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Added: incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/CosineDistanceHash.html
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--- incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/CosineDistanceHash.html (added)
+++ incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/CosineDistanceHash.html Wed Aug 10 22:07:27 2016
@@ -0,0 +1,467 @@
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+<!-- ======== START OF CLASS DATA ======== -->
+<div class="header">
+<div class="subTitle">datafu.pig.hash.lsh</div>
+<h2 title="Class CosineDistanceHash" class="title">Class CosineDistanceHash</h2>
+</div>
+<div class="contentContainer">
+<ul class="inheritance">
+<li>java.lang.Object</li>
+<li>
+<ul class="inheritance">
+<li>org.apache.pig.EvalFunc<org.apache.pig.data.DataBag></li>
+<li>
+<ul class="inheritance">
+<li><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">datafu.pig.hash.lsh.LSHFunc</a></li>
+<li>
+<ul class="inheritance">
+<li>datafu.pig.hash.lsh.CosineDistanceHash</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<div class="description">
+<ul class="blockList">
+<li class="blockList">
+<hr>
+<br>
+<pre>public class <span class="strong">CosineDistanceHash</span>
+extends <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></pre>
+<div class="block">From wikipedia's article on <a href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing" target="_blank">Locality Sensitive Hashing</a>:
+ <pre>
+ Locality-sensitive hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
+ The basic idea is to hash the input items so that similar items are mapped to the same buckets with high probability
+ (the number of buckets being much smaller than the universe of possible input items).
+ </pre>
+
+ In particular, this implementation implements a locality sensitive hashing scheme which maps high-dimensional vectors which are
+ close together (with high probability) according to <a href="http://en.wikipedia.org/wiki/Cosine_similarity" target="_blank">Cosine Similarity</a>
+ into the same buckets. Each LSH maps a vector onto one side or the other of a random hyperplane, thereby producing a single
+ bit as the hash value. Multiple, independent, hashes can be run on the same input and aggregated together to form a more
+ broad domain than a single bit.
+
+ For more information, see Charikar, Moses S.. (2002). "Similarity Estimation Techniques from Rounding Algorithms". Proceedings of the 34th Annual ACM Symposium on Theory of Computing 2002.</div>
+</li>
+</ul>
+</div>
+<div class="summary">
+<ul class="blockList">
+<li class="blockList">
+<!-- ======== NESTED CLASS SUMMARY ======== -->
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+<li class="blockList"><a name="nested_classes_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Nested classes/interfaces inherited from class org.apache.pig.EvalFunc</h3>
+<code>org.apache.pig.EvalFunc.SchemaType</code></li>
+</ul>
+</li>
+</ul>
+<!-- =========== FIELD SUMMARY =========== -->
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+<!-- -->
+</a>
+<h3>Fields inherited from class datafu.pig.hash.lsh.<a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></h3>
+<code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#lsh">lsh</a></code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="fields_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Fields inherited from class org.apache.pig.EvalFunc</h3>
+<code>log, pigLogger, reporter, returnType</code></li>
+</ul>
+</li>
+</ul>
+<!-- ======== CONSTRUCTOR SUMMARY ======== -->
+<ul class="blockList">
+<li class="blockList"><a name="constructor_summary">
+<!-- -->
+</a>
+<h3>Constructor Summary</h3>
+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Constructor Summary table, listing constructors, and an explanation">
+<caption><span>Constructors</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colOne" scope="col">Constructor and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/CosineDistanceHash.html#CosineDistanceHash(java.lang.String,%20java.lang.String,%20java.lang.String)">CosineDistanceHash</a></strong>(java.lang.String sDim,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes)</code> </td>
+</tr>
+<tr class="rowColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/CosineDistanceHash.html#CosineDistanceHash(java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String)">CosineDistanceHash</a></strong>(java.lang.String sDim,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes,
+ java.lang.String sSeed)</code>
+<div class="block">Locality sensitive hash that maps vectors onto 0,1 in such a way that colliding
+ vectors are "near" one another according to cosine similarity with high probability.</div>
+</td>
+</tr>
+</table>
+</li>
+</ul>
+<!-- ========== METHOD SUMMARY =========== -->
+<ul class="blockList">
+<li class="blockList"><a name="method_summary">
+<!-- -->
+</a>
+<h3>Method Summary</h3>
+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
+<caption><span>Methods</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colFirst" scope="col">Modifier and Type</th>
+<th class="colLast" scope="col">Method and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colFirst"><code>protected <a href="../../../../datafu/pig/hash/lsh/interfaces/LSHCreator.html" title="class in datafu.pig.hash.lsh.interfaces">LSHCreator</a></code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/CosineDistanceHash.html#createLSHCreator()">createLSHCreator</a></strong>()</code> </td>
+</tr>
+<tr class="rowColor">
+<td class="colFirst"><code>protected int</code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/CosineDistanceHash.html#getDimension()">getDimension</a></strong>()</code> </td>
+</tr>
+</table>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">
+<!-- -->
+</a>
+<h3>Methods inherited from class datafu.pig.hash.lsh.<a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></h3>
+<code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#exec(org.apache.pig.data.Tuple)">exec</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#getSeed()">getSeed</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#outputSchema(org.apache.pig.impl.logicalLayer.schema.Schema)">outputSchema</a></code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Methods inherited from class org.apache.pig.EvalFunc</h3>
+<code>allowCompileTimeCalculation, finish, getArgToFuncMapping, getCacheFiles, getInputSchema, getLogger, getPigLogger, getReporter, getReturnType, getSchemaName, getSchemaType, getShipFiles, isAsynchronous, progress, setInputSchema, setPigLogger, setReporter, setUDFContextSignature, warn</code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_java.lang.Object">
+<!-- -->
+</a>
+<h3>Methods inherited from class java.lang.Object</h3>
+<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="details">
+<ul class="blockList">
+<li class="blockList">
+<!-- ========= CONSTRUCTOR DETAIL ======== -->
+<ul class="blockList">
+<li class="blockList"><a name="constructor_detail">
+<!-- -->
+</a>
+<h3>Constructor Detail</h3>
+<a name="CosineDistanceHash(java.lang.String, java.lang.String, java.lang.String, java.lang.String)">
+<!-- -->
+</a>
+<ul class="blockList">
+<li class="blockList">
+<h4>CosineDistanceHash</h4>
+<pre>public CosineDistanceHash(java.lang.String sDim,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes,
+ java.lang.String sSeed)</pre>
+<div class="block">Locality sensitive hash that maps vectors onto 0,1 in such a way that colliding
+ vectors are "near" one another according to cosine similarity with high probability.
