
+closest_column( m, x )
+</pre><p><b>Argument</b> </p><dl class="arglist">
+<dt>m </dt>
+<dd>DOUBLE PRECISION[][]. The learned centroids from the training function. </dd>
+<dt>x </dt>
+<dd>DOUBLE PRECISION[]. The data point. </dd>
+</dl>
+<p><b>Output format</b> </p><table  class="output">
+<tr>
+<th>column_id </th><td>INTEGER. The cluster assignment (zero-based). </td></tr>
+<tr>
+<th>distance </th><td>DOUBLE PRECISION. The distance to the cluster centroid. </td></tr>
+</table>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Prepare some input data. <pre class="example">
+CREATE TABLE public.km_sample(pid int, points double precision[]);
+COPY km_sample (pid, points) FROM stdin DELIMITER '|';
+1 | {14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065}
+2 | {13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050}
+3 | {13.16, 2.36,  2.67, 18.6, 101, 2.8,  3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185}
+4 | {14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480}
+5 | {13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735}
+6 | {14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450}
+7 | {14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290}
+8 | {14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295}
+9 | {14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045}
+10 | {13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045}
+\.
+</pre></li>
+<li>Run k-means clustering using kmeans++ for centroid seeding: <pre class="example">
+\x on;
+SELECT * FROM madlib.kmeanspp( 'km_sample',
+                               'points',
+                               2,
+                               'madlib.squared_dist_norm2',
+                               'madlib.avg',
+                               20,
+                               0.001
+                             );
+</pre> Result: <pre class="result">
+centroids       | {{13.872,1.814,2.376,15.56,88.2,2.806,2.928,0.288,1.844,5.35198,1.044,3.348,988},
+                   {14.036,2.018,2.536,16.56,108.6,3.004,3.03,0.298,2.038,6.10598,1.004,3.326,1340}}
+objective_fn    | 151184.962672
+frac_reassigned | 0
+num_iterations  | 2
+</pre></li>
+<li>Calculate the simplified silhouette coefficient: <pre class="example">
+SELECT * FROM madlib.simple_silhouette( 'km_sample',
+                                        'points',
+                                        (SELECT centroids FROM
+                                            madlib.kmeanspp('km_sample',
+                                                            'points',
+                                                            2,
+                                                            'madlib.squared_dist_norm2',
+                                                            'madlib.avg',
+                                                            20,
+                                                            0.001)),
+                                        'madlib.dist_norm2'
+                                      );
+</pre> Result: <pre class="result">
+simple_silhouette | 0.68978804882941
+</pre></li>
+<li>Find the cluster assignment for each point <pre class="example">
+\x off;
+SELECT data.*, (madlib.closest_column(centroids, points)).column_id as cluster_id
+FROM public.km_sample as data,
+     (SELECT centroids
+      FROM madlib.kmeanspp('km_sample', 'points', 2,
+                           'madlib.squared_dist_norm2',
+                           'madlib.avg', 20, 0.001)) as centroids
+ORDER BY data.pid;
+</pre> <pre class="result">
+ pid |                               points                               | cluster_id
+-----+--------------------------------------------------------------------+------------
+   1 | {14.23,1.71,2.43,15.6,127,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065}  |          0
+   2 | {13.2,1.78,2.14,11.2,1,2.65,2.76,0.26,1.28,4.38,1.05,3.49,1050}    |          0
+   3 | {13.16,2.36,2.67,18.6,101,2.8,3.24,0.3,2.81,5.6799,1.03,3.17,1185} |          1
+   4 | {14.37,1.95,2.5,16.8,113,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480}   |          1
+   5 | {13.24,2.59,2.87,21,118,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735}     |          0
+   6 | {14.2,1.76,2.45,15.2,112,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450}  |          1
+   7 | {14.39,1.87,2.45,14.6,96,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290}    |          1
+   8 | {14.06,2.15,2.61,17.6,121,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295}  |          1
+   9 | {14.83,1.64,2.17,14,97,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045}      |          0
+  10 | {13.86,1.35,2.27,16,98,2.98,3.15,0.22,1.85,7.2199,1.01,3.55,1045}  |          0
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
+<p>The algorithm stops when one of the following conditions is met:</p><ul>
+<li>The fraction of updated points is smaller than the convergence threshold (<em>min_frac_reassigned</em> argument). (Default: 0.001).</li>
+<li>The algorithm reaches the maximum number of allowed iterations (<em>max_num_iterations</em> argument). (Default: 20).</li>
+</ul>
+<p>A popular method to assess the quality of the clustering is the <em>silhouette coefficient</em>, a simplified version of which is provided as part of the k-means module. Note that for large data sets, this computation is expensive.</p>
+<p>The silhouette function has the following syntax: </p><pre class="syntax">
+simple_silhouette( rel_source,
+                   expr_point,
+                   centroids,
+                   fn_dist
+                 )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>rel_source </dt>
+<dd>TEXT. The name of the relation containing the input point. </dd>
+<dt>expr_point </dt>
+<dd>TEXT. An expression evaluating to point coordinates for each row in the relation. </dd>
+<dt>centroids </dt>
+<dd>TEXT. An expression evaluating to an array of centroids.  </dd>
+<dt>fn_dist (optional) </dt>
+<dd>TEXT, default 'dist_norm2', The name of a function to calculate the distance of a point from a centroid. See the <em>fn_dist</em> argument of the k-means training function. </dd>
+</dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Formally, we wish to minimize the following objective function: </p><p class="formulaDsp">
+\[ (c_1, \dots, c_k) \mapsto \sum_{i=1}^n \min_{j=1}^k \operatorname{dist}(x_i, c_j) \]
+</p>
+<p> In the most common case, \( \operatorname{dist} \) is the square of the Euclidean distance.</p>
+<p>This problem is computationally difficult (NP-hard), yet the local-search heuristic proposed by Lloyd [4] performs reasonably well in practice. In fact, it is so ubiquitous today that it is often referred to as the <em>standard algorithm</em> or even just the <em>k-means algorithm</em> [1]. It works as follows:</p>
+<ol type="1">
+<li>Seed the \( k \) centroids (see below)</li>
+<li>Repeat until convergence:<ol type="a">
+<li>Assign each point to its closest centroid</li>
+<li>Move each centroid to a position that minimizes the sum of distances in this cluster</li>
+</ol>
+</li>
+<li>Convergence is achieved when no points change their assignments during step 2a.</li>
+</ol>
+<p>Since the objective function decreases in every step, this algorithm is guaranteed to converge to a local optimum.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p><a class="anchor" id="kmeans-lit-1"></a>[1] Wikipedia, K-means Clustering, <a href="http://en.wikipedia.org/wiki/K-means_clustering">http://en.wikipedia.org/wiki/K-means_clustering</a></p>
+<p><a class="anchor" id="kmeans-lit-2"></a>[2] David Arthur, Sergei Vassilvitskii: k-means++: the advantages of careful seeding, Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'07), pp. 1027-1035, <a href="http://www.stanford.edu/~darthur/kMeansPlusPlus.pdf">http://www.stanford.edu/~darthur/kMeansPlusPlus.pdf</a></p>
+<p><a class="anchor" id="kmeans-lit-3"></a>[3] E. R. Hruschka, L. N. C. Silva, R. J. G. B. Campello: Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search. In: Studies in Computational Intelligence - Hybrid Evolutionary Algorithms. pp. 313-335. Springer. 2007.</p>
+<p><a class="anchor" id="kmeans-lit-4"></a>[4] Lloyd, Stuart: Least squares quantization in PCM. Technical Note, Bell Laboratories. Published much later in: IEEE Transactions on Information Theory 28(2), pp. 128-137. 1982.</p>
+<p><a class="anchor" id="kmeans-lit-5"></a>[5] Leisch, Friedrich: A Toolbox for K-Centroids Cluster Analysis. In: Computational Statistics and Data Analysis, 51(2). pp. 526-544. 2006.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="kmeans_8sql__in.html" title="Set of functions for k-means clustering. ">kmeans.sql_in</a> documenting the k-Means SQL functions</p>
+<p><a class="el" href="group__grp__svec.html">Sparse Vectors</a></p>
+<p>simple_silhouette()</p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[13/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+<tr class="memitem:group__grp__kernmach"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__kernmach.html">Support Vector Machines</a></td></tr>
+<tr class="memdesc:group__grp__kernmach"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generates support vector machines for classification, regression, and novelty detection. <br /></td></tr>
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+<div class="title">Elastic Net Regularization<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a class="el" href="group__grp__regml.html">Regression Models</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#optimizer">Optimizer Parameters</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Functions</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module implements elastic net regularization for linear and logistic regression problems.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training Function</dt><dd>The training function has the following syntax: <pre class="syntax">
+elastic_net_train( tbl_source,
+                   tbl_result,
+                   col_dep_var,
+                   col_ind_var,
+                   regress_family,
+                   alpha,
+                   lambda_value,
+                   standardize,
+                   grouping_col,
+                   optimizer,
+                   optimizer_params,
+                   excluded,
+                   max_iter,
+                   tolerance
+                 )
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>tbl_source </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>tbl_result </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the output model. The output table produced by the <a class="el" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" title="Interface for elastic net. ">elastic_net_train()</a> function has the following columns: </p><table  class="output">
+<tr>
+<th>regress_family </th><td>The regression type: 'gaussian' or 'binomial'.  </td></tr>
+<tr>
+<th>features </th><td>An array of the features (independent variables) passed into the analysis.  </td></tr>
+<tr>
+<th>features_selected </th><td>An array of the features selected by the analysis.  </td></tr>
+<tr>
+<th>coef_nonzero </th><td>Fitting coefficients for the selected features.  </td></tr>
+<tr>
+<th>coef_all </th><td>Coefficients for all selected and unselected features  </td></tr>
+<tr>
+<th>intercept </th><td>Fitting intercept for the model.  </td></tr>
+<tr>
+<th>log_likelihood </th><td>The negative value of the first equation above (up to a constant depending on the data set).  </td></tr>
+<tr>
+<th>standardize </th><td>BOOLEAN. Whether the data was normalized (<em>standardize</em> argument was TRUE).  </td></tr>
+<tr>
+<th>iteration_run </th><td>The number of iterations executed.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>col_dep_var </dt>
+<dd><p class="startdd">TEXT. An expression for the dependent variable.</p>
+<p>Both <em>col_dep_var</em> and <em>col_ind_var</em> can be valid Postgres expressions. For example, <code>col_dep_var = 'log(y+1)'</code>, and <code>col_ind_var = 'array[exp(x[1]), x[2], 1/(1+x[3])]'</code>. In the binomial case, you can use a Boolean expression, for example, <code>col_dep_var = 'y &lt; 0'</code>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_ind_var </dt>
+<dd><p class="startdd">TEXT. An expression for the independent variables. Use <code>'*'</code> to specify all columns of <em>tbl_source</em> except those listed in the <em>excluded</em> string. If <em>col_dep_var</em> is a column name, it is automatically excluded from the independent variables. However, if <em>col_dep_var</em> is a valid Postgres expression, any column names used within the expression are only excluded if they are explicitly included in the <em>excluded</em> argument. It is a good idea to add all column names involved in the dependent variable expression to the <em>excluded</em> string.</p>
+<p class="enddd"></p>
+</dd>
+<dt>regress_family </dt>
+<dd><p class="startdd">TEXT. The regression type, either 'gaussian' ('linear') or 'binomial' ('logistic').</p>
+<p class="enddd"></p>
+</dd>
+<dt>alpha </dt>
+<dd><p class="startdd">FLOAT8. Elastic net control parameter, value in [0, 1], 1 for L-1 regularization, 0 for L-2.</p>
+<p class="enddd"></p>
+</dd>
+<dt>lambda_value </dt>
+<dd><p class="startdd">FLOAT8. Regularization parameter, positive.</p>
+<p class="enddd"></p>
+</dd>
+<dt>standardize (optional) </dt>
