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<!doctype html> <html> <head> <meta name="generator" content="JSDoc 3"> <meta charset="utf-8"> <title>Class: DpMeans</title> <link rel="stylesheet" href="https://brick.a.ssl.fastly.net/Karla:400,400i,700,700i" type="text/css"> <link rel="stylesheet" href="https://brick.a.ssl.fastly.net/Noto+Serif:400,400i,700,700i" type="text/css"> <link rel="stylesheet" href="https://brick.a.ssl.fastly.net/Inconsolata:500" type="text/css"> <link href="css/baseline.css" rel="stylesheet"> </head> <body onload="prettyPrint()"> <nav id="jsdoc-navbar" role="navigation" class="jsdoc-navbar"> <div id="jsdoc-navbar-container"> <div id="jsdoc-navbar-content"> <a href="index.html" class="jsdoc-navbar-package-name">QMiner JavaScript API v9.4.0</a> </div> </div> </nav> <div id="jsdoc-body-container"> <div id="jsdoc-content"> <div id="jsdoc-content-container"> <div id="jsdoc-main" role="main"> <header class="page-header"> <div class="symbol-detail-labels"><span class="label label-kind">class</span> <span class="label label-static">static</span></div> <h1><small><a href="module-analytics.html">analytics</a>.<wbr></small><span class="symbol-name">DpMeans</span></h1> <p class="source-link">Source: <a href="analyticsdoc.js.html#source-line-1942">analyticsdoc.<wbr>js:1942</a></p> <div class="symbol-classdesc"> <p>DpMeans Clustering is an iterative, data-partitioning algorithm that assigns observations into clusters with the nearest centroid according to some metric. The number of clusters is not known in advance, but can be upper and lower bounded. Rather the parameter is the maximum radius of a cluster <code>lambda</code>.</p> </div> <dl class="dl-compact"> </dl> </header> <section id="summary"> <div class="summary-callout"> <h2 class="summary-callout-heading">Properties</h2> <div class="summary-content"> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics.DpMeans.html#centroids">centroids</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#idxv">idxv</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics.DpMeans.html#medoids">medoids</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#relMeanCentroidDist">relMeanCentroidDist</a></dt> <dd> </dd> </dl> </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.DpMeans.html#explain">explain(X)</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#fit">fit(X)</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#getModel">getModel()</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics.DpMeans.html#getParams">getParams()</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#permuteCentroids">permuteCentroids(mapping)</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#predict">predict(A)</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics.DpMeans.html#save">save(fout)</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#setParams">setParams(params)</a></dt> <dd> </dd> <dt><a href="module-analytics.DpMeans.html#transform">transform(A)</a></dt> <dd> </dd> </dl> </div> </div> </div> </section> <section> <h2 id="DpMeans">new <span class="symbol-name">DpMeans</span><span class="signature"><span class="signature-params">([arg])</span></span></h2> <p>DpMeans Clustering</p> <section> <h3> Example </h3> <div> <pre class="prettyprint"><code>// import analytics and la modules var analytics = require('qminer').analytics; var la = require('qminer').la; // create a KMeans object var dpmeans = new analytics.DpMeans(); // create the matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model dpmeans.fit(X); // predict where the columns of the matrix will be assigned var Y = new la.Matrix([[1, 1, 0], [-2, 3, 1]]); var prediction = dpmeans.predict(Y);</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#~DpMeansParam">module:analytics~DpMeansParam</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#~DpMeansParam">module:analytics~DpMeansParam</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>Properties</h2> <section> <h3 id="centroids"><span class="symbol-name">centroids</span></h3> <p>The centroids created with the fit method. Type <a href="module-la.Matrix.html">module:la.Matrix</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 DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fit(X); // get the centroids var centroids = DpMeans.centroids; // print the first centroid console.log(centroids.getCol(0));</code></pre> </div> </section> <dl class="dl-compact"> </dl> <h3 id="idxv"><span class="symbol-name">idxv</span></h3> <p>The integer vector containing the cluster ids of the training set created with the fit method. Type <a href="module-la.IntVector.html">module:la.IntVector</a>.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import the modules var analytics = require('qminer').analytics; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // get the idxv var idxv = DpMeans.idxv;</code></pre> </div> </section> <dl class="dl-compact"> </dl> <h3 id="medoids"><span class="symbol-name">medoids</span></h3> <p>The medoids created with the fit method. Type <a href="module-la.IntVector.html">module:la.IntVector</a>.