+
+ <p>
+ Generally, multiple LSH are combined via repetition to increase the range of the hash function to the full set of longs.
+ The number of functions which you want to internally repeat is specified by the sRepeat parameter.
+
+ The size of the hash family corresponds to the number of independent hashes you want to apply to the data.
+ In a k-near neighbors style of searching, this corresponds to the number of neighbors you want to find
+ (i.e. the number of vectors within a distance according to cosine similarity).
+
+ <p>
+ Consider the following example where we input some 3-dimensional points and a set of 3-dimensional queries
+ and find the nearest neighbors of the query points:
+ <pre>
+ -- Create a CosineDistanceHash of
+ -- 3 dimensional data
+ -- 1500 internal hashes (being combined into one hash)
+ -- family of 5 hashes
+ -- with a seed of 0
+
+ -- This creates a bag of tuples:
+ -- lsh_id:Integer the family ID (in this case, 0-4)
+ -- hash:Long the hash
+
+ define LSH datafu.pig.hash.lsh.CosineDistanceHash('3', '1500', '5', '0');
+ define METRIC datafu.pig.hash.lsh.metric.L2();
+
+ PTS = LOAD 'input' AS (dim1:double, dim2:double, dim3:double);
+
+ --hash the input points
+ PTS_HASHED = foreach PTS generate TOTUPLE(dim1, dim2, dim3) as pt
+ , FLATTEN(LSH(TOTUPLE(dim1, dim2, dim3)));
+
+ -- the hash family ID and the hash should group the input points into partitions
+ PARTITIONS = group PTS_HASHED by (lsh_id, hash);
+
+ -- take in the query points and hash them
+ QUERIES = LOAD 'queries' as (dim1:double, dim2:double, dim3:double);
+ QUERIES_HASHED = foreach QUERIES generate TOTUPLE(dim1, dim2, dim3) as query_pt
+ , FLATTEN(LSH(TOTUPLE(dim1, dim2, dim3)))
+ ;
+
+ -- join the hashed query points with the (presumably larger) list of input data split by partitions
+ QUERIES_W_PARTS = join QUERIES_HASHED by (lsh_id, hash), PARTITIONS by (group.$0, group.$1);
+
+ -- Now, use the appropriate METRIC UDF (in this case Cosine distance) to find the first point within
+ -- a parameterized threshold (in this case, .001). It takes:
+ -- query_pt:Tuple the query point
+ -- threshold:Double the threshold, so that if the distance between the query point and a point
+ -- in the partition is less than this threshold, it returns the point (and stops searching)
+ -- partition:Bag The bag of tuples in the partition.
+
+ tuples from
+ NEAR_NEIGHBORS = foreach QUERIES_W_PARTS generate query_pt as query_pt
+ , METRIC(query_pt, .001, PTS_HASHED) as neighbor
+ ;
+ describe NEAR_NEIGHBORS;
+ -- {query_pt: (dim1: double,dim2: double,dim3: double)
+ -- ,neighbor: (pt: (dim1: double,dim2: double,dim3: double)
+ -- ,lsh::lsh_id: int
+ -- ,lsh::hash: long
+ -- )
+ -- }
+
+ -- project out the query and the matching point
+ NEIGHBORS_PROJ = foreach NEAR_NEIGHBORS {
+ generate query_pt as query_pt, neighbor.pt as matching_pts;
+ };
+
+ -- Filter out the hashes which resulted in no matches
+ NOT_NULL = filter NEIGHBORS_PROJ by SIZE(matching_pts) > 0;
+
+ -- group by the query
+ NEIGHBORS_GRP = group NOT_NULL by query_pt;
+ describe NEIGHBORS_GRP;
+
+ -- Generate the query, the number of matches and the bag of matching points
+ NEIGHBOR_CNT = foreach NEIGHBORS_GRP{
+ MATCHING_PTS = foreach NOT_NULL generate FLATTEN(matching_pts);
+ DIST_MATCHING_PTS = DISTINCT MATCHING_PTS;
+ generate group as query_pt, COUNT(NOT_NULL), DIST_MATCHING_PTS;
+ };
+ describe NEIGHBOR_CNT;
+ -- NEIGHBOR_CNT: {query_pt: (dim1: double,dim2: double,dim3: double)
+ -- ,long
+ -- ,DIST_MATCHING_PTS: { (matching_pts::dim1: double,matching_pts::dim2: double,matching_pts::dim3: double)
+ -- }
+ -- }
+ STORE NEIGHBOR_CNT INTO 'neighbors';
+ </pre></div>
+<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sDim</code> - Dimension of the vectors</dd><dd><code>sRepeat</code> - Number of internal repetitions</dd><dd><code>sNumHashes</code> - Size of the hash family (if you're looking for k near neighbors, this is the k)</dd><dd><code>sSeed</code> - Seed to use when constructing LSH family</dd></dl>
+</li>
+</ul>
+<a name="CosineDistanceHash(java.lang.String, java.lang.String, java.lang.String)">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>CosineDistanceHash</h4>
+<pre>public CosineDistanceHash(java.lang.String sDim,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes)</pre>
+</li>
+</ul>
+</li>
+</ul>
+<!-- ============ METHOD DETAIL ========== -->
+<ul class="blockList">
+<li class="blockList"><a name="method_detail">
+<!-- -->
+</a>
+<h3>Method Detail</h3>
+<a name="createLSHCreator()">
+<!-- -->
+</a>
+<ul class="blockList">
+<li class="blockList">
+<h4>createLSHCreator</h4>
+<pre>protected <a href="../../../../datafu/pig/hash/lsh/interfaces/LSHCreator.html" title="class in datafu.pig.hash.lsh.interfaces">LSHCreator</a> createLSHCreator()</pre>
+<dl>
+<dt><strong>Specified by:</strong></dt>
+<dd><code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#createLSHCreator()">createLSHCreator</a></code> in class <code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></code></dd>
+</dl>
+</li>
+</ul>
+<a name="getDimension()">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>getDimension</h4>
+<pre>protected int getDimension()</pre>
+<dl>
+<dt><strong>Specified by:</strong></dt>
+<dd><code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#getDimension()">getDimension</a></code> in class <code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></code></dd>
+</dl>
+</li>
+</ul>
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+</div>
+<!-- ========= END OF CLASS DATA ========= -->
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Added: incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L1PStableHash.html
