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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<div class="symbol-detail-labels"><span class="label label-kind">module</span></div>
<h1><small></small><span class="symbol-name">analytics</span></h1>
<p class="source-link">Source: <a href="analyticsdoc.js.html#source-line-8">analyticsdoc.<wbr>js:8</a></p>
<div class="symbol-description">
<p>Analytics module.</p>
</div>
<section>
<h2>
Example
</h2>
<div>
<pre class="prettyprint"><code>// import modules
var qm = require('qminer');
var analytics = qm.analytics;
// load dataset, create model, evaluate model</code></pre>
</div>
</section>
<dl class="dl-compact">
</dl>
</header>
<section id="summary">
<div class="summary-callout">
<h2 class="summary-callout-heading">Child classes</h2>
<div class="summary-content">
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.BiasedGk.html">BiasedGk([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.BufferedTDigest.html">BufferedTDigest([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.DpMeans.html">DpMeans([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.Gk.html">Gk([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.KMeans.html">KMeans([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.LogReg.html">LogReg([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.MDS.html">MDS([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.NearestNeighborAD.html">NearestNeighborAD([arg])</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.NNet.html">NNet([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.OneVsAll.html">OneVsAll([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.PCA.html">PCA([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.PropHazards.html">PropHazards([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.RecLinReg.html">RecLinReg(arg)</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.RecommenderSys.html">RecommenderSys([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.RidgeReg.html">RidgeReg([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.Sigmoid.html">Sigmoid([arg])</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.SVC.html">SVC([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.SVR.html">SVR([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.TDigest.html">TDigest([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.ThresholdModel.html">ThresholdModel([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.Tokenizer.html">Tokenizer([arg])</a></dt>
<dd>
</dd>
<dt><a href="module-analytics-ActiveLearner.html">ActiveLearner([arg])</a></dt>
<dd>
</dd>
</dl>
</div>
</div>
</div>
<div class="summary-callout">
<h2 class="summary-callout-heading">Namespaces</h2>
<div class="summary-content">
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics-metrics.html">metrics</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics-preprocessing.html">preprocessing</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
</div>
</div>
</div>
<div class="summary-callout">
<h2 class="summary-callout-heading">Method</h2>
<div class="summary-content">
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.html#nmf">nmf(mat, k[, json])</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">Abstract types</h2>
<div class="summary-content">
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.html#~ActiveLearnerParam">ActiveLearnerParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~BiasedGkParam">BiasedGkParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~BufferedDigestParam">BufferedDigestParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~detectorParam">detectorParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~DpMeansExplain">DpMeansExplain</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~DpMeansParam">DpMeansParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~GkParam">GkParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~hazardModelParam">hazardModelParam</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.html#~KMeansExplain">KMeansExplain</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~KMeansParam">KMeansParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~logisticRegParam">logisticRegParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~MDSParam">MDSParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~NearestNeighborADExplain">NearestNeighborADExplain</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~NearestNeighborADFeatureContribution">NearestNeighborADFeatureContribution</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~nnetParam">nnetParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~oneVsAllParam">oneVsAllParam</a></dt>
<dd>
</dd>
</dl>
</div>
<div class="summary-column">
<dl class="dl-summary-callout">
