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<!doctype html> <html> <head> <meta name="generator" content="JSDoc 3"> <meta charset="utf-8"> <title>Class: SparseMatrix</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-la.html">la</a>.<wbr></small><span class="symbol-name">SparseMatrix</span></h1> <p class="source-link">Source: <a href="ladoc.js.html#source-line-705">ladoc.<wbr>js:705</a></p> <div class="symbol-classdesc"> <p>Sparse Matrix is represented as a dense vector of sparse vectors which correspond to matrix columns.</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-la.SparseMatrix.html#cols">cols</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-la.SparseMatrix.html#rows">rows</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-la.SparseMatrix.html#at">at(rowIdx, colIdx)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#clear">clear()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#colNorms">colNorms()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#frob">frob()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#frob2">frob2()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#full">full()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#getCol">getCol(colIdx)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#getColSubmatrix">getColSubmatrix(columnIdVec)</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-la.SparseMatrix.html#load">load(fin)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#minus">minus(mat)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#multiply">multiply(arg)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#multiplyT">multiplyT(arg)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#nnz">nnz()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#normalizeCols">normalizeCols()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#plus">plus(mat)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#print">print()</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-la.SparseMatrix.html#push">push(spVec)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#put">put(rowIdx, colIdx, num)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#save">save(fout[, saveMatlab])</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#setCol">setCol(colIdx, spVec)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#setRowDim">setRowDim(rowDim)</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#toString">toString()</a></dt> <dd> </dd> <dt><a href="module-la.SparseMatrix.html#transpose">transpose()</a></dt> <dd> </dd> </dl> </div> </div> </div> </section> <section> <h2 id="SparseMatrix">new <span class="symbol-name">SparseMatrix</span><span class="signature"><span class="signature-params">([arg][, rows])</span></span></h2> <p>Sparse Matrix</p> <section> <h3> Example </h3> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix with array var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]); // create a new sparse matrix with specified max rows var mat2 = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]], 3);</code></pre> </div> </section> <section> <h3>Parameters</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>(Array of Array of Array of number or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>)</p> </td> <td> <p>Yes</p> </td> <td> <p>Constructor arguments. There are two ways of constructing: <br>1. using a nested array of sparse vectors (columns). A sparse vector is a nested array of pairs, first value is index, second is value. Example: [[[0, 2]], [[0, 1], [2, 3]]] has 2 columns. The second non-zero element in second column has a value 3 at index 2, <br>2. using a sparse matrix (copy constructor). </p> </td> </tr> <tr> <td> <p>rows</p> </td> <td> <p>number</p> </td> <td> <p>Yes</p> </td> <td> <p>Maximal number of rows in sparse vector. <i>It is only in combinantion with nested array of vector elements.</i></p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> </dl> </section> <section> <h2>Properties</h2> <section> <h3 id="cols"><span class="symbol-name">cols</span></h3> <p>Gives the number of columns of sparse matrix. Type <code>number</code>.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]); // check the number of columns in sparse matrix mat.cols;</code></pre> </div> </section> <dl class="dl-compact"> </dl> <h3 id="rows"><span class="symbol-name">rows</span></h3> <p>Gives the number of rows of sparse matrix. Type <code>number</code>.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]); // check the number of rows in sparse matrix mat.rows;</code></pre> </div> </section> <dl class="dl-compact"> </dl> </section> <h2>Methods</h2> <section> <h3 id="at"><span class="symbol-name">at</span><span class="signature"><span class="signature-params">(rowIdx, colIdx)</span> → <span class="signature-returns"> number</span></span></h3> <p>Returns an element of the sparse matrix at the given location.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a sparse matrix var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]); // get the value at the position (1, 1) mat.at(1, 1); // returns 3</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>rowIdx</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Row index (zero based).</p> </td> </tr> <tr> <td> <p>colIdx</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Column index (zero based).