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import the modules var analytics = require('qminer').analytics; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // get the centroids var medoids = DpMeans.medoids;</code></pre> </div> </section> <dl class="dl-compact"> </dl> <h3 id="relMeanCentroidDist"><span class="symbol-name">relMeanCentroidDist</span></h3> <p>Returns the normalized weighted distance between the vectors and their centroids using the following formula: d = \frac{sum_i p_i*sum_j d(x_j,c_i) / n_i}{sum_{k=1}^n d(x_k, mu) / n} = \frac{sum_{i,j} d(c_i,x_j)}{sum_k d(x_k, mu)}</p> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B relMeanDist</p> </dd> </dl> </section> <h2>Methods</h2> <section> <h3 id="explain"><span class="symbol-name">explain</span><span class="signature"><span class="signature-params">(X)</span> → <span class="signature-returns"> Array of <a href="module-analytics.html#~DpMeansExplain">module:analytics~DpMeansExplain</a></span></span></h3> <p>Returns the IDs of the nearest medoid for each example.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import analytics module var analytics = require('qminer').analytics; // import linear algebra module var la = require('qminer').la; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, k: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X using the column IDs [0,1,2] DpMeans.fit(X, [1234,1142,2355]); // create the matrix of the prediction vectors var test = new la.Matrix([[2, -1, 1], [1, 0, -3]]); // predict/explain - return the closest medoids var explanation = DpMeans.explain(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.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>Matrix whose columns correspond to examples.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>Array of <a href="module-analytics.html#~DpMeansExplain">module:analytics~DpMeansExplain</a></code>B Array containing the DpMeans explanantions.</p> </dd> </dl> <h3 id="fit"><span class="symbol-name">fit</span><span class="signature"><span class="signature-params">(X)</span> → <span class="signature-returns"> <a href="module-analytics.DpMeans.html">module:analytics.DpMeans</a></span></span></h3> <p>Calculates the centroids.</p> <section> <h4> Examples </h4> <div> <p> Asynchronous function </p> <pre class="prettyprint"><code>// import analytics module var analytics = require('qminer').analytics; var la = require('qminer').la; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fitAsync(X, function (err) { if (err) console.log(err); // successful calculation });</code></pre> </div> <div> <p> Synchronous function </p> <pre class="prettyprint"><code>var analytics = require('qminer').analytics; var la = require('qminer').la; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fit(X);</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.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>Matrix whose columns correspond to examples.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-analytics.DpMeans.html">module:analytics.DpMeans</a></code>B Self. The model has been updated.</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>Returns the model.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import modules var analytics = require('qminer').analytics; var la = require('qminer').la; // create the KMeans object var dpmeans = new analytics.DpMeans({ iter: 1000 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model dpmeans.fit(X); // get the model var model = dpmeans.getModel();</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>Object</code>B The <code>DpMeansModel</code> object containing the properites: <br> 1. <code>DpMeansModel.C</code> - The <a href="module-la.Matrix.html">module:la.Matrix</a> or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a> containing the centroids, <br> 2. <code>DpMeansModel.medoids</code> - The <a href="module-la.IntVector.html">module:la.IntVector</a> of cluster medoids of the training data, <br> 3. <code>DpMeansModel.idxv</code> - The <a href="module-la.IntVector.html">module:la.IntVector</a> of cluster IDs of the training data. </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#~KMeansParam">module:analytics~KMeansParam</a></span></span></h3> <p>Returns the parameters.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import analytics module var analytics = require('qminer').analytics; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 5 }); // get the parameters var json = DpMeans.getParams(); console.log(json.lambda);</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-analytics.html#~KMeansParam">module:analytics~KMeansParam</a></code>B The construction parameters.