<dt><a href="module-analytics.html#~PCAParam">PCAParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~recLinRegParam">recLinRegParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~RecSysParam">RecSysParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~ridgeRegParam">ridgeRegParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~SVMParam">SVMParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~TDigestParam">TDigestParam</a></dt>
<dd>
</dd>
<dt><a href="module-analytics.html#~tokenizerParam">tokenizerParam</a></dt>
<dd>
</dd>
</dl>
</div>
</div>
</div>
</section>
<section>
<h2>Classes</h2>
<section id='members-links'>
<h3><a href="module-analytics.BiasedGk.html">BiasedGk</a></h3>
<h3><a href="module-analytics.BufferedTDigest.html">BufferedTDigest</a></h3>
<h3><a href="module-analytics.DpMeans.html">DpMeans</a></h3>
<h3><a href="module-analytics.Gk.html">Gk</a></h3>
<h3><a href="module-analytics.KMeans.html">KMeans</a></h3>
<h3><a href="module-analytics.LogReg.html">LogReg</a></h3>
<h3><a href="module-analytics.MDS.html">MDS</a></h3>
<h3><a href="module-analytics.NearestNeighborAD.html">NearestNeighborAD</a></h3>
<h3><a href="module-analytics.NNet.html">NNet</a></h3>
<h3><a href="module-analytics.OneVsAll.html">OneVsAll</a></h3>
<h3><a href="module-analytics.PCA.html">PCA</a></h3>
<h3><a href="module-analytics.PropHazards.html">PropHazards</a></h3>
<h3><a href="module-analytics.RecLinReg.html">RecLinReg</a></h3>
<h3><a href="module-analytics.RecommenderSys.html">RecommenderSys</a></h3>
<h3><a href="module-analytics.RidgeReg.html">RidgeReg</a></h3>
<h3><a href="module-analytics.Sigmoid.html">Sigmoid</a></h3>
<h3><a href="module-analytics.SVC.html">SVC</a></h3>
<h3><a href="module-analytics.SVR.html">SVR</a></h3>
<h3><a href="module-analytics.TDigest.html">TDigest</a></h3>
<h3><a href="module-analytics.ThresholdModel.html">ThresholdModel</a></h3>
<h3><a href="module-analytics.Tokenizer.html">Tokenizer</a></h3>
<h3><a href="module-analytics-ActiveLearner.html">ActiveLearner</a></h3>
</section>
<h2>Namespaces</h2>
<section id='members-links'>
<h3><a href="module-analytics-metrics.html">metrics</a></h3>
<h3><a href="module-analytics-preprocessing.html">preprocessing</a></h3>
</section>
<h2>Method</h2>
<section>
<h3 id="nmf"><span class="symbol-name">nmf</span><span class="signature"><span class="signature-params">(mat, k[, json])</span> → <span class="signature-returns"> Object</span></span></h3>
<p>Calculates the non-negative matrix factorization, see: <a href="https://en.wikipedia.org/wiki/Non-negative_matrix_factorization">https://en.wikipedia.org/wiki/Non-negative_matrix_factorization</a>.</p>
<section>
<h4>
Examples
</h4>
<div>
<p>Asynchronous function</p>
<pre class="prettyprint"><code>// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create a matrix
var mat = new la.Matrix({ rows: 10, cols: 5, random: true });
// compute the non-negative matrix factorization
analytics.nmfAsync(mat, 3, { iter: 100, tol: 1e-4 }, function (err, result) {
if (err) { console.log(err); }
// calculation successful
var U = result.U;
var V = result.V;
});</code></pre>
</div>
<div>
<p>Synchronous function</p>
<pre class="prettyprint"><code>// import modules
var analytics = require('qminer').analytics;
var la = require('qminer').la;
// create a matrix
var mat = new la.Matrix({ rows: 10, cols: 5, random: true });
// compute the non-negative matrix factorization
var result = analytics.nmf(mat, 3, { iter: 100, tol: 1e-4 });
var U = result.U;
var V = result.V;</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>mat</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>The non-negative matrix.</p>
</td>
</tr>
<tr>
<td>
<p>k</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The reduced rank, e.g. number of columns in matrix U and number of rows in matrix V. Must be between 0 and <code>min(mat.rows, mat.cols)</code>.</p>
</td>
</tr>
<tr>
<td>
<p>json</p>
</td>
<td>
<p>Object</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Algorithm options.</p>
<p>Values in <code>json</code> have the following properties:</p>
<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>iter</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The number of iterations used for the algorithm.</p>
<p>Defaults to <code>100</code>.</p>
</td>
</tr>
<tr>
<td>
<p>tol</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The tolerance.</p>
<p>Defaults to <code>1e-3</code>.</p>
</td>
</tr>
<tr>
<td>
<p>verbose</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>If false, the console output is supressed.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
</tbody>
</table>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
<dt>Returns</dt>
<dd>
<p><code>Object</code>B The json object <code>nmfRes</code> containing the non-negative matrices U and V:
<br> <code>nmfRes.U</code>- The <a href="module-la.Matrix.html">module:la.Matrix</a> representation of the matrix U,
<br> <code>nmfRes.V</code>- The <a href="module-la.Matrix.html">module:la.Matrix</a> representation of the matrix V.
