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">SVR</span></h1>
<p class="source-link">Source: <a href="analyticsdoc.js.html#source-line-264">analyticsdoc.<wbr>js:264</a></p>
<div class="symbol-classdesc">
<p>Support Vector Machine Regression. Implements a soft margin linear support vector regression using the PEGASOS algorithm with epsilon insensitive loss, see: <a href="http://ttic.uchicago.edu/~nati/Publications/PegasosMPB.pdf">Pegasos: Primal Estimated sub-GrAdient SOlver for SVM</a>.</p>
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<dl class="dl-compact">
</dl>
</header>
<section id="summary">
<div class="summary-callout">
<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.SVR.html#weights">weights</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
</div>
<div class="summary-column">
</div>
</div>
</div>
<div class="summary-callout">
<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.SVR.html#decisionFunction">decisionFunction(X)</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.SVR.html#fit">fit(X, y)</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.SVR.html#getModel">getModel()</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.SVR.html#getParams">getParams()</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.SVR.html#predict">predict(X)</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.SVR.html#save">save(fout)</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.SVR.html#setParams">setParams(param)</a></dt>
<dd>
</dd>
</dl>
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</div>
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</section>
<section>
<h2 id="SVR">new <span class="symbol-name">SVR</span><span class="signature"><span class="signature-params">([arg])</span></span></h2>
<p>SVR</p>
<section>
<h3>
Example
</h3>
<div>
<pre class="prettyprint"><code>// import module
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// REGRESSION WITH SVR
// Set up fake train and test data.
// Four training examples with, number of features = 2
var featureMatrix = new la.Matrix({ rows: 2, cols: 4, random: true });
// Regression targets for four examples
var targets = new la.Vector([1.1, -2, 3, 4.2]);
// Set up the regression model
var SVR = new analytics.SVR({ verbose: false });
// Train regression
SVR.fit(featureMatrix, targets);
// Set up a fake test vector
var test = new la.Vector([1.1, -0.8]);
// Predict the target value
var prediction = SVR.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#~SVMParam">module:analytics~SVMParam</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#~SVMParam">module:analytics~SVMParam</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>The vector of coefficients of the linear model. Type <a href="module-la.Vector.html">module:la.Vector</a>.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import the modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10 });
// create a matrix and vector for the model
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
// create the model by fitting the values
SVR.fit(matrix, vector);
// get the coeficients of the linear model
var coef = SVR.weights;</code></pre>
</div>
</section>
<dl class="dl-compact">
</dl>
</section>
<h2>Methods</h2>
<section>
<h3 id="decisionFunction"><span class="symbol-name">decisionFunction</span><span class="signature"><span class="signature-params">(X)</span> → <span class="signature-returns"> (number or <a href="module-la.Vector.html">module:la.Vector</a>)</span></span></h3>
<p>Sends vector through the model and returns the scalar product as a real number.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import the modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10 });
// create a matrix and vector for the model
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
// create the model by fitting the values
SVR.fit(matrix, vector);
// get the distance between the model and the given vector
var vec2 = new la.Vector([-5, 1]);
var distance = SVR.decisionFunction(vec2);</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>, <a href="module-la.SparseVector.html">module:la.SparseVector</a>, <a href="module-la.Matrix.html">module:la.Matrix</a>, or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>)</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Input feature vector or matrix with feature vectors as columns.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code>(number or <a href="module-la.Vector.html">module:la.Vector</a>)</code>B Distance:
<br>1. Real number if <code>X</code> is <a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.SparseVector.html">module:la.SparseVector</a>.
<br>2. <a href="module-la.Vector.html">module:la.Vector</a>, if <code>X</code> is <a href="module-la.Matrix.html">module:la.Matrix</a> or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>.
</p>
</dd>
</dl>
<h3 id="fit"><span class="symbol-name">fit</span><span class="signature"><span class="signature-params">(X, y)</span> → <span class="signature-returns"> <a href="module-analytics.SVR.html">module:analytics.SVR</a></span></span></h3>
<p>Fits a SVM regression model, given column examples in a matrix and vector of targets.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import the modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10 });
// create a matrix and vector for the model
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
// create the model by fitting the values
SVR.fit(matrix, vector);</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> or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>)</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Input feature matrix where columns correspond to 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>Input vector of targets, one for each column of X.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-analytics.SVR.html">module:analytics.SVR</a></code>B Self. The model has been created.</p>
</dd>
</dl>
<h3 id="getModel"><span class="symbol-name">getModel</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> Object</span></span></h3>
<p>Get the model.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import analytics module
var analytics = require('qminer').analytics;
// create a SVR model
var SVR = new analytics.SVR();
// get the properties of the model
var model = SVR.getModel();</code></pre>
</div>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code>Object</code>B The <code>svmModel</code> object containing the property:
<br> 1. <code>svmModel.weights</code> - The weights of the model. Type <a href="module-la.Vector.html">module:la.Vector</a>.
</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#~SVMParam">module:analytics~SVMParam</a></span></span></h3>
<p>Gets the SVR parameters.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import analytics module
var analytics = require('qminer').analytics;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10, eps: 1e-10, maxTime: 12000, verbose: true });
// get the parameters of SVR
var params = SVR.getParams();</code></pre>
</div>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-analytics.html#~SVMParam">module:analytics~SVMParam</a></code>B Parameters of the regression 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 or <a href="module-la.Vector.html">module:la.Vector</a>)</span></span></h3>
<p>Sends vector through the model and returns the prediction as a real number.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import the modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10 });
// create a matrix and vector for the model
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
// create the model by fitting the values
SVR.fit(matrix, vector);
// predict the value of the given vector
var vec2 = new la.Vector([-5, 1]);
var prediction = SVR.predict(vec2);</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>, <a href="module-la.SparseVector.html">module:la.SparseVector</a>, <a href="module-la.Matrix.html">module:la.Matrix</a>, or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>)</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Input feature vector or matrix with feature vectors as columns.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code>(number or <a href="module-la.Vector.html">module:la.Vector</a>)</code>B Prediction:
<br>1. Real number, if <code>X</code> is <a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.SparseVector.html">module:la.SparseVector</a>.
<br>2. <a href="module-la.Vector.html">module:la.Vector</a>, if <code>X</code> is <a href="module-la.Matrix.html">module:la.Matrix</a> or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>.
</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 model to output file stream.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import the modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
var fs = require('qminer').fs;
// create a new SVR object
var SVR = new analytics.SVR({ c: 10 });
// create a matrix and vector for the model
var matrix = new la.Matrix([[1, -1], [1, 1]]);
var vector = new la.Vector([1, 1]);
// create the model by fitting the values
SVR.fit(matrix, vector);
// save the model in a binary file
var fout = fs.openWrite('svr_example.bin');
SVR.save(fout);
fout.close();
// construct a SVR model by loading from the binary file
var fin = fs.openRead('svr_example.bin');
var SVR2 = new analytics.SVR(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>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.SVR.html">module:analytics.SVR</a></span></span></h3>
<p>Sets the SVR parameters.</p>
<section>
<h4>
Example
</h4>
<div>
<pre class="prettyprint"><code>// import analytics module
var analytics = require('qminer').analytics;
// create a new SVR object
var SVR = new analytics.SVR();
// set the parameters of the SVR object
SVR.setParams({ c: 10, maxTime: 12000 });</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#~SVMParam">module:analytics~SVMParam</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Regression training parameters.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code><a href="module-analytics.SVR.html">module:analytics.SVR</a></code>B Self. Updated the training parameters.</p>
</dd>
</dl>
</section>
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