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Posted to commits@madlib.apache.org by ri...@apache.org on 2017/05/16 20:29:18 UTC
[03/51] [partial] incubator-madlib-site git commit: Add v1.11 docs
http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/modules.html
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+ <span id="projectnumber">1.11</span>
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+ <div id="projectbrief">User Documentation for MADlib</div>
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+<div class="title">Modules</div> </div>
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+<div class="contents">
+<div class="textblock">Here is a list of all modules:</div><div class="directory">
+<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span>]</div><table class="directory">
+<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><a class="el" href="group__grp__datatrans.html" target="_self">Data Types and Transformations</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_0_" class="arrow" onclick="toggleFolder('0_0_')">▼</span><a class="el" href="group__grp__arraysmatrix.html" target="_self">Arrays and Matrices</a></td><td class="desc">Mathematical operations for arrays and matrices </td></tr>
+<tr id="row_0_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__array.html" target="_self">Array Operations</a></td><td class="desc">Provides fast array operations supporting other MADlib modules </td></tr>
+<tr id="row_0_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__matrix.html" target="_self">Matrix Operations</a></td><td class="desc">Provides fast matrix operations supporting other MADlib modules </td></tr>
+<tr id="row_0_0_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_0_0_2_" class="arrow" onclick="toggleFolder('0_0_2_')">▼</span><a class="el" href="group__grp__matrix__factorization.html" target="_self">Matrix Factorization</a></td><td class="desc">Matrix Factorization methods including Singular Value Decomposition and Low-rank Matrix Factorization </td></tr>
+<tr id="row_0_0_2_0_"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__lmf.html" target="_self">Low-rank Matrix Factorization</a></td><td class="desc">Performs low-rank matrix factorization for an incomplete matrix </td></tr>
+<tr id="row_0_0_2_1_" class="even"><td class="entry"><span style="width:64px;display:inline-block;"> </span><a class="el" href="group__grp__svd.html" target="_self">Singular Value Decomposition</a></td><td class="desc">Performs factorization of dense and sparse matrices </td></tr>
+<tr id="row_0_0_3_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__linalg.html" target="_self">Norms and Distance functions</a></td><td class="desc">Provides utility functions for basic linear algebra operations </td></tr>
+<tr id="row_0_0_4_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__svec.html" target="_self">Sparse Vectors</a></td><td class="desc">Implements a sparse vector data type that provides compressed storage of vectors that may have many duplicate elements </td></tr>
+<tr id="row_0_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_0_1_" class="arrow" onclick="toggleFolder('0_1_')">▼</span><a class="el" href="group__grp__pca.html" target="_self">Dimensionality Reduction</a></td><td class="desc">A collection of methods for dimensionality reduction </td></tr>
+<tr id="row_0_1_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__pca__train.html" target="_self">Principal Component Analysis</a></td><td class="desc">Produces a model that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components </td></tr>
+<tr id="row_0_1_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__pca__project.html" target="_self">Principal Component Projection</a></td><td class="desc">Projects a higher dimensional data point to a lower dimensional subspace spanned by principal components learned through the PCA training procedure </td></tr>
+<tr id="row_0_2_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__encode__categorical.html" target="_self">Encoding Categorical Variables</a></td><td class="desc">Provides functions to encode categorical variables </td></tr>
+<tr id="row_0_3_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pivot.html" target="_self">Pivot</a></td><td class="desc">Provides pivoting functions helpful for data preparation before modeling </td></tr>
+<tr id="row_0_4_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__stemmer.html" target="_self">Stemming</a></td><td class="desc">Provides porter stemmer operations supporting other MADlib modules </td></tr>
+<tr id="row_1_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><a class="el" href="group__grp__graph.html" target="_self">Graph</a></td><td class="desc"></td></tr>
+<tr id="row_1_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pagerank.html" target="_self">PageRank</a></td><td class="desc">Find the PageRank of all vertices in a directed graph </td></tr>
+<tr id="row_1_1_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sssp.html" target="_self">Single Source Shortest Path</a></td><td class="desc">Finds the shortest path from a single source vertex to every other vertex in a given graph </td></tr>
+<tr id="row_2_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_2_" class="arrow" onclick="toggleFolder('2_')">▼</span><a class="el" href="group__grp__mdl.html" target="_self">Model Evaluation</a></td><td class="desc"></td></tr>
+<tr id="row_2_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__validation.html" target="_self">Cross Validation</a></td><td class="desc">Estimates the fit of a predictive model given a data set and specifications for the training, prediction, and error estimation functions </td></tr>
+<tr id="row_2_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pred.html" target="_self">Prediction Metrics</a></td><td class="desc">Provides various prediction accuracy metrics </td></tr>
+<tr id="row_3_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_3_" class="arrow" onclick="toggleFolder('3_')">▼</span><a class="el" href="group__grp__stats.html" target="_self">Statistics</a></td><td class="desc"></td></tr>