+<dd><p class="startdd">BOOLEAN, default: TRUE. Whether to normalize the data. Setting this to TRUE usually yields better results and faster convergence.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL <code>GROUP BY</code> clause. When this value is NULL, no grouping is used and a single result model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer (optional) </dt>
+<dd><p class="startdd">TEXT, default: 'fista'. Name of optimizer, either 'fista' or 'igd'.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer_params (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. Optimizer parameters, delimited with commas. The parameters differ depending on the value of <em>optimizer</em>. See the descriptions below for details.</p>
+<p class="enddd"></p>
+</dd>
+<dt>excluded (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. If the <em>col_ind_var</em> input is '*' then <em>excluded</em> can be provided as a comma-delimited list of column names that are to be excluded from the features. For example, <code>'col1, col2'</code>. If the <em>col_ind_var</em> is an array, <em>excluded</em> must be a list of the integer array positions to exclude, for example <code>'1,2'</code>. If this argument is NULL or an empty string <code>''</code>, no columns are excluded.</p>
+<p class="enddd"></p>
+</dd>
+<dt>max_iter (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 1000. The maximum number of iterations that are allowed.</p>
+<p class="enddd"></p>
+</dd>
+<dt>tolerance </dt>
+<dd>FLOAT8, default: default is 1e-6. The criteria to end iterations. Both the 'fista' and 'igd' optimizers compute the difference between the loglikelihood of two consecutive iterations, and when the difference is smaller than <em>tolerance</em> or the iteration number is larger than <em>max_iter</em>, the computation stops. </dd>
+</dl>
+<p><a class="anchor" id="optimizer"></a></p><dl class="section user"><dt>Optimizer Parameters</dt><dd>Optimizer parameters are supplied in a string containing a comma-delimited list of name-value pairs. All of these named parameters are optional, and their order does not matter. You must use the format "&lt;param_name&gt; = &lt;value&gt;" to specify the value of a parameter, otherwise the parameter is ignored.</dd></dl>
+<p>When the <a class="el" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83">elastic_net_train()</a> <em>optimizer</em> argument value is <b>'fista'</b>, the <em>optimizer_params</em> argument is a string containing name-value pairs with the following format. (Line breaks are inserted for readability.) </p><pre class="syntax">
+  'max_stepsize = &lt;value&gt;,
+   eta = &lt;value&gt;,
+   warmup = &lt;value&gt;,
+   warmup_lambdas = &lt;value&gt;,
+   warmup_lambda_no = &lt;value&gt;,
+   warmup_tolerance = &lt;value&gt;,
+   use_active_set = &lt;value&gt;,
+   activeset_tolerance = &lt;value&gt;,
+   random_stepsize = &lt;value&gt;'
+</pre><p> <b>Parameters</b> </p><dl class="arglist">
+<dt>max_stepsize </dt>
+<dd>Default: 4.0. Initial backtracking step size. At each iteration, the algorithm first tries <em>stepsize = max_stepsize</em>, and if it does not work out, it then tries a smaller step size, <em>stepsize = stepsize/eta</em>, where <em>eta</em> must be larger than 1. At first glance, this seems to perform repeated iterations for even one step, but using a larger step size actually greatly increases the computation speed and minimizes the total number of iterations. A careful choice of <em>max_stepsize</em> can decrease the computation time by more than 10 times. </dd>
+<dt>eta </dt>
+<dd><p class="startdd">Default: 2. If stepsize does not work <em>stepsize</em> / <em>eta</em> is tried. Must be greater than 1. </p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup </dt>
+<dd><p class="startdd">Default: FALSE. If <em>warmup</em> is TRUE, a series of lambda values, which is strictly descent and ends at the lambda value that the user wants to calculate, is used. The larger lambda gives very sparse solution, and the sparse solution again is used as the initial guess for the next lambda's solution, which speeds up the computation for the next lambda. For larger data sets, this can sometimes accelerate the whole computation and may be faster than computation on only one lambda value.</p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup_lambdas </dt>
+<dd><p class="startdd">Default: NULL. The lambda value series to use when <em>warmup</em> is True. The default is NULL, which means that lambda values will be automatically generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup_lambda_no </dt>
+<dd><p class="startdd">Default: 15. How many lambdas are used in warm-up. If <em>warmup_lambdas</em> is not NULL, this value is overridden by the number of provided lambda values.</p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup_tolerance </dt>
+<dd><p class="startdd">The value of tolerance used during warmup. The default is the same as the <em>tolerance</em> argument.</p>
+<p class="enddd"></p>
+</dd>
+<dt>use_active_set </dt>
+<dd><p class="startdd">Default: FALSE. If <em>use_active_set</em> is TRUE, an active-set method is used to speed up the computation. Considerable speedup is obtained by organizing the iterations around the active set of features&mdash;those with nonzero coefficients. After a complete cycle through all the variables, we iterate on only the active set until convergence. If another complete cycle does not change the active set, we are done, otherwise the process is repeated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>activeset_tolerance </dt>
+<dd><p class="startdd">Default: the value of the tolerance argument. The value of tolerance used during active set calculation. </p>
+<p class="enddd"></p>
+</dd>
+<dt>random_stepsize </dt>
+<dd>Default: FALSE. Whether to add some randomness to the step size. Sometimes, this can speed up the calculation. </dd>
+</dl>
+<p>When the <a class="el" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83">elastic_net_train()</a> <em>optimizer</em> argument value is <b>'igd'</b>, the <em>optimizer_params</em> argument is a string containing name-value pairs with the following format. (Line breaks are inserted for readability.) </p><pre class="syntax">
+  'stepsize = &lt;value&gt;,
+   step_decay = &lt;value&gt;,
+   threshold = &lt;value&gt;,
+   warmup = &lt;value&gt;,
+   warmup_lambdas = &lt;value&gt;,
+   warmup_lambda_no = &lt;value&gt;,
+   warmup_tolerance = &lt;value&gt;,
+   parallel = &lt;value&gt;'
+</pre><p> <b>Parameters</b> </p><dl class="arglist">
+<dt>stepsize </dt>
+<dd>The default is 0.01. </dd>
+<dt>step_decay </dt>
+<dd>The actual setpsize used for current step is (previous stepsize) / exp(setp_decay). The default value is 0, which means that a constant stepsize is used in IGD. </dd>
+<dt>threshold </dt>
+<dd><p class="startdd">Default: 1e-10. When a coefficient is really small, set this coefficient to be 0.</p>
+<p class="enddd">Due to the stochastic nature of SGD, we can only obtain very small values for the fitting coefficients. Therefore, <em>threshold</em> is needed at the end of the computation to screen out tiny values and hard-set them to zeros. This is accomplished as follows: (1) multiply each coefficient with the standard deviation of the corresponding feature; (2) compute the average of absolute values of re-scaled coefficients; (3) divide each rescaled coefficient with the average, and if the resulting absolute value is smaller than <em>threshold</em>, set the original coefficient to zero. </p>
+</dd>
+<dt>warmup </dt>
+<dd>Default: FALSE. If <em>warmup</em> is TRUE, a series of lambda values, which is strictly descent and ends at the lambda value that the user wants to calculate, is used. The larger lambda gives very sparse solution, and the sparse solution again is used as the initial guess for the next lambda's solution, which speeds up the computation for the next lambda. For larger data sets, this can sometimes accelerate the whole computation and may be faster than computation on only one lambda value. </dd>
+<dt>warmup_lambdas </dt>
+<dd>Default: NULL. An array of lambda values to use for warmup. </dd>
+<dt>warmup_lambda_no </dt>
+<dd>The number of lambdas used in warm-up. The default is 15. If <em>warmup_lambdas</em> is not NULL, this argument is overridden by the size of the <em>warmup_lambdas</em> array. </dd>
+<dt>warmup_tolerance </dt>
+<dd>The value of tolerance used during warmup.The default is the same as the <em>tolerance</em> argument. </dd>
+<dt>parallel </dt>
+<dd><p class="startdd">Whether to run the computation on multiple segments. The default is True.</p>
+<p class="enddd">SGD is a sequential algorithm in nature. When running in a distributed manner, each segment of the data runs its own SGD model and then the models are averaged to get a model for each iteration. This averaging might slow down the convergence speed, although we also acquire the ability to process large datasets on multiple machines. This algorithm, therefore, provides the <em>parallel</em> option to allow you to choose whether to do parallel computation.  </p>
+</dd>
+</dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section user"><dt>Prediction Function</dt><dd></dd></dl>
+<h4>Per-Tuple Prediction</h4>
+<p>The prediction function returns a double value for Gaussian family and boolean value for Binomial family.</p>
+<p>The predict function has the following syntax (<a class="el" href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae" title="Prediction for linear models use learned coefficients for a given example. ">elastic_net_gaussian_predict()</a> and <a class="el" href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793" title="Prediction for logistic models use learned coefficients for a given example. ">elastic_net_binomial_predict()</a>): </p><pre class="syntax">
+elastic_net_&lt;family&gt;_predict(
+                     coefficients,
+                     intercept,
+                     ind_var
+                   )
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>coefficients </dt>
+<dd>DOUBLE PRECISION[]. Fitting coefficients, usually coef_all or coef_nonzero. </dd>
+<dt>intercept </dt>
+<dd>DOUBLE PRECISION. The intercept for the model. </dd>
+<dt>ind_var </dt>
+<dd>DOUBLE PRECISION[]. Independent variables that correspond to coefficients, use <em>features</em> column in <em>tbl_result</em> for coef_all, and <em>features_selected</em> for coef_nonzero. See also <a href="#additional_example">examples</a>. Note that unexpected results or errors may be returned in the case that this argument is not given properly. </dd>
+</dl>
+<p>For binomial family, there is a function (<a class="el" href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71" title="Compute the probability of belonging to the True class for a given observation. ">elastic_net_binomial_prob()</a>) that outputs the probability of the instance being True: </p><pre class="syntax">
+elastic_net_binomial_prob(
+                     coefficients,
+                     intercept,
+                     ind_var
+                   )
+</pre><h4>Per-Table Prediction</h4>
+<p>Alternatively, you can use another prediction function that stores the prediction result in a table (<a class="el" href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b" title="Prediction and put the result in a table can be used together with General-CV. ">elastic_net_predict()</a>). This is useful if you want to use elastic net together with the general cross validation function. </p><pre class="syntax">
+elastic_net_predict( tbl_model,
+                     tbl_new_sourcedata,
+                     col_id,
+                     tbl_predict
+                   )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>tbl_model </dt>
+<dd>TEXT. The name of the table containing the output from the training function. </dd>
+<dt>tbl_new_sourcedata </dt>
+<dd>TEXT. The name of the table containing the new source data. </dd>
+<dt>col_id </dt>
+<dd>TEXT. The unique ID associated with each row. </dd>
+<dt>tbl_predict </dt>
+<dd>TEXT. The name of table to store the prediction result.  </dd>
+</dl>
+<p>You do not need to specify whether the model is "linear" or "logistic" because this information is already included in the <em>tbl_model</em> table.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Display online help for the <a class="el" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" title="Interface for elastic net. ">elastic_net_train()</a> function. <pre class="example">
+SELECT madlib.elastic_net_train();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+DROP TABLE IF EXISTS houses;
+CREATE TABLE houses ( id INT,
+                      tax INT,
+                      bedroom INT,
+                      bath FLOAT,
+                      price INT,
+                      size INT,
+                      lot INT
+                    );
+COPY houses FROM STDIN WITH DELIMITER '|';
+  1 |  590 |       2 |    1 |  50000 |  770 | 22100
+  2 | 1050 |       3 |    2 |  85000 | 1410 | 12000
+  3 |   20 |       3 |    1 |  22500 | 1060 |  3500
+  4 |  870 |       2 |    2 |  90000 | 1300 | 17500
+  5 | 1320 |       3 |    2 | 133000 | 1500 | 30000
+  6 | 1350 |       2 |    1 |  90500 |  820 | 25700
+  7 | 2790 |       3 |  2.5 | 260000 | 2130 | 25000
+  8 |  680 |       2 |    1 | 142500 | 1170 | 22000
+  9 | 1840 |       3 |    2 | 160000 | 1500 | 19000
+ 10 | 3680 |       4 |    2 | 240000 | 2790 | 20000
+ 11 | 1660 |       3 |    1 |  87000 | 1030 | 17500
+ 12 | 1620 |       3 |    2 | 118600 | 1250 | 20000
+ 13 | 3100 |       3 |    2 | 140000 | 1760 | 38000
+ 14 | 2070 |       2 |    3 | 148000 | 1550 | 14000
+ 15 |  650 |       3 |  1.5 |  65000 | 1450 | 12000
+\.
