http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__early__stage.js
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__early__stage.js 
b/docs/v1.9.1/group__grp__early__stage.js
new file mode 100644
index 0000000..48f836c
--- /dev/null
+++ b/docs/v1.9.1/group__grp__early__stage.js
@@ -0,0 +1,7 @@
+var group__grp__early__stage =
+[
+    [ "Cardinality Estimators", "group__grp__sketches.html", 
"group__grp__sketches" ],
+    [ "Conjugate Gradient", "group__grp__cg.html", null ],
+    [ "Naive Bayes Classification", "group__grp__bayes.html", null ],
+    [ "Random Sampling", "group__grp__sample.html", null ]
+];
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__elasticnet.html
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__elasticnet.html 
b/docs/v1.9.1/group__grp__elasticnet.html
new file mode 100644
index 0000000..320bf19
--- /dev/null
+++ b/docs/v1.9.1/group__grp__elasticnet.html
@@ -0,0 +1,543 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" 
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";>
+<html xmlns="http://www.w3.org/1999/xhtml";>
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data 
mining,deep learning,ensemble methods,data science,market basket 
analysis,affinity analysis,pca,lda,regression,elastic net,huber 
white,proportional hazards,k-means,latent dirichlet allocation,bayes,support 
vector machines,svm"/>
+<title>MADlib: Elastic Net Regularization</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="eigen_navtree_hacks.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new 
Date();a=s.createElement(o),
+  
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'madlib.net');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.net";><img alt="Logo" 
src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.9.1</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" 
href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" 
border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__elasticnet.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Elastic Net Regularization<div class="ingroups"><a 
class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a 
class="el" href="group__grp__regml.html">Regression Models</a></div></div>  
</div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#optimizer">Optimizer Parameters</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Functions</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#background">Technical Background</a> </li>
+<li class="level1">
+<a href="#literature">Literature</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>This module implements elastic net regularization for linear and 
logistic regression problems.</p>
+<p><a class="anchor" id="train"></a></p><dl class="section user"><dt>Training 
Function</dt><dd>The training function has the following syntax: <pre 
class="syntax">
+elastic_net_train( tbl_source,
+                   tbl_result,
+                   col_dep_var,
+                   col_ind_var,
+                   regress_family,
+                   alpha,
+                   lambda_value,
+                   standardize,
+                   grouping_col,
+                   optimizer,
+                   optimizer_params,
+                   excluded,
+                   max_iter,
+                   tolerance
+                 )
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>tbl_source </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training 
data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>tbl_result </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the output 
model. The output table produced by the <a class="el" 
href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" 
title="Interface for elastic net. ">elastic_net_train()</a> function has the 
following columns: </p><table  class="output">
+<tr>
+<th>regress_family </th><td>The regression type: 'gaussian' or 'binomial'.  
</td></tr>
+<tr>
+<th>features </th><td>An array of the features (independent variables) passed 
into the analysis.  </td></tr>
+<tr>
+<th>features_selected </th><td>An array of the features selected by the 
analysis.  </td></tr>
+<tr>
+<th>coef_nonzero </th><td>Fitting coefficients for the selected features.  
</td></tr>
+<tr>
+<th>coef_all </th><td>Coefficients for all selected and unselected features  
</td></tr>
+<tr>
+<th>intercept </th><td>Fitting intercept for the model.  </td></tr>
+<tr>
+<th>log_likelihood </th><td>The negative value of the first equation above (up 
to a constant depending on the data set).  </td></tr>
+<tr>
+<th>standardize </th><td>BOOLEAN. Whether the data was normalized 
(<em>standardize</em> argument was TRUE).  </td></tr>
+<tr>
+<th>iteration_run </th><td>The number of iterations executed.  </td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>col_dep_var </dt>
+<dd><p class="startdd">TEXT. An expression for the dependent variable.</p>
+<p>Both <em>col_dep_var</em> and <em>col_ind_var</em> can be valid Postgres 
expressions. For example, <code>col_dep_var = 'log(y+1)'</code>, and 
<code>col_ind_var = 'array[exp(x[1]), x[2], 1/(1+x[3])]'</code>. In the 
binomial case, you can use a Boolean expression, for example, <code>col_dep_var 
= 'y &lt; 0'</code>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_ind_var </dt>
+<dd><p class="startdd">TEXT. An expression for the independent variables. Use 
<code>'*'</code> to specify all columns of <em>tbl_source</em> except those 
listed in the <em>excluded</em> string. If <em>col_dep_var</em> is a column 
name, it is automatically excluded from the independent variables. However, if 
<em>col_dep_var</em> is a valid Postgres expression, any column names used 
within the expression are only excluded if they are explicitly included in the 
<em>excluded</em> argument. It is a good idea to add all column names involved 
in the dependent variable expression to the <em>excluded</em> string.</p>
+<p class="enddd"></p>
+</dd>
+<dt>regress_family </dt>
+<dd><p class="startdd">TEXT. The regression type, either 'gaussian' ('linear') 
or 'binomial' ('logistic').</p>
+<p class="enddd"></p>
+</dd>
+<dt>alpha </dt>
+<dd><p class="startdd">FLOAT8. Elastic net control parameter, value in [0, 1], 
1 for L-1 regularization, 0 for L-2.</p>
+<p class="enddd"></p>
+</dd>
+<dt>lambda_value </dt>
+<dd><p class="startdd">FLOAT8. Regularization parameter, positive.</p>
+<p class="enddd"></p>
+</dd>
+<dt>standardize (optional) </dt>
+<dd><p class="startdd">BOOLEAN, default: TRUE. Whether to normalize the data. 
Setting this to TRUE usually yields better results and faster convergence.</p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL.</p>
+<dl class="section note"><dt>Note</dt><dd><em>Not currently implemented. Any 
non-NULL value is ignored. Grouping support will be added in a future release. 
</em> When implemented, an expression list will be used to group the input 
dataset into discrete groups, running one regression per group. Similar to the 
SQL <code>GROUP BY</code> clause. When this value is NULL, no grouping is used 
and a single result model is generated.</dd></dl>
+</dd>
+<dt>optimizer (optional) </dt>
+<dd><p class="startdd">TEXT, default: 'fista'. Name of optimizer, either 
'fista' or 'igd'.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optimizer_params (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. Optimizer parameters, delimited 
with commas. The parameters differ depending on the value of 
<em>optimizer</em>. See the descriptions below for details.</p>
+<p class="enddd"></p>
+</dd>
+<dt>excluded (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. If the <em>col_ind_var</em> input 
is '*' then <em>excluded</em> can be provided as a comma-delimited list of 
column names that are to be excluded from the features. For example, 
<code>'col1, col2'</code>. If the <em>col_ind_var</em> is an array, 
<em>excluded</em> must be a list of the integer array positions to exclude, for 
example <code>'1,2'</code>. If this argument is NULL or an empty string 
<code>''</code>, no columns are excluded.</p>
+<p class="enddd"></p>
+</dd>
+<dt>max_iter (optional) </dt>
+<dd><p class="startdd">INTEGER, default: 1000. The maximum number of 
iterations that are allowed.</p>
+<p class="enddd"></p>
+</dd>
+<dt>tolerance </dt>
+<dd>FLOAT8, default: default is 1e-6. The criteria to end iterations. Both the 
'fista' and 'igd' optimizers compute the difference between the loglikelihood 
of two consecutive iterations, and when the difference is smaller than 
<em>tolerance</em> or the iteration number is larger than <em>max_iter</em>, 
the computation stops. </dd>
+</dl>
+<p><a class="anchor" id="optimizer"></a></p><dl class="section 
user"><dt>Optimizer Parameters</dt><dd>Optimizer parameters are supplied in a 
string containing a comma-delimited list of name-value pairs. All of these 
named parameters are optional, and their order does not matter. You must use 
the format "&lt;param_name&gt; = &lt;value&gt;" to specify the value of a 
parameter, otherwise the parameter is ignored.</dd></dl>
+<p>When the <a class="el" 
href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83">elastic_net_train()</a>
 <em>optimizer</em> argument value is <b>'fista'</b>, the 
<em>optimizer_params</em> argument is a string containing name-value pairs with 
the following format. (Line breaks are inserted for readability.) </p><pre 
class="syntax">
+  'max_stepsize = &lt;value&gt;,
+   eta = &lt;value&gt;,
+   warmup = &lt;value&gt;,
+   warmup_lambdas = &lt;value&gt;,
+   warmup_lambda_no = &lt;value&gt;,
+   warmup_tolerance = &lt;value&gt;,
+   use_active_set = &lt;value&gt;,
+   activeset_tolerance = &lt;value&gt;,
+   random_stepsize = &lt;value&gt;'
+</pre><p> <b>Parameters</b> </p><dl class="arglist">
+<dt>max_stepsize </dt>
+<dd>Default: 4.0. Initial backtracking step size. At each iteration, the 
algorithm first tries <em>stepsize = max_stepsize</em>, and if it does not work 
out, it then tries a smaller step size, <em>stepsize = stepsize/eta</em>, where 
<em>eta</em> must be larger than 1. At first glance, this seems to perform 
repeated iterations for even one step, but using a larger step size actually 
greatly increases the computation speed and minimizes the total number of 
iterations. A careful choice of <em>max_stepsize</em> can decrease the 
computation time by more than 10 times. </dd>
+<dt>eta </dt>
+<dd><p class="startdd">Default: 2. If stepsize does not work <em>stepsize</em> 
/ <em>eta</em> is tried. Must be greater than 1. </p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup </dt>
+<dd><p class="startdd">Default: FALSE. If <em>warmup</em> is TRUE, a series of 
lambda values, which is strictly descent and ends at the lambda value that the 
user wants to calculate, is used. The larger lambda gives very sparse solution, 
and the sparse solution again is used as the initial guess for the next 
lambda's solution, which speeds up the computation for the next lambda. For 
larger data sets, this can sometimes accelerate the whole computation and may 
be faster than computation on only one lambda value.</p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup_lambdas </dt>
+<dd><p class="startdd">Default: NULL. The lambda value series to use when 
<em>warmup</em> is True. The default is NULL, which means that lambda values 
will be automatically generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup_lambda_no </dt>
+<dd><p class="startdd">Default: 15. How many lambdas are used in warm-up. If 
<em>warmup_lambdas</em> is not NULL, this value is overridden by the number of 
provided lambda values.</p>
+<p class="enddd"></p>
+</dd>
+<dt>warmup_tolerance </dt>
+<dd><p class="startdd">The value of tolerance used during warmup. The default 
is the same as the <em>tolerance</em> argument.</p>
+<p class="enddd"></p>
+</dd>
+<dt>use_active_set </dt>
+<dd><p class="startdd">Default: FALSE. If <em>use_active_set</em> is TRUE, an 
active-set method is used to speed up the computation. Considerable speedup is 
obtained by organizing the iterations around the active set of 
features&mdash;those with nonzero coefficients. After a complete cycle through 
all the variables, we iterate on only the active set until convergence. If 
another complete cycle does not change the active set, we are done, otherwise 
the process is repeated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>activeset_tolerance </dt>
+<dd><p class="startdd">Default: the value of the tolerance argument. The value 
of tolerance used during active set calculation. </p>
+<p class="enddd"></p>
+</dd>
+<dt>random_stepsize </dt>
+<dd>Default: FALSE. Whether to add some randomness to the step size. 