+<tr id="row_3_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_3_0_" class="arrow" onclick="toggleFolder('3_0_')">▼</span><a class="el" href="group__grp__desc__stats.html" target="_self">Descriptive Statistics</a></td><td class="desc"></td></tr>
+<tr id="row_3_0_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__correlation.html" target="_self">Pearson's Correlation</a></td><td class="desc">Generates a cross-correlation matrix for all pairs of numeric columns in a table </td></tr>
+<tr id="row_3_0_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__summary.html" target="_self">Summary</a></td><td class="desc">Calculates general descriptive statistics for any data table </td></tr>
+<tr id="row_3_1_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_3_1_" class="arrow" onclick="toggleFolder('3_1_')">▼</span><a class="el" href="group__grp__inf__stats.html" target="_self">Inferential Statistics</a></td><td class="desc"></td></tr>
+<tr id="row_3_1_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__stats__tests.html" target="_self">Hypothesis Tests</a></td><td class="desc">Provides functions to perform statistical hypothesis tests </td></tr>
+<tr id="row_3_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__prob.html" target="_self">Probability Functions</a></td><td class="desc">Provides cumulative distribution, density/mass, and quantile functions for a wide range of probability distributions </td></tr>
+<tr id="row_4_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_4_" class="arrow" onclick="toggleFolder('4_')">▼</span><a class="el" href="group__grp__super.html" target="_self">Supervised Learning</a></td><td class="desc"></td></tr>
+<tr id="row_4_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__crf.html" target="_self">Conditional Random Field</a></td><td class="desc">Constructs a Conditional Random Fields (CRF) model for labeling sequential data </td></tr>
+<tr id="row_4_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_4_1_" class="arrow" onclick="toggleFolder('4_1_')">▼</span><a class="el" href="group__grp__regml.html" target="_self">Regression Models</a></td><td class="desc"></td></tr>
+<tr id="row_4_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__clustered__errors.html" target="_self">Clustered Variance</a></td><td class="desc">Calculates clustered variance for linear, logistic, and multinomial logistic regression models, and Cox proportional hazards models </td></tr>
+<tr id="row_4_1_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__cox__prop__hazards.html" target="_self">Cox-Proportional Hazards Regression</a></td><td class="desc">Models the relationship between one or more independent predictor variables and the amount of time before an event occurs </td></tr>
+<tr id="row_4_1_2_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__elasticnet.html" target="_self">Elastic Net Regularization</a></td><td class="desc">Generates a regularized regression model for variable selection in linear and logistic regression problems, combining the L1 and L2 penalties of the lasso and ridge methods </td></tr>
+<tr id="row_4_1_3_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__glm.html" target="_self">Generalized Linear Models</a></td><td class="desc">Estimate generalized linear model (GLM). GLM is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value </td></tr>
+<tr id="row_4_1_4_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__linreg.html" target="_self">Linear Regression</a></td><td class="desc">Also called Ordinary Least Squares Regression, models linear relationship between a dependent variable and one or more independent variables </td></tr>
+<tr id="row_4_1_5_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__logreg.html" target="_self">Logistic Regression</a></td><td class="desc">Models the relationship between one or more predictor variables and a binary categorical dependent variable by predicting the probability of the dependent variable using a logistic function </td></tr>
+<tr id="row_4_1_6_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__marginal.html" target="_self">Marginal Effects</a></td><td class="desc">Calculates marginal effects for the coefficients in regression problems </td></tr>
+<tr id="row_4_1_7_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__multinom.html" target="_self">Multinomial Regression</a></td><td class="desc">Multinomial regression is to model the conditional distribution of the multinomial response variable using a linear combination of predictors </td></tr>
+<tr id="row_4_1_8_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__ordinal.html" target="_self">Ordinal Regression</a></td><td class="desc">Regression to model data with ordinal response variable </td></tr>
+<tr id="row_4_1_9_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__robust.html" target="_self">Robust Variance</a></td><td class="desc">Calculates Huber-White variance estimates for linear, logistic, and multinomial regression models, and for Cox proportional hazards models </td></tr>
+<tr id="row_4_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__svm.html" target="_self">Support Vector Machines</a></td><td class="desc">Solves classification and regression problems by separating data with a hyperplane or other nonlinear decision boundary </td></tr>
+<tr id="row_4_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_4_3_" class="arrow" onclick="toggleFolder('4_3_')">▼</span><a class="el" href="group__grp__tree.html" target="_self">Tree Methods</a></td><td class="desc"></td></tr>
+<tr id="row_4_3_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__decision__tree.html" target="_self">Decision Tree</a></td><td class="desc">Decision Trees. Decision trees use a tree-based model to predict the value of a target variable based on several input variables </td></tr>
+<tr id="row_4_3_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__random__forest.html" target="_self">Random Forest</a></td><td class="desc">Random forests are an ensemble learning method for classification and regression that construct a multitude of decision trees at training time, then produce the class that is the mode of the classes of the individual trees </td></tr>