+</pre></li>
+<li>Train the model. <pre class="example">
+DROP TABLE IF EXISTS houses_en;
+SELECT madlib.elastic_net_train( 'houses',
+                                 'houses_en',
+                                 'price',
+                                 'array[tax, bath, size]',
+                                 'gaussian',
+                                 0.5,
+                                 0.1,
+                                 TRUE,
+                                 NULL,
+                                 'fista',
+                                 '',
+                                 NULL,
+                                 10000,
+                                 1e-6
+                               );
+</pre></li>
+<li>View the resulting model. <pre class="example">
+-- Turn on expanded display to make it easier to read results.
+\x on
+SELECT * FROM houses_en;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-----+--------------------------------------------
+family            | gaussian
+features          | {tax,bath,size}
+features_selected | {tax,bath,size}
+coef_nonzero      | {27.6945611671,11509.0099734,49.0945557639}
+coef_all          | {27.6945611671,11509.0099734,49.0945557639}
+intercept         | -11145.5061503
+log_likelihood    | -490118975.406
+standardize       | t
+iteration_run     | 322
+</pre></li>
+<li>Use the prediction function to evaluate residuals. <pre class="example">
+\x off
+SELECT id, price, predict, price - predict AS residual
+FROM (
+    SELECT
+        houses.*,
+        madlib.elastic_net_gaussian_predict(
+            m.coef_all,
+            m.intercept,
+            ARRAY[tax,bath,size]
+            ) AS predict
+    FROM houses, houses_en m) s
+ORDER BY id;
+</pre></li>
+</ol>
+<p><a class="anchor" id="additional_example"></a></p><h4>Additional Example (when coef_nonzero is different from coef_all)</h4>
+<ol type="1">
+<li>Reuse the <a href="#examples">houses</a> table above and train the model with alpha=1 (L-1) and a large lambda (30000). <pre class="example">
+DROP TABLE IF EXISTS houses_en2;
+SELECT madlib.elastic_net_train( 'houses',
+                                 'houses_en2',
+                                 'price',
+                                 'array[tax, bath, size]',
+                                 'gaussian',
+                                 1,
+                                 30000,
+                                 TRUE,
+                                 NULL,
+                                 'fista',
+                                 '',
+                                 NULL,
+                                 10000,
+                                 1e-6
+                               );
+</pre></li>
+<li>View the resulting model and see coef_nonzero is different from coef_all. <pre class="example">
+-- Turn on expanded display to make it easier to read results.
+\x on
+SELECT * FROM houses_en2;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-----+--------------------------------------------
+features          | {tax,bath,size}
+features_selected | {tax,size}
+coef_nonzero      | {13.3261747481,22.7347986045}
+coef_all          | {13.3261747481,0,22.7347986045}
+intercept         | 68877.5045405
+log_likelihood    | -1694746275.43
+standardize       | t
+iteration_run     | 115
+</pre></li>
+<li>We can still use the prediction function with coef_all to evaluate residuals. <pre class="example">
+\x off
+SELECT id, price, predict, price - predict AS residual
+FROM (
+    SELECT
+        houses.*,
+        madlib.elastic_net_gaussian_predict(
+            m.coef_all,
+            m.intercept,
+            ARRAY[tax,bath,size]
+            ) AS predict
+    FROM houses, houses_en2 m) s
+ORDER BY id;
+</pre></li>
+<li>While we can also speed up the prediction function with coef_nonzero to evaluate residuals. This requires user to examine the feature_selected column in the result table to construct the correct independent variables. <pre class="example">
+\x off
+SELECT id, price, predict, price - predict AS residual
+FROM (
+    SELECT
+        houses.*,
+        madlib.elastic_net_gaussian_predict(
+            m.coef_nonzero,
+            m.intercept,
+            ARRAY[tax,size]
+            ) AS predict
+    FROM houses, houses_en2 m) s
+ORDER BY id;
+</pre> The two queries are expected to give same residuals: <pre class="result">
+ id | price  |     predict      |     residual
+----+--------+------------------+-------------------
+  1 |  50000 |  94245.742567344 |  -44245.742567344
+  3 |  22500 |  93242.914556232 |  -70742.914556232
+  5 | 133000 | 120570.253114742 |   12429.746885258
+  7 | 260000 | 154482.653115284 |  105517.346884716
+  9 | 160000 | 127499.863983754 |   32500.136016246
+ 11 |  87000 | 114415.797184981 |  -27415.797184981
+ 13 | 140000 |  150201.89180353 |   -10201.89180353
+ 15 |  65000 |  110504.97610329 |   -45504.97610329
+  2 |  85000 |  114926.05405835 |   -29926.05405835
+  4 |  90000 | 110026.514757197 |  -20026.514757197
+  6 |  90500 | 105510.375306125 |  -15010.375306125
+  8 | 142500 | 104539.017736473 |   37960.982263527
+ 10 | 240000 | 181347.915720063 |   58652.084279937
+ 12 | 118600 | 118884.405888047 | -284.405888046997
+ 14 | 148000 | 131701.624106042 |   16298.375893958
+(15 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Note</dt><dd>It is <b>strongly</b> <b>recommended</b> that you run <code><a class="el" href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" title="Interface for elastic net. ">elastic_net_train()</a></code> on a subset of the data with a limited <em>max_iter</em> before applying it to the full data set with a large <em>max_iter</em>. In the pre-run, you can adjust the parameters to get the best performance and then apply the best set of parameters to the whole data set.</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Elastic net regularization seeks to find a weight vector that, for any given training example set, minimizes: </p><p class="formulaDsp">
+\[\min_{w \in R^N} L(w) + \lambda \left(\frac{(1-\alpha)}{2} \|w\|_2^2 + \alpha \|w\|_1 \right)\]
+</p>
+<p> where \(L\) is the metric function that the user wants to minimize. Here \( \alpha \in [0,1] \) and \( lambda \geq 0 \). If \(alpha = 0\), we have the ridge regularization (known also as Tikhonov regularization), and if \(\alpha = 1\), we have the LASSO regularization.</p>
+<p>For the Gaussian response family (or linear model), we have </p><p class="formulaDsp">
+\[L(\vec{w}) = \frac{1}{2}\left[\frac{1}{M} \sum_{m=1}^M (w^{t} x_m + w_{0} - y_m)^2 \right] \]
+</p>
+<p>For the Binomial response family (or logistic model), we have </p><p class="formulaDsp">
+\[ L(\vec{w}) = \sum_{m=1}^M\left[y_m \log\left(1 + e^{-(w_0 + \vec{w}\cdot\vec{x}_m)}\right) + (1-y_m) \log\left(1 + e^{w_0 + \vec{w}\cdot\vec{x}_m}\right)\right]\ , \]
+</p>
+<p> where \(y_m \in {0,1}\).</p>
+<p>To get better convergence, one can rescale the value of each element of x </p><p class="formulaDsp">
+\[ x&#39; \leftarrow \frac{x - \bar{x}}{\sigma_x} \]
+</p>
+<p> and for Gaussian case we also let </p><p class="formulaDsp">
+\[y&#39; \leftarrow y - \bar{y} \]
+</p>
+<p> and then minimize with the regularization terms. At the end of the calculation, the orginal scales will be restored and an intercept term will be obtained at the same time as a by-product.</p>
+<p>Note that fitting after scaling is not equivalent to directly fitting.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Elastic net regularization. <a href="http://en.wikipedia.org/wiki/Elastic_net_regularization">http://en.wikipedia.org/wiki/Elastic_net_regularization</a></p>
+<p>[2] Beck, A. and M. Teboulle (2009), A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. on Imaging Sciences 2(1), 183-202.</p>
+<p>[3] Shai Shalev-Shwartz and Ambuj Tewari, Stochastic Methods for l1 Regularized Loss Minimization. Proceedings of the 26th International Conference on Machine Learning, Montreal, Canada, 2009.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="elastic__net_8sql__in.html" title="SQL functions for elastic net regularization. ">elastic_net.sql_in</a> documenting the SQL functions.</p>
+<p>grp_validation </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:37:45 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

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+<title>MADlib: FM (Flajolet-Martin)</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.8</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
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+var searchBox = new SearchBox("searchBox", "search",false,'Search');
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+       class="ui-resizable-handle">
+  </div>
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+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__fmsketch.html','');});
+</script>
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+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">FM (Flajolet-Martin)<div class="ingroups"><a class="el" href="group__grp__early__stage.html">Early Stage Development</a> &raquo; <a class="el" href="group__grp__sketches.html">Cardinality Estimators</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method is still in early stage development. There may be some issues that will be addressed in a future version. Interface and implementation is subject to change. </em></dd></dl>