</p>
</dd>
</dl>
</section>
<h2>Abstract types</h2>
<section>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~ActiveLearnerParam"><span class="symbol-name">ActiveLearnerParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of module:analytics.ActiveLearner.</p>
<section>
<h4>Properties</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>learner</p>
</td>
<td>
<p>Object</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Learner parameters</p>
<p>Values in <code>learner</code> have the following properties:</p>
<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>disableAsserts</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Disable input asserting</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
</tbody>
</table>
</td>
</tr>
<tr>
<td>
<p>SVC</p>
</td>
<td>
<p><a href="module-analytics.html#~SVMParam">module:analytics~SVMParam</a></p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Support vector classifier parameters.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~BiasedGkParam"><span class="symbol-name">BiasedGkParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of module:analytics.quantiles.BiasedGk.</p>
<section>
<h4>Properties</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>targetProb</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The probability where the algorithm is most accurate. Its accuracy is determined as eps<em>max(p, targetProb) when targetProb < 0.5 and eps</em>max(1-p, 1-targetProb) when targetProb >= 0.5. Higher values of <code>targetProb</code> allow for a smaller memory footprint.</p>
<p>Defaults to <code>0.01</code>.</p>
</td>
</tr>
<tr>
<td>
<p>eps</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Parameter which determines the accuracy.</p>
<p>Defaults to <code>0.1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>compression</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Determines when the algorithm compresses its summary. Options are: "periodic", "aggressive" and "manual".</p>
<p>Defaults to <code>"periodic"</code>.</p>
</td>
</tr>
<tr>
<td>
<p>useBands</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether the algorithm should use the 'band' subprocedure. Using this subprocedure should result in a smaller summary.</p>
<p>Defaults to <code>true</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~BufferedDigestParam"><span class="symbol-name">BufferedDigestParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of module:analytics.quantiles.BufferedTDigest.</p>
<section>
<h4>Properties</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>delta</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The number of clusters in the summary is bounded by floor(minClusters) <= clusters < 2*ceil(minClusters)</p>
<p>Defaults to <code>100</code>.</p>
</td>
</tr>
<tr>
<td>
<p>bufferLen</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>the size of the buffer is minClusters<em>bufferLenFactor, when the buffer fills it is merged with the summary. Also, the algorithm initializes after seeing minClusters</em>bufferLenFactor examples.</p>
<p>Defaults to <code>1000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>seed</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>random seed (values above 1 are deterministic)</p>
<p>Defaults to <code>0</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~detectorParam"><span class="symbol-name">detectorParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.NearestNeighborAD.html">module:analytics.NearestNeighborAD</a>.</p>
<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>rate</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The expected fracton of emmited anomalies (0.05 -> 5% of cases will be classified as anomalies).</p>
<p>Defaults to <code>0.05</code>.</p>
</td>
</tr>
<tr>
<td>
<p>windowSize</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Number of most recent instances kept in the model.</p>
<p>Defaults to <code>100</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~DpMeansExplain"><span class="symbol-name">DpMeansExplain</span><small class="property-type">
Object</small></h3>
<p>The examplanation returned by <a href="module-analytics.KMeans.html#explain">module:analytics.KMeans#explain</a>.</p>
<section>
<h4>Properties</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>medoidID</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The ID of the nearest medoids.</p>
</td>
</tr>
<tr>
<td>
<p>featureIDs</p>
</td>
<td>
<p><a href="module-la.IntVector.html">module:la.IntVector</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The IDs of features, sorted by contribution.</p>
</td>
</tr>
<tr>
<td>
<p>featureContributions</p>
</td>
<td>
<p><a href="module-la.Vector.html">module:la.Vector</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Weights of each feature contribution (sum to 1.0).</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~DpMeansParam"><span class="symbol-name">DpMeansParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.KMeans.html">module:analytics.KMeans</a>.</p>
<section>
<h4>Properties</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>iter</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The maximum number of iterations.</p>
<p>Defaults to <code>10000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>lambda</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Maximum radius of the clusters</p>