+<tr id="row_5_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_5_" class="arrow" onclick="toggleFolder('5_')">▼</span><a class="el" href="group__grp__tsa.html" target="_self">Time Series Analysis</a></td><td class="desc"></td></tr>
+<tr id="row_5_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__arima.html" target="_self">ARIMA</a></td><td class="desc">Generates a model with autoregressive, moving average, and integrated components for a time series dataset </td></tr>
+<tr id="row_6_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_6_" class="arrow" onclick="toggleFolder('6_')">▼</span><a class="el" href="group__grp__unsupervised.html" target="_self">Unsupervised Learning</a></td><td class="desc"></td></tr>
+<tr id="row_6_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_0_" class="arrow" onclick="toggleFolder('6_0_')">▼</span><a class="el" href="group__grp__association__rules.html" target="_self">Association Rules</a></td><td class="desc"></td></tr>
+<tr id="row_6_0_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__assoc__rules.html" target="_self">Apriori Algorithm</a></td><td class="desc">Computes association rules for a given set of data </td></tr>
+<tr id="row_6_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_1_" class="arrow" onclick="toggleFolder('6_1_')">▼</span><a class="el" href="group__grp__clustering.html" target="_self">Clustering</a></td><td class="desc"></td></tr>
+<tr id="row_6_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__kmeans.html" target="_self">k-Means Clustering</a></td><td class="desc">Partitions a set of observations into clusters by finding centroids that minimize the sum of observations' distances from their closest centroid </td></tr>
+<tr id="row_6_2_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_2_" class="arrow" onclick="toggleFolder('6_2_')">▼</span><a class="el" href="group__grp__topic__modelling.html" target="_self">Topic Modelling</a></td><td class="desc"></td></tr>
+<tr id="row_6_2_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__lda.html" target="_self">Latent Dirichlet Allocation</a></td><td class="desc">Generates a Latent Dirichlet Allocation predictive model for a collection of documents </td></tr>
+<tr id="row_7_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_7_" class="arrow" onclick="toggleFolder('7_')">▼</span><a class="el" href="group__grp__utility__functions.html" target="_self">Utility Functions</a></td><td class="desc"></td></tr>
+<tr id="row_7_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__utilities.html" target="_self">Developer Database Functions</a></td><td class="desc">Provides a collection of user-defined functions for performing common tasks in the database </td></tr>
+<tr id="row_7_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_7_1_" class="arrow" onclick="toggleFolder('7_1_')">▼</span><a class="el" href="group__grp__linear__solver.html" target="_self">Linear Solvers</a></td><td class="desc"></td></tr>
+<tr id="row_7_1_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__dense__linear__solver.html" target="_self">Dense Linear Systems</a></td><td class="desc">Implements solution methods for large dense linear systems. Currently, restricted to problems that fit in memory </td></tr>
+<tr id="row_7_1_1_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__sparse__linear__solver.html" target="_self">Sparse Linear Systems</a></td><td class="desc">Implements solution methods for linear systems with sparse matrix input. Currently, restricted to problems that fit in memory </td></tr>
+<tr id="row_7_2_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__path.html" target="_self">Path</a></td><td class="desc">Path Functions </td></tr>
+<tr id="row_7_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__pmml.html" target="_self">PMML Export</a></td><td class="desc">Implements the PMML XML standard to describe and exchange models produced by data mining and machine learning algorithms </td></tr>
+<tr id="row_7_4_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__sessionize.html" target="_self">Sessionize</a></td><td class="desc">Sessionize </td></tr>
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+<tr id="row_8_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_8_" class="arrow" onclick="toggleFolder('8_')">▼</span><a class="el" href="group__grp__early__stage.html" target="_self">Early Stage Development</a></td><td class="desc">A collection of implementations which are in early stage of development. There may be some issues that will be addressed in a future version. Interface and implementation are subject to change </td></tr>
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+<tr id="row_8_0_0_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__countmin.html" target="_self">CountMin (Cormode-Muthukrishnan)</a></td><td class="desc">Implements Cormode-Mathukrishnan <em>CountMin</em> sketches on integer values as a user-defined aggregate </td></tr>
+<tr id="row_8_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__fmsketch.html" target="_self">FM (Flajolet-Martin)</a></td><td class="desc">Implements Flajolet-Martin's distinct count estimation as a user-defined aggregate </td></tr>
+<tr id="row_8_0_2_" class="even"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__mfvsketch.html" target="_self">MFV (Most Frequent Values)</a></td><td class="desc">Implements the most frequent values variant of the CountMin sketch as a user-defined aggregate </td></tr>
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+<tr id="row_8_4_0_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><a class="el" href="group__grp__knn.html" target="_self">k-Nearest Neighbors</a></td><td class="desc">Finds k nearest data points to the given data point and outputs majority vote value of output classes for classification, and average value of target values for regression </td></tr>