Sometimes, this can speed up the calculation. </dd>
+</dl>
+<p>When the <a class="el" 
href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83">elastic_net_train()</a>
 <em>optimizer</em> argument value is <b>'igd'</b>, the 
<em>optimizer_params</em> argument is a string containing name-value pairs with 
the following format. (Line breaks are inserted for readability.) </p><pre 
class="syntax">
+  'stepsize = &lt;value&gt;,
+   step_decay = &lt;value&gt;,
+   threshold = &lt;value&gt;,
+   warmup = &lt;value&gt;,
+   warmup_lambdas = &lt;value&gt;,
+   warmup_lambda_no = &lt;value&gt;,
+   warmup_tolerance = &lt;value&gt;,
+   parallel = &lt;value&gt;'
+</pre><p> <b>Parameters</b> </p><dl class="arglist">
+<dt>stepsize </dt>
+<dd>The default is 0.01. </dd>
+<dt>step_decay </dt>
+<dd>The actual setpsize used for current step is (previous stepsize) / 
exp(setp_decay). The default value is 0, which means that a constant stepsize 
is used in IGD. </dd>
+<dt>threshold </dt>
+<dd><p class="startdd">Default: 1e-10. When a coefficient is really small, set 
this coefficient to be 0.</p>
+<p class="enddd">Due to the stochastic nature of SGD, we can only obtain very 
small values for the fitting coefficients. Therefore, <em>threshold</em> is 
needed at the end of the computation to screen out tiny values and hard-set 
them to zeros. This is accomplished as follows: (1) multiply each coefficient 
with the standard deviation of the corresponding feature; (2) compute the 
average of absolute values of re-scaled coefficients; (3) divide each rescaled 
coefficient with the average, and if the resulting absolute value is smaller 
than <em>threshold</em>, set the original coefficient to zero. </p>
+</dd>
+<dt>warmup </dt>
+<dd>Default: FALSE. If <em>warmup</em> is TRUE, a series of lambda values, 
which is strictly descent and ends at the lambda value that the user wants to 
calculate, is used. The larger lambda gives very sparse solution, and the 
sparse solution again is used as the initial guess for the next lambda's 
solution, which speeds up the computation for the next lambda. For larger data 
sets, this can sometimes accelerate the whole computation and may be faster 
than computation on only one lambda value. </dd>
+<dt>warmup_lambdas </dt>
+<dd>Default: NULL. An array of lambda values to use for warmup. </dd>
+<dt>warmup_lambda_no </dt>
+<dd>The number of lambdas used in warm-up. The default is 15. If 
<em>warmup_lambdas</em> is not NULL, this argument is overridden by the size of 
the <em>warmup_lambdas</em> array. </dd>
+<dt>warmup_tolerance </dt>
+<dd>The value of tolerance used during warmup.The default is the same as the 
<em>tolerance</em> argument. </dd>
+<dt>parallel </dt>
+<dd><p class="startdd">Whether to run the computation on multiple segments. 
The default is True.</p>
+<p class="enddd">SGD is a sequential algorithm in nature. When running in a 
distributed manner, each segment of the data runs its own SGD model and then 
the models are averaged to get a model for each iteration. This averaging might 
slow down the convergence speed, although we also acquire the ability to 
process large datasets on multiple machines. This algorithm, therefore, 
provides the <em>parallel</em> option to allow you to choose whether to do 
parallel computation.  </p>
+</dd>
+</dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section 
user"><dt>Prediction Function</dt><dd></dd></dl>
+<h4>Per-Tuple Prediction</h4>
+<p>The prediction function returns a double value for Gaussian family and 
boolean value for Binomial family.</p>
+<p>The predict function has the following syntax (<a class="el" 
href="elastic__net_8sql__in.html#a96db4ff4ba3ea363fafbf6c036c19fae" 
title="Prediction for linear models use learned coefficients for a given 
example. ">elastic_net_gaussian_predict()</a> and <a class="el" 
href="elastic__net_8sql__in.html#aa78cde79f1f2caa7c5b38f933001d793" 
title="Prediction for logistic models use learned coefficients for a given 
example. ">elastic_net_binomial_predict()</a>): </p><pre class="syntax">
+elastic_net_&lt;family&gt;_predict(
+                     coefficients,
+                     intercept,
+                     ind_var
+                   )
+</pre><p><b>Arguments</b> </p><dl class="arglist">
+<dt>coefficients </dt>
+<dd>DOUBLE PRECISION[]. Fitting coefficients, usually coef_all or 
coef_nonzero. </dd>
+<dt>intercept </dt>
+<dd>DOUBLE PRECISION. The intercept for the model. </dd>
+<dt>ind_var </dt>
+<dd>DOUBLE PRECISION[]. Independent variables that correspond to coefficients, 
use <em>features</em> column in <em>tbl_result</em> for coef_all, and 
<em>features_selected</em> for coef_nonzero. See also <a 
href="#additional_example">examples</a>. Note that unexpected results or errors 
may be returned in the case that this argument is not given properly. </dd>
+</dl>
+<p>For binomial family, there is a function (<a class="el" 
href="elastic__net_8sql__in.html#a308718fd5234bc1007b971a639aadf71" 
title="Compute the probability of belonging to the True class for a given 
observation. ">elastic_net_binomial_prob()</a>) that outputs the probability of 
the instance being True: </p><pre class="syntax">
+elastic_net_binomial_prob(
+                     coefficients,
+                     intercept,
+                     ind_var
+                   )
+</pre><h4>Per-Table Prediction</h4>
+<p>Alternatively, you can use another prediction function that stores the 
prediction result in a table (<a class="el" 
href="elastic__net_8sql__in.html#a3578608204ac9b2d3442ff42977f632b" 
title="Prediction and put the result in a table can be used together with 
General-CV. ">elastic_net_predict()</a>). This is useful if you want to use 
elastic net together with the general cross validation function. </p><pre 
class="syntax">
+elastic_net_predict( tbl_model,
+                     tbl_new_sourcedata,
+                     col_id,
+                     tbl_predict
+                   )
+</pre><p> <b>Arguments</b> </p><dl class="arglist">
+<dt>tbl_model </dt>
+<dd>TEXT. The name of the table containing the output from the training 
function. </dd>
+<dt>tbl_new_sourcedata </dt>
+<dd>TEXT. The name of the table containing the new source data. </dd>
+<dt>col_id </dt>
+<dd>TEXT. The unique ID associated with each row. </dd>
+<dt>tbl_predict </dt>
+<dd>TEXT. The name of table to store the prediction result.  </dd>
+</dl>
+<p>You do not need to specify whether the model is "linear" or "logistic" 
because this information is already included in the <em>tbl_model</em> 
table.</p>
+<p><a class="anchor" id="examples"></a></p><dl class="section 
user"><dt>Examples</dt><dd></dd></dl>
+<ol type="1">
+<li>Display online help for the <a class="el" 
href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" 
title="Interface for elastic net. ">elastic_net_train()</a> function. <pre 
class="example">
+SELECT madlib.elastic_net_train();
+</pre></li>
+<li>Create an input data set. <pre class="example">
+DROP TABLE IF EXISTS houses;
+CREATE TABLE houses ( id INT,
+                      tax INT,
+                      bedroom INT,
+                      bath FLOAT,
+                      price INT,
+                      size INT,
+                      lot INT
+                    );
+COPY houses FROM STDIN WITH DELIMITER '|';
+  1 |  590 |       2 |    1 |  50000 |  770 | 22100
+  2 | 1050 |       3 |    2 |  85000 | 1410 | 12000
+  3 |   20 |       3 |    1 |  22500 | 1060 |  3500
+  4 |  870 |       2 |    2 |  90000 | 1300 | 17500
+  5 | 1320 |       3 |    2 | 133000 | 1500 | 30000
+  6 | 1350 |       2 |    1 |  90500 |  820 | 25700
+  7 | 2790 |       3 |  2.5 | 260000 | 2130 | 25000
+  8 |  680 |       2 |    1 | 142500 | 1170 | 22000
+  9 | 1840 |       3 |    2 | 160000 | 1500 | 19000
+ 10 | 3680 |       4 |    2 | 240000 | 2790 | 20000
+ 11 | 1660 |       3 |    1 |  87000 | 1030 | 17500
+ 12 | 1620 |       3 |    2 | 118600 | 1250 | 20000
+ 13 | 3100 |       3 |    2 | 140000 | 1760 | 38000
+ 14 | 2070 |       2 |    3 | 148000 | 1550 | 14000
+ 15 |  650 |       3 |  1.5 |  65000 | 1450 | 12000
+\.