<p>Defaults to <code>1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>minClusters</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Minimum number of clusters</p>
<p>Defaults to <code>2</code>.</p>
</td>
</tr>
<tr>
<td>
<p>maxClusters</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Maximum number of clusters</p>
<p>Defaults to <code>inf</code>.</p>
</td>
</tr>
<tr>
<td>
<p>allowEmpty</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether to allow empty clusters to be generated.</p>
<p>Defaults to <code>true</code>.</p>
</td>
</tr>
<tr>
<td>
<p>calcDistQual</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether to calculate the quality measure based on distance, if false relMeanCentroidDist will return 'undefined'</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
<tr>
<td>
<p>centroidType</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The type of centroids. Possible options are <code>'Dense'</code> and <code>'Sparse'</code>.</p>
<p>Defaults to <code>"Dense"</code>.</p>
</td>
</tr>
<tr>
<td>
<p>distanceType</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The distance type used at the calculations. Possible options are <code>'Euclid'</code> and <code>'Cos'</code>.</p>
<p>Defaults to <code>"Euclid"</code>.</p>
</td>
</tr>
<tr>
<td>
<p>verbose</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>If <code>false</code>, the console output is supressed.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
<tr>
<td>
<p>fitIdx</p>
</td>
<td>
<p>Array of number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The index array used for the construction of the initial centroids.</p>
</td>
</tr>
<tr>
<td>
<p>fitStart</p>
</td>
<td>
<p>Object</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The KMeans model returned by module:analytics.KMeans.prototype.getModel used for centroid initialization.</p>
<p>Values in <code>fitStart</code> have the following properties:</p>
<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>C</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>The centroid matrix.</p>
</td>
</tr>
</tbody>
</table>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~GkParam"><span class="symbol-name">GkParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of module:analytics.quantiles.Gk.</p>
<section>
<h4>Properties</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>eps</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Determines the relative error of the algorithm.</p>
<p>Defaults to <code>0.01</code>.</p>
</td>
</tr>
<tr>
<td>
<p>autoCompress</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether the summary should be compresses automatically or manually.</p>
<p>Defaults to <code>true</code>.</p>
</td>
</tr>
<tr>
<td>
<p>useBands</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether the algorithm should use the 'band' subprocedure. Using this subprocedure should result in a smaller summary.</p>
<p>Defaults to <code>true</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~hazardModelParam"><span class="symbol-name">hazardModelParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.PropHazards.html">module:analytics.PropHazards</a>.</p>
<section>
<h4>Property</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>lambda</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The regularization parameter.</p>
<p>Defaults to <code>0</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~KMeansExplain"><span class="symbol-name">KMeansExplain</span><small class="property-type">
Object</small></h3>
<p>The examplanation returned by <a href="module-analytics.KMeans.html#explain">module:analytics.KMeans#explain</a>.</p>
<section>
<h4>Properties</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>medoidID</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The ID of the nearest medoids.</p>
</td>
</tr>
<tr>
<td>
<p>featureIDs</p>
</td>
<td>
<p><a href="module-la.IntVector.html">module:la.IntVector</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The IDs of features, sorted by contribution.</p>
</td>
</tr>
<tr>
<td>
<p>featureContributions</p>
</td>
<td>
<p><a href="module-la.Vector.html">module:la.Vector</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Weights of each feature contribution (sum to 1.0).</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~KMeansParam"><span class="symbol-name">KMeansParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.KMeans.html">module:analytics.KMeans</a>.</p>
<section>
<h4>Properties</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>iter</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The maximum number of iterations.</p>
<p>Defaults to <code>10000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>k</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The number of centroids.</p>
<p>Defaults to <code>2</code>.</p>
</td>
</tr>
<tr>
<td>
<p>allowEmpty</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether to allow empty clusters to be generated.</p>
<p>Defaults to <code>true</code>.</p>
</td>
</tr>
<tr>
<td>
<p>calcDistQual</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Whether to calculate the quality measure based on distance, if false relMeanCentroidDist will return 'undefined'</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
<tr>
<td>