</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Matrix value.</p> </dd> </dl> <h3 id="clear"><span class="symbol-name">clear</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Clear content of the matrix and sets its row dimension to -1.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>//import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]); // clear the matrix mat.clear();</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B Self. All the content has been cleared.</p> </dd> </dl> <h3 id="colNorms"><span class="symbol-name">colNorms</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> <a href="module-la.Vector.html">module:la.Vector</a></span></span></h3> <p>Returns the vector of column norms of sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>//import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]); // get the column norms var norms = mat.colNorms();</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.Vector.html">module:la.Vector</a></code>B Vector of column norms. Ihe i-th value of the return vector is the norm of i-th column of sparse matrix.</p> </dd> </dl> <h3 id="frob"><span class="symbol-name">frob</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> number</span></span></h3> <p>Returns the frobenious norm of sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 1], [1, 3]], [[0, 2], [1, 4]]]); // get the frobenious norm of sparse matrix var norm = mat.frob(); // returns sqrt(30)</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Frobenious norm of sparse matrix.</p> </dd> </dl> <h3 id="frob2"><span class="symbol-name">frob2</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> number</span></span></h3> <p>Calculates the frobenious norm squared of the matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a sparse matrix var spMat = new la.SparseMatrix([[[0, 1], [1, 5]], [[0, 2], [2, -3]]]); // get the forbenious norm squared of the sparse matrix var frob = spMat.frob2();</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Frobenious norm squared.</p> </dd> </dl> <h3 id="full"><span class="symbol-name">full</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> <a href="module-la.Matrix.html">module:la.Matrix</a></span></span></h3> <p>Returns the dense representation of sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]); // create a dense representation of sparse matrix // returns the dense matrix: // 2 1 // 0 0 // 0 3 mat.full();</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.Matrix.html">module:la.Matrix</a></code>B Dense representation of sparse matrix.</p> </dd> </dl> <h3 id="getCol"><span class="symbol-name">getCol</span><span class="signature"><span class="signature-params">(colIdx)</span> → <span class="signature-returns"> <a href="module-la.SparseVector.html">module:la.SparseVector</a></span></span></h3> <p>Returns the column of the sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a sparse matrix var mat = new la.SparseMatrix([[[0, 3], [1, 2]], [[1, -2], [3, 4]], [[10, 8]]]); // get the first column as a vector var first = mat.getCol(0); // returns the first column of the sparse 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>colIdx</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>The column index (zero based).</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseVector.html">module:la.SparseVector</a></code>B Sparse vector corresponding to the <code>colIdx</code>-th column of sparse matrix.</p> </dd> </dl> <h3 id="getColSubmatrix"><span class="symbol-name">getColSubmatrix</span><span class="signature"><span class="signature-params">(columnIdVec)</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Returns a submatrix containing only selected columns. Columns are identified by a vector of ids.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>//import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2], [2, -3]], [[1, 1], [3, -2]]]); // get the submatrix containing the 1, 2 and 4 column var submat = mat.getColSubmatrix(new la.IntVector([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>columnIdVec</p> </td> <td> <p><a href="module-la.IntVector.html">module:la.IntVector</a></p> </td> <td> <p> </p> </td> <td> <p>Integer vector containing selected column ids.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B The submatrix containing the the columns of the original matrix.</p> </dd> </dl> <h3 id="load"><span class="symbol-name">load</span><span class="signature"><span class="signature-params">(fin)</span> → <span class="signature-returns"> <a href="module-la.Matrix.html">module:la.Matrix</a></span></span></h3> <p>Loads the sparse matrix from input stream.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import the modules var fs = require('qminer').fs; var la = require('qminer').la; // create an empty matrix var mat = new la.SparseMatrix(); // open a read stream ('mat.dat' was previously created) var fin = fs.openRead('mat.dat'); // load the matrix mat.load(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>fin</p> </td> <td> <p><a href="module-fs.FIn.html">module:fs.FIn</a></p> </td> <td> <p> </p> </td> <td> <p>Input stream.</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 Self. The content has been loaded using <code>fin</code>.