+</pre></li>
+<li>Train the model. <pre class="example">
+DROP TABLE IF EXISTS houses_en;
+SELECT madlib.elastic_net_train( 'houses',
+                                 'houses_en',
+                                 'price',
+                                 'array[tax, bath, size]',
+                                 'gaussian',
+                                 0.5,
+                                 0.1,
+                                 TRUE,
+                                 NULL,
+                                 'fista',
+                                 '',
+                                 NULL,
+                                 10000,
+                                 1e-6
+                               );
+</pre></li>
+<li>View the resulting model. <pre class="example">
+-- Turn on expanded display to make it easier to read results.
+\x on
+SELECT * FROM houses_en;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-----+--------------------------------------------
+family            | gaussian
+features          | {tax,bath,size}
+features_selected | {tax,bath,size}
+coef_nonzero      | {27.6945611671,11509.0099734,49.0945557639}
+coef_all          | {27.6945611671,11509.0099734,49.0945557639}
+intercept         | -11145.5061503
+log_likelihood    | -490118975.406
+standardize       | t
+iteration_run     | 322
+</pre></li>
+<li>Use the prediction function to evaluate residuals. <pre class="example">
+\x off
+SELECT id, price, predict, price - predict AS residual
+FROM (
+    SELECT
+        houses.*,
+        madlib.elastic_net_gaussian_predict(
+            m.coef_all,
+            m.intercept,
+            ARRAY[tax,bath,size]
+            ) AS predict
+    FROM houses, houses_en m) s
+ORDER BY id;
+</pre></li>
+</ol>
+<p><a class="anchor" id="additional_example"></a></p><h4>Additional Example 
(when coef_nonzero is different from coef_all)</h4>
+<ol type="1">
+<li>Reuse the <a href="#examples">houses</a> table above and train the model 
with alpha=1 (L-1) and a large lambda (30000). <pre class="example">
+DROP TABLE IF EXISTS houses_en2;
+SELECT madlib.elastic_net_train( 'houses',
+                                 'houses_en2',
+                                 'price',
+                                 'array[tax, bath, size]',
+                                 'gaussian',
+                                 1,
+                                 30000,
+                                 TRUE,
+                                 NULL,
+                                 'fista',
+                                 '',
+                                 NULL,
+                                 10000,
+                                 1e-6
+                               );
+</pre></li>
+<li>View the resulting model and see coef_nonzero is different from coef_all. 
<pre class="example">
+-- Turn on expanded display to make it easier to read results.
+\x on
+SELECT * FROM houses_en2;
+</pre> Result: <pre class="result">
+-[ RECORD 1 ]-----+--------------------------------------------
+features          | {tax,bath,size}
+features_selected | {tax,size}
+coef_nonzero      | {13.3261747481,22.7347986045}
+coef_all          | {13.3261747481,0,22.7347986045}
+intercept         | 68877.5045405
+log_likelihood    | -1694746275.43
+standardize       | t
+iteration_run     | 115
+</pre></li>
+<li>We can still use the prediction function with coef_all to evaluate 
residuals. <pre class="example">
+\x off
+SELECT id, price, predict, price - predict AS residual
+FROM (
+    SELECT
+        houses.*,
+        madlib.elastic_net_gaussian_predict(
+            m.coef_all,
+            m.intercept,
+            ARRAY[tax,bath,size]
+            ) AS predict
+    FROM houses, houses_en2 m) s
+ORDER BY id;
+</pre></li>
+<li>While we can also speed up the prediction function with coef_nonzero to 
evaluate residuals. This requires user to examine the feature_selected column 
in the result table to construct the correct independent variables. <pre 
class="example">
+\x off
+SELECT id, price, predict, price - predict AS residual
+FROM (
+    SELECT
+        houses.*,
+        madlib.elastic_net_gaussian_predict(
+            m.coef_nonzero,
+            m.intercept,
+            ARRAY[tax,size]
+            ) AS predict
+    FROM houses, houses_en2 m) s
+ORDER BY id;
+</pre> The two queries are expected to give same residuals: <pre 
class="result">
+ id | price  |     predict      |     residual
+----+--------+------------------+-------------------
+  1 |  50000 |  94245.742567344 |  -44245.742567344
+  3 |  22500 |  93242.914556232 |  -70742.914556232
+  5 | 133000 | 120570.253114742 |   12429.746885258
+  7 | 260000 | 154482.653115284 |  105517.346884716
+  9 | 160000 | 127499.863983754 |   32500.136016246
+ 11 |  87000 | 114415.797184981 |  -27415.797184981
+ 13 | 140000 |  150201.89180353 |   -10201.89180353
+ 15 |  65000 |  110504.97610329 |   -45504.97610329
+  2 |  85000 |  114926.05405835 |   -29926.05405835
+  4 |  90000 | 110026.514757197 |  -20026.514757197
+  6 |  90500 | 105510.375306125 |  -15010.375306125
+  8 | 142500 | 104539.017736473 |   37960.982263527
+ 10 | 240000 | 181347.915720063 |   58652.084279937
+ 12 | 118600 | 118884.405888047 | -284.405888046997
+ 14 | 148000 | 131701.624106042 |   16298.375893958
+(15 rows)
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section 
user"><dt>Note</dt><dd>It is <b>strongly</b> <b>recommended</b> that you run 
<code><a class="el" 
href="elastic__net_8sql__in.html#a735038a5090c112505c740a90a203e83" 
title="Interface for elastic net. ">elastic_net_train()</a></code> on a subset 
of the data with a limited <em>max_iter</em> before applying it to the full 
data set with a large <em>max_iter</em>. In the pre-run, you can adjust the 
parameters to get the best performance and then apply the best set of 
parameters to the whole data set.</dd></dl>
+<p><a class="anchor" id="background"></a></p><dl class="section 
user"><dt>Technical Background</dt><dd></dd></dl>
+<p>Elastic net regularization seeks to find a weight vector that, for any 
given training example set, minimizes: </p><p class="formulaDsp">
+<img class="formulaDsp" alt="\[\min_{w \in R^N} L(w) + \lambda 
\left(\frac{(1-\alpha)}{2} \|w\|_2^2 + \alpha \|w\|_1 \right)\]" 
src="form_81.png"/>
+</p>
+<p> where <img class="formulaInl" alt="$L$" src="form_82.png"/> is the metric 
function that the user wants to minimize. Here <img class="formulaInl" alt="$ 
\alpha \in [0,1] $" src="form_83.png"/> and <img class="formulaInl" alt="$ 
lambda \geq 0 $" src="form_84.png"/>. If <img class="formulaInl" alt="$alpha = 
0$" src="form_85.png"/>, we have the ridge regularization (known also as 
Tikhonov regularization), and if <img class="formulaInl" alt="$\alpha = 1$" 
src="form_86.png"/>, we have the LASSO regularization.</p>
+<p>For the Gaussian response family (or linear model), we have </p><p 
class="formulaDsp">
+<img class="formulaDsp" alt="\[L(\vec{w}) = \frac{1}{2}\left[\frac{1}{M} 
\sum_{m=1}^M (w^{t} x_m + w_{0} - y_m)^2 \right] \]" src="form_87.png"/>
+</p>
+<p>For the Binomial response family (or logistic model), we have </p><p 
class="formulaDsp">
+<img class="formulaDsp" alt="\[ L(\vec{w}) = \sum_{m=1}^M\left[y_m \log\left(1 
+ e^{-(w_0 + \vec{w}\cdot\vec{x}_m)}\right) + (1-y_m) \log\left(1 + e^{w_0 + 
\vec{w}\cdot\vec{x}_m}\right)\right]\ , \]" src="form_88.png"/>
+</p>
+<p> where <img class="formulaInl" alt="$y_m \in {0,1}$" 
src="form_89.png"/>.</p>
+<p>To get better convergence, one can rescale the value of each element of x 
</p><p class="formulaDsp">
+<img class="formulaDsp" alt="\[ x' \leftarrow \frac{x - \bar{x}}{\sigma_x} \]" 
src="form_90.png"/>
+</p>
+<p> and for Gaussian case we also let </p><p class="formulaDsp">
+<img class="formulaDsp" alt="\[y' \leftarrow y - \bar{y} \]" 
src="form_91.png"/>
+</p>
+<p> and then minimize with the regularization terms. At the end of the 
calculation, the orginal scales will be restored and an intercept term will be 
obtained at the same time as a by-product.</p>
+<p>Note that fitting after scaling is not equivalent to directly fitting.</p>
+<p><a class="anchor" id="literature"></a></p><dl class="section 
user"><dt>Literature</dt><dd></dd></dl>
+<p>[1] Elastic net regularization. <a 
href="http://en.wikipedia.org/wiki/Elastic_net_regularization";>http://en.wikipedia.org/wiki/Elastic_net_regularization</a></p>
+<p>[2] Beck, A. and M. Teboulle (2009), A fast iterative 
