qminer
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A C++ based data analytics platform for processing large-scale real-time streams containing structured and unstructured data
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<a href="index.html" class="jsdoc-navbar-package-name">QMiner JavaScript API v9.4.0</a>
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<h1><small><a href="module-analytics.html">analytics</a>.<wbr></small><span class="symbol-name">LogReg</span></h1>
<p class="source-link">Source: <a href="analyticsdoc.js.html#source-line-1121">analyticsdoc.<wbr>js:1121</a></p>
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<p>Uses Newtons method to compute the weights. <b>Before use: QMiner must be built with the OpenBLAS library.</b></p>
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<dl class="dl-compact">
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<section id="summary">
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<h2 class="summary-callout-heading">Property</h2>
<div class="summary-content">
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.LogReg.html#weights">weights</a></dt>
<dd>
</dd>
</dl>
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<div class="summary-column">
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<div class="summary-column">
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<h2 class="summary-callout-heading">Methods</h2>
<div class="summary-content">
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.LogReg.html#fit">fit(X, y[, eps])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.LogReg.html#getParams">getParams()</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.LogReg.html#predict">predict(x)</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.LogReg.html#save">save(fout)</a></dt>
<dd>
</dd>
</dl>
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<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.LogReg.html#setParams">setParams(param)</a></dt>
<dd>
</dd>
</dl>
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</section>
<section>
<h2 id="LogReg">new <span class="symbol-name">LogReg</span><span class="signature"><span class="signature-params">([arg])</span></span></h2>
<p>Logistic regression model.</p>
<section>
<h3>
Example
</h3>
<div>
<pre class="prettyprint"><code>// import analytics module
var analytics = require('qminer').analytics;
// create the Logistic Regression model
var logreg = new analytics.LogReg({ lambda: 2 });
// create the input matrix and vector for fitting the model
var mat = new la.Matrix([[1, 0, -1, 0], [0, 1, 0, -1]]);
var vec = new la.Vector([1, 0, -1, -2]);
// if OpenBLAS is used, fit the model
if (require('qminer').flags.blas) {
logreg.fit(mat, vec);
// create the vector for the prediction
var test = new la.Vector([1, 1]);
// get the prediction
var prediction = logreg.predict(test);
}</code></pre>
</div>
</section>
<section>
<h3>Parameter</h3>
<table class="jsdoc-details-table">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Optional</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<p>arg</p>
</td>
<td>
<p>(<a href="module-analytics.html#~logisticRegParam">module:analytics~logisticRegParam</a> or <a href="module-fs.FIn.html">module:fs.FIn</a>)</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Construction arguments. There are two ways of constructing:
<br>1. Using the <a href="module-analytics.html#~logisticRegParam">module:analytics~logisticRegParam</a> object,
<br>2. using the file input stream <a href="module-fs.FIn.html">module:fs.FIn</a>.
</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
</section>
<section>
<h2>Property</h2>
<section>
<h3 id="weights"><span class="symbol-name">weights</span></h3>
<p>Gives the weights of the model. Type <a href="module-la.Vector.html">module:la.Vector</a>.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create the logistic regression model
var logreg = new analytics.LogReg();
// get the weights of the model
var weights = logreg.weights;</code></pre>
</div>
</section>
<dl class="dl-compact">
</dl>
</section>
<h2>Methods</h2>
<section>
<h3 id="fit"><span class="symbol-name">fit</span><span class="signature"><span class="signature-params">(X, y[, eps])</span> → <span class="signature-returns"> <a href="module-analytics.LogReg.html">module:analytics.LogReg</a></span></span></h3>
<p>Fits a column matrix of feature vectors <code>X</code> onto the response variable <code>y</code>.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create the logistic regression model
var logreg = new analytics.LogReg();
// create the input matrix and vector for fitting the model
var mat = new la.Matrix([[1, 0, -1, 0], [0, 1, 0, -1]]);
var vec = new la.Vector([1, 0, -1, -2]);
// if OpenBLAS is used, fit the model
if (require('qminer').flags.blas) {
logreg.fit(mat, vec);
}</code></pre>
</div>
</section>
<section>
<h4>Parameters</h4>
<table class="jsdoc-details-table">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Optional</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<p>X</p>
</td>
<td>
<p><a href="module-la.Matrix.html">module:la.Matrix</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>the column matrix which stores the feature vectors.</p>
