qminer
Version:
A C++ based data analytics platform for processing large-scale real-time streams containing structured and unstructured data
1,249 lines • 52.3 kB
HTML
<!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>// import la module
var la = require('qminer').la;
// create a new sparse matrix
var mat = new la.SparseMatrix([[[0, 1]], [[0, 2], [1, 8]]]);
// create the string
var text = mat.toString(); // returns 'rows: -1, cols: 2, nnz: 3'</code></pre>
</div>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code>string</code>B String displaying row, columns and number of non-zero elements.</p>
</dd>
</dl>
<h3 id="transpose"><span class="symbol-name">transpose</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>Returns the transposed 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]]]);
// transpose the sparse matrix
// returns the transposed matrix (in sparse form)
// 2 0 -3 0
// 0 1 0 -2
mat.transpose();</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 Transposed sparse matrix.</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>