+<p><a class="el" href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b">fmsketch_dcount</a> can be run on a column of any type. It returns an approximation to the number of distinct values (a la <code>COUNT(DISTINCT x)</code>), but faster and approximate. Like any aggregate, it can be combined with a GROUP BY clause to do distinct counts per group.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section user"><dt>Syntax</dt><dd></dd></dl>
+<p>Get the number of distinct values in a designated column. </p><pre class="syntax">
+fmsketch_dcount( col_name )
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd><ol type="1">
+<li>Generate some data. <pre class="example">
+CREATE TABLE data(class INT, a1 INT);
+INSERT INTO data SELECT 1,1 FROM generate_series(1,10000);
+INSERT INTO data SELECT 1,2 FROM generate_series(1,15000);
+INSERT INTO data SELECT 1,3 FROM generate_series(1,10000);
+INSERT INTO data SELECT 2,5 FROM generate_series(1,1000);
+INSERT INTO data SELECT 2,6 FROM generate_series(1,1000);
+</pre></li>
+<li>Find the distinct number of values for each class. <pre class="example">
+SELECT class, fmsketch_dcount(a1)
+FROM data
+GROUP BY data.class;
+</pre> Result: <pre class="result">
+class | fmsketch_dcount
+&#160;------+-----------------
+    2 |               2
+    1 |               3
+(2 rows)
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd>[1] P. Flajolet and N.G. Martin. Probabilistic counting algorithms for data base applications, Journal of Computer and System Sciences 31(2), pp 182-209, 1985. <a href="http://algo.inria.fr/flajolet/Publications/FlMa85.pdf">http://algo.inria.fr/flajolet/Publications/FlMa85.pdf</a></dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="sketch_8sql__in.html" title="SQL functions for sketch-based approximations of descriptive statistics. ">sketch.sql_in</a> documenting the SQL function. </dd></dl>
+</div><!-- contents -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

[23/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+&#160;
+
+<h3><a class="anchor" id="index_m"></a>- m -</h3><ul>
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+: <a class="el" href="marginal_8sql__in.html#a3286b644d2d51a8f2874896dc1b5d925">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_final()
+: <a class="el" href="marginal_8sql__in.html#ac4e06a28a76651de70768457575bb9e0">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#ae44a992f8bd650b5c1f0c2ffae7bb542">marginal.sql_in</a>
+</li>
+<li>marginal_logregr_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a2115a795562bc2928c3858fc55104c4b">marginal.sql_in</a>
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+: <a class="el" href="linalg_8sql__in.html#a24a4f14f171a12ec31f609af2f07ac18">linalg.sql_in</a>
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+<li>matrix_densify()
+: <a class="el" href="matrix__op_8sql__in.html#ad56abb5f2271700423a51147b8b890b6">matrix_op.sql_in</a>
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+: <a class="el" href="matrix__op_8sql__in.html#a91383f59dcb8b7ca2fba89ea350d113b">matrix_op.sql_in</a>
+</li>
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+: <a class="el" href="matrix__op_8sql__in.html#a0731901b985a90c3aa2f686a7ce95978">matrix_op.sql_in</a>
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+<li>matrix_mult()
+: <a class="el" href="matrix__op_8sql__in.html#a716158fc895763243a9b222733cbdd9a">matrix_op.sql_in</a>
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+: <a class="el" href="matrix__op_8sql__in.html#aa3e1bf84295bb870c097bb293dc56d5b">matrix_op.sql_in</a>
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+: <a class="el" href="matrix__op_8sql__in.html#ac5937bd13fa68864318970bc48425f59">matrix_op.sql_in</a>
+</li>
+<li>matrix_square()
+: <a class="el" href="matrix__op_8sql__in.html#a1cd4cba697f163a4e2ec2fe2ca0c8f46">matrix_op.sql_in</a>
+</li>
+<li>matrix_trans()
+: <a class="el" href="matrix__op_8sql__in.html#a505b024301dd1a7ceb9ee01e1bf572d3">matrix_op.sql_in</a>
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+<li>matrix_unblockize()
+: <a class="el" href="matrix__op_8sql__in.html#ad48d14a3b941b2c5ba61a76128f9e597">matrix_op.sql_in</a>
+</li>
+<li>mean()
+: <a class="el" href="svec__util_8sql__in.html#a546b947319c9f48237120a31929bdfd9">svec_util.sql_in</a>
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+<li>mfvsketch_quick_histogram()
+: <a class="el" href="sketch_8sql__in.html#aa2d4c0af50b242731f841c2dd355fc72">sketch.sql_in</a>
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+: <a class="el" href="multilogistic_8sql__in.html#a116c95de21b112dedf99035a9b243fd7">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_final()
+: <a class="el" href="marginal_8sql__in.html#ad80d7df29226a2529f9f80607f2a983e">marginal.sql_in</a>
+</li>
+<li>mlogregr_marginal_step_merge_states()
+: <a class="el" href="marginal_8sql__in.html#a82ac74fd7aec70b7596bc2b4776742fa">marginal.sql_in</a>
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+<li>mlogregr_marginal_step_transition()
+: <a class="el" href="marginal_8sql__in.html#a7b8a7cf298ea51fc16de975e2503615a">marginal.sql_in</a>
+</li>
+<li>mlogregr_predict()
+: <a class="el" href="multilogistic_8sql__in.html#a0fdfed54d63cefe260a0b74b9c7bbad5">multilogistic.sql_in</a>
+</li>
+<li>mlogregr_robust_step_final()
+: <a class="el" href="robust_8sql__in.html#a6bfc968b23887a0a8ddd69a5c3350ba9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#a123149abbf366743e04ecb6fc7f8b9d9">robust.sql_in</a>
+</li>
+<li>mlogregr_robust_step_transition()
+: <a class="el" href="robust_8sql__in.html#a3928ec1e3935e8dc4dbcb9e705805660">robust.sql_in</a>
+</li>
+<li>mlogregr_train()
+: <a class="el" href="multilogistic_8sql__in.html#ab8b5a7eb69a945435cba5a068576f2e4">multilogistic.sql_in</a>
+</li>
+<li>mode()
+: <a class="el" href="utilities_8sql__in.html#ac25ef90f8a8e2f9b2244a57d92086c91">utilities.sql_in</a>
+</li>
+<li>mse_error()
+: <a class="el" href="cross__validation_8sql__in.html#a2cd71e756f90b0c4fa46f508a1ca261e">cross_validation.sql_in</a>
+</li>
+<li>mw_test_final()
+: <a class="el" href="hypothesis__tests_8sql__in.html#ac18e666088117997da2d22236e982f5e">hypothesis_tests.sql_in</a>
+</li>
+<li>mw_test_transition()
+: <a class="el" href="hypothesis__tests_8sql__in.html#a4e324c82b069ebf7b498012aa83931c5">hypothesis_tests.sql_in</a>
+</li>
+</ul>
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
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+</html>

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+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
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+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_n"></a>- n -</h3><ul>
+<li>negative_binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#ad0a7e4474f828869fb90e62f8e6f04d7">prob.sql_in</a>
+</li>
+<li>negative_binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#ab9cbc30424eba30f2df2a32a7e45f138">prob.sql_in</a>
+</li>
+<li>negative_binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#ad9e541de8b41da2e7b7434f862db4845">prob.sql_in</a>
+</li>
+<li>non_central_beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a1361569bd86e41f796c70f8cb277010e">prob.sql_in</a>
+</li>
+<li>non_central_beta_pdf()
+: <a class="el" href="prob_8sql__in.html#ad4a12c083054f0e2d316ae76c9aaeef7">prob.sql_in</a>
+</li>
+<li>non_central_beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a3073b409eaee3faa6d43df014662c279">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_cdf()
+: <a class="el" href="prob_8sql__in.html#ab4b7d2cf10bb031328dcc34c6ff494ad">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_pdf()
+: <a class="el" href="prob_8sql__in.html#aa7a563183224593d1e0d623a3c5489d8">prob.sql_in</a>
+</li>
+<li>non_central_chi_squared_quantile()
+: <a class="el" href="prob_8sql__in.html#ad694e29187b629ae683ef1235d2b9270">prob.sql_in</a>
+</li>
+<li>non_central_f_cdf()
+: <a class="el" href="prob_8sql__in.html#a00051df630007b530ce86b4ab44a0434">prob.sql_in</a>
+</li>
+<li>non_central_f_pdf()
+: <a class="el" href="prob_8sql__in.html#a3d94edcf90fca1fa52671293a9ea9c2f">prob.sql_in</a>
+</li>
+<li>non_central_f_quantile()
+: <a class="el" href="prob_8sql__in.html#a92b2a978db480a6c78cfb708107ecb92">prob.sql_in</a>
+</li>
+<li>non_central_t_cdf()
+: <a class="el" href="prob_8sql__in.html#afaf4374d2720b230a54713e21ecb1955">prob.sql_in</a>
+</li>
+<li>non_central_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a4799e3bb68a496d9bc1ef1ea85265409">prob.sql_in</a>
+</li>
+<li>non_central_t_quantile()
+: <a class="el" href="prob_8sql__in.html#af50865aba2ece2e23b2af461a02f7d12">prob.sql_in</a>
+</li>
+<li>noop()
+: <a class="el" href="utilities_8sql__in.html#ab194d6a1222672c314ea39b78aad8fd0">utilities.sql_in</a>
+</li>
+<li>norm1()
+: <a class="el" href="linalg_8sql__in.html#a300300fe4b8576ba0b97b95d8dea3057">linalg.sql_in</a>
+</li>
+<li>norm2()
+: <a class="el" href="linalg_8sql__in.html#a50fdfe30cc0edc6888a909dbb4b4c239">linalg.sql_in</a>
+</li>
+<li>normal_cdf()
+: <a class="el" href="prob_8sql__in.html#a370e31a46781ed8832b31625a683d053">prob.sql_in</a>
+</li>
+<li>normal_pdf()
+: <a class="el" href="prob_8sql__in.html#a63f555f36385d86e229cdca223e39567">prob.sql_in</a>
+</li>
+<li>normal_quantile()
+: <a class="el" href="prob_8sql__in.html#a53d56b672fe4cd1277cb5eac5de5118f">prob.sql_in</a>