+<tr id="row_9_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_9_" class="arrow" onclick="toggleFolder('9_')">▼</span><a class="el" href="group__grp__deprecated.html" target="_self">Deprecated Modules</a></td><td class="desc">A collection of deprecated modules. These functions will be removed in the next major version (2.0) </td></tr>
+<tr id="row_9_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__indicator.html" target="_self">Create Indicator Variables</a></td><td class="desc">Provides utility functions helpful for data preparation before modeling </td></tr>
+<tr id="row_9_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><a class="el" href="group__grp__mlogreg.html" target="_self">Multinomial Logistic Regression</a></td><td class="desc">Also called as softmax regression, models the relationship between one or more independent variables and a categorical dependent variable </td></tr>
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+</div><!-- contents -->
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+ <li class="footer">Generated on Tue May 16 2017 13:24:39 for MADlib by
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+ <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li>
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http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/b5b51c69/docs/v1.11/multilogistic_8sql__in.html
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+ name="MSearchResults" id="MSearchResults">
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+ <div class="summary">
+<a href="#func-members">Functions</a> </div>
+ <div class="headertitle">
+<div class="title">multilogistic.sql_in File Reference</div> </div>
+</div><!--header-->
+<div class="contents">
+
+<p>SQL functions for multinomial logistic 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:aef43e4a6363495901045daf339d5c6d7"><td class="memItemLeft" align="right" valign="top">float8 [] </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#aef43e4a6363495901045daf339d5c6d7">__mlogregr_irls_step_transition</a> (float8[] state, integer y, integer num_categories, integer ref_category, float8[] x, float8[] prev_state)</td></tr>
+<tr class="separator:aef43e4a6363495901045daf339d5c6d7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7cae4ea602fc9c159fa2cc8c1a7653a6"><td class="memItemLeft" align="right" valign="top">float8 [] </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a7cae4ea602fc9c159fa2cc8c1a7653a6">__mlogregr_irls_step_merge_states</a> (float8[] state1, float8[] state2)</td></tr>
+<tr class="separator:a7cae4ea602fc9c159fa2cc8c1a7653a6"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a690a74b753dceec66c4e0ad22f50c51e"><td class="memItemLeft" align="right" valign="top">float8 [] </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a690a74b753dceec66c4e0ad22f50c51e">__mlogregr_irls_step_final</a> (float8[] state)</td></tr>
+<tr class="separator:a690a74b753dceec66c4e0ad22f50c51e"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a695395e04b6d95afafc7c8ac9e01b7b2"><td class="memItemLeft" align="right" valign="top">aggregate float8 [] </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a695395e04b6d95afafc7c8ac9e01b7b2">__mlogregr_irls_step</a> (integer y, integer numcategories, integer ref_category, float8[] x, float8[] previous_state)</td></tr>
+<tr class="separator:a695395e04b6d95afafc7c8ac9e01b7b2"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aad300d3120db2ecaabf4809cf6be81e7"><td class="memItemLeft" align="right" valign="top">float8 </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#aad300d3120db2ecaabf4809cf6be81e7">__internal_mlogregr_irls_step_distance</a> (float8[] state1, float8[] state2)</td></tr>
+<tr class="separator:aad300d3120db2ecaabf4809cf6be81e7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae890704e55f57bd9105b63021e0f86ae"><td class="memItemLeft" align="right" valign="top">mlogregr_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ae890704e55f57bd9105b63021e0f86ae">__internal_mlogregr_irls_result</a> (float8[] state)</td></tr>
+<tr class="separator:ae890704e55f57bd9105b63021e0f86ae"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a48e0f07fcd855a9abcdf6ff070474b73"><td class="memItemLeft" align="right" valign="top">mlogregr_summary_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a48e0f07fcd855a9abcdf6ff070474b73">__internal_mlogregr_summary_results</a> (float8[] state)</td></tr>
+<tr class="separator:a48e0f07fcd855a9abcdf6ff070474b73"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aedc13474e6abbc88451d120ad97e44d4"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#aedc13474e6abbc88451d120ad97e44d4">mlogregr_train</a> (varchar source_table, varchar output_table, varchar dependent_varname, varchar independent_varname, integer ref_category, varchar optimizer_params)</td></tr>
+<tr class="memdesc:aedc13474e6abbc88451d120ad97e44d4"><td class="mdescLeft"> </td><td class="mdescRight">Compute multinomial logistic regression coefficients. <a href="#aedc13474e6abbc88451d120ad97e44d4">More...</a><br /></td></tr>
+<tr class="separator:aedc13474e6abbc88451d120ad97e44d4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:afea8bd51ec241fa7a749a7c74ae0f580"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#afea8bd51ec241fa7a749a7c74ae0f580">mlogregr_train</a> (varchar source_table, varchar output_table, varchar dependent_varname, varchar independent_varname, integer ref_category)</td></tr>
+<tr class="separator:afea8bd51ec241fa7a749a7c74ae0f580"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:aa596f23c7bcfcd47b051d78de0b99c36"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#aa596f23c7bcfcd47b051d78de0b99c36">mlogregr_train</a> (varchar source_table, varchar output_table, varchar dependent_varname, varchar independent_varname)</td></tr>
+<tr class="separator:aa596f23c7bcfcd47b051d78de0b99c36"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0b4643da1ecfcfaf3a1563c820b3347d"><td class="memItemLeft" align="right" valign="top">varchar </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a0b4643da1ecfcfaf3a1563c820b3347d">mlogregr_train</a> (varchar message)</td></tr>