shrinkage-thresholding algorithm for linear inverse problems. SIAM J. on 
Imaging Sciences 2(1), 183-202.</p>
+<p>[3] Shai Shalev-Shwartz and Ambuj Tewari, Stochastic Methods for l1 
Regularized Loss Minimization. Proceedings of the 26th International Conference 
on Machine Learning, Montreal, Canada, 2009.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related 
Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="elastic__net_8sql__in.html" title="SQL functions 
for elastic net regularization. ">elastic_net.sql_in</a> documenting the SQL 
functions.</p>
+<p>grp_validation </p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Tue Sep 20 2016 11:27:01 for MADlib by
+    <a href="http://www.doxygen.org/index.html";>
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__fmsketch.html
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__fmsketch.html 
b/docs/v1.9.1/group__grp__fmsketch.html
new file mode 100644
index 0000000..b84f5cb
--- /dev/null
+++ b/docs/v1.9.1/group__grp__fmsketch.html
@@ -0,0 +1,162 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" 
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";>
+<html xmlns="http://www.w3.org/1999/xhtml";>
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data 
mining,deep learning,ensemble methods,data science,market basket 
analysis,affinity analysis,pca,lda,regression,elastic net,huber 
white,proportional hazards,k-means,latent dirichlet allocation,bayes,support 
vector machines,svm"/>
+<title>MADlib: FM (Flajolet-Martin)</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="eigen_navtree_hacks.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new 
Date();a=s.createElement(o),
+  
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'madlib.net');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.net";><img alt="Logo" 
src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.9.1</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" 
href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" 
border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__fmsketch.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">FM (Flajolet-Martin)<div class="ingroups"><a class="el" 
href="group__grp__early__stage.html">Early Stage Development</a> &raquo; <a 
class="el" href="group__grp__sketches.html">Cardinality 
Estimators</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b> </p><ul>
+<li>
+<a href="#syntax">Syntax</a> </li>
+<li>
+<a href="#examples">Examples</a> </li>
+<li>
+<a href="#literature">Literature</a> </li>
+<li>
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><dl class="section warning"><dt>Warning</dt><dd><em> This MADlib method 
is still in early stage development. There may be some issues that will be 
addressed in a future version. Interface and implementation is subject to 
change. </em></dd></dl>
+<p><a class="el" 
href="sketch_8sql__in.html#ae27d5aaa5e4b426bcfe55e05a89c8e0b">fmsketch_dcount</a>
 can be run on a column of any type. It returns an approximation to the number 
of distinct values (a la <code>COUNT(DISTINCT x)</code>), but faster and 
approximate. Like any aggregate, it can be combined with a GROUP BY clause to 
do distinct counts per group.</p>
+<p><a class="anchor" id="syntax"></a></p><dl class="section 
user"><dt>Syntax</dt><dd></dd></dl>
+<p>Get the number of distinct values in a designated column. </p><pre 
class="syntax">
+fmsketch_dcount( col_name )
+</pre><p><a class="anchor" id="examples"></a></p><dl class="section 
user"><dt>Examples</dt><dd><ol type="1">
+<li>Generate some data. <pre class="example">
+CREATE TABLE data(class INT, a1 INT);
+INSERT INTO data SELECT 1,1 FROM generate_series(1,10000);
+INSERT INTO data SELECT 1,2 FROM generate_series(1,15000);
+INSERT INTO data SELECT 1,3 FROM generate_series(1,10000);
+INSERT INTO data SELECT 2,5 FROM generate_series(1,1000);
+INSERT INTO data SELECT 2,6 FROM generate_series(1,1000);
+</pre></li>
+<li>Find the distinct number of values for each class. <pre class="example">
+SELECT class, fmsketch_dcount(a1)
+FROM data
+GROUP BY data.class;
+</pre> Result: <pre class="result">
+class | fmsketch_dcount
+&#160;------+-----------------
+    2 |               2
+    1 |               3
+(2 rows)
+</pre></li>
+</ol>
+</dd></dl>
+<p><a class="anchor" id="literature"></a></p><dl class="section 
user"><dt>Literature</dt><dd>[1] P. Flajolet and N.G. Martin. Probabilistic 
counting algorithms for data base applications, Journal of Computer and System 
Sciences 31(2), pp 182-209, 1985. <a 
href="http://algo.inria.fr/flajolet/Publications/FlMa85.pdf";>http://algo.inria.fr/flajolet/Publications/FlMa85.pdf</a></dd></dl>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related 
Topics</dt><dd>File <a class="el" href="sketch_8sql__in.html" title="SQL 
functions for sketch-based approximations of descriptive statistics. 
">sketch.sql_in</a> documenting the SQL function. </dd></dl>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Tue Sep 20 2016 11:27:01 for MADlib by
+    <a href="http://www.doxygen.org/index.html";>
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__glm.html
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__glm.html b/docs/v1.9.1/group__grp__glm.html
new file mode 100644
index 0000000..aac40a8
--- /dev/null
+++ b/docs/v1.9.1/group__grp__glm.html
@@ -0,0 +1,573 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" 
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";>
+<html xmlns="http://www.w3.org/1999/xhtml";>
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data 
mining,deep learning,ensemble methods,data science,market basket 
analysis,affinity analysis,pca,lda,regression,elastic net,huber 
white,proportional hazards,k-means,latent dirichlet allocation,bayes,support 
vector machines,svm"/>
+<title>MADlib: Generalized Linear Models</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="eigen_navtree_hacks.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new 
Date();a=s.createElement(o),
+  
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'madlib.net');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.net";><img alt="Logo" 
src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.9.1</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" 
href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" 
border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__glm.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Generalized Linear Models<div class="ingroups"><a 
class="el" href="group__grp__super.html">Supervised Learning</a> &raquo; <a 
class="el" href="group__grp__regml.html">Regression Models</a></div></div>  
</div>
+</div><!--header-->
+<div class="contents">
+<div class="toc"><b>Contents</b></p><ul>
+<li class="level1">
+<a href="#train">Training Function</a> </li>
+<li class="level1">
+<a href="#predict">Prediction Function</a> </li>
+<li class="level1">
+<a href="#examples">Examples</a> </li>
+<li class="level1">
+<a href="#related">Related Topics</a> </li>
+</ul>
+</div><p>Generalized linear models extends ordinary linear regression by 
allowing the response variable to follow a more general set of distributions 
(rather than simply Gaussian distributions), and for a general family of 
functions of the response variable (the link function) to vary linearly with 
the predicted values (rather than assuming that the response itself must vary 
linearly).</p>
+<p>For example, data of counts would typically be modeled with a Poisson 
distribution and a log link, while binary outcomes would typically be modeled 
with a Bernoulli distribution (or binomial distribution, depending on exactly 
how the problem is phrased) and a log-odds (or logit) link function.</p>
+<p>Currently, the implemented distribution families are </p><center> <table 
class="doxtable">
+<tr>
+<th>Distribution Family </th><th>Link Functions  </th></tr>
+<tr>
+<td>Binomial </td><td>logit, probit  </td></tr>
+<tr>
+<td>Gamma </td><td>inverse, identity, log  </td></tr>
+<tr>
+<td>Gaussian </td><td>identity, inverse, log  </td></tr>
+<tr>
+<td>Inverse Gaussian </td><td>inverse of square, inverse, identity, log  
</td></tr>
+<tr>
+<td>Poisson </td><td>log, identity, square-root<br />
+  </td></tr>
+</table>
+</center><p><a class="anchor" id="train"></a></p><dl class="section 
user"><dt>Training Function</dt><dd>GLM training function has the following 
format: <pre class="syntax">
+glm(source_table,
+    model_table,