+</li>
+<li>normalize()
+: <a class="el" href="svec__util_8sql__in.html#aace16abea7c63f136520fde6c28f1f28">svec_util.sql_in</a>
+</li>
+<li>normalized_avg()
+: <a class="el" href="linalg_8sql__in.html#a0b04663ca206f03e66aed5ea2b4cc461">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#a1c22ee0672d6c550f5bb966f78ce738a">linalg.sql_in</a>
+</li>
+<li>normalized_avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#ad5863fed5eb4813ba0044abc268d03ce">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
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+: <a class="el" href="hypothesis__tests_8sql__in.html#aa694f4ce95280210a3887773bb3f417b">hypothesis_tests.sql_in</a>
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+</ul>
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
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+</body>
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+: <a class="el" href="pca_8sql__in.html#ace5236fb2133c297eca85ab788e6dfc2">pca.sql_in</a>
+</li>
+<li>pca_train()
+: <a class="el" href="pca_8sql__in.html#a2d1cfcf6a7c8a9c69808ec8927b900d1">pca.sql_in</a>
+</li>
+<li>pmml()
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+<li>poisson_pmf()
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+<li>poisson_quantile()
+: <a class="el" href="prob_8sql__in.html#a032d26db18b2ee1034085f5521939c61">prob.sql_in</a>
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+<li>poisson_random()
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+<li>profile()
+: <a class="el" href="profile_8sql__in.html#a22f0136bb3d7740262b13030a2925a80">profile.sql_in</a>
+</li>
+<li>profile_full()
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+</ul>
+</div><!-- contents -->
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+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
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+<h3><a class="anchor" id="index_q"></a>- q -</h3><ul>
+<li>quantile()
+: <a class="el" href="quantile_8sql__in.html#a6955e44659a0817803ab7a11a9a7e8a5">quantile.sql_in</a>
+</li>
+<li>quantile_big()
+: <a class="el" href="quantile_8sql__in.html#a154435ddf6200110013dc05b1a6207fe">quantile.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x72.html
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diff --git a/docs/latest/globals_func_0x72.html b/docs/latest/globals_func_0x72.html
new file mode 100644
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.4"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+</script><script src="../mathjax/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
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+ <tbody>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
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+   <div id="projectbrief">User Documentation</div>
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+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
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+          <img id="MSearchSelect" src="search/mag_sel.png"
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+          </span><span class="right">
+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.4 -->
+<script type="text/javascript">
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+       class="ui-resizable-handle">
+  </div>
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+<script type="text/javascript">
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+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
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+</div>
+
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+&#160;
+
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+: <a class="el" href="prob_8sql__in.html#aab0ddb8a5348cfa387d777043a3cb6d0">prob.sql_in</a>
+</li>
+<li>rayleigh_pdf()
+: <a class="el" href="prob_8sql__in.html#a798541736d9255bdd5c0bd94924d47bc">prob.sql_in</a>
+</li>
+<li>rayleigh_quantile()
+: <a class="el" href="prob_8sql__in.html#acd6757acab1683c735e2b57901494336">prob.sql_in</a>
+</li>
+<li>rb_coxph_step_final()
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+</li>
+<li>rb_coxph_step_transition()
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+</li>
+<li>rb_coxph_strata_step_final()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a9826d93b250c1fb6e06d412181545754">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#ad8fc6f9cca64b26bc7a12f15abfc6234">robust_variance_coxph.sql_in</a>
+</li>
+<li>rb_sum_strata_final()
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+</li>
+<li>rb_sum_strata_transition()
+: <a class="el" href="robust__variance__coxph_8sql__in.html#a4bf2a610417ee174074b2191fb921256">robust_variance_coxph.sql_in</a>
+</li>
+<li>relative_error()
+: <a class="el" href="utilities_8sql__in.html#a4d49e00b4dd6059f61ffb9fdc0a696f7">utilities.sql_in</a>
+</li>
+<li>rf_classify()
+: <a class="el" href="rf_8sql__in.html#a116584e8a5194a4277e964900ee5383d">rf.sql_in</a>
+</li>
+<li>rf_clean()
+: <a class="el" href="rf_8sql__in.html#af33b77b75df225ee65a8acf18705256e">rf.sql_in</a>
+</li>
+<li>rf_display()
+: <a class="el" href="rf_8sql__in.html#a60b3f093409a14331b7601522ac0ac42">rf.sql_in</a>
+</li>
+<li>rf_score()
+: <a class="el" href="rf_8sql__in.html#ac6745c3d4cae3443b217f3ba66d11ac4">rf.sql_in</a>
+</li>
+<li>rf_train()
+: <a class="el" href="rf_8sql__in.html#a3cf718282802b63dc0a0d19b34f6829b">rf.sql_in</a>
+</li>
+<li>robust_linregr()
+: <a class="el" href="robust_8sql__in.html#ade1af85b04294ee404a638aa2bfbf051">robust.sql_in</a>
+</li>
+<li>robust_linregr_final()
+: <a class="el" href="robust_8sql__in.html#a458741a15dc6c2ca627f0d1c1d19b7f8">robust.sql_in</a>
+</li>
+<li>robust_linregr_merge_states()
+: <a class="el" href="robust_8sql__in.html#ad85d9925cbab4b59acd798a7b32671ff">robust.sql_in</a>
+</li>
+<li>robust_linregr_transition()
+: <a class="el" href="robust_8sql__in.html#a3f978229f9a2e229276278985d76c438">robust.sql_in</a>
+</li>
+<li>robust_logregr()
+: <a class="el" href="robust_8sql__in.html#a6a9290aafa048bcb7cdc13410d5814aa">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_final()
+: <a class="el" href="robust_8sql__in.html#af8efdd02ad3155411507bf786095a562">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_merge_states()
+: <a class="el" href="robust_8sql__in.html#aa715a6f6885b56d919a243ec4ff07fde">robust.sql_in</a>
+</li>
+<li>robust_logregr_step_transition()
+: <a class="el" href="robust_8sql__in.html#a840ddaa14013c4445b7a0d55e9489d8d">robust.sql_in</a>
+</li>
+<li>robust_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a209021ac52461f029353cec7fb900cd4">robust.sql_in</a>
+</li>
+<li>robust_variance_coxph()
+: <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">clustered_variance_coxph.sql_in</a>
+, <a class="el" href="robust__variance__coxph_8sql__in.html#a9d08b7ab1efa1f58543214a311344741">robust_variance_coxph.sql_in</a>
+</li>
+<li>robust_variance_linregr()
+: <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust.sql_in</a>
+</li>
+<li>robust_variance_logregr()
+: <a class="el" href="robust_8sql__in.html#a815cadfeb54447993db9ce2bd2aa709e">robust.sql_in</a>
+</li>
+<li>robust_variance_mlogregr()
+: <a class="el" href="robust_8sql__in.html#a93031a31e4c93d3caa22d963927e75c8">robust.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x73.html
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diff --git a/docs/latest/globals_func_0x73.html b/docs/latest/globals_func_0x73.html
new file mode 100644
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+++ b/docs/latest/globals_func_0x73.html
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+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
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+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data mining,deep learning,ensemble methods,data science,market basket analysis,affinity analysis,pca,lda,regression,elastic net,huber white,proportional hazards,k-means,latent dirichlet allocation,bayes,support vector machines,svm"/>
+<title>MADlib: File Members</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
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+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
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+          <input type="text" id="MSearchField" value="Search" accesskey="S"
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+            <a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
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+</div>
+<!-- end header part -->
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+<script type="text/javascript">
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+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('globals_func_0x73.html','');});
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+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+<a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(0)"><span class="SelectionMark">&#160;</span>All</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(1)"><span class="SelectionMark">&#160;</span>Files</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(2)"><span class="SelectionMark">&#160;</span>Functions</a><a class="SelectItem" href="javascript:void(0)" onclick="searchBox.OnSelectItem(3)"><span class="SelectionMark">&#160;</span>Groups</a></div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_s"></a>- s -</h3><ul>