<p>centroidType</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The type of centroids. Possible options are <code>'Dense'</code> and <code>'Sparse'</code>.</p>
<p>Defaults to <code>"Dense"</code>.</p>
</td>
</tr>
<tr>
<td>
<p>distanceType</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The distance type used at the calculations. Possible options are <code>'Euclid'</code> and <code>'Cos'</code>.</p>
<p>Defaults to <code>"Euclid"</code>.</p>
</td>
</tr>
<tr>
<td>
<p>verbose</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>If <code>false</code>, the console output is supressed.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
<tr>
<td>
<p>fitIdx</p>
</td>
<td>
<p>Array of number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The index array used for the construction of the initial centroids.</p>
</td>
</tr>
<tr>
<td>
<p>fitStart</p>
</td>
<td>
<p>Object</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The KMeans model returned by module:analytics.KMeans.prototype.getModel used for centroid initialization.</p>
<p>Values in <code>fitStart</code> have the following properties:</p>
<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>C</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>The centroid matrix.</p>
</td>
</tr>
</tbody>
</table>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~logisticRegParam"><span class="symbol-name">logisticRegParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.LogReg.html">module:analytics.LogReg</a>.</p>
<section>
<h4>Properties</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>lambda</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The regularization parameter.</p>
<p>Defaults to <code>1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>intercept</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Indicates whether to automatically include the intercept.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~MDSParam"><span class="symbol-name">MDSParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.MDS.html">module:analytics.MDS</a>.</p>
<section>
<h4>Properties</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>maxSecs</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The maximum time period to compute Multidimensional Scaling of a matrix.</p>
<p>Defaults to <code>500</code>.</p>
</td>
</tr>
<tr>
<td>
<p>maxStep</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The maximum number of iterations.</p>
<p>Defaults to <code>5000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>minDiff</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The minimum difference criteria in MDS.</p>
<p>Defaults to <code>1e-4</code>.</p>
</td>
</tr>
<tr>
<td>
<p>distType</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The type of distance used. Available types: "Euclid", "Cos", "SqrtCos".</p>
<p>Defaults to <code>"Euclid"</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~NearestNeighborADExplain"><span class="symbol-name">NearestNeighborADExplain</span><small class="property-type">
Object</small></h3>
<p>An object used for interpreting the predictions of <a href="module-analytics.NearestNeighborAD.html#explain">module:analytics.NearestNeighborAD#explain</a>.</p>
<section>
<h4>Properties</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>nearestID</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The ID of the nearest neighbor.</p>
</td>
</tr>
<tr>
<td>
<p>distance</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The distance to the nearest neighbor.</p>
</td>
</tr>
<tr>
<td>
<p>features</p>
</td>
<td>
<p>Array of <a href="module-analytics.html#~NearestNeighborADFeatureContribution">module:analytics~NearestNeighborADFeatureContribution</a></p>
</td>
<td>
<p> </p>
</td>
<td>
<p>An array with feature contributions.</p>
</td>
</tr>
<tr>
<td>
<p>oldestID</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The ID of the oldest record in the internal buffer (the record that was added first).</p>
</td>
</tr>
<tr>
<td>
<p>newestID</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The ID of the newest record in the internal buffer (the record that was added last).</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~NearestNeighborADFeatureContribution"><span class="symbol-name">NearestNeighborADFeatureContribution</span><small class="property-type">
Object</small></h3>
<p>An object explaining the prediction of <a href="module-analytics.NearestNeighborAD.html#explain">module:analytics.NearestNeighborAD#explain</a> in terms of a single feature.
Contained in the object <a href="module-analytics.html#~NearestNeighborADExplain">module:analytics~NearestNeighborADExplain</a>.</p>
<section>
<h4>Properties</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>id</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The ID of the feature.</p>
</td>
</tr>
<tr>
<td>
<p>val</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The value of the feature for the vector we are explaining.</p>
</td>
</tr>
<tr>
<td>
<p>nearVal</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The the value of the feature for the nearest neighbor.</p>
</td>
</tr>
<tr>
<td>
<p>contribution</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>Fraction of the total distance <code>(v(i) - n(i))^2 / ||v - n||^2</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~nnetParam"><span class="symbol-name">nnetParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.NNet.html">module:analytics.NNet</a>.</p>