URL: http://svn.apache.org/viewvc/incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L1PStableHash.html?rev=1755883&view=auto
==============================================================================
--- incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L1PStableHash.html (added)
+++ incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L1PStableHash.html Wed Aug 10 22:07:27 2016
@@ -0,0 +1,483 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
+<!-- NewPage -->
+<html lang="en">
+<head>
+<!-- Generated by javadoc (version 1.7.0_79) on Wed Aug 10 15:01:03 PDT 2016 -->
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+<li><a href="#fields_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">Field</a> | </li>
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+<li><a href="#method_summary">Method</a></li>
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+<!-- ========= END OF TOP NAVBAR ========= -->
+<!-- ======== START OF CLASS DATA ======== -->
+<div class="header">
+<div class="subTitle">datafu.pig.hash.lsh</div>
+<h2 title="Class L1PStableHash" class="title">Class L1PStableHash</h2>
+</div>
+<div class="contentContainer">
+<ul class="inheritance">
+<li>java.lang.Object</li>
+<li>
+<ul class="inheritance">
+<li>org.apache.pig.EvalFunc<org.apache.pig.data.DataBag></li>
+<li>
+<ul class="inheritance">
+<li><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">datafu.pig.hash.lsh.LSHFunc</a></li>
+<li>
+<ul class="inheritance">
+<li>datafu.pig.hash.lsh.L1PStableHash</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<div class="description">
+<ul class="blockList">
+<li class="blockList">
+<hr>
+<br>
+<pre>public class <span class="strong">L1PStableHash</span>
+extends <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></pre>
+<div class="block">From wikipedia's article on <a href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing" target="_blank">Locality Sensitive Hashing</a>:
+ <pre>
+ Locality-sensitive hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
+ The basic idea is to hash the input items so that similar items are mapped to the same buckets with high probability
+ (the number of buckets being much smaller than the universe of possible input items).
+ </pre>
+
+ In particular, this implementation implements a locality sensitive hashing scheme which maps high-dimensional vectors which are
+ close together (with high probability) according to the <a href="http://en.wikipedia.org/wiki/Lp_space" target="_blank">L1</a>
+ distance metric into the same buckets. This implementation uses a 1-stable distribution (a Cauchy distribution) in order
+ to accomplish this.
+
+ For more information, see Datar, M.; Immorlica, N.; Indyk, P.; Mirrokni, V.S. (2004). "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions". Proceedings of the Symposium on Computational Geometry.</div>
+</li>
+</ul>
+</div>
+<div class="summary">
+<ul class="blockList">
+<li class="blockList">
+<!-- ======== NESTED CLASS SUMMARY ======== -->
+<ul class="blockList">
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+</a>
+<h3>Nested Class Summary</h3>
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+<li class="blockList"><a name="nested_classes_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Nested classes/interfaces inherited from class org.apache.pig.EvalFunc</h3>
+<code>org.apache.pig.EvalFunc.SchemaType</code></li>
+</ul>
+</li>
+</ul>
+<!-- =========== FIELD SUMMARY =========== -->
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+<h3>Field Summary</h3>
+<ul class="blockList">
+<li class="blockList"><a name="fields_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">
+<!-- -->
+</a>
+<h3>Fields inherited from class datafu.pig.hash.lsh.<a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></h3>
+<code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#lsh">lsh</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#seed">seed</a></code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="fields_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Fields inherited from class org.apache.pig.EvalFunc</h3>
+<code>log, pigLogger, reporter, returnType</code></li>
+</ul>
+</li>
+</ul>
+<!-- ======== CONSTRUCTOR SUMMARY ======== -->
+<ul class="blockList">
+<li class="blockList"><a name="constructor_summary">
+<!-- -->
+</a>
+<h3>Constructor Summary</h3>
+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Constructor Summary table, listing constructors, and an explanation">
+<caption><span>Constructors</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colOne" scope="col">Constructor and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/L1PStableHash.html#L1PStableHash(java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String)">L1PStableHash</a></strong>(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes)</code> </td>
+</tr>
+<tr class="rowColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/L1PStableHash.html#L1PStableHash(java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String)">L1PStableHash</a></strong>(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes,
+ java.lang.String sSeed)</code>
+<div class="block">Locality sensitive hash that maps vectors onto a long in such a way that colliding
+ vectors are "near" one another according to cosine similarity with high probability.</div>
+</td>
+</tr>
+</table>
+</li>
+</ul>
+<!-- ========== METHOD SUMMARY =========== -->
+<ul class="blockList">
+<li class="blockList"><a name="method_summary">
+<!-- -->
+</a>
+<h3>Method Summary</h3>
+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
+<caption><span>Methods</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colFirst" scope="col">Modifier and Type</th>