+<li>sparse_direct_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8d22e4088dedd57a931b465a73a0357a">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a37d42899e8ec22d8642aa2c943689d19">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_direct_linear_system_merge_states()
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+</li>
+<li>sparse_direct_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a8175311c8e181a3ac73d9cc87814bbb0">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#ace39ff8e846754e3e0709bfbbbee9a60">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_final()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5ff2f1afd945580fc9cb9bedba84695b">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_merge_states()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a3daa8daf05cda974b4137ce07c694e2e">sparse_linear_systems.sql_in</a>
+</li>
+<li>sparse_inmem_iterative_linear_system_transition()
+: <a class="el" href="sparse__linear__systems_8sql__in.html#a5463eaef5dc5e4040acd56a1035ecc91">sparse_linear_systems.sql_in</a>
+</li>
+<li>squared_dist_norm2()
+: <a class="el" href="linalg_8sql__in.html#a00a08e69f27524f2096032214e15b668">linalg.sql_in</a>
+</li>
+<li>students_t_cdf()
+: <a class="el" href="prob_8sql__in.html#a5322531131074c23a2dbf067ee504ef7">prob.sql_in</a>
+</li>
+<li>students_t_pdf()
+: <a class="el" href="prob_8sql__in.html#a8815c21670fff9d31946553a84b845b1">prob.sql_in</a>
+</li>
+<li>students_t_quantile()
+: <a class="el" href="prob_8sql__in.html#a7d64add02af21a95d73502b2dd466a75">prob.sql_in</a>
+</li>
+<li>sum()
+: <a class="el" href="array__ops_8sql__in.html#a26e8508a2bae10a6574cec697a270eea">array_ops.sql_in</a>
+</li>
+<li>summary()
+: <a class="el" href="summary_8sql__in.html#ae39edc1569d25b212ca5ebc06703c6d6">summary.sql_in</a>
+</li>
+<li>svd()
+: <a class="el" href="svd_8sql__in.html#a8dbab74f5bde863a63431a6ec6f66a61">svd.sql_in</a>
+</li>
+<li>svd_block()
+: <a class="el" href="svd_8sql__in.html#a96fb5620eed9c936b5bbfe24d7e37ad6">svd.sql_in</a>
+</li>
+<li>svd_sparse()
+: <a class="el" href="svd_8sql__in.html#a511f83e3eba19f6f41f8e3f7122ddb3a">svd.sql_in</a>
+</li>
+<li>svd_sparse_native()
+: <a class="el" href="svd_8sql__in.html#a0225b28fd4b71cf6e7072c034f3296c6">svd.sql_in</a>
+</li>
+<li>svdmf_run()
+: <a class="el" href="svdmf_8sql__in.html#a6cff34415cca23aa0a826cc08a6283f5">svdmf.sql_in</a>
+</li>
+<li>svec_append()
+: <a class="el" href="svec__util_8sql__in.html#a3950b17b103ceeddca520a0f0d097ddc">svec_util.sql_in</a>
+</li>
+<li>svec_cast_float4()
+: <a class="el" href="svec_8sql__in.html#a1f1025124d5423cc1505991414f17582">svec.sql_in</a>
+</li>
+<li>svec_cast_float8()
+: <a class="el" href="svec_8sql__in.html#af42e4b15879949032a68139c59443aac">svec.sql_in</a>
+</li>
+<li>svec_cast_float8arr()
+: <a class="el" href="svec_8sql__in.html#a96025250e64f049a053c5696b3b96e02">svec.sql_in</a>
+</li>
+<li>svec_cast_int2()
+: <a class="el" href="svec_8sql__in.html#af69458bf9b2906ca471d027966b13e6f">svec.sql_in</a>
+</li>
+<li>svec_cast_int4()
+: <a class="el" href="svec_8sql__in.html#abc01ac6b36ea9d689d338fd72abbd540">svec.sql_in</a>
+</li>
+<li>svec_cast_int8()
+: <a class="el" href="svec_8sql__in.html#a9ea167c01fb2999dc5aaf5e8e21f5e8f">svec.sql_in</a>
+</li>
+<li>svec_cast_numeric()
+: <a class="el" href="svec_8sql__in.html#a50de0278ff7ceeff33a2ea4ca5fdf1a0">svec.sql_in</a>
+</li>
+<li>svec_cast_positions_float8arr()
+: <a class="el" href="svec_8sql__in.html#a0ee423729fbca5abd46c86fa81d51f23">svec.sql_in</a>
+</li>
+<li>svec_change()
+: <a class="el" href="svec__util_8sql__in.html#a59407764a1cbf1937da39cf39a2f447c">svec_util.sql_in</a>
+</li>
+<li>svec_cmp()
+: <a class="el" href="svec_8sql__in.html#a813762d8bd2a7fda083880e5bd8c355b">svec.sql_in</a>
+</li>
+<li>svec_concat()
+: <a class="el" href="svec_8sql__in.html#a24f184bef083fd9068c28fc94dad8054">svec.sql_in</a>
+</li>
+<li>svec_concat_replicate()
+: <a class="el" href="svec_8sql__in.html#a34cb5f83e0c010aacc719b8b078bfeb9">svec.sql_in</a>
+</li>
+<li>svec_contains()
+: <a class="el" href="svec__util_8sql__in.html#afdef1c6bf65ffc0e838cf10f33ae5557">svec_util.sql_in</a>
+</li>
+<li>svec_count()
+: <a class="el" href="svec__util_8sql__in.html#a6a21e25d3f50053c888a0bd126f831f0">svec_util.sql_in</a>
+</li>
+<li>svec_count_nonzero()
+: <a class="el" href="svec__util_8sql__in.html#abebdcbe45de346aff874db008e842e65">svec_util.sql_in</a>
+</li>
+<li>svec_dimension()
+: <a class="el" href="svec__util_8sql__in.html#ab8d1acbe4e8551755fef217b506bed8a">svec_util.sql_in</a>
+</li>
+<li>svec_div()
+: <a class="el" href="svec_8sql__in.html#a78a38512c860085e31f6153b8118cbe9">svec.sql_in</a>
+</li>
+<li>svec_div_float8arr()
+: <a class="el" href="svec_8sql__in.html#a211cf37584f54087dabc1fac14ed12dd">svec.sql_in</a>
+</li>
+<li>svec_dmax()
+: <a class="el" href="svec__util_8sql__in.html#aa05abd8cb32253c07c0f2224861256a5">svec_util.sql_in</a>
+</li>
+<li>svec_dmin()
+: <a class="el" href="svec__util_8sql__in.html#ad1011a13839994adbb1eb3e643c9074d">svec_util.sql_in</a>
+</li>
+<li>svec_dot()
+: <a class="el" href="svec_8sql__in.html#a47b111663b39ec8646891edec8ab3925">svec.sql_in</a>
+</li>
+<li>svec_elsum()
+: <a class="el" href="svec__util_8sql__in.html#a00a7b3260b9fde9b55061e6bf58a028a">svec_util.sql_in</a>
+</li>
+<li>svec_eq()
+: <a class="el" href="svec_8sql__in.html#adceeff7e2f2e43b3e17219bca5d4fea0">svec.sql_in</a>
+</li>
+<li>svec_eq_non_zero()
+: <a class="el" href="svec__util_8sql__in.html#ab3a1cccd709379973f100e5eb35559d2">svec_util.sql_in</a>
+</li>
+<li>svec_from_string()
+: <a class="el" href="svec__util_8sql__in.html#a1f5e8911574cf30cc979478040bd3262">svec_util.sql_in</a>
+</li>
+<li>svec_ge()
+: <a class="el" href="svec_8sql__in.html#ad08b0cadbcf50ff4685228f917cafd1f">svec.sql_in</a>
+</li>
+<li>svec_gt()
+: <a class="el" href="svec_8sql__in.html#a3e5d57b7a4473904e4d053884468e540">svec.sql_in</a>
+</li>
+<li>svec_hash()
+: <a class="el" href="svec__util_8sql__in.html#a0bfa8f8d083282bc9e8caf6f03bb5251">svec_util.sql_in</a>
+</li>
+<li>svec_in()
+: <a class="el" href="svec_8sql__in.html#abe896192db26c02d5c517a5ccf2144f1">svec.sql_in</a>
+</li>
+<li>svec_l1norm()
+: <a class="el" href="svec__util_8sql__in.html#ae60f7aaa5710c765d08b6c3906e0c9f0">svec_util.sql_in</a>
+</li>
+<li>svec_l2_cmp()
+: <a class="el" href="svec_8sql__in.html#a8f72c8cf6d39f08d939f23cf1338a3c6">svec.sql_in</a>
+</li>
+<li>svec_l2_eq()
+: <a class="el" href="svec_8sql__in.html#a9268d26c54c4b2905eb7938df95af8c0">svec.sql_in</a>
+</li>
+<li>svec_l2_ge()
+: <a class="el" href="svec_8sql__in.html#ac22b72fbae2a8f2b35b1c571d9f54dad">svec.sql_in</a>
+</li>
+<li>svec_l2_gt()
+: <a class="el" href="svec_8sql__in.html#ab2414d0d78286884cd199cf44c1d17b9">svec.sql_in</a>
+</li>
+<li>svec_l2_le()
+: <a class="el" href="svec_8sql__in.html#a4045437bce8b9a9bdb0eb3115c97ca99">svec.sql_in</a>
+</li>
+<li>svec_l2_lt()
+: <a class="el" href="svec_8sql__in.html#ab53b85cb3412c9172bf0ee04f6856c69">svec.sql_in</a>
+</li>
+<li>svec_l2_ne()
+: <a class="el" href="svec_8sql__in.html#af35c08aadf164fa3bbc8678765bca643">svec.sql_in</a>
+</li>
+<li>svec_l2norm()
+: <a class="el" href="svec__util_8sql__in.html#a475f0f3e4602e8278b582e099fce2487">svec_util.sql_in</a>
+</li>
+<li>svec_lapply()
+: <a class="el" href="svec__util_8sql__in.html#a0d94c44dde95a00e3d802dee6d7c01eb">svec_util.sql_in</a>
+</li>
+<li>svec_le()
+: <a class="el" href="svec_8sql__in.html#aa6f5715df728db0783b5dcb8db82a2a2">svec.sql_in</a>
+</li>
+<li>svec_log()
+: <a class="el" href="svec__util_8sql__in.html#aab641f9db3deed5bbfa7e050c531e9c1">svec_util.sql_in</a>
+</li>
+<li>svec_lt()
+: <a class="el" href="svec_8sql__in.html#ad7f05a6ce6efc51517bab6ccdb79ee23">svec.sql_in</a>
+</li>
+<li>svec_mean_final()
+: <a class="el" href="svec__util_8sql__in.html#ac3fc45511b50b5d054bf7bbeb952b98d">svec_util.sql_in</a>
+</li>
+<li>svec_mean_prefunc()
+: <a class="el" href="svec__util_8sql__in.html#ab161647a1498c85762779966790eab71">svec_util.sql_in</a>
+</li>
+<li>svec_mean_transition()
+: <a class="el" href="svec__util_8sql__in.html#afcf2f75cd00ab0ba269caf79dcfcc62c">svec_util.sql_in</a>
+</li>
+<li>svec_median()
+: <a class="el" href="svec__util_8sql__in.html#a2284a118fe03465203ecea8c34acefa5">svec_util.sql_in</a>
+</li>
+<li>svec_median_inmemory()
+: <a class="el" href="svec__util_8sql__in.html#a93400e39ecce2f11b9ceb08403420a43">svec_util.sql_in</a>
+</li>
+<li>svec_minus()
+: <a class="el" href="svec_8sql__in.html#a170890a165bf662d343339ffc2a187cf">svec.sql_in</a>
+</li>
+<li>svec_minus_float8arr()
+: <a class="el" href="svec_8sql__in.html#adf39975fbbd8a39629569c622db33ab9">svec.sql_in</a>
+</li>
+<li>svec_mult()
+: <a class="el" href="svec_8sql__in.html#a27f0629942dba2e395b005c728d53ac3">svec.sql_in</a>
+</li>
+<li>svec_mult_float8arr()
+: <a class="el" href="svec_8sql__in.html#a6e6916baaf0a7ae0079424ce0565c8b4">svec.sql_in</a>
+</li>
+<li>svec_ne()
+: <a class="el" href="svec_8sql__in.html#a80d9692f67e7cb482c4bda6dbaacd82b">svec.sql_in</a>
+</li>
+<li>svec_nonbase_positions()
+: <a class="el" href="svec__util_8sql__in.html#a5bc053a080fb38601ad91045dc3ffe74">svec_util.sql_in</a>
+</li>
+<li>svec_nonbase_values()
+: <a class="el" href="svec__util_8sql__in.html#a076dcbc0a278bb3b5161b0db2ad8fa44">svec_util.sql_in</a>
+</li>
+<li>svec_out()
+: <a class="el" href="svec_8sql__in.html#a3db4af36acd318030ae57d2bfd19387a">svec.sql_in</a>