<section>
<h4>Properties</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>layout</p>
</td>
<td>
<p>Array of number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The array representing the network schema.</p>
<p>Defaults to <code>[1, 2, 1]</code>.</p>
</td>
</tr>
<tr>
<td>
<p>learnRate</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The learning rate.</p>
<p>Defaults to <code>0.1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>momentum</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The momentum of optimization.</p>
<p>Defaults to <code>0.5</code>.</p>
</td>
</tr>
<tr>
<td>
<p>tFuncHidden</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Type of activation function used on hidden nevrons. Possible options are <code>'tanHyper'</code>, <code>'sigmoid'</code>, <code>'fastTanh'</code>, <code>'softPlus'</code>, <code>'fastSigmoid'</code> and <code>'linear'</code>.</p>
<p>Defaults to <code>'tanHyper'</code>.</p>
</td>
</tr>
<tr>
<td>
<p>tFuncOut</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Type of activation function used on output nevrons. Possible options are <code>'tanHyper'</code>, <code>'sigmoid'</code>, <code>'fastTanh'</code>, <code>'softPlus'</code>, <code>'fastSigmoid'</code> and <code>'linear'</code>.</p>
<p>Defaults to <code>'tanHyper'</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~oneVsAllParam"><span class="symbol-name">oneVsAllParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.OneVsAll.html">module:analytics.OneVsAll</a>.</p>
<section>
<h4>Properties</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>model</p>
</td>
<td>
<p>function()</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Constructor for binary model to be
used internaly. Constructor should expect only one parameter.</p>
</td>
</tr>
<tr>
<td>
<p>modelParam</p>
</td>
<td>
<p>Object</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Parameter for <code>oneVsAllParam.model</code> constructor.</p>
</td>
</tr>
<tr>
<td>
<p>categories</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Number of categories.</p>
</td>
</tr>
<tr>
<td>
<p>verbose</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>If false, the console output is supressed.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~PCAParam"><span class="symbol-name">PCAParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.PCA.html">module:analytics.PCA</a>.</p>
<section>
<h4>Properties</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>k</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Number of eigenvectors to be computed.</p>
<p>Defaults to <code>null</code>.</p>
</td>
</tr>
<tr>
<td>
<p>iter</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Number of iterations.</p>
<p>Defaults to <code>100</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~recLinRegParam"><span class="symbol-name">recLinRegParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.RecLinReg.html">module:analytics.RecLinReg</a>.</p>
<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>dim</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p> </p>
</td>
<td>
<p>The dimension of the model.</p>
</td>
</tr>
<tr>
<td>
<p>regFact</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The regularization factor.</p>
<p>Defaults to <code>1.0</code>.</p>
</td>
</tr>
<tr>
<td>
<p>forgetFact</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The forgetting factor.</p>
<p>Defaults to <code>1.0</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~RecSysParam"><span class="symbol-name">RecSysParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.RecommenderSys.html">module:analytics.RecommenderSys</a>.</p>
<section>
<h4>Properties</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>iter</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The maximum number of iterations.</p>
<p>Defaults to <code>10000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>k</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The number of centroids.</p>
<p>Defaults to <code>2</code>.</p>
</td>
</tr>
<tr>
<td>
<p>tol</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The tolerance.</p>
<p>Defaults to <code>1e-3</code>.</p>
</td>
</tr>
<tr>
<td>
<p>verbose</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>If false, the console output is supressed.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~ridgeRegParam"><span class="symbol-name">ridgeRegParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.RidgeReg.html">module:analytics.RidgeReg</a>.</p>
<section>
<h4>Property</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>gamma</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The gamma value.</p>
<p>Defaults to <code>0.0</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~SVMParam"><span class="symbol-name">SVMParam</span><small class="property-type">
Object</small></h3>
<p>SVM constructor parameters. Used for the construction of <a href="module-analytics.SVC.html">module:analytics.SVC</a> and <a href="module-analytics.SVR.html">module:analytics.SVR</a>.</p>
<section>
<h4>Properties</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>algorithm</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The algorithm procedure. Possible options are <code>'SGD'</code> and <code>'LIBSVM'</code>. <code>'PR_LOQO'</code>is not supported anymore.</p>