+<tr class="separator:a0b4643da1ecfcfaf3a1563c820b3347d"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ab8b5a7eb69a945435cba5a068576f2e4"><td class="memItemLeft" align="right" valign="top">varchar </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ab8b5a7eb69a945435cba5a068576f2e4">mlogregr_train</a> ()</td></tr>
+<tr class="separator:ab8b5a7eb69a945435cba5a068576f2e4"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ac7da2fbd9877d94b2f1f013cc000566b"><td class="memItemLeft" align="right" valign="top">integer </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ac7da2fbd9877d94b2f1f013cc000566b">__compute_mlogregr</a> (varchar source_table, varchar dependent_varname, varchar independent_varname, integer num_categories, integer max_iter, varchar optimizer, float8 precision, integer ref_category)</td></tr>
+<tr class="separator:ac7da2fbd9877d94b2f1f013cc000566b"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7be20ccb465d47808e18149140fc666f"><td class="memItemLeft" align="right" valign="top">mlogregr_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a7be20ccb465d47808e18149140fc666f">mlogregr</a> (varchar source, varchar depvar, varchar indepvar, integer max_num_iterations=20, varchar optimizer="irls", float8 precision=0.0001, integer ref_category)</td></tr>
+<tr class="memdesc:a7be20ccb465d47808e18149140fc666f"><td class="mdescLeft"> </td><td class="mdescRight">Compute logistic-regression coefficients and diagnostic statistics. <a href="#a7be20ccb465d47808e18149140fc666f">More...</a><br /></td></tr>
+<tr class="separator:a7be20ccb465d47808e18149140fc666f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a116c95de21b112dedf99035a9b243fd7"><td class="memItemLeft" align="right" valign="top">mlogregr_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a116c95de21b112dedf99035a9b243fd7">mlogregr</a> (varchar source, varchar depvar, varchar indepvar)</td></tr>
+<tr class="separator:a116c95de21b112dedf99035a9b243fd7"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a1ab68f7396f53ae3e32362240d077cbf"><td class="memItemLeft" align="right" valign="top">mlogregr_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a1ab68f7396f53ae3e32362240d077cbf">mlogregr</a> (varchar source, varchar depvar, varchar indepvar, integer max_num_iterations)</td></tr>
+<tr class="separator:a1ab68f7396f53ae3e32362240d077cbf"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ae1b5954340a0c98a6ed06eb5f3bb43b8"><td class="memItemLeft" align="right" valign="top">mlogregr_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ae1b5954340a0c98a6ed06eb5f3bb43b8">mlogregr</a> (varchar source, varchar depvar, varchar indepvar, integer max_num_iterations, varchar optimizer)</td></tr>
+<tr class="separator:ae1b5954340a0c98a6ed06eb5f3bb43b8"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a55ffab7c3e95b301b3ad5733f185a1ec"><td class="memItemLeft" align="right" valign="top">set< __mlogregr_cat_coef > </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a55ffab7c3e95b301b3ad5733f185a1ec">__mlogregr_format</a> (float8[] coef, integer num_feature, integer num_category, integer ref_category)</td></tr>
+<tr class="separator:a55ffab7c3e95b301b3ad5733f185a1ec"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a7418e1ce65432793d0889f7e53f668cd"><td class="memItemLeft" align="right" valign="top">float8 [] </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a7418e1ce65432793d0889f7e53f668cd">__mlogregr_predict_prob</a> (float8[] coef, integer ref_category, float8[] col_ind_var)</td></tr>
+<tr class="separator:a7418e1ce65432793d0889f7e53f668cd"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:ad5e9a79ac38439db8ecd25148ca6f244"><td class="memItemLeft" align="right" valign="top">integer </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ad5e9a79ac38439db8ecd25148ca6f244">__mlogregr_predict_response</a> (float8[] coef, integer ref_category, float8[] col_ind_var)</td></tr>
+<tr class="separator:ad5e9a79ac38439db8ecd25148ca6f244"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a00f0c988e1b2b2fee9e4021450840061"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a00f0c988e1b2b2fee9e4021450840061">mlogregr_predict</a> (text model, text source, text id_col_name, text output, text pred_type)</td></tr>
+<tr class="separator:a00f0c988e1b2b2fee9e4021450840061"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a4c61f2fd5a67a7babb700fdfbd4d146f"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a4c61f2fd5a67a7babb700fdfbd4d146f">mlogregr_predict</a> (text model, text source, text id_col_name, text output)</td></tr>
+<tr class="separator:a4c61f2fd5a67a7babb700fdfbd4d146f"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a0fdfed54d63cefe260a0b74b9c7bbad5"><td class="memItemLeft" align="right" valign="top">text </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a0fdfed54d63cefe260a0b74b9c7bbad5">mlogregr_predict</a> (text message)</td></tr>
+<tr class="separator:a0fdfed54d63cefe260a0b74b9c7bbad5"><td class="memSeparator" colspan="2"> </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 multinomial <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e" title="Evaluate the usual logistic function in an under-/overflow-safe way. ">logistic</a> regression, see the module description <a class="el" href="group__grp__mlogreg.html">Multinomial Logistic Regression</a>. </dd></dl>
+</div><h2 class="groupheader">Function Documentation</h2>