+    dependent_varname,
+    independent_varname,
+    family_params,
+    grouping_col,
+    optim_params,
+    verbose
+    )
+</pre> <b>Arguments</b> <dl class="arglist">
+<dt>source_table </dt>
+<dd><p class="startdd">TEXT. The name of the table containing the training 
data.</p>
+<p class="enddd"></p>
+</dd>
+<dt>model_table </dt>
+<dd><p class="startdd">TEXT. Name of the generated table containing the 
model.</p>
+<p>The model table produced by glm contains the following columns:</p>
+<table  class="output">
+<tr>
+<th>&lt;...&gt; </th><td><p class="starttd">Text. Grouping columns, if 
provided in input. This could be multiple columns depending on the 
<code>grouping_col</code> input. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>coef </th><td><p class="starttd">FLOAT8. Vector of the coefficients in 
linear predictor. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>log_likelihood </th><td><p class="starttd">FLOAT8. The log-likelihood <img 
class="formulaInl" alt="$ l(\boldsymbol \beta) $" src="form_92.png"/>. We use 
the maximum likelihood estimate of dispersion parameter to calculate the 
log-likelihood while R and Python use deviance estimate and Pearson estimate 
respectively. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>std_err </th><td><p class="starttd">FLOAT8[]. Vector of the standard error 
of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>z_stats or t_stats </th><td><p class="starttd">FLOAT8[]. Vector of the 
z-statistics (in Poisson distribtuion and Binomial distribution) or the 
t-statistics (in all other distributions) of the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>p_values </th><td><p class="starttd">FLOAT8[]. Vector of the p-values of 
the coefficients. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dispersion </th><td><p class="starttd">FLOAT8. The dispersion value 
(Pearson estimate). When family=poisson or family=binomial, the dispersion is 
always 1. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_processed </th><td><p class="starttd">BIGINT. Numbers of rows 
processed. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_rows_skipped </th><td><p class="starttd">BIGINT. Numbers of rows 
skipped due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_iterations </th><td>INTEGER. The number of iterations actually 
completed. This would be different from the <code>nIterations</code> argument 
if a <code>tolerance</code> parameter is provided and the algorithm converges 
before all iterations are completed.  </td></tr>
+</table>
+<p>A summary table named &lt;model_table&gt;_summary is also created at the 
same time, which has the following columns: </p><table  class="output">
+<tr>
+<th>method </th><td><p class="starttd">'glm' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>source_table </th><td><p class="starttd">The data source table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>model_table </th><td><p class="starttd">The model table name. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>dependent_varname </th><td><p class="starttd">The dependent variable. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>independent_varname </th><td><p class="starttd">The independent variables 
</p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>family_params </th><td><p class="starttd">A string that contains family 
parameters, and has the form of 'family=..., link=...' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>grouping_col </th><td><p class="starttd">Name of grouping columns. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>optimizer_params </th><td><p class="starttd">A string that contains 
optimizer parameters, and has the form of 'optimizer=..., max_iter=..., 
tolerance=...' </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_all_groups </th><td><p class="starttd">Number of groups in glm 
training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>num_failed_groups </th><td><p class="starttd">Number of failed groups in 
glm training. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_processed </th><td><p class="starttd">BIGINT. Total numbers of 
rows processed in all groups. </p>
+<p class="endtd"></p>
+</td></tr>
+<tr>
+<th>total_rows_skipped </th><td><p class="starttd">BIGINT. Total numbers of 
rows skipped in all groups due to missing values or failures. </p>
+<p class="endtd"></p>
+</td></tr>
+</table>
+<p class="enddd"></p>
+</dd>
+<dt>dependent_varname </dt>
+<dd><p class="startdd">TEXT. Name of the dependent variable column.</p>
+<p class="enddd"></p>
+</dd>
+<dt>independent_varname </dt>
+<dd><p class="startdd">TEXT. Expression list to evaluate for the independent 
variables. An intercept variable is not assumed. It is common to provide an 
explicit intercept term by including a single constant <code>1</code> term in 
the independent variable list.</p>
+<p class="enddd"></p>
+</dd>
+<dt>family_params (optional) </dt>
+<dd><p class="startdd">TEXT, Parameters for distribution family. Currently, we 
support</p>
+<p>(1) family=poisson and link=[log or identity or sqrt].</p>
+<p>(2) family=gaussian and link=[identity or log or inverse]. And when 
family=gaussian and link=identity, the GLM model is exactly the same as the 
linear regression.</p>
+<p>(3) family=gamma and link=[inverse or identity or log].</p>
+<p>(4) family=inverse_gaussian and link=[sqr_inverse or log or identity or 
inverse].</p>
+<p>(5) family=binomial and link=[probit or logit]. </p>
+<p class="enddd"></p>
+</dd>
+<dt>grouping_col (optional) </dt>
+<dd><p class="startdd">TEXT, default: NULL. An expression list used to group 
the input dataset into discrete groups, running one regression per group. 
Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is 
used and a single model is generated.</p>
+<p class="enddd"></p>
+</dd>
+<dt>optim_params (optional) </dt>
+<dd><p class="startdd">TEXT, default: 
'max_iter=100,optimizer=irls,tolerance=1e-6'. Parameters for optimizer. 
Currently, we support tolerance=[tolerance for relative error between 
log-likelihoods], max_iter=[maximum iterations to run], optimizer=irls.</p>
+<p class="enddd"></p>
+</dd>
+<dt>verbose (optional) </dt>
+<dd>BOOLEAN, default: FALSE. Provides verbose output of the results of 
training. </dd>
+</dl>
+</dd></dl>
+<dl class="section note"><dt>Note</dt><dd>For p-values, we just return the 
computation result directly. Other statistical packages, like 'R', produce the 
same result, but on printing the result to screen, another format function is 
used and any p-value that is smaller than the machine epsilon (the smallest 
positive floating-point number 'x' such that '1 + x != 1') will be printed on 
screen as "&lt; xxx" (xxx is the value of the machine epsilon). Although the 
results may look different, they are in fact the same. </dd></dl>
+<p><a class="anchor" id="predict"></a></p><dl class="section 
user"><dt>Prediction Function</dt><dd>The prediction function is provided to 
estimate the conditional mean given a new predictor. It has the following 
syntax: <pre class="syntax">
+glm_predict(coef,
+            col_ind_var
+            link)
+</pre></dd></dl>
+<p><b>Arguments</b> </p><dl class="arglist">
+<dt>coef </dt>
+<dd><p class="startdd">DOUBLE PRECISION[]. Model coefficients obtained from <a 
class="el" 
href="glm_8sql__in.html#a3f8eb219013e05675626acb8cf4612cc">glm()</a>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>col_ind_var </dt>
+<dd><p class="startdd">New predictor, as a DOUBLE array. This should be the 
same length as the array obtained by evaluation of the 'independent_varname' 
argument in <a class="el" 
href="glm_8sql__in.html#a3f8eb219013e05675626acb8cf4612cc">glm()</a>.</p>
+<p class="enddd"></p>
+</dd>
+<dt>link </dt>
+<dd>link function, as a string. This should match the link function the user 
inputted in <a class="el" 
href="glm_8sql__in.html#a3f8eb219013e05675626acb8cf4612cc">glm()</a>. </dd>
+</dl>
+<p><a class="anchor" id="examples"></a></p><dl class="section 
user"><dt>Examples</dt><dd><ol type="1">
+<li>Create the training data table. <pre class="example">
+CREATE TABLE warpbreaks(
+    id      serial,
+    breaks  integer,
+    wool    char(1),
+    tension char(1)
+);
+INSERT INTO warpbreaks(breaks, wool, tension) VALUES
+(26, 'A', 'L'),
+(30, 'A', 'L'),
+(54, 'A', 'L'),
+(25, 'A', 'L'),
+(70, 'A', 'L'),
+(52, 'A', 'L'),
+(51, 'A', 'L'),
+(26, 'A', 'L'),
+(67, 'A', 'L'),
+(18, 'A', 'M'),
+(21, 'A', 'M'),
+(29, 'A', 'M'),
+(17, 'A', 'M'),