</p> </dd> </dl> <h3 id="minus"><span class="symbol-name">minus</span><span class="signature"><span class="signature-params">(mat)</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Substraction of two matrices.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create two sparse matrices var mat = new la.SparseMatrix([[[0, 1], [3, 2]], [[1, -3]]]); var mat2 = new la.SparseMatrix([[[0, 3]],[[2, 1]]]); // get the sum of the two matrices // returns the sum ( insparse form) // -2 0 // 0 -3 // 0 -1 // 2 0 var diff = mat.minus(mat2);</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>mat</p> </td> <td> <p><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></p> </td> <td> <p> </p> </td> <td> <p>The second sparse matrix.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B The difference of the two sparse matrices.</p> </dd> </dl> <h3 id="multiply"><span class="symbol-name">multiply</span><span class="signature"><span class="signature-params">(arg)</span> → <span class="signature-returns"> (<a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.Matrix.html">module:la.Matrix</a>)</span></span></h3> <p>Multiplies argument with sparse maatrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create new sparse matrix var mat = new la.SparseMatrix([[[0, 2], [3, 5]], [[1, -3]]]); // create a vector var vec = new la.Vector([3, -2]); // multiply the matrix and vector var vec2 = mat.multiply(vec);</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>arg</p> </td> <td> <p>(number, <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>Multiplication input.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>(<a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.Matrix.html">module:la.Matrix</a>)</code>B <br>1. <a href="module-la.Matrix.html">module:la.Matrix</a>, if <code>arg</code> is number, <a href="module-la.Matrix.html">module:la.Matrix</a> or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>. <br>2. <a href="module-la.Vector.html">module:la.Vector</a>, if <code>arg</code> is <a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.SparseVector.html">module:la.SparseVector</a>. </p> </dd> </dl> <h3 id="multiplyT"><span class="symbol-name">multiplyT</span><span class="signature"><span class="signature-params">(arg)</span> → <span class="signature-returns"> (<a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.Matrix.html">module:la.Matrix</a>)</span></span></h3> <p>Sparse matrix transpose and multiplies with argument.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create new sparse matrix var mat = new la.SparseMatrix([[[0, 2], [3, 5]], [[1, -3]]]); // create a dense matrix var mat2 = new la.Matrix([[0, 1], [2, 3], [4, 5], [-1, 3]]); // transpose mat and multiply it with mat2 var mat3 = mat.multiplyT(mat2);</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>arg</p> </td> <td> <p>(number, <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>Multiplication input.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>(<a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.Matrix.html">module:la.Matrix</a>)</code>B <br>1. <a href="module-la.Matrix.html">module:la.Matrix</a>, if <code>arg</code> is number, <a href="module-la.Matrix.html">module:la.Matrix</a> or <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a>. <br>2. <a href="module-la.Vector.html">module:la.Vector</a>, if <code>arg</code> is <a href="module-la.Vector.html">module:la.Vector</a> or <a href="module-la.SparseVector.html">module:la.SparseVector</a>. </p> </dd> </dl> <h3 id="nnz"><span class="symbol-name">nnz</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> number</span></span></h3> <p>Returns the number of non-zero elements of sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a sparse matrix var spMat = new la.SparseMatrix([[[0, 1], [1, 5]], [[0, 2], [2, -3]]]); // get the number of non-zero elements // returns 4 var nnz = spMat.nnz(); </code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Number of non-zero elements.</p> </dd> </dl> <h3 id="normalizeCols"><span class="symbol-name">normalizeCols</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Normalizes columns of sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 2]], [[0, 1], [2, 3]]]); // normalize matrix columns // The new matrix elements are: // 1 0.316227 // 0 0 // 0 0.948683 mat.normalizeCols();</code></pre> </div> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B Self. The columns of the sparse matrix are normalized.</p> </dd> </dl> <h3 id="plus"><span class="symbol-name">plus</span><span class="signature"><span class="signature-params">(mat)</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Addition of two matrices.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create two sparse matrices var mat = new la.SparseMatrix([[[0, 1], [3, 2]], [[1, -3]]]); var mat2 = new la.SparseMatrix([[[0, 3]],[[2, 1]]]); // get the sum of the two matrices // returns the sum ( insparse form) // 4 0 // 0 -3 // 0 1 // 2 0 var sum = mat.plus(mat2);</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>mat</p> </td> <td> <p><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></p> </td> <td> <p> </p> </td> <td> <p>The second sparse matrix.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B Sum of the two sparse matrices.</p> </dd> </dl> <h3 id="print"><span class="symbol-name">print</span><span class="signature"><span class="signature-params">()</span></span></h3> <p>Prints sparse matrix on screen.