+<th class="colLast" scope="col">Method and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colFirst"><code>protected <a href="../../../../datafu/pig/hash/lsh/interfaces/LSHCreator.html" title="class in datafu.pig.hash.lsh.interfaces">LSHCreator</a></code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/L1PStableHash.html#createLSHCreator()">createLSHCreator</a></strong>()</code> </td>
+</tr>
+<tr class="rowColor">
+<td class="colFirst"><code>protected int</code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/L1PStableHash.html#getDimension()">getDimension</a></strong>()</code> </td>
+</tr>
+</table>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">
+<!-- -->
+</a>
+<h3>Methods inherited from class datafu.pig.hash.lsh.<a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></h3>
+<code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#exec(org.apache.pig.data.Tuple)">exec</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#getSeed()">getSeed</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#outputSchema(org.apache.pig.impl.logicalLayer.schema.Schema)">outputSchema</a></code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Methods inherited from class org.apache.pig.EvalFunc</h3>
+<code>allowCompileTimeCalculation, finish, getArgToFuncMapping, getCacheFiles, getInputSchema, getLogger, getPigLogger, getReporter, getReturnType, getSchemaName, getSchemaType, getShipFiles, isAsynchronous, progress, setInputSchema, setPigLogger, setReporter, setUDFContextSignature, warn</code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_java.lang.Object">
+<!-- -->
+</a>
+<h3>Methods inherited from class java.lang.Object</h3>
+<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="details">
+<ul class="blockList">
+<li class="blockList">
+<!-- ========= CONSTRUCTOR DETAIL ======== -->
+<ul class="blockList">
+<li class="blockList"><a name="constructor_detail">
+<!-- -->
+</a>
+<h3>Constructor Detail</h3>
+<a name="L1PStableHash(java.lang.String, java.lang.String, java.lang.String, java.lang.String, java.lang.String)">
+<!-- -->
+</a>
+<ul class="blockList">
+<li class="blockList">
+<h4>L1PStableHash</h4>
+<pre>public L1PStableHash(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes,
+ java.lang.String sSeed)</pre>
+<div class="block">Locality sensitive hash that maps vectors onto a long in such a way that colliding
+ vectors are "near" one another according to cosine similarity with high probability.
+
+ <p>
+ Generally, multiple LSH are combined via repetition to increase the range of the hash function to the full set of longs.
+ The number of functions which you want to internally repeat is specified by the sRepeat parameter.
+
+ The size of the hash family corresponds to the number of independent hashes you want to apply to the data.
+ In a k-near neighbors style of searching, this corresponds to the number of neighbors you want to find
+ (i.e. the number of vectors within a distance according to cosine similarity).
+
+ This UDF, indeed all p-stable LSH functions, are parameterized with a quantization parameter (w or r in the literature
+ , depending on where you look). Consider the following excerpt from Datar, M.; Immorlica, N.; Indyk, P.; Mirrokni, V.S. (2004). "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions". Proceedings of the Symposium on Computational Geometry.
+
+ <pre>
+ Decreasing the width of the projection (w) decreases the probability of collision for any two points.
+ Thus, it has the same effect as increasing k . As a result, we would like to set w as small as possible
+ and in this way decrease the number of projections we need to make.
+ </pre>
+
+ In the literature, the quantization parameter (or width of the projection) is found empirically given a sample of
+ the data and the likely threshold of for the metric. Tuning this parameter is very important for the performance
+ of this algorithm.
+
+ <p>
+ Consider the following example where we input some 3-dimensional points and a set of 3-dimensional queries
+ and find the nearest neighbors of the query points:
+ <pre>
+ -- Create a L1PStableHash of
+ -- 3 dimensional data
+ -- projection width of 150
+ -- 1 internal hashes
+ -- family of 5 hashes
+ -- with a seed of 0
+
+ -- This creates a bag of tuples:
+ -- lsh_id:Integer the family ID (in this case, 0-4)
+ -- hash:Long the hash
+
+ define LSH datafu.pig.hash.lsh.L1PStableHash('3', '150', '1', '5', '0');
+ define METRIC datafu.pig.hash.lsh.metric.L1();
+
+ PTS = LOAD 'input' AS (dim1:double, dim2:double, dim3:double);
+
+ --hash the input points
+ PTS_HASHED = foreach PTS generate TOTUPLE(dim1, dim2, dim3) as pt
+ , FLATTEN(LSH(TOTUPLE(dim1, dim2, dim3)));
+
+ -- the hash family ID and the hash should group the input points into partitions
+ PARTITIONS = group PTS_HASHED by (lsh_id, hash);
+
+ -- take in the query points and hash them
+ QUERIES = LOAD 'queries' as (dim1:double, dim2:double, dim3:double);
+ QUERIES_HASHED = foreach QUERIES generate TOTUPLE(dim1, dim2, dim3) as query_pt
+ , FLATTEN(LSH(TOTUPLE(dim1, dim2, dim3)))
+ ;
+
+ -- join the hashed query points with the (presumably larger) list of input data split by partitions
+ QUERIES_W_PARTS = join QUERIES_HASHED by (lsh_id, hash), PARTITIONS by (group.$0, group.$1);
+
+ -- Now, use the appropriate METRIC UDF (in this case L1 (aka city block) distance) to find the first point within
+ -- a parameterized threshold (in this case, 1000). It takes:
+ -- query_pt:Tuple the query point
+ -- threshold:Double the threshold, so that if the distance between the query point and a point
+ -- in the partition is less than this threshold, it returns the point (and stops searching)
+ -- partition:Bag The bag of tuples in the partition.