</p> </dd> </dl> <h3 id="permuteCentroids"><span class="symbol-name">permuteCentroids</span><span class="signature"><span class="signature-params">(mapping)</span> → <span class="signature-returns"> <a href="module-analytics.DpMeans.html">module:analytics.DpMeans</a></span></span></h3> <p>Permutates the clusters, and with it <a href="module-analytics.DpMeans.html#centroids">module:analytics.DpMeans#centroids</a>, <a href="module-analytics.DpMeans.html#medoids">module:analytics.DpMeans#medoids</a> and <a href="module-analytics.DpMeans.html#idxv">module:analytics.DpMeans#idxv</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 DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fit(X); if (DpMeans.centroids.cols > 1) { // create the mapping vector: swap first two centroids var Mapping = new la.IntVector([1, 0, 2, 3, 4, 5].splice(0,DpMeans.centroids.cols)); console.log(DpMeans.centroids.toString()); // permutate the clusters. DpMeans.permuteCentroids(Mapping); console.log(DpMeans.centroids.toString()); }</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>mapping</p> </td> <td> <p><a href="module-la.IntVector.html">module:la.IntVector</a></p> </td> <td> <p> </p> </td> <td> <p>The mapping, where <code>mapping[4] = 2</code> means "map cluster 4 into cluster 2".</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-analytics.DpMeans.html">module:analytics.DpMeans</a></code>B Self. The clusters have been permuted.</p> </dd> </dl> <h3 id="predict"><span class="symbol-name">predict</span><span class="signature"><span class="signature-params">(A)</span> → <span class="signature-returns"> <a href="module-la.IntVector.html">module:la.IntVector</a></span></span></h3> <p>Returns an vector of cluster id assignments.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import analytics module var analytics = require('qminer').analytics; var la = require('qminer').la; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fit(X); // create the matrix of the prediction vectors var pred = new la.Matrix([[2, -1, 1], [1, 0, -3]]); // predict the values var prediction = DpMeans.predict(pred);</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>A</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>Matrix whose columns correspond to examples.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.IntVector.html">module:la.IntVector</a></code>B Vector of cluster assignments.</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 DpMeans internal state into (binary) file.</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 DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fit(X); // create the file output stream var fout = new fs.openWrite('DpMeans.bin'); // save the DpMeans instance DpMeans.save(fout); fout.close(); // load the DpMeans instance var fin = fs.openRead('DpMeans.bin'); var KMeans2 = new analytics.DpMeans(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">(params)</span> → <span class="signature-returns"> <a href="module-analytics.DpMeans.html">module:analytics.DpMeans</a></span></span></h3> <p>Sets the parameters.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import analytics module var analytics = require('qminer').analytics; // create a new DpMeans object var DpMeans = new analytics.DpMeans(); // change the parameters of the DpMeans object DpMeans.setParams({ iter: 1000, lambda: 5 });</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>params</p> </td> <td> <p><a href="module-analytics.html#~KMeansParam">module:analytics~KMeansParam</a></p> </td> <td> <p> </p> </td> <td> <p>The construction parameters.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-analytics.DpMeans.html">module:analytics.DpMeans</a></code>B Self. The model parameters have been updated.</p> </dd> </dl> <h3 id="transform"><span class="symbol-name">transform</span><span class="signature"><span class="signature-params">(A)</span> → <span class="signature-returns"> <a href="module-la.Matrix.html">module:la.Matrix</a></span></span></h3> <p>Transforms the points to vectors of distances to centroids.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import modules var analytics = require('qminer').analytics; var la = require('qminer').la; // create a new DpMeans object var DpMeans = new analytics.DpMeans({ iter: 1000, lambda: 3 }); // create a matrix to be fitted var X = new la.Matrix([[1, -2, -1], [1, 1, -3]]); // create the model with the matrix X DpMeans.fit(X); // create the matrix of the transform vectors var matrix = new la.Matrix([[-2, 0], [0, -3]]); // get the transform values of matrix // returns the matrix // 10 17 // 1 20 // 10 1 DpMeans.transform(matrix);</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>A</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>Matrix whose columns correspond to examples.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.Matrix.html">module:la.Matrix</a></code>B Matrix where each column represents the squared distances to the centroid vectors.</p> </dd> </dl> </section> </section> </div> </div> <nav id="jsdoc-toc-nav" role="navigation"></nav> </div> </div> <footer id="jsdoc-footer" class="jsdoc-footer"> <div id="jsdoc-footer-container"> <p> </p> </div> </footer> <script src="scripts/jquery.min.js"></script> <script src="scripts/tree.jquery.js"></script> <script src="scripts/prettify.js"></script> <script src="scripts/jsdoc-toc.js"></script> <script src="scripts/linenumber.js"></script> <script src="scripts/scrollanchor.js"></script> </body> </html>