+</li>
+<li>svec_pivot()
+: <a class="el" href="svec__util_8sql__in.html#a929bde3bc91b67300d9d370b6a1495f1">svec_util.sql_in</a>
+</li>
+<li>svec_plus()
+: <a class="el" href="svec_8sql__in.html#a612d8920c61e6fdf18dd4e3e9c69c979">svec.sql_in</a>
+</li>
+<li>svec_plus_float8arr()
+: <a class="el" href="svec_8sql__in.html#aaeb54c952836c83c7f21a1c62a77955f">svec.sql_in</a>
+</li>
+<li>svec_pow()
+: <a class="el" href="svec_8sql__in.html#ab5166565f3482768db33ee677acc3d96">svec.sql_in</a>
+</li>
+<li>svec_proj()
+: <a class="el" href="svec__util_8sql__in.html#a8787222aec691f94d9808d1369aa401c">svec_util.sql_in</a>
+</li>
+<li>svec_recv()
+: <a class="el" href="svec_8sql__in.html#a585ea8bcb1d0647ef1295187b87405b2">svec.sql_in</a>
+</li>
+<li>svec_return_array()
+: <a class="el" href="svec_8sql__in.html#a7decda276d7ff1e9340dd44e0e7419c0">svec.sql_in</a>
+</li>
+<li>svec_reverse()
+: <a class="el" href="svec__util_8sql__in.html#a4a221435ca91b6fa45114e689ee96010">svec_util.sql_in</a>
+</li>
+<li>svec_send()
+: <a class="el" href="svec_8sql__in.html#a617557435170136b2781070c0a268b83">svec.sql_in</a>
+</li>
+<li>svec_sfv()
+: <a class="el" href="svec__util_8sql__in.html#a375acd521ed9cb05f63b3696dcc10bf4">svec_util.sql_in</a>
+</li>
+<li>svec_sort()
+: <a class="el" href="svec__util_8sql__in.html#a91c23a7ce58932f48f5ab3579ea0309e">svec_util.sql_in</a>
+</li>
+<li>svec_subvec()
+: <a class="el" href="svec__util_8sql__in.html#a5cb3446de5fc117befe88ccb1ebb0e4e">svec_util.sql_in</a>
+</li>
+<li>svec_sum()
+: <a class="el" href="svec__util_8sql__in.html#af1ce6cdd7244ac0832d15c65b7ccc782">svec_util.sql_in</a>
+</li>
+<li>svec_to_string()
+: <a class="el" href="svec__util_8sql__in.html#a1de746ebd23530e72ba4f031ac41d155">svec_util.sql_in</a>
+</li>
+<li>svec_unnest()
+: <a class="el" href="svec__util_8sql__in.html#a87b45a983da4066fa7f35b17ccecf699">svec_util.sql_in</a>
+</li>
+<li>svm_classification()
+: <a class="el" href="online__sv_8sql__in.html#ad90b6bf3b807f22d37b0e2b1893262f0">online_sv.sql_in</a>
+</li>
+<li>svm_cls_agg()
+: <a class="el" href="online__sv_8sql__in.html#a4fd5a4571d959a892ab28dab1fea92f3">online_sv.sql_in</a>
+</li>
+<li>svm_cls_update()
+: <a class="el" href="online__sv_8sql__in.html#a837bd8cfd5f3200e985febf0ff7ec013">online_sv.sql_in</a>
+</li>
+<li>svm_data_normalization()
+: <a class="el" href="online__sv_8sql__in.html#a78bb07d8f4aee80c3bbc8e8176e512be">online_sv.sql_in</a>
+</li>
+<li>svm_dot()
+: <a class="el" href="online__sv_8sql__in.html#ab53cac5790dafd7230359e08c72af4f1">online_sv.sql_in</a>
+</li>
+<li>svm_drop_model()
+: <a class="el" href="online__sv_8sql__in.html#ab54d33f13c0e00faa358e3e3f17c10fb">online_sv.sql_in</a>
+</li>
+<li>svm_gaussian()
+: <a class="el" href="online__sv_8sql__in.html#a9f2a96e1a241ecc66386a78b110777d3">online_sv.sql_in</a>
+</li>
+<li>svm_generate_cls_data()
+: <a class="el" href="online__sv_8sql__in.html#a1cf6d0632772caf6643212a4698d2507">online_sv.sql_in</a>
+</li>
+<li>svm_generate_nd_data()
+: <a class="el" href="online__sv_8sql__in.html#a8be25cfa6134cc12bbc6d31c04712c23">online_sv.sql_in</a>
+</li>
+<li>svm_generate_reg_data()
+: <a class="el" href="online__sv_8sql__in.html#adc9fe3f9b4a04dff13004edbaab8240f">online_sv.sql_in</a>
+</li>
+<li>svm_nd_agg()
+: <a class="el" href="online__sv_8sql__in.html#a9e0abf5bf91bd0833bf80b9d9a5ae8bf">online_sv.sql_in</a>
+</li>
+<li>svm_nd_update()
+: <a class="el" href="online__sv_8sql__in.html#ae6aef7b5654b6da487d0178adf443ee9">online_sv.sql_in</a>
+</li>
+<li>svm_novelty_detection()
+: <a class="el" href="online__sv_8sql__in.html#a75b773bcf4b5242c673d0e54128ed159">online_sv.sql_in</a>
+</li>
+<li>svm_polynomial()
+: <a class="el" href="online__sv_8sql__in.html#a1ac76fdf9623e0a4db47665f2a80be90">online_sv.sql_in</a>
+</li>
+<li>svm_predict()
+: <a class="el" href="online__sv_8sql__in.html#adc8c1c1d5e2deb20f5436790468b052f">online_sv.sql_in</a>
+</li>
+<li>svm_predict_batch()
+: <a class="el" href="online__sv_8sql__in.html#a91ac71354e9dec74e25339bf168c2e5b">online_sv.sql_in</a>
+</li>
+<li>svm_predict_sub()
+: <a class="el" href="online__sv_8sql__in.html#a5988c209ceb6d15b9139eef0c16d7b7d">online_sv.sql_in</a>
+</li>
+<li>svm_reg_agg()
+: <a class="el" href="online__sv_8sql__in.html#a225bdb74eaa687cef0ddbb35145318fc">online_sv.sql_in</a>
+</li>
+<li>svm_reg_update()
+: <a class="el" href="online__sv_8sql__in.html#a0eb68a44edff3fa53891a6a39de61e57">online_sv.sql_in</a>
+</li>
+<li>svm_regression()
+: <a class="el" href="online__sv_8sql__in.html#acaf1f4aa3eec5710de5c03e368a4b106">online_sv.sql_in</a>
+</li>
+<li>svm_store_model()
+: <a class="el" href="online__sv_8sql__in.html#ad2cd899daaa2036b475f71c28cc031ae">online_sv.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
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+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

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+</li>
+<li>triangular_cdf()
+: <a class="el" href="prob_8sql__in.html#abf9c7d870bcfe68cacaa421749bbdf35">prob.sql_in</a>
+</li>
+<li>triangular_pdf()
+: <a class="el" href="prob_8sql__in.html#a0c511b9748b2f7a21fe56aaf5f66d188">prob.sql_in</a>
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+<li>triangular_quantile()
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+</li>
+</ul>
+</div><!-- contents -->
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+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x75.html
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+</ul>
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+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
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[25/51] [partial] incubator-madlib-site git commit: Add all files from old site (madlib.net)

[ri...@apache.org]

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func.html
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+<script type="text/javascript" src="jquery.js"></script>
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+</li>
+<li>__arima_diff()
+: <a class="el" href="arima_8sql__in.html#ab686e554403a61a5404d83e692735ada">arima.sql_in</a>
+</li>
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+: <a class="el" href="arima_8sql__in.html#aadd3c7627043810a7e650e80b7fafd52">arima.sql_in</a>
+</li>
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+<li>__arima_lm_result_pfunc()
+: <a class="el" href="arima_8sql__in.html#a76b2c9d653383c81acde8383bb29aa97">arima.sql_in</a>
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+<li>__arima_lm_result_sfunc()
+: <a class="el" href="arima_8sql__in.html#aa919ba0d4fe79c3b55ee60b7ad17c7de">arima.sql_in</a>
+</li>
+<li>__arima_lm_stat_agg()
+: <a class="el" href="arima_8sql__in.html#ae9b8675db85344ac26484f8c125f57b6">arima.sql_in</a>
+</li>
+<li>__arima_lm_stat_ffunc()
+: <a class="el" href="arima_8sql__in.html#abb44a6b3bf206e966a9746dde942ddc3">arima.sql_in</a>
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+<li>__arima_lm_stat_sfunc()
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+: <a class="el" href="arima_8sql__in.html#a6955c191d95e1531655cea4617caf12c">arima.sql_in</a>
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+: <a class="el" href="decision__tree_8sql__in.html#a71e8f6ae4227a3cd3734d8c8f7ef54fe">decision_tree.sql_in</a>
+</li>
+<li>_dst_compute_entropy_final()
+: <a class="el" href="decision__tree_8sql__in.html#ac81d2cf47356702c5bc30790f505c05f">decision_tree.sql_in</a>
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+<li>_dst_compute_entropy_merge()
+: <a class="el" href="decision__tree_8sql__in.html#a992c692244ec9f1dd38557bc8cf5c2ef">decision_tree.sql_in</a>
+</li>
+<li>_dst_compute_entropy_transition()
+: <a class="el" href="decision__tree_8sql__in.html#a88589c4a865221cd97af52284e222195">decision_tree.sql_in</a>
+</li>
+<li>_dt_apply()
+: <a class="el" href="decision__tree_8sql__in.html#a164580846362686fe4ab55db7c4fa52a">decision_tree.sql_in</a>
+</li>
+<li>_dt_surr_apply()
+: <a class="el" href="decision__tree_8sql__in.html#a05665f86bc28346cdf12e4b4d05aa3d9">decision_tree.sql_in</a>
+</li>
+<li>_final_mode()
+: <a class="el" href="utilities_8sql__in.html#a378fd0afa1245520f2270e602ed9e6b6">utilities.sql_in</a>
+</li>
+<li>_gen_cat_levels_set()
+: <a class="el" href="decision__tree_8sql__in.html#a658412807f7b28e9e367a1421cb8ad96">decision_tree.sql_in</a>
+</li>
+<li>_gen_state()
+: <a class="el" href="utilities_8sql__in.html#a2dc21ec6de26adef2cf1699ed8dab5aa">utilities.sql_in</a>
+</li>
+<li>_get_bin_index_by_value()
+: <a class="el" href="random__forest_8sql__in.html#a5abaf34ca61c9870463291b0851ef261">random_forest.sql_in</a>
+</li>
+<li>_get_bin_indices_by_values()
+: <a class="el" href="random__forest_8sql__in.html#a022b4a4701e439538bd8aad7891ecee5">random_forest.sql_in</a>
+</li>
+<li>_get_bin_value_by_index()
+: <a class="el" href="random__forest_8sql__in.html#a807c2d0ab7a4c3b56d52aad07aedd02a">random_forest.sql_in</a>
+</li>
+<li>_initialize_decision_tree()
+: <a class="el" href="decision__tree_8sql__in.html#ad63f7b9a78190275509744f9517dd370">decision_tree.sql_in</a>
+</li>
+<li>_map_catlevel_to_int()
+: <a class="el" href="decision__tree_8sql__in.html#a247bb7f7065a506279627028b49af5e9">decision_tree.sql_in</a>
+</li>
+<li>_predict_dt_prob()
+: <a class="el" href="decision__tree_8sql__in.html#a25ef01785744c7cf005e6b517602b497">decision_tree.sql_in</a>
+</li>