+
+
+ NEAR_NEIGHBORS = foreach QUERIES_W_PARTS generate query_pt as query_pt
+ , METRIC(query_pt, 1000, PTS_HASHED) as neighbor
+ ;
+ describe NEAR_NEIGHBORS;
+ -- {query_pt: (dim1: double,dim2: double,dim3: double)
+ -- ,neighbor: (pt: (dim1: double,dim2: double,dim3: double)
+ -- ,lsh::lsh_id: int
+ -- ,lsh::hash: long
+ -- )
+ -- }
+
+ -- project out the query and the matching point
+ NEIGHBORS_PROJ = foreach NEAR_NEIGHBORS {
+ generate query_pt as query_pt, neighbor.pt as matching_pts;
+ };
+
+ -- Filter out the hashes which resulted in no matches
+ NOT_NULL = filter NEIGHBORS_PROJ by SIZE(matching_pts) > 0;
+
+ -- group by the query
+ NEIGHBORS_GRP = group NOT_NULL by query_pt;
+ describe NEIGHBORS_GRP;
+
+ -- Generate the query, the number of matches and the bag of matching points
+ NEIGHBOR_CNT = foreach NEIGHBORS_GRP{
+ DIST_MATCHING_PTS = DISTINCT MATCHING_PTS;
+ generate group as query_pt, COUNT(NOT_NULL), DIST_MATCHING_PTS;
+ };
+ describe NEIGHBOR_CNT;
+ -- NEIGHBOR_CNT: {query_pt: (dim1: double,dim2: double,dim3: double)
+ -- ,long
+ -- ,DIST_MATCHING_PTS: { (matching_pts::dim1: double,matching_pts::dim2: double,matching_pts::dim3: double)
+ -- }
+ -- }
+ STORE NEIGHBOR_CNT INTO 'neighbors';
+ </pre></div>
+<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sDim</code> - Dimension of the vectors</dd><dd><code>sW</code> - A double representing the quantization parameter (also known as the projection width)</dd><dd><code>sRepeat</code> - Number of internal repetitions (generally this should be 1 as the p-stable hashes have a larger range than one bit)</dd><dd><code>sNumHashes</code> - Size of the hash family (if you're looking for k near neighbors, this is the k)</dd><dd><code>sSeed</code> - Seed to use when constructing LSH family</dd></dl>
+</li>
+</ul>
+<a name="L1PStableHash(java.lang.String, java.lang.String, java.lang.String, java.lang.String)">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>L1PStableHash</h4>
+<pre>public L1PStableHash(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes)</pre>
+</li>
+</ul>
+</li>
+</ul>
+<!-- ============ METHOD DETAIL ========== -->
+<ul class="blockList">
+<li class="blockList"><a name="method_detail">
+<!-- -->
+</a>
+<h3>Method Detail</h3>
+<a name="createLSHCreator()">
+<!-- -->
+</a>
+<ul class="blockList">
+<li class="blockList">
+<h4>createLSHCreator</h4>
+<pre>protected <a href="../../../../datafu/pig/hash/lsh/interfaces/LSHCreator.html" title="class in datafu.pig.hash.lsh.interfaces">LSHCreator</a> createLSHCreator()</pre>
+<dl>
+<dt><strong>Specified by:</strong></dt>
+<dd><code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#createLSHCreator()">createLSHCreator</a></code> in class <code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></code></dd>
+</dl>
+</li>
+</ul>
+<a name="getDimension()">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>getDimension</h4>
+<pre>protected int getDimension()</pre>
+<dl>
+<dt><strong>Specified by:</strong></dt>
+<dd><code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#getDimension()">getDimension</a></code> in class <code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></code></dd>
+</dl>
+</li>
+</ul>
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+</div>
+</div>
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Added: incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L2PStableHash.html
URL: http://svn.apache.org/viewvc/incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L2PStableHash.html?rev=1755883&view=auto
==============================================================================
--- incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L2PStableHash.html (added)
+++ incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/L2PStableHash.html Wed Aug 10 22:07:27 2016
@@ -0,0 +1,485 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
+<!-- NewPage -->
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+<li>Summary: </li>
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+<li><a href="#fields_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">Field</a> | </li>
+<li><a href="#constructor_summary">Constr</a> | </li>
+<li><a href="#method_summary">Method</a></li>
+</ul>
+<ul class="subNavList">
+<li>Detail: </li>
+<li>Field | </li>
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+</ul>
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+<a name="skip-navbar_top">
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+<!-- ========= END OF TOP NAVBAR ========= -->
+<!-- ======== START OF CLASS DATA ======== -->
+<div class="header">
+<div class="subTitle">datafu.pig.hash.lsh</div>
+<h2 title="Class L2PStableHash" class="title">Class L2PStableHash</h2>
+</div>
+<div class="contentContainer">
+<ul class="inheritance">
+<li>java.lang.Object</li>
+<li>
+<ul class="inheritance">
+<li>org.apache.pig.EvalFunc<org.apache.pig.data.DataBag></li>
+<li>
+<ul class="inheritance">
+<li><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">datafu.pig.hash.lsh.LSHFunc</a></li>
+<li>
+<ul class="inheritance">
+<li>datafu.pig.hash.lsh.L2PStableHash</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+<div class="description">
+<ul class="blockList">
+<li class="blockList">
+<hr>
+<br>
+<pre>public class <span class="strong">L2PStableHash</span>
+extends <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></pre>
+<div class="block">From wikipedia's article on <a href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing" target="_blank">Locality Sensitive Hashing</a>:
+ <pre>
+ Locality-sensitive hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional data.
+ The basic idea is to hash the input items so that similar items are mapped to the same buckets with high probability
+ (the number of buckets being much smaller than the universe of possible input items).
+ </pre>
+
+ In particular, this implementation implements a locality sensitive hashing scheme which maps high-dimensional vectors which are
+ close together (with high probability) according to the <a href="http://en.wikipedia.org/wiki/Lp_space" target="_blank">L2</a>
+ distance metric into the same buckets. This implementation uses a 2-stable distribution (a Gaussian distribution) in order
+ to accomplish this.