+(12, 'A', 'M'),
+(18, 'A', 'M'),
+(35, 'A', 'M'),
+(30, 'A', 'M'),
+(36, 'A', 'M'),
+(36, 'A', 'H'),
+(21, 'A', 'H'),
+(24, 'A', 'H'),
+(18, 'A', 'H'),
+(10, 'A', 'H'),
+(43, 'A', 'H'),
+(28, 'A', 'H'),
+(15, 'A', 'H'),
+(26, 'A', 'H'),
+(27, 'B', 'L'),
+(14, 'B', 'L'),
+(29, 'B', 'L'),
+(19, 'B', 'L'),
+(29, 'B', 'L'),
+(31, 'B', 'L'),
+(41, 'B', 'L'),
+(20, 'B', 'L'),
+(44, 'B', 'L'),
+(42, 'B', 'M'),
+(26, 'B', 'M'),
+(19, 'B', 'M'),
+(16, 'B', 'M'),
+(39, 'B', 'M'),
+(28, 'B', 'M'),
+(21, 'B', 'M'),
+(39, 'B', 'M'),
+(29, 'B', 'M'),
+(20, 'B', 'H'),
+(21, 'B', 'H'),
+(24, 'B', 'H'),
+(17, 'B', 'H'),
+(13, 'B', 'H'),
+(15, 'B', 'H'),
+(15, 'B', 'H'),
+(16, 'B', 'H'),
+(28, 'B', 'H');
+SELECT create_indicator_variables('warpbreaks', 'warpbreaks_dummy', 
'wool,tension');
+</pre></li>
+<li>Train a GLM model. <pre class="example">
+SELECT glm('warpbreaks_dummy',
+           'glm_model',
+           'breaks',
+           'ARRAY[1.0,"wool_B","tension_M", "tension_H"]',
+           'family=poisson, link=log');
+</pre></li>
+<li>View the regression results. <pre class="example">
+-- Set extended display on for easier reading of output
+\x on
+SELECT * FROM glm_model;
+</pre> Result: <pre class="result">
+coef               | 
{3.69196314494079,-0.205988442638621,-0.321320431600611,-0.51848849651156}
+log_likelihood     | -242.527983208979
+std_err            | 
{0.04541079434248,0.0515712427835191,0.0602659166951256,0.0639595193956924}
+z_stats            | 
{81.3014438174473,-3.99425011926316,-5.3317106786264,-8.10651020224019}
+p_values           | 
{0,6.48993254938271e-05,9.72918600322907e-08,5.20943463005751e-16}
+num_rows_processed | 54
+num_rows_skipped   | 0
+iteration          | 5
+</pre> Alternatively, unnest the arrays in the results for easier reading of 
output: <pre class="example">
+\x off
+SELECT unnest(coef) as coefficient,
+       unnest(std_err) as standard_error,
+       unnest(z_stats) as z_stat,
+       unnest(p_values) as pvalue
+FROM glm_model;
+</pre></li>
+<li>Predicting dependent variable using GLM model. (This example uses the 
original data table to perform the prediction. Typically a different test 
dataset with the same features as the original training dataset would be used 
for prediction.) <pre class="example">
+\x off
+-- Display predicted mean value on the original dataset
+SELECT
+    w.id,
+    madlib.glm_predict(
+        coef,
+        ARRAY[1, "wool_B", "tension_M", "tension_H"]::float8[],
+        'log') AS mu
+FROM warpbreaks_dummy w, glm_model m
+ORDER BY w.id;
+</pre> <pre class="example">
+-- Display predicted counts (which are predicted mean values rounded to the 
nearest integral value) on the original dataset
+SELECT
+    w.id,
+    madlib.glm_predict_poisson(
+        coef,
+        ARRAY[1, "wool_B", "tension_M", "tension_H"]::float8[],
+        'log') AS poisson_count
+FROM warpbreaks_dummy w, glm_model m
+ORDER BY w.id;
+</pre></li>
+</ol>
+</dd></dl>
+<p><b>Example for Gaussian family:</b></p>
+<ol type="1">
+<li>Create a testing data table <pre class="example">
+CREATE TABLE abalone (
+    id integer,
+    sex text,
+    length double precision,
+    diameter double precision,
+    height double precision,
+    whole double precision,
+    shucked double precision,
+    viscera double precision,
+    shell double precision,
+    rings integer
+);
+INSERT INTO abalone VALUES
+(3151, 'F', 0.655000000000000027, 0.505000000000000004, 0.165000000000000008, 
1.36699999999999999, 0.583500000000000019, 0.351499999999999979, 
0.396000000000000019, 10),
+(2026, 'F', 0.550000000000000044, 0.469999999999999973, 0.149999999999999994, 
0.920499999999999985, 0.381000000000000005, 0.243499999999999994, 
0.267500000000000016, 10),
+(3751, 'I', 0.434999999999999998, 0.375, 0.110000000000000001, 
0.41549999999999998, 0.170000000000000012, 0.0759999999999999981, 
0.14499999999999999, 8),
+(720, 'I', 0.149999999999999994, 0.100000000000000006, 0.0250000000000000014, 
0.0149999999999999994, 0.00449999999999999966, 0.00400000000000000008, 
0.0050000000000000001, 2),
+(1635, 'F', 0.574999999999999956, 0.469999999999999973, 0.154999999999999999, 
1.1160000000000001, 0.509000000000000008, 0.237999999999999989, 
0.340000000000000024, 10),
+(2648, 'I', 0.5, 0.390000000000000013, 0.125, 0.582999999999999963, 
0.293999999999999984, 0.132000000000000006, 0.160500000000000004, 8),
+(1796, 'F', 0.57999999999999996, 0.429999999999999993, 0.170000000000000012, 
1.47999999999999998, 0.65349999999999997, 0.32400000000000001, 
0.41549999999999998, 10),
+(209, 'F', 0.525000000000000022, 0.41499999999999998, 0.170000000000000012, 
0.832500000000000018, 0.275500000000000023, 0.168500000000000011, 
0.309999999999999998, 13),
+(1451, 'I', 0.455000000000000016, 0.33500000000000002, 0.135000000000000009, 
0.501000000000000001, 0.274000000000000021, 0.0995000000000000051, 
0.106499999999999997, 7),
+(1108, 'I', 0.510000000000000009, 0.380000000000000004, 0.115000000000000005, 
0.515499999999999958, 0.214999999999999997, 0.113500000000000004, 
0.166000000000000009, 8),
+(3675, 'F', 0.594999999999999973, 0.450000000000000011, 0.165000000000000008, 
1.08099999999999996, 0.489999999999999991, 0.252500000000000002, 
0.279000000000000026, 12),
+(2108, 'F', 0.675000000000000044, 0.550000000000000044, 0.179999999999999993, 
1.68849999999999989, 0.562000000000000055, 0.370499999999999996, 
0.599999999999999978, 15),
+(3312, 'F', 0.479999999999999982, 0.380000000000000004, 0.135000000000000009, 
0.507000000000000006, 0.191500000000000004, 0.13650000000000001, 
0.154999999999999999, 12),
+(882, 'M', 0.655000000000000027, 0.520000000000000018, 0.165000000000000008, 
1.40949999999999998, 0.585999999999999965, 0.290999999999999981, 
0.405000000000000027, 9),
+(3402, 'M', 0.479999999999999982, 0.395000000000000018, 0.149999999999999994, 
0.681499999999999995, 0.214499999999999996, 0.140500000000000014, 0.2495, 18),
+(829, 'I', 0.409999999999999976, 0.325000000000000011, 0.100000000000000006, 
0.394000000000000017, 0.20799999999999999, 0.0655000000000000027, 
0.105999999999999997, 6),
+(1305, 'M', 0.535000000000000031, 0.434999999999999998, 0.149999999999999994, 
0.716999999999999971, 0.347499999999999976, 0.14449999999999999, 
0.194000000000000006, 9),
+(3613, 'M', 0.599999999999999978, 0.46000000000000002, 0.179999999999999993, 
1.1399999999999999, 0.422999999999999987, 0.257500000000000007, 
0.364999999999999991, 10),
+(1068, 'I', 0.340000000000000024, 0.265000000000000013, 0.0800000000000000017, 
0.201500000000000012, 0.0899999999999999967, 0.0475000000000000006, 
0.0550000000000000003, 5),
+(2446, 'M', 0.5, 0.380000000000000004, 0.135000000000000009, 
0.583500000000000019, 0.22950000000000001, 0.126500000000000001, 
0.179999999999999993, 12),
+(1393, 'M', 0.635000000000000009, 0.474999999999999978, 0.170000000000000012, 
1.19350000000000001, 0.520499999999999963, 0.269500000000000017, 
0.366499999999999992, 10),
+(359, 'M', 0.744999999999999996, 0.584999999999999964, 0.214999999999999997, 
2.49900000000000011, 0.92649999999999999, 0.471999999999999975, 
0.699999999999999956, 17),
+(549, 'F', 0.564999999999999947, 0.450000000000000011, 0.160000000000000003, 
0.79500000000000004, 0.360499999999999987, 0.155499999999999999, 
0.23000000000000001, 12),
+(1154, 'F', 0.599999999999999978, 0.474999999999999978, 0.160000000000000003, 
1.02649999999999997, 0.484999999999999987, 0.2495, 0.256500000000000006, 9),
+(1790, 'F', 0.54500000000000004, 0.385000000000000009, 0.149999999999999994, 
1.11850000000000005, 0.542499999999999982, 0.244499999999999995, 
0.284499999999999975, 9),
+(3703, 'F', 0.665000000000000036, 0.540000000000000036, 0.195000000000000007, 
1.76400000000000001, 0.850500000000000034, 0.361499999999999988, 
0.469999999999999973, 11),
+(1962, 'F', 0.655000000000000027, 0.515000000000000013, 0.179999999999999993, 
1.41199999999999992, 0.619500000000000051, 0.248499999999999999, 
0.496999999999999997, 11),
+(1665, 'I', 0.604999999999999982, 0.469999999999999973, 0.14499999999999999, 
0.802499999999999991, 0.379000000000000004, 0.226500000000000007, 
0.220000000000000001, 9),
+(635, 'M', 0.359999999999999987, 0.294999999999999984, 0.100000000000000006, 
0.210499999999999993, 0.0660000000000000031, 0.0524999999999999981, 
0.0749999999999999972, 9),
+(3901, 'M', 0.445000000000000007, 0.344999999999999973, 0.140000000000000013, 
0.475999999999999979, 0.205499999999999988, 0.101500000000000007, 