+<li>_predict_dt_response()
+: <a class="el" href="decision__tree_8sql__in.html#a59b5f555e1f8d7c57830c9cfe3401615">decision_tree.sql_in</a>
+</li>
+<li>_print_decision_tree()
+: <a class="el" href="decision__tree_8sql__in.html#a32e1056ceed38cc5da2e022e455b7de6">decision_tree.sql_in</a>
+</li>
+<li>_rf_cat_imp_score()
+: <a class="el" href="random__forest_8sql__in.html#a9b188037fd90e41b9590d8296351b645">random_forest.sql_in</a>
+</li>
+<li>_rf_con_imp_score()
+: <a class="el" href="random__forest_8sql__in.html#aa237f4a451150d5a44f7e871a62b4621">random_forest.sql_in</a>
+</li>
+<li>_split_final()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#ac5ba4af69415de3372e5a9d9f97a96af">cox_prop_hazards.sql_in</a>
+</li>
+<li>_split_merge()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a2e641566026ce44f191e722db6d4f4d3">cox_prop_hazards.sql_in</a>
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+: <a class="el" href="cox__prop__hazards_8sql__in.html#a842f88fb0d1f17d5410ac30fb1445deb">cox_prop_hazards.sql_in</a>
+</li>
+<li>_string_to_array()
+: <a class="el" href="utilities_8sql__in.html#a967afc3041b28c8ceedcef8d25c26b9b">utilities.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Mon Jul 27 2015 20:27:22 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x61.html
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+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
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+<title>MADlib: File Members</title>
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+  <td id="projectlogo"><a href="http://madlib.incubator.apache.org"><img alt="Logo" src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.7.1</span>
+   </div>
+   <div id="projectbrief">User Documentation</div>
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+
+<div class="contents">
+&#160;
+
+<h3><a class="anchor" id="index_a"></a>- a -</h3><ul>
+<li>angle()
+: <a class="el" href="svec__util_8sql__in.html#a7e395ee71f6cf05afa76d89a72d45bec">svec_util.sql_in</a>
+</li>
+<li>argmax()
+: <a class="el" href="bayes_8sql__in.html#ad47c68a2b8aeedf66d509f0c32a9732d">bayes.sql_in</a>
+</li>
+<li>argmax_combine()
+: <a class="el" href="bayes_8sql__in.html#ab150442ec577f4a0a3948176baf5ebd0">bayes.sql_in</a>
+</li>
+<li>argmax_final()
+: <a class="el" href="bayes_8sql__in.html#aa7017323fd6fad40c9acbbee5f1fec7e">bayes.sql_in</a>
+</li>
+<li>argmax_transition()
+: <a class="el" href="bayes_8sql__in.html#a0671e6ac35a1967f41d2efb9e8417e1b">bayes.sql_in</a>
+</li>
+<li>arima_forecast()
+: <a class="el" href="arima_8sql__in.html#a08645edc97c591bce174964ac309a3a0">arima.sql_in</a>
+</li>
+<li>arima_train()
+: <a class="el" href="arima_8sql__in.html#a9af899e0260745b75b1872159fea1281">arima.sql_in</a>
+</li>
+<li>array_add()
+: <a class="el" href="array__ops_8sql__in.html#a91c8d3715142927b3967f05a4fbf1575">array_ops.sql_in</a>
+</li>
+<li>array_avg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a5734f5796a403ebf2e765cba136f54c8">cox_prop_hazards.sql_in</a>
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+</li>
+<li>array_contains()
+: <a class="el" href="array__ops_8sql__in.html#aedf6cb13eb4803bcc12dc4d95ea8ff4e">array_ops.sql_in</a>
+</li>
+<li>array_div()
+: <a class="el" href="array__ops_8sql__in.html#a6cc05e7052495f8b64692faf40219576">array_ops.sql_in</a>
+</li>
+<li>array_dot()
+: <a class="el" href="array__ops_8sql__in.html#acde10964ed23b7c8da515fb84cb8d5e0">array_ops.sql_in</a>
+</li>
+<li>array_elem_corr_agg()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a8928da50ddcf85de812ab058214324db">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_max()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a1de2b192c73ede707b42726e9469664d">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_element_min()
+: <a class="el" href="cox__prop__hazards_8sql__in.html#a2ec0ea5a3faba236a75d4d9cc16e40a4">cox_prop_hazards.sql_in</a>
+</li>
+<li>array_fill()
+: <a class="el" href="array__ops_8sql__in.html#a065a5323f3b742be47e39ad8b4c90fc2">array_ops.sql_in</a>
+</li>
+<li>array_filter()
+: <a class="el" href="array__ops_8sql__in.html#acc295a568878940ffc3e2c9a75990efb">array_ops.sql_in</a>
+</li>
+<li>array_max()
+: <a class="el" href="array__ops_8sql__in.html#ae891429cc50705c530f3e5ca15541849">array_ops.sql_in</a>
+</li>
+<li>array_mean()
+: <a class="el" href="array__ops_8sql__in.html#a407598f9eb70637798b02fd731bfca2c">array_ops.sql_in</a>
+</li>
+<li>array_min()
+: <a class="el" href="array__ops_8sql__in.html#a6659bf9d9363eb179fab34f81f8ac59b">array_ops.sql_in</a>
+</li>
+<li>array_mult()
+: <a class="el" href="array__ops_8sql__in.html#a652d70c480d484c4a1a92ded384b0dd7">array_ops.sql_in</a>
+</li>
+<li>array_of_bigint()
+: <a class="el" href="array__ops_8sql__in.html#ab7d8550e66d2e0bd54b8f0997d93880c">array_ops.sql_in</a>
+</li>
+<li>array_of_float()
+: <a class="el" href="array__ops_8sql__in.html#ab066e65a41db78b00b4532996b2a6efc">array_ops.sql_in</a>
+</li>
+<li>array_scalar_add()
+: <a class="el" href="array__ops_8sql__in.html#a0b6ffe59b12c3dee076c3059f9ab363f">array_ops.sql_in</a>
+</li>
+<li>array_scalar_mult()
+: <a class="el" href="array__ops_8sql__in.html#ae6881cc5c86941b6ffca35d7f3cd5c12">array_ops.sql_in</a>
+</li>
+<li>array_stddev()
+: <a class="el" href="array__ops_8sql__in.html#a3b6c2d173a611e6d6b184d825c2b336d">array_ops.sql_in</a>
+</li>
+<li>array_sub()
+: <a class="el" href="array__ops_8sql__in.html#a2875a161a01c7dcdea9a4997b074eefc">array_ops.sql_in</a>
+</li>
+<li>array_sum()
+: <a class="el" href="array__ops_8sql__in.html#a4c98f20e6a737358806f63318daea5ec">array_ops.sql_in</a>
+</li>
+<li>array_sum_big()
+: <a class="el" href="array__ops_8sql__in.html#a418de59800833aa95f9b7cbd6b12901c">array_ops.sql_in</a>
+</li>
+<li>array_to_1d()
+: <a class="el" href="linalg_8sql__in.html#a71304c333a999e69ac1430c285c35181">linalg.sql_in</a>
+</li>
+<li>array_to_2d()
+: <a class="el" href="linalg_8sql__in.html#ac3cb4979f60ce86579b7ba4a5dd8bb3e">linalg.sql_in</a>
+</li>
+<li>assert()
+: <a class="el" href="utilities_8sql__in.html#a76cfdbed8a6782f37f50710d855434bd">utilities.sql_in</a>
+</li>
+<li>assoc_rules()
+: <a class="el" href="assoc__rules_8sql__in.html#a68a256d98b82ac15bac7df92e806f6f8">assoc_rules.sql_in</a>
+</li>
+<li>avg()
+: <a class="el" href="linalg_8sql__in.html#a1aa37f73fb1cd8d7d106aa518dd8c0b4">linalg.sql_in</a>
+</li>
+<li>avg_vector_final()
+: <a class="el" href="linalg_8sql__in.html#ad468b52a7caa9b47a0650d39e32d9c50">linalg.sql_in</a>
+</li>
+<li>avg_vector_merge()
+: <a class="el" href="linalg_8sql__in.html#a5ac255c13e1f2d91274b829ff0383e44">linalg.sql_in</a>
+</li>
+<li>avg_vector_transition()
+: <a class="el" href="linalg_8sql__in.html#aaea269f4746f952faadffed9d9b107d7">linalg.sql_in</a>
+</li>
+</ul>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Thu Apr 30 2015 11:29:34 for MADlib by
+    <a href="http://www.doxygen.org/index.html">
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.4 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/7258f51a/docs/latest/globals_func_0x62.html
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diff --git a/docs/latest/globals_func_0x62.html b/docs/latest/globals_func_0x62.html
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+<title>MADlib: File Members</title>
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+<script type="text/javascript" src="dynsections.js"></script>
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+<h3><a class="anchor" id="index_b"></a>- b -</h3><ul>
+<li>bernoulli_cdf()
+: <a class="el" href="prob_8sql__in.html#aea21a931dc5578a570e3370af3d8d43a">prob.sql_in</a>
+</li>
+<li>bernoulli_pmf()
+: <a class="el" href="prob_8sql__in.html#a434b3ad1f3964835834dc2a942b820ef">prob.sql_in</a>
+</li>
+<li>bernoulli_quantile()
+: <a class="el" href="prob_8sql__in.html#a7133c2e86fd2f6384416ee0e4fd3a60b">prob.sql_in</a>
+</li>
+<li>beta_cdf()
+: <a class="el" href="prob_8sql__in.html#a72e1cca872da35592075dbcfb18aed3f">prob.sql_in</a>
+</li>
+<li>beta_pdf()
+: <a class="el" href="prob_8sql__in.html#aa105049e6e3bb9b3891b0ed1b343e28e">prob.sql_in</a>
+</li>
+<li>beta_quantile()
+: <a class="el" href="prob_8sql__in.html#a32433aa742c0504d33e98e28a3e2f190">prob.sql_in</a>
+</li>
+<li>big_or()
+: <a class="el" href="sketch_8sql__in.html#a335fba86bbb3fb5a3284808cc64b8f16">sketch.sql_in</a>
+</li>
+<li>binomial_cdf()
+: <a class="el" href="prob_8sql__in.html#aa5000bad6e2e4af1c8cbfec7ea884476">prob.sql_in</a>
+</li>
+<li>binomial_pmf()
+: <a class="el" href="prob_8sql__in.html#aa0614475b8685bf8e37533d2ac5bb116">prob.sql_in</a>
+</li>
+<li>binomial_quantile()
+: <a class="el" href="prob_8sql__in.html#a49f421c58d2e1abd63b83d71af9edf21">prob.sql_in</a>
+</li>
+<li>bool_to_text()
+: <a class="el" href="utilities_8sql__in.html#abc867482053e6692047d051b679953a5">utilities.sql_in</a>
+</li>
+<li>bytea8in()
+: <a class="el" href="utilities_8sql__in.html#a60c4765ce8dc639cbb0ad4c389f5f1ad">utilities.sql_in</a>
+</li>
+<li>bytea8out()
+: <a class="el" href="utilities_8sql__in.html#adff923d42d4bdb2b631d6cafd5f37a43">utilities.sql_in</a>
+</li>
+<li>bytea8recv()
+: <a class="el" href="utilities_8sql__in.html#a244a7ff30f2788a6f4816417982eb13b">utilities.sql_in</a>
+</li>
+<li>bytea8send()
+: <a class="el" href="utilities_8sql__in.html#aeb90c740e8e6b67aa60a11c430fa9050">utilities.sql_in</a>
+</li>
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