+
+ For more information, see Datar, M.; Immorlica, N.; Indyk, P.; Mirrokni, V.S. (2004). "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions". Proceedings of the Symposium on Computational Geometry.</div>
+</li>
+</ul>
+</div>
+<div class="summary">
+<ul class="blockList">
+<li class="blockList">
+<!-- ======== NESTED CLASS SUMMARY ======== -->
+<ul class="blockList">
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+</a>
+<h3>Nested Class Summary</h3>
+<ul class="blockList">
+<li class="blockList"><a name="nested_classes_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Nested classes/interfaces inherited from class org.apache.pig.EvalFunc</h3>
+<code>org.apache.pig.EvalFunc.SchemaType</code></li>
+</ul>
+</li>
+</ul>
+<!-- =========== FIELD SUMMARY =========== -->
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+<!-- -->
+</a>
+<h3>Field Summary</h3>
+<ul class="blockList">
+<li class="blockList"><a name="fields_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">
+<!-- -->
+</a>
+<h3>Fields inherited from class datafu.pig.hash.lsh.<a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></h3>
+<code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#lsh">lsh</a></code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="fields_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Fields inherited from class org.apache.pig.EvalFunc</h3>
+<code>log, pigLogger, reporter, returnType</code></li>
+</ul>
+</li>
+</ul>
+<!-- ======== CONSTRUCTOR SUMMARY ======== -->
+<ul class="blockList">
+<li class="blockList"><a name="constructor_summary">
+<!-- -->
+</a>
+<h3>Constructor Summary</h3>
+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Constructor Summary table, listing constructors, and an explanation">
+<caption><span>Constructors</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colOne" scope="col">Constructor and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/L2PStableHash.html#L2PStableHash(java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String)">L2PStableHash</a></strong>(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes)</code> </td>
+</tr>
+<tr class="rowColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/L2PStableHash.html#L2PStableHash(java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String,%20java.lang.String)">L2PStableHash</a></strong>(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes,
+ java.lang.String sSeed)</code>
+<div class="block">Locality sensitive hash that maps vectors onto a long in such a way that colliding
+ vectors are "near" one another according to cosine similarity with high probability.</div>
+</td>
+</tr>
+</table>
+</li>
+</ul>
+<!-- ========== METHOD SUMMARY =========== -->
+<ul class="blockList">
+<li class="blockList"><a name="method_summary">
+<!-- -->
+</a>
+<h3>Method Summary</h3>
+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
+<caption><span>Methods</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colFirst" scope="col">Modifier and Type</th>
+<th class="colLast" scope="col">Method and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colFirst"><code>protected <a href="../../../../datafu/pig/hash/lsh/interfaces/LSHCreator.html" title="class in datafu.pig.hash.lsh.interfaces">LSHCreator</a></code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/L2PStableHash.html#createLSHCreator()">createLSHCreator</a></strong>()</code> </td>
+</tr>
+<tr class="rowColor">
+<td class="colFirst"><code>protected int</code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/L2PStableHash.html#getDimension()">getDimension</a></strong>()</code> </td>
+</tr>
+</table>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">
+<!-- -->
+</a>
+<h3>Methods inherited from class datafu.pig.hash.lsh.<a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></h3>
+<code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#exec(org.apache.pig.data.Tuple)">exec</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#getSeed()">getSeed</a>, <a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#outputSchema(org.apache.pig.impl.logicalLayer.schema.Schema)">outputSchema</a></code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_org.apache.pig.EvalFunc">
+<!-- -->
+</a>
+<h3>Methods inherited from class org.apache.pig.EvalFunc</h3>
+<code>allowCompileTimeCalculation, finish, getArgToFuncMapping, getCacheFiles, getInputSchema, getLogger, getPigLogger, getReporter, getReturnType, getSchemaName, getSchemaType, getShipFiles, isAsynchronous, progress, setInputSchema, setPigLogger, setReporter, setUDFContextSignature, warn</code></li>
+</ul>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_java.lang.Object">
+<!-- -->
+</a>
+<h3>Methods inherited from class java.lang.Object</h3>
+<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="details">
+<ul class="blockList">
+<li class="blockList">
+<!-- ========= CONSTRUCTOR DETAIL ======== -->
+<ul class="blockList">
+<li class="blockList"><a name="constructor_detail">
+<!-- -->
+</a>
+<h3>Constructor Detail</h3>
+<a name="L2PStableHash(java.lang.String, java.lang.String, java.lang.String, java.lang.String, java.lang.String)">
+<!-- -->
+</a>
+<ul class="blockList">
+<li class="blockList">
+<h4>L2PStableHash</h4>
+<pre>public L2PStableHash(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes,
+ java.lang.String sSeed)</pre>
+<div class="block">Locality sensitive hash that maps vectors onto a long in such a way that colliding
+ vectors are "near" one another according to cosine similarity with high probability.
+
+ <p>
+ Generally, multiple LSH are combined via repetition to increase the range of the hash function to the full set of longs.
+ The number of functions which you want to internally repeat is specified by the sRepeat parameter.
+
+ The size of the hash family corresponds to the number of independent hashes you want to apply to the data.
+ In a k-near neighbors style of searching, this corresponds to the number of neighbors you want to find
+ (i.e. the number of vectors within a distance according to cosine similarity).
+
+ This UDF, indeed all p-stable LSH functions are parameterized with a quantization parameter (w or r in the literature
+ , depending on where you look). Consider the following excerpt from Datar, M.; Immorlica, N.; Indyk, P.; Mirrokni, V.S. (2004). "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions". Proceedings of the Symposium on Computational Geometry.
+
+ <pre>
+ Decreasing the width of the projection (w) decreases the probability of collision for any two points.
+ Thus, it has the same effect as increasing k . As a result, we would like to set w as small as possible
+ and in this way decrease the number of projections we need to make.
+ </pre>
+
+ In the literature, the quantization parameter (or width of the projection) is found empirically given a sample of
+ the data and the likely threshold of for the metric. Tuning this parameter is very important for the performance
+ of this algorithm.