+<a id="ac7da2fbd9877d94b2f1f013cc000566b"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ac7da2fbd9877d94b2f1f013cc000566b">◆ </a></span>__compute_mlogregr()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">integer __compute_mlogregr </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>dependent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>independent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>num_categories</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>max_iter</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>optimizer</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>precision</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="ae890704e55f57bd9105b63021e0f86ae"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae890704e55f57bd9105b63021e0f86ae">◆ </a></span>__internal_mlogregr_irls_result()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">mlogregr_result __internal_mlogregr_irls_result </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="aad300d3120db2ecaabf4809cf6be81e7"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aad300d3120db2ecaabf4809cf6be81e7">◆ </a></span>__internal_mlogregr_irls_step_distance()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 __internal_mlogregr_irls_step_distance </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state1</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state2</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a48e0f07fcd855a9abcdf6ff070474b73"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a48e0f07fcd855a9abcdf6ff070474b73">◆ </a></span>__internal_mlogregr_summary_results()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">mlogregr_summary_result __internal_mlogregr_summary_results </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a55ffab7c3e95b301b3ad5733f185a1ec"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a55ffab7c3e95b301b3ad5733f185a1ec">◆ </a></span>__mlogregr_format()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">set<__mlogregr_cat_coef> __mlogregr_format </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>coef</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>num_feature</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>num_category</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a695395e04b6d95afafc7c8ac9e01b7b2"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a695395e04b6d95afafc7c8ac9e01b7b2">◆ </a></span>__mlogregr_irls_step()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">aggregate float8 [] __mlogregr_irls_step </td>
+ <td>(</td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>y</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>numcategories</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>x</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>previous_state</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a690a74b753dceec66c4e0ad22f50c51e"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a690a74b753dceec66c4e0ad22f50c51e">◆ </a></span>__mlogregr_irls_step_final()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 [] __mlogregr_irls_step_final </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a7cae4ea602fc9c159fa2cc8c1a7653a6"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a7cae4ea602fc9c159fa2cc8c1a7653a6">◆ </a></span>__mlogregr_irls_step_merge_states()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 [] __mlogregr_irls_step_merge_states </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state1</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state2</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="aef43e4a6363495901045daf339d5c6d7"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aef43e4a6363495901045daf339d5c6d7">◆ </a></span>__mlogregr_irls_step_transition()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 [] __mlogregr_irls_step_transition </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>state</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>y</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>num_categories</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>x</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>prev_state</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a7418e1ce65432793d0889f7e53f668cd"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a7418e1ce65432793d0889f7e53f668cd">◆ </a></span>__mlogregr_predict_prob()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">float8 [] __mlogregr_predict_prob </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>coef</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>col_ind_var</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="ad5e9a79ac38439db8ecd25148ca6f244"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad5e9a79ac38439db8ecd25148ca6f244">◆ </a></span>__mlogregr_predict_response()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">integer __mlogregr_predict_response </td>
+ <td>(</td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>coef</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 [] </td>
+ <td class="paramname"><em>col_ind_var</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a7be20ccb465d47808e18149140fc666f"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a7be20ccb465d47808e18149140fc666f">◆ </a></span>mlogregr() <span class="overload">[1/4]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">mlogregr_result mlogregr </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>depvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>indepvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>max_num_iterations</em> = <code>20</code>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>optimizer</em> = <code>"irls"</code>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">float8 </td>
+ <td class="paramname"><em>precision</em> = <code>0.0001</code>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em> </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</td><td>Name of the source relation containing the training data </td></tr>
+ <tr><td class="paramname">depvar</td><td>Name of the dependent column (of type INTEGER < numcategories) </td></tr>
+ <tr><td class="paramname">indepvar</td><td>Name of the independent column (of type DOUBLE PRECISION[]) </td></tr>
+ <tr><td class="paramname">max_num_iterations</td><td>The maximum number of iterations </td></tr>
+ <tr><td class="paramname">optimizer</td><td>The optimizer to use ( <code>'irls'</code>/<code>'newton'</code> for iteratively reweighted least squares) </td></tr>
+ <tr><td class="paramname">precision</td><td>The difference between log-likelihood values in successive iterations that should indicate convergence. Note that a non-positive value here disables the convergence criterion, and execution will only stop after \ max_num_iterations iterations. </td></tr>