<p>Defaults to <code>'SGD'</code>.</p>
</td>
</tr>
<tr>
<td>
<p>c</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Cost parameter. Increasing the parameter forces the model to fit the training data more accurately (setting it too large may lead to overfitting) .</p>
<p>Defaults to <code>1.0</code>.</p>
</td>
</tr>
<tr>
<td>
<p>j</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Unbalance parameter. Increasing it gives more weight to the positive examples (getting a better fit on the positive training examples gets a higher priority). Setting j=n is like adding n-1 copies of the positive training examples to the data set.</p>
<p>Defaults to <code>1.0</code>.</p>
</td>
</tr>
<tr>
<td>
<p>eps</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Epsilon insensitive loss parameter. Larger values result in fewer support vectors (smaller model complexity)</p>
<p>Defaults to <code>1e-3</code>.</p>
</td>
</tr>
<tr>
<td>
<p>batchSize</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Number of examples used in the subgradient estimation. Higher number of samples slows down the algorithm, but makes the local steps more accurate.</p>
<p>Defaults to <code>1000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>maxIterations</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Maximum number of iterations.</p>
<p>Defaults to <code>10000</code>.</p>
</td>
</tr>
<tr>
<td>
<p>maxTime</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Maximum runtime in seconds.</p>
<p>Defaults to <code>1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>minDiff</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Stopping criterion tolerance.</p>
<p>Defaults to <code>1e-6</code>.</p>
</td>
</tr>
<tr>
<td>
<p>type</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The subalgorithm procedure in LIBSVM. Possible options are <code>'C_SVC'</code>, <code>'NU_SVC'</code> and <code>'ONE_CLASS'</code> for classification and <code>'EPSILON_SVR'</code>, <code>'NU_SVR'</code> and <code>'ONE_CLASS'</code> for regression.</p>
<p>Defaults to <code>'C_SVC'</code>.</p>
</td>
</tr>
<tr>
<td>
<p>kernel</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Kernel type in LIBSVM. Possible options are <code>'LINEAR'</code>, <code>'POLY'</code>, 'RBF'<code>, 'SIGMOID'</code> and <code>'PRECOMPUTED'</code>.</p>
<p>Defaults to <code>'LINEAR'</code>.</p>
</td>
</tr>
<tr>
<td>
<p>gamma</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Gamma parameter in LIBSVM. Set gamma in kernel function.</p>
<p>Defaults to <code>1.0</code>.</p>
</td>
</tr>
<tr>
<td>
<p>p</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>P parameter in LIBSVM. Set the epsilon in loss function of epsilon-SVR.</p>
<p>Defaults to <code>1e-1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>degree</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Degree parameter in LIBSVM. Set degree in kernel function.</p>
<p>Defaults to <code>1</code>.</p>
</td>
</tr>
<tr>
<td>
<p>nu</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Nu parameter in LIBSVM. Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR.</p>
<p>Defaults to <code>1e-2</code>.</p>
</td>
</tr>
<tr>
<td>
<p>coef0</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Coef0 parameter in LIBSVM. Set coef0 in kernel function.</p>
<p>Defaults to <code>1.0</code>.</p>
</td>
</tr>
<tr>
<td>
<p>cacheSize</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Set cache memory size in MB (default 100) in LIBSVM.</p>
<p>Defaults to <code>100</code>.</p>
</td>
</tr>
<tr>
<td>
<p>verbose</p>
</td>
<td>
<p>boolean</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>Toggle verbose output in the console.</p>
<p>Defaults to <code>false</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~TDigestParam"><span class="symbol-name">TDigestParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of module:analytics.quantiles.TDigest.</p>
<section>
<h4>Properties</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>minCount</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The minimal number of examples before the model is initialized.</p>
<p>Defaults to <code>0</code>.</p>
</td>
</tr>
<tr>
<td>
<p>clusters</p>
</td>
<td>
<p>number</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The number of 1-d clusters (large values lead to higher memory usage).</p>
<p>Defaults to <code>100</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
<div class="symbol-detail-labels"><span class="label label-inner">inner</span></div>
<h3 id="~tokenizerParam"><span class="symbol-name">tokenizerParam</span><small class="property-type">
Object</small></h3>
<p>An object used for the construction of <a href="module-analytics.Tokenizer.html">module:analytics.Tokenizer</a>.</p>
<section>
<h4>Property</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>type</p>
</td>
<td>
<p>string</p>
</td>
<td>
<p>Yes</p>
</td>
<td>
<p>The type of the tokenizer. The different types are:
<br>1. 'simple' - Creates break on white spaces.
<br>2. 'html' - Creates break on white spaces and ignores html tags.
<br>3. 'unicode' - Creates break on white spaces and normalizes unicode letters, e.g. o?=o?= changes to cso?=z.
</p>
<p>Defaults to <code>'unicode'</code>.</p>
</td>
</tr>
</tbody>
</table>
</section>
<dl class="dl-compact">
</dl>
</section>
</section>
</div>
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