</td>
</tr>
<tr>
<td>
<p>y</p>
</td>
<td>
<p><a href="module-la.Vector.html">module:la.Vector</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>the response variable.</p>
</td>
</tr>
<tr>
<td>
<p>eps</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>the epsilon used for convergence.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-analytics.LogReg.html">module:analytics.LogReg</a></code>B Self. The model has been updated.</p>
</dd>
</dl>
<h3 id="getParams"><span class="symbol-name">getParams</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> <a href="module-analytics.html#~logisticRegParam">module:analytics~logisticRegParam</a></span></span></h3>
<p>Gets the parameters.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import analytics module
var analytics = require('qminer').analytics;
// create the Logistic Regression model
var logreg = new analytics.LogReg({ lambda: 10 });
// get the parameters of the model
var param = logreg.getParams(); // returns { lambda: 10, intercept: false }</code></pre>
</div>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-analytics.html#~logisticRegParam">module:analytics~logisticRegParam</a></code>B The parameters of the model.</p>
</dd>
</dl>
<h3 id="predict"><span class="symbol-name">predict</span><span class="signature"><span class="signature-params">(x)</span> → <span class="signature-returns"> number</span></span></h3>
<p>Returns the expected response for the provided feature vector.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create the logistic regression model
var logreg = new analytics.LogReg();
// create the input matrix and vector for fitting the model
var mat = new la.Matrix([[1, 0, -1, 0], [0, 1, 0, -1]]);
var vec = new la.Vector([1, 0, -1, -2]);
// if openblas is used, fit the model and predict the value
if (require('qminer').flags.blas) {
// fit the model
logreg.fit(mat, vec);
// create the vector for the prediction
var test = new la.Vector([1, 1]);
// get the prediction
var prediction = logreg.predict(test);
};</code></pre>
</div>
</section>
<section>
<h4>Parameter</h4>
<table class="jsdoc-details-table">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Optional</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<p>x</p>
</td>
<td>
<p><a href="module-la.Vector.html">module:la.Vector</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>the feature vector.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code>number</code>B the expected response.</p>
</dd>
</dl>
<h3 id="save"><span class="symbol-name">save</span><span class="signature"><span class="signature-params">(fout)</span> → <span class="signature-returns"> <a href="module-fs.FOut.html">module:fs.FOut</a></span></span></h3>
<p>Saves the model into the output stream.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create the logistic regression model
var logreg = new analytics.LogReg();
// create the input matrix and vector for fitting the model
var mat = new la.Matrix([[1, 0, -1, 0], [0, 1, 0, -1]]);
var vec = new la.Vector([1, 0, -1, -2]);
// if openblas is used, fit the model
if (require('qminer').flags.blas) {
logreg.fit(mat, vec);
};
// create an output stream object and save the model
var fout = fs.openWrite('logreg_example.bin');
logreg.save(fout);
fout.close();
// create input stream
var fin = fs.openRead('logreg_example.bin');
// create a Logistic Regression object that loads the model and parameters from input stream
var logreg2 = new analytics.LogReg(fin);</code></pre>
</div>
</section>
<section>
<h4>Parameter</h4>
<table class="jsdoc-details-table">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Optional</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<p>fout</p>
</td>
<td>
<p><a href="module-fs.FOut.html">module:fs.FOut</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>the output stream.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-fs.FOut.html">module:fs.FOut</a></code>B The output stream <code>fout</code>.</p>
</dd>
</dl>
<h3 id="setParams"><span class="symbol-name">setParams</span><span class="signature"><span class="signature-params">(param)</span> → <span class="signature-returns"> <a href="module-analytics.LogReg.html">module:analytics.LogReg</a></span></span></h3>
<p>Set the parameters.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import analytics module
var analytics = require('qminer').analytics;
// create a logistic regression model
var logreg = new analytics.LogReg({ lambda: 10 });
// set the parameters of the model
logreg.setParams({ lambda: 1 });</code></pre>
</div>
</section>
<section>
<h4>Parameter</h4>
<table class="jsdoc-details-table">
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Optional</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<p>param</p>
</td>
<td>
<p><a href="module-analytics.html#~logisticRegParam">module:analytics~logisticRegParam</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The new parameters.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-analytics.LogReg.html">module:analytics.LogReg</a></code>B Self. The parameters are updated.</p>
</dd>
</dl>
</section>
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