+ <tr><td class="paramname">ref_category</td><td>The reference category specified by the user</td></tr>
+ </table>
+ </dd>
+</dl>
+<dl class="section return"><dt>Returns</dt><dd>A composite value:<ul>
+<li><code>ref_category INTEGER</code> - Reference category</li>
+<li><code>coef FLOAT8[]</code> - Array of coefficients, <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/></li>
+<li><code>log_likelihood FLOAT8</code> - Log-likelihood <img class="formulaInl" alt="$ l(\boldsymbol c) $" src="form_80.png"/></li>
+<li><code>std_err FLOAT8[]</code> - Array of standard errors, <img class="formulaInl" alt="$ \mathit{se}(c_1), \dots, \mathit{se}(c_k) $" src="form_350.png"/></li>
+<li><code>z_stats FLOAT8[]</code> - Array of Wald z-statistics, <img class="formulaInl" alt="$ \boldsymbol z $" src="form_358.png"/></li>
+<li><code>p_values FLOAT8[]</code> - Array of Wald p-values, <img class="formulaInl" alt="$ \boldsymbol p $" src="form_352.png"/></li>
+<li><code>odds_ratios FLOAT8[]</code>: Array of odds ratios, <img class="formulaInl" alt="$ \mathit{odds}(c_1), \dots, \mathit{odds}(c_k) $" src="form_359.png"/></li>
+<li><code>condition_no FLOAT8</code> - The condition number of matrix <img class="formulaInl" alt="$ X^T A X $" src="form_360.png"/> during the iteration immediately <em>preceding</em> convergence (i.e., <img class="formulaInl" alt="$ A $" src="form_14.png"/> is computed using the coefficients of the previous iteration)</li>
+<li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Get vector of coefficients <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/> and all diagnostic statistics:<br />
+ <pre>SELECT * FROM mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
+ '<em>numCategories</em>', '<em>independentVariables</em>');</pre></li>
+<li>Get vector of coefficients <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/>:<br />
+ <pre>SELECT (mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
+ '<em>numCategories</em>', '<em>independentVariables</em>')).coef;</pre></li>
+<li>Get a subset of the output columns, e.g., only the array of coefficients <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/>, the log-likelihood of determination <img class="formulaInl" alt="$ l(\boldsymbol c) $" src="form_80.png"/>, and the array of p-values <img class="formulaInl" alt="$ \boldsymbol p $" src="form_352.png"/>: <pre>SELECT coef, log_likelihood, p_values
+ FROM mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
+ '<em>numCategories</em>', '<em>independentVariables</em>');</pre></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 id="a116c95de21b112dedf99035a9b243fd7"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a116c95de21b112dedf99035a9b243fd7">◆ </a></span>mlogregr() <span class="overload">[2/4]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">mlogregr_result mlogregr </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>depvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>indepvar</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a1ab68f7396f53ae3e32362240d077cbf"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a1ab68f7396f53ae3e32362240d077cbf">◆ </a></span>mlogregr() <span class="overload">[3/4]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">mlogregr_result mlogregr </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>depvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>indepvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>max_num_iterations</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="ae1b5954340a0c98a6ed06eb5f3bb43b8"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae1b5954340a0c98a6ed06eb5f3bb43b8">◆ </a></span>mlogregr() <span class="overload">[4/4]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">mlogregr_result mlogregr </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>depvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>indepvar</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>max_num_iterations</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>optimizer</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a00f0c988e1b2b2fee9e4021450840061"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a00f0c988e1b2b2fee9e4021450840061">◆ </a></span>mlogregr_predict() <span class="overload">[1/3]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void mlogregr_predict </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>source</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>id_col_name</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>pred_type</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a4c61f2fd5a67a7babb700fdfbd4d146f"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a4c61f2fd5a67a7babb700fdfbd4d146f">◆ </a></span>mlogregr_predict() <span class="overload">[2/3]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void mlogregr_predict </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>model</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>source</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>id_col_name</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>output</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a0fdfed54d63cefe260a0b74b9c7bbad5"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a0fdfed54d63cefe260a0b74b9c7bbad5">◆ </a></span>mlogregr_predict() <span class="overload">[3/3]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">text mlogregr_predict </td>
+ <td>(</td>
+ <td class="paramtype">text </td>
+ <td class="paramname"><em>message</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="aedc13474e6abbc88451d120ad97e44d4"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aedc13474e6abbc88451d120ad97e44d4">◆ </a></span>mlogregr_train() <span class="overload">[1/5]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void mlogregr_train </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>output_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>dependent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>independent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>optimizer_params</em> </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 resulting model </td></tr>