+
+ <p>
+ Consider the following example where we input some 3-dimensional points and a set of 3-dimensional queries
+ and find the nearest neighbors of the query points:
+ <pre>
+ -- Create a L2PStableHash of
+ -- 3 dimensional data
+ -- projection width of 200
+ -- 1 internal hashes
+ -- family of 5 hashes
+ -- with a seed of 0
+
+ -- This creates a bag of tuples:
+ -- lsh_id:Integer the family ID (in this case, 0-4)
+ -- hash:Long the hash
+
+ define LSH datafu.pig.hash.lsh.L2PStableHash('3', '200', '1', '5', '0');
+ define METRIC datafu.pig.hash.lsh.metric.L2();
+
+ PTS = LOAD 'input' AS (dim1:double, dim2:double, dim3:double);
+
+ --hash the input points
+ PTS_HASHED = foreach PTS generate TOTUPLE(dim1, dim2, dim3) as pt
+ , FLATTEN(LSH(TOTUPLE(dim1, dim2, dim3)));
+
+ -- the hash family ID and the hash should group the input points into partitions
+ PARTITIONS = group PTS_HASHED by (lsh_id, hash);
+
+ -- take in the query points and hash them
+ QUERIES = LOAD 'queries' as (dim1:double, dim2:double, dim3:double);
+ QUERIES_HASHED = foreach QUERIES generate TOTUPLE(dim1, dim2, dim3) as query_pt
+ , FLATTEN(LSH(TOTUPLE(dim1, dim2, dim3)))
+ ;
+
+ -- join the hashed query points with the (presumably larger) list of input data split by partitions
+ QUERIES_W_PARTS = join QUERIES_HASHED by (lsh_id, hash), PARTITIONS by (group.$0, group.$1);
+
+ -- Now, use the appropriate METRIC UDF (in this case L2 (aka Euclidean) distance) to find the first point within
+ -- a parameterized threshold (in this case, 1000). It takes:
+ -- query_pt:Tuple the query point
+ -- threshold:Double the threshold, so that if the distance between the query point and a point
+ -- in the partition is less than this threshold, it returns the point (and stops searching)
+ -- partition:Bag The bag of tuples in the partition.
+
+ tuples from
+ NEAR_NEIGHBORS = foreach QUERIES_W_PARTS generate query_pt as query_pt
+ , METRIC(query_pt, 1000, PTS_HASHED) as neighbor
+ ;
+ describe NEAR_NEIGHBORS;
+ describe NEAR_NEIGHBORS;
+ -- {query_pt: (dim1: double,dim2: double,dim3: double)
+ -- ,neighbor: (pt: (dim1: double,dim2: double,dim3: double)
+ -- ,lsh::lsh_id: int
+ -- ,lsh::hash: long
+ -- )
+ -- }
+
+ -- project out the query and the matching point
+ NEIGHBORS_PROJ = foreach NEAR_NEIGHBORS {
+ generate query_pt as query_pt, neighbor.pt as matching_pts;
+ };
+
+ -- Filter out the hashes which resulted in no matches
+ NOT_NULL = filter NEIGHBORS_PROJ by SIZE(matching_pts) > 0;
+
+ -- group by the query
+ NEIGHBORS_GRP = group NOT_NULL by query_pt;
+ describe NEIGHBORS_GRP;
+
+ -- Generate the query, the number of matches and the bag of matching points
+ NEIGHBOR_CNT = foreach NEIGHBORS_GRP{
+ MATCHING_PTS = foreach NOT_NULL generate FLATTEN(matching_pts);
+ DIST_MATCHING_PTS = DISTINCT MATCHING_PTS;
+ generate group as query_pt, COUNT(NOT_NULL), DIST_MATCHING_PTS;
+ };
+ describe NEIGHBOR_CNT;
+ -- NEIGHBOR_CNT: {query_pt: (dim1: double,dim2: double,dim3: double)
+ -- ,long
+ -- ,DIST_MATCHING_PTS: { (matching_pts::dim1: double,matching_pts::dim2: double,matching_pts::dim3: double)
+ -- }
+ -- }
+ STORE NEIGHBOR_CNT INTO 'neighbors';
+ </pre></div>
+<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sDim</code> - Dimension of the vectors</dd><dd><code>sW</code> - A double representing the quantization parameter (also known as the projection width)</dd><dd><code>sRepeat</code> - Number of internal repetitions (generally this should be 1 as the p-stable hashes have a larger range than one bit)</dd><dd><code>sNumHashes</code> - Size of the hash family (if you're looking for k near neighbors, this is the k)</dd><dd><code>sSeed</code> - Seed to use when constructing LSH family</dd></dl>
+</li>
+</ul>
+<a name="L2PStableHash(java.lang.String, java.lang.String, java.lang.String, java.lang.String)">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>L2PStableHash</h4>
+<pre>public L2PStableHash(java.lang.String sDim,
+ java.lang.String sW,
+ java.lang.String sRepeat,
+ java.lang.String sNumHashes)</pre>
+</li>
+</ul>
+</li>
+</ul>
+<!-- ============ METHOD DETAIL ========== -->
+<ul class="blockList">
+<li class="blockList"><a name="method_detail">
+<!-- -->
+</a>
+<h3>Method Detail</h3>
+<a name="createLSHCreator()">
+<!-- -->
+</a>
+<ul class="blockList">
+<li class="blockList">
+<h4>createLSHCreator</h4>
+<pre>protected <a href="../../../../datafu/pig/hash/lsh/interfaces/LSHCreator.html" title="class in datafu.pig.hash.lsh.interfaces">LSHCreator</a> createLSHCreator()</pre>
+<dl>
+<dt><strong>Specified by:</strong></dt>
+<dd><code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#createLSHCreator()">createLSHCreator</a></code> in class <code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></code></dd>
+</dl>
+</li>
+</ul>
+<a name="getDimension()">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>getDimension</h4>
+<pre>protected int getDimension()</pre>
+<dl>
+<dt><strong>Specified by:</strong></dt>
+<dd><code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html#getDimension()">getDimension</a></code> in class <code><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh">LSHFunc</a></code></dd>
+</dl>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+<!-- ========= END OF CLASS DATA ========= -->
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+<li><a href="#fields_inherited_from_class_datafu.pig.hash.lsh.LSHFunc">Field</a> | </li>
+<li><a href="#constructor_summary">Constr</a> | </li>
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+<ul class="subNavList">