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var spMat = new la.SparseMatrix([[[0, 1]], [[0, 3], [1, 8]]]); // print sparse matrix on screen // each row represents a nonzero element, where first value is row index, second // value is column index and third value is element value. For this matrix: // 0 0 1.000000 // 0 1 3.000000 // 1 1 8.000000 spMat.print();</code></pre> </div> </section> <dl class="dl-compact"> </dl> <h3 id="push"><span class="symbol-name">push</span><span class="signature"><span class="signature-params">(spVec)</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Attaches a column to the sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse vector var mat = new la.SparseMatrix([[[0, 2], [3, 5]], [[1, -3]]]); // create a new vector var vec = new la.SparseVector([[0, 2], [2, -3]]); // push the newly created vector to the matrix // the new matrix is going to be (in sparse form) // 2 0 2 // 0 -3 0 // 0 0 -3 // 5 0 0 mat.push(vec);</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>spVec</p> </td> <td> <p><a href="module-la.SparseVector.html">module:la.SparseVector</a></p> </td> <td> <p> </p> </td> <td> <p>Attached column as sparse vector.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B Self. The last column is now the added <code>spVec</code> and the number of columns is now bigger by one.</p> </dd> </dl> <h3 id="put"><span class="symbol-name">put</span><span class="signature"><span class="signature-params">(rowIdx, colIdx, num)</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Puts an element in sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 3], [1, 2]], [[1, -2], [3, 4]], [[10, 8]]]); // set the value at position (2, 2) to -4 mat.put(2, 2, -4);</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>rowIdx</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Row index (zero based).</p> </td> </tr> <tr> <td> <p>colIdx</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Column index (zero based).</p> </td> </tr> <tr> <td> <p>num</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Element value.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B Self. The value at position (<code>rowIdx</code>, <code>colIdx</code>) is changed.</p> </dd> </dl> <h3 id="save"><span class="symbol-name">save</span><span class="signature"><span class="signature-params">(fout[, saveMatlab])</span> → <span class="signature-returns"> <a href="module-fs.FOut.html">module:fs.FOut</a></span></span></h3> <p>Saves the sparse matrix as output stream.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import the modules var fs = require('qminer').fs; var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 1]], [[0, 3], [1, 12]]]); // open write stream var fout = fs.openWrite('mat.dat'); // save matrix and close write stream mat.save(fout).close();</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>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> <tr> <td> <p>saveMatlab</p> </td> <td> <p>boolean</p> </td> <td> <p>Yes</p> </td> <td> <p>If true, saves using matlab three column text format. Otherwise, saves using binary format.</p> <p>Defaults to <code>false</code>.</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="setCol"><span class="symbol-name">setCol</span><span class="signature"><span class="signature-params">(colIdx, spVec)</span> → <span class="signature-returns"> <a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></span></span></h3> <p>Sets a column in sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import la module var la = require('qminer').la; // create a new sparse matrix var mat = new la.SparseMatrix([[[0, 3], [1, 2]], [[1, -2], [3, 4]], [[10, 8]]]); // create a new sparse vector to replace the third column var vec = new la.SparseVector([[0, 3], [2, -5]]); // set the third column of mat to vec mat.setCol(2, vec); // returns mat with the third column changed</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>colIdx</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Column index (zero based).</p> </td> </tr> <tr> <td> <p>spVec</p> </td> <td> <p><a href="module-la.SparseVector.html">module:la.SparseVector</a></p> </td> <td> <p> </p> </td> <td> <p>The new column sparse vector.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.SparseMatrix.html">module:la.SparseMatrix</a></code>B Self. The <code>colIdx</code>-th column has been replaced with <code>spVec</code>.</p> </dd> </dl> <h3 id="setRowDim"><span class="symbol-name">setRowDim</span><span class="signature"><span class="signature-params">(rowDim)</span></span></h3> <p>Sets the row dimension.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// import the modules var la = require('qminer').la; // create an empty matrix var mat = new la.SparseMatrix(); mat.setRowDim(2); mat.rows // prints 2</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>rowDim</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Row dimension.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> </dl> <h3 id="toString"><span class="symbol-name">toString</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> string</span></span></h3> <p>Returns a string displaying rows, columns and number of non-zero elements of sparse matrix.</p> <section> <h4> Example </h4> <div> <pre class="prettyprint"><code>// imp