0.108499999999999999, 15),
+(2734, 'I', 0.41499999999999998, 0.33500000000000002, 0.100000000000000006, 
0.357999999999999985, 0.169000000000000011, 0.067000000000000004, 
0.104999999999999996, 7),
+(3856, 'M', 0.409999999999999976, 0.33500000000000002, 0.115000000000000005, 
0.440500000000000003, 0.190000000000000002, 0.0850000000000000061, 
0.135000000000000009, 8),
+(827, 'I', 0.395000000000000018, 0.28999999999999998, 0.0950000000000000011, 
0.303999999999999992, 0.127000000000000002, 0.0840000000000000052, 
0.076999999999999999, 6),
+(3381, 'I', 0.190000000000000002, 0.130000000000000004, 0.0449999999999999983, 
0.0264999999999999993, 0.00899999999999999932, 0.0050000000000000001, 
0.00899999999999999932, 5),
+(3972, 'I', 0.400000000000000022, 0.294999999999999984, 0.0950000000000000011, 
0.252000000000000002, 0.110500000000000001, 0.0575000000000000025, 
0.0660000000000000031, 6),
+(1155, 'M', 0.599999999999999978, 0.455000000000000016, 0.170000000000000012, 
1.1915, 0.695999999999999952, 0.239499999999999991, 0.239999999999999991, 8),
+(3467, 'M', 0.640000000000000013, 0.5, 0.170000000000000012, 
1.4544999999999999, 0.642000000000000015, 0.357499999999999984, 
0.353999999999999981, 9),
+(2433, 'F', 0.609999999999999987, 0.484999999999999987, 0.165000000000000008, 
1.08699999999999997, 0.425499999999999989, 0.232000000000000012, 
0.380000000000000004, 11),
+(552, 'I', 0.614999999999999991, 0.489999999999999991, 0.154999999999999999, 
0.988500000000000045, 0.41449999999999998, 0.195000000000000007, 
0.344999999999999973, 13),
+(1425, 'F', 0.729999999999999982, 0.57999999999999996, 0.190000000000000002, 
1.73750000000000004, 0.678499999999999992, 0.434499999999999997, 
0.520000000000000018, 11),
+(2402, 'F', 0.584999999999999964, 0.41499999999999998, 0.154999999999999999, 
0.69850000000000001, 0.299999999999999989, 0.145999999999999991, 
0.195000000000000007, 12),
+(1748, 'F', 0.699999999999999956, 0.535000000000000031, 0.174999999999999989, 
1.77299999999999991, 0.680499999999999994, 0.479999999999999982, 
0.512000000000000011, 15),
+(3983, 'I', 0.57999999999999996, 0.434999999999999998, 0.149999999999999994, 
0.891499999999999959, 0.362999999999999989, 0.192500000000000004, 
0.251500000000000001, 6),
+(335, 'F', 0.739999999999999991, 0.599999999999999978, 0.195000000000000007, 
1.97399999999999998, 0.597999999999999976, 0.408499999999999974, 
0.709999999999999964, 16),
+(1587, 'I', 0.515000000000000013, 0.349999999999999978, 0.104999999999999996, 
0.474499999999999977, 0.212999999999999995, 0.122999999999999998, 
0.127500000000000002, 10),
+(2448, 'I', 0.275000000000000022, 0.204999999999999988, 0.0800000000000000017, 
0.096000000000000002, 0.0359999999999999973, 0.0184999999999999991, 
0.0299999999999999989, 6),
+(1362, 'F', 0.604999999999999982, 0.474999999999999978, 0.174999999999999989, 
1.07600000000000007, 0.463000000000000023, 0.219500000000000001, 
0.33500000000000002, 9),
+(2799, 'M', 0.640000000000000013, 0.484999999999999987, 0.149999999999999994, 
1.09800000000000009, 0.519499999999999962, 0.222000000000000003, 
0.317500000000000004, 10),
+(1413, 'F', 0.67000000000000004, 0.505000000000000004, 0.174999999999999989, 
1.01449999999999996, 0.4375, 0.271000000000000019, 0.3745, 10),
+(1739, 'F', 0.67000000000000004, 0.540000000000000036, 0.195000000000000007, 
1.61899999999999999, 0.739999999999999991, 0.330500000000000016, 
0.465000000000000024, 11),
+(1152, 'M', 0.584999999999999964, 0.465000000000000024, 0.160000000000000003, 
0.955500000000000016, 0.45950000000000002, 0.235999999999999988, 
0.265000000000000013, 7),
+(2427, 'I', 0.564999999999999947, 0.434999999999999998, 0.154999999999999999, 
0.782000000000000028, 0.271500000000000019, 0.16800000000000001, 
0.284999999999999976, 14),
+(1777, 'M', 0.484999999999999987, 0.369999999999999996, 0.154999999999999999, 
0.967999999999999972, 0.418999999999999984, 0.245499999999999996, 
0.236499999999999988, 9),
+(3294, 'M', 0.574999999999999956, 0.455000000000000016, 0.184999999999999998, 
1.15599999999999992, 0.552499999999999991, 0.242999999999999994, 
0.294999999999999984, 13),
+(1403, 'M', 0.650000000000000022, 0.510000000000000009, 0.190000000000000002, 
1.54200000000000004, 0.715500000000000025, 0.373499999999999999, 0.375, 9),
+(2256, 'M', 0.510000000000000009, 0.395000000000000018, 0.14499999999999999, 
0.61850000000000005, 0.215999999999999998, 0.138500000000000012, 
0.239999999999999991, 12),
+(3984, 'F', 0.584999999999999964, 0.450000000000000011, 0.125, 
0.873999999999999999, 0.354499999999999982, 0.20749999999999999, 
0.225000000000000006, 6),
+(1116, 'M', 0.525000000000000022, 0.405000000000000027, 0.119999999999999996, 
0.755499999999999949, 0.3755, 0.155499999999999999, 0.201000000000000012, 9),
+(1366, 'M', 0.609999999999999987, 0.474999999999999978, 0.170000000000000012, 
1.02649999999999997, 0.434999999999999998, 0.233500000000000013, 
0.303499999999999992, 10),
+(3759, 'I', 0.525000000000000022, 0.400000000000000022, 0.140000000000000013, 
0.605500000000000038, 0.260500000000000009, 0.107999999999999999, 
0.209999999999999992, 9);
+</pre></li>
+<li>Train a model with family=gaussian and link=identity <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=gaussian, link=identity');
+</pre></li>
+</ol>
+<p><b>Example for Gamma family:</b> (reuse the dataset in Gaussian case)</p>
+<ol type="1">
+<li>Reuse the test data set in Gaussian</li>
+<li>Train a model with family=gamma and link=inverse <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=gamma, link=inverse');
+</pre></li>
+</ol>
+<p><b>Example for Inverse Gaussian family:</b> (reuse the dataset in Gaussian 
case)</p>
+<ol type="1">
+<li>Reuse the test data set in Gaussian</li>
+<li>Train a model with family=inverse_gaussian and link=sqr_inverse <pre 
class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=inverse_gaussian, link=sqr_inverse');
+</pre></li>
+</ol>
+<p><b>Example for Binomial family:</b> (reuse the dataset in Gaussian case)</p>
+<ol type="1">
+<li>Reuse the test data set in Gaussian</li>
+<li>Train a model with family=binomial and link=probit <pre class="example">
+SELECT madlib.glm(
+    'abalone',
+    'abalone_out',
+    'rings &lt; 10',
+    'ARRAY[1, length, diameter, height, whole, shucked, viscera, shell]',
+    'family=binomial, link=probit');
+</pre></li>
+<li>Predict output probabilities <pre class="example">
+SELECT madlib.glm_predict(
+    coef,
+    ARRAY[1, length, diameter, height, whole, shucked, viscera, 
shell]::float8[],
+    'probit')
+FROM abalone_out, abalone;
+</pre></li>
+<li>Predict output categories <pre class="example">
+SELECT madlib.glm_predict(
+SELECT madlib.glm_predict_binomial(
+    coef,
+    ARRAY[1, length, diameter, height, whole, shucked, viscera, 
shell]::float8[],
+    'probit')
+FROM abalone_out, abalone;
+</pre></li>
+</ol>
+<p><a class="anchor" id="notes"></a></p><dl class="section 
user"><dt>Notes</dt><dd>All table names can be optionally schema qualified 
(current_schemas() would be searched if a schema name is not provided) and all 
table and column names should follow case-sensitivity and quoting rules per the 
database. (For instance, 'mytable' and 'MyTable' both resolve to the same 
entity, i.e. 'mytable'. If mixed-case or multi-byte characters are desired for 
entity names then the string should be double-quoted; in this case the input 
would be '"MyTable"').</dd></dl>
+<p>Currently implementation uses Newton's method and, according to performance 
tests, when number of features are over 1000, this GLM function could be 
running slowly.</p>
+<p>Functions in <a class="el" href="group__grp__linreg.html">Linear 
Regression</a> is prefered to GLM with family=gaussian,link=identity, as the 
former require only a single pass over the training data. In addition, if user 
expects to use robust variance, clustered variance, or marginal effects on top 
of the trained model, functions in <a class="el" 
href="group__grp__linreg.html">Linear Regression</a> and <a class="el" 
href="group__grp__logreg.html">Logistic Regression</a> should be used.</p>
+<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related 
Topics</dt><dd></dd></dl>
+<p>File <a class="el" href="glm_8sql__in.html" title="SQL functions for GLM 
(Poisson) ">glm.sql_in</a> documenting the training function</p>
+<p><a class="el" href="group__grp__linreg.html">Linear Regression</a></p>
+<p><a class="el" href="group__grp__logreg.html">Logistic Regression</a></p>