+ <tr><td class="paramname">dependent_varname</td><td>Name of the dependent column (of type INTEGER) </td></tr>
+ <tr><td class="paramname">independent_varname</td><td>Name of the independent column (or an array expression) </td></tr>
+ <tr><td class="paramname">ref_category</td><td>The reference category specified by the user </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 (named 'output_table' above) containing following columns:<ul>
+<li><code>ref_category INTEGER</code> - Reference category</li>
+<li><code>coef FLOAT8[]</code> - Array of coefficients, <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/></li>
+<li><code>log_likelihood FLOAT8</code> - Log-likelihood <img class="formulaInl" alt="$ l(\boldsymbol c) $" src="form_80.png"/></li>
+<li><code>std_err FLOAT8[]</code> - Array of standard errors, <img class="formulaInl" alt="$ \mathit{se}(c_1), \dots, \mathit{se}(c_k) $" src="form_350.png"/></li>
+<li><code>z_stats FLOAT8[]</code> - Array of Wald z-statistics, <img class="formulaInl" alt="$ \boldsymbol z $" src="form_358.png"/></li>
+<li><code>p_values FLOAT8[]</code> - Array of Wald p-values, <img class="formulaInl" alt="$ \boldsymbol p $" src="form_352.png"/></li>
+<li><code>odds_ratios FLOAT8[]</code>: Array of odds ratios, <img class="formulaInl" alt="$ \mathit{odds}(c_1), \dots, \mathit{odds}(c_k) $" src="form_359.png"/></li>
+<li><code>condition_no FLOAT8</code> - The condition number of matrix <img class="formulaInl" alt="$ X^T A X $" src="form_360.png"/> during the iteration immediately <em>preceding</em> convergence (i.e., <img class="formulaInl" alt="$ A $" src="form_14.png"/> is computed using the coefficients of the previous iteration) An output table (named 'output_table'_summary) containing following columns:</li>
+<li><code>regression_type VARCHAR</code> - The regression type run (in this case it will be 'mlogit')</li>
+<li><code>source_table VARCHAR</code> - Source table containing the training data</li>
+<li><code>output_table VARCHAR</code> - Output table containing the trained model</li>
+<li><code>dependent_varname VARCHAR</code> - Name of the dependent column used for training</li>
+<li><code>independent_varname VARCHAR</code> - Name of the independent column used for training (or the ARRAY expression used for training)</li>
+<li><code>ref_category INTEGER</code> - The reference category specified by the user</li>
+<li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated</li>
+<li><code>num_rows_processed INTEGER</code> - The number of rows from training data used for training</li>
+<li><code>num_missing_rows_skipped INTEGER</code> - The number of rows skipped during training</li>
+</ul>
+</dd></dl>
+<dl class="section user"><dt>Usage</dt><dd><ul>
+<li>Get vector of coefficients <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/> and all diagnostic statistics:<br />
+ <pre>SELECT mlogregr_train('<em>sourceName</em>', '<em>outputName</em>',
+ '<em>dependentVariable</em>', '<em>independentVariables</em>');
+ SELECT * from <em>outputName</em>;
+ </pre></li>
+<li>Get vector of coefficients <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/>:<br />
+ <pre>SELECT coef from <em>outputName</em>;</pre></li>
+<li>Get a subset of the output columns, e.g., only the array of coefficients <img class="formulaInl" alt="$ \boldsymbol c $" src="form_79.png"/>, the log-likelihood of determination <img class="formulaInl" alt="$ l(\boldsymbol c) $" src="form_80.png"/>, and the array of p-values <img class="formulaInl" alt="$ \boldsymbol p $" src="form_352.png"/>: <pre>SELECT coef, log_likelihood, p_values
+ FROM <em>outputName</em>;</pre> </li>
+</ul>
+</dd></dl>
+
+</div>
+</div>
+<a id="afea8bd51ec241fa7a749a7c74ae0f580"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#afea8bd51ec241fa7a749a7c74ae0f580">◆ </a></span>mlogregr_train() <span class="overload">[2/5]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void mlogregr_train </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>output_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>dependent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>independent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">integer </td>
+ <td class="paramname"><em>ref_category</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="aa596f23c7bcfcd47b051d78de0b99c36"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#aa596f23c7bcfcd47b051d78de0b99c36">◆ </a></span>mlogregr_train() <span class="overload">[3/5]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">void mlogregr_train </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>source_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>output_table</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>dependent_varname</em>, </td>
+ </tr>
+ <tr>
+ <td class="paramkey"></td>
+ <td></td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>independent_varname</em> </td>
+ </tr>
+ <tr>
+ <td></td>
+ <td>)</td>
+ <td></td><td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="a0b4643da1ecfcfaf3a1563c820b3347d"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a0b4643da1ecfcfaf3a1563c820b3347d">◆ </a></span>mlogregr_train() <span class="overload">[4/5]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">varchar mlogregr_train </td>
+ <td>(</td>
+ <td class="paramtype">varchar </td>
+ <td class="paramname"><em>message</em></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+<a id="ab8b5a7eb69a945435cba5a068576f2e4"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ab8b5a7eb69a945435cba5a068576f2e4">◆ </a></span>mlogregr_train() <span class="overload">[5/5]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname">varchar mlogregr_train </td>
+ <td>(</td>
+ <td class="paramname"></td><td>)</td>
+ <td></td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+</div>
+</div>
+</div><!-- contents -->
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