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Added: incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/LSHFamily.html
URL: http://svn.apache.org/viewvc/incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/LSHFamily.html?rev=1755883&view=auto
==============================================================================
--- incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/LSHFamily.html (added)
+++ incubator/datafu/site/docs/datafu/1.3.1/datafu/pig/hash/lsh/LSHFamily.html Wed Aug 10 22:07:27 2016
@@ -0,0 +1,264 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
+<!-- NewPage -->
+<html lang="en">
+<head>
+<!-- Generated by javadoc (version 1.7.0_79) on Wed Aug 10 15:01:03 PDT 2016 -->
+<title>LSHFamily (datafu-pig 1.3.1 API)</title>
+<meta name="date" content="2016-08-10">
+<link rel="stylesheet" type="text/css" href="../../../../stylesheet.css" title="Style">
+</head>
+<body>
+<script type="text/javascript"><!--
+ if (location.href.indexOf('is-external=true') == -1) {
+ parent.document.title="LSHFamily (datafu-pig 1.3.1 API)";
+ }
+//-->
+</script>
+<noscript>
+<div>JavaScript is disabled on your browser.</div>
+</noscript>
+<!-- ========= START OF TOP NAVBAR ======= -->
+<div class="topNav"><a name="navbar_top">
+<!-- -->
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+<li><a href="../../../../datafu/pig/hash/lsh/LSHFunc.html" title="class in datafu.pig.hash.lsh"><span class="strong">Next Class</span></a></li>
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+<!-- ======== START OF CLASS DATA ======== -->
+<div class="header">
+<div class="subTitle">datafu.pig.hash.lsh</div>
+<h2 title="Class LSHFamily" class="title">Class LSHFamily</h2>
+</div>
+<div class="contentContainer">
+<ul class="inheritance">
+<li>java.lang.Object</li>
+<li>
+<ul class="inheritance">
+<li>datafu.pig.hash.lsh.LSHFamily</li>
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+<br>
+<pre>public class <span class="strong">LSHFamily</span>
+extends java.lang.Object</pre>
+<div class="block">A family of k locality sensitive hashes. For a given point, k hashes will be computed.</div>
+</li>
+</ul>
+</div>
+<div class="summary">
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+<!-- ======== CONSTRUCTOR SUMMARY ======== -->
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+<table class="overviewSummary" border="0" cellpadding="3" cellspacing="0" summary="Constructor Summary table, listing constructors, and an explanation">
+<caption><span>Constructors</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colOne" scope="col">Constructor and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colOne"><code><strong><a href="../../../../datafu/pig/hash/lsh/LSHFamily.html#LSHFamily(java.util.List)">LSHFamily</a></strong>(java.util.List<<a href="../../../../datafu/pig/hash/lsh/interfaces/LSH.html" title="class in datafu.pig.hash.lsh.interfaces">LSH</a>> hashes)</code>
+<div class="block">Construct a family of hashes</div>
+</td>
+</tr>
+</table>
+</li>
+</ul>
+<!-- ========== METHOD SUMMARY =========== -->
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+<li class="blockList"><a name="method_summary">
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+<h3>Method Summary</h3>
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+<caption><span>Methods</span><span class="tabEnd"> </span></caption>
+<tr>
+<th class="colFirst" scope="col">Modifier and Type</th>
+<th class="colLast" scope="col">Method and Description</th>
+</tr>
+<tr class="altColor">
+<td class="colFirst"><code>java.lang.Iterable<java.lang.Long></code></td>
+<td class="colLast"><code><strong><a href="../../../../datafu/pig/hash/lsh/LSHFamily.html#apply(org.apache.commons.math.linear.RealVector)">apply</a></strong>(org.apache.commons.math.linear.RealVector vector)</code>
+<div class="block">Compute the family of k-hashes for a vector.</div>
+</td>
+</tr>
+</table>
+<ul class="blockList">
+<li class="blockList"><a name="methods_inherited_from_class_java.lang.Object">
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+<h3>Methods inherited from class java.lang.Object</h3>
+<code>clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
+</ul>
+</li>
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+<li class="blockList"><a name="constructor_detail">
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+</a>
+<h3>Constructor Detail</h3>
+<a name="LSHFamily(java.util.List)">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>LSHFamily</h4>
+<pre>public LSHFamily(java.util.List<<a href="../../../../datafu/pig/hash/lsh/interfaces/LSH.html" title="class in datafu.pig.hash.lsh.interfaces">LSH</a>> hashes)</pre>
+<div class="block">Construct a family of hashes</div>
+<dl><dt><span class="strong">Parameters:</span></dt><dd><code>hashes</code> - Hashes which will be applied in turn to a given point</dd></dl>
+</li>
+</ul>
+</li>
+</ul>
+<!-- ============ METHOD DETAIL ========== -->
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+<h3>Method Detail</h3>
+<a name="apply(org.apache.commons.math.linear.RealVector)">
+<!-- -->
+</a>
+<ul class="blockListLast">
+<li class="blockList">
+<h4>apply</h4>
+<pre>public java.lang.Iterable<java.lang.Long> apply(org.apache.commons.math.linear.RealVector vector)</pre>
+<div class="block">Compute the family of k-hashes for a vector.</div>
+<dl><dt><span class="strong">Parameters:</span></dt><dd><code>vector</code> - the vector</dd>
+<dt><span class="strong">Returns:</span></dt><dd>An iterable of hashes</dd></dl>
+</li>
+</ul>
+</li>
+</ul>
+</li>
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