+<p><a class="el" href="group__grp__mlogreg.html">Multinomial Logistic 
Regression</a></p>
+<p><a class="el" href="group__grp__robust.html">Robust Variance</a></p>
+<p><a class="el" href="group__grp__clustered__errors.html">Clustered 
Variance</a></p>
+<p><a class="el" href="group__grp__validation.html">Cross Validation</a></p>
+<p><a class="el" href="group__grp__marginal.html">Marginal Effects</a></p>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Tue Sep 20 2016 11:27:01 for MADlib by
+    <a href="http://www.doxygen.org/index.html";>
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__inf__stats.html
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__inf__stats.html 
b/docs/v1.9.1/group__grp__inf__stats.html
new file mode 100644
index 0000000..2166179
--- /dev/null
+++ b/docs/v1.9.1/group__grp__inf__stats.html
@@ -0,0 +1,134 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" 
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";>
+<html xmlns="http://www.w3.org/1999/xhtml";>
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data 
mining,deep learning,ensemble methods,data science,market basket 
analysis,affinity analysis,pca,lda,regression,elastic net,huber 
white,proportional hazards,k-means,latent dirichlet allocation,bayes,support 
vector machines,svm"/>
+<title>MADlib: Inferential Statistics</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="eigen_navtree_hacks.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new 
Date();a=s.createElement(o),
+  
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'madlib.net');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.net";><img alt="Logo" 
src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.9.1</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" 
href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" 
border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__inf__stats.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="summary">
+<a href="#groups">Modules</a>  </div>
+  <div class="headertitle">
+<div class="title">Inferential Statistics<div class="ingroups"><a class="el" 
href="group__grp__stats.html">Statistics</a></div></div>  </div>
+</div><!--header-->
+<div class="contents">
+<a name="details" id="details"></a><h2 class="groupheader">Detailed 
Description</h2>
+<p>A collection of methods to compute inferential statistics on a dataset. </p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a 
name="groups"></a>
+Modules</h2></td></tr>
+<tr class="memitem:group__grp__stats__tests"><td class="memItemLeft" 
align="right" valign="top">&#160;</td><td class="memItemRight" 
valign="bottom"><a class="el" href="group__grp__stats__tests.html">Hypothesis 
Tests</a></td></tr>
+<tr class="memdesc:group__grp__stats__tests"><td 
class="mdescLeft">&#160;</td><td class="mdescRight">Provides functions to 
perform statistical hypothesis tests. <br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Tue Sep 20 2016 11:27:01 for MADlib by
+    <a href="http://www.doxygen.org/index.html";>
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__inf__stats.js
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__inf__stats.js 
b/docs/v1.9.1/group__grp__inf__stats.js
new file mode 100644
index 0000000..7bfc14a
--- /dev/null
+++ b/docs/v1.9.1/group__grp__inf__stats.js
@@ -0,0 +1,4 @@
+var group__grp__inf__stats =
+[
+    [ "Hypothesis Tests", "group__grp__stats__tests.html", null ]
+];
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/bed9253d/docs/v1.9.1/group__grp__kernmach.html
----------------------------------------------------------------------
diff --git a/docs/v1.9.1/group__grp__kernmach.html 
b/docs/v1.9.1/group__grp__kernmach.html
new file mode 100644
index 0000000..4b9a08b
--- /dev/null
+++ b/docs/v1.9.1/group__grp__kernmach.html
@@ -0,0 +1,123 @@
+<!-- HTML header for doxygen 1.8.4-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" 
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";>
+<html xmlns="http://www.w3.org/1999/xhtml";>
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen 1.8.10"/>
+<meta name="keywords" content="madlib,postgres,greenplum,machine learning,data 
mining,deep learning,ensemble methods,data science,market basket 
analysis,affinity analysis,pca,lda,regression,elastic net,huber 
white,proportional hazards,k-means,latent dirichlet allocation,bayes,support 
vector machines,svm"/>
+<title>MADlib: Support Vector Machines</title>
+<link href="tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="jquery.js"></script>
+<script type="text/javascript" src="dynsections.js"></script>
+<link href="navtree.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="resize.js"></script>
+<script type="text/javascript" src="navtreedata.js"></script>
+<script type="text/javascript" src="navtree.js"></script>
+<script type="text/javascript">
+  $(document).ready(initResizable);
+  $(window).load(resizeHeight);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/javascript">
+  $(document).ready(function() { init_search(); });
+</script>
+<!-- hack in the navigation tree -->
+<script type="text/javascript" src="eigen_navtree_hacks.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="madlib_extra.css" rel="stylesheet" type="text/css"/>
+<!-- google analytics -->
+<script>
+  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new 
Date();a=s.createElement(o),
+  
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+  })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
+  ga('create', 'UA-45382226-1', 'madlib.net');
+  ga('send', 'pageview');
+</script>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <td id="projectlogo"><a href="http://madlib.net";><img alt="Logo" 
src="madlib.png" height="50" style="padding-left:0.5em;" border="0"/ ></a></td>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   <span id="projectnumber">1.9dev</span>
+   </div>
+   <div id="projectbrief">User Documentation for MADlib</div>
+  </td>
+   <td>        <div id="MSearchBox" class="MSearchBoxInactive">
+        <span class="left">
+          <img id="MSearchSelect" src="search/mag_sel.png"
+               onmouseover="return searchBox.OnSearchSelectShow()"
+               onmouseout="return searchBox.OnSearchSelectHide()"
+               alt=""/>
+          <input type="text" id="MSearchField" value="Search" accesskey="S"
+               onfocus="searchBox.OnSearchFieldFocus(true)" 
+               onblur="searchBox.OnSearchFieldFocus(false)" 
+               onkeyup="searchBox.OnSearchFieldChange(event)"/>
+          </span><span class="right">
+            <a id="MSearchClose" 
href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" 
border="0" src="search/close.png" alt=""/></a>
+          </span>
+        </div>
+</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.10 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+</div><!-- top -->
+<div id="side-nav" class="ui-resizable side-nav-resizable">
+  <div id="nav-tree">
+    <div id="nav-tree-contents">
+      <div id="nav-sync" class="sync"></div>
+    </div>
+  </div>
+  <div id="splitbar" style="-moz-user-select:none;" 
+       class="ui-resizable-handle">
+  </div>
+</div>
+<script type="text/javascript">
+$(document).ready(function(){initNavTree('group__grp__kernmach.html','');});
+</script>
+<div id="doc-content">
+<!-- window showing the filter options -->
+<div id="MSearchSelectWindow"
+     onmouseover="return searchBox.OnSearchSelectShow()"
+     onmouseout="return searchBox.OnSearchSelectHide()"
+     onkeydown="return searchBox.OnSearchSelectKey(event)">
+</div>
+
+<!-- iframe showing the search results (closed by default) -->
+<div id="MSearchResultsWindow">
+<iframe src="javascript:void(0)" frameborder="0" 
+        name="MSearchResults" id="MSearchResults">
+</iframe>
+</div>
+
+<div class="header">
+  <div class="headertitle">
+<div class="title">Support Vector Machines<div class="ingroups"><a class="el" 
href="group__grp__early__stage.html">Early Stage Development</a></div></div>  
</div>
+</div><!--header-->
+<div class="contents">
+</div><!-- contents -->
+</div><!-- doc-content -->
+<!-- start footer part -->
+<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
+  <ul>
+    <li class="footer">Generated on Tue Jun 7 2016 09:40:56 for MADlib by
+    <a href="http://www.doxygen.org/index.html";>
+    <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.10 </li>
+  </ul>
+</div>
+</body>
+</html>


Reply via email to