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        <h3>Contents</h3>
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<li><a class="reference internal" href="#">4.1. Gaussian mixture models</a><ul>
<li><a class="reference internal" href="#gmm-classifier">4.1.1. GMM classifier</a></li>
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  <div class="section" id="gaussian-mixture-models">
<span id="mixture"></span><h1>4.1. Gaussian mixture models<a class="headerlink" href="#gaussian-mixture-models" title="Permalink to this headline">¶</a></h1>
<p><cite>scikits.learn.mixture</cite> is a package which enables to create Mixture
Models (diagonal, spherical, tied and full covariance matrices
supported), to sample them, and to estimate them from data using
Expectation Maximization algorithm.  It can also draw confidence
ellipsoides for multivariate models, and compute the Bayesian
Information Criterion to assess the number of clusters in the data.</p>
<p>For the moment, only Gaussian Mixture Models (GMM) are
implemented. These are a class of probabilistic models describing the
data as drawn from a mixture of Gaussian probability
distributions. The challenge that is GMM tackles is to learn the
parameters of these Gaussians from the data.</p>
<div class="section" id="gmm-classifier">
<h2>4.1.1. GMM classifier<a class="headerlink" href="#gmm-classifier" title="Permalink to this headline">¶</a></h2>
<p>The <tt class="xref py py-class docutils literal"><span class="pre">GMM</span></tt> object implements a <tt class="xref py py-meth docutils literal"><span class="pre">GMM.fit()</span></tt> method to learn a
Gaussian Mixture Models from train data. Given test data, it can assign
to each sample the class of the Gaussian it mostly probably belong to
using the <tt class="xref py py-meth docutils literal"><span class="pre">GMM.predict()</span></tt> method.</p>
<div class="figure align-center">
<a class="reference external image-reference" href="../auto_examples/cluster/plot_gmm_classifier.html"><img alt="auto_examples/mixture/images/plot_gmm_classifier.png" src="auto_examples/mixture/images/plot_gmm_classifier.png" /></a>
</div>
<div class="topic">
<p class="topic-title first">Examples:</p>
<ul class="simple">
<li>See <a class="reference internal" href="../auto_examples/mixture/plot_gmm_classifier.html#example-mixture-plot-gmm-classifier-py"><em>GMM classification</em></a> for an example of
using a GMM as a classifier on the iris dataset.</li>
<li>See <a class="reference internal" href="../auto_examples/mixture/plot_gmm.html#example-mixture-plot-gmm-py"><em>Gaussian Mixture Model Ellipsoids</em></a> for an example on plotting the
confidence ellipsoids.</li>
<li>See <a class="reference internal" href="../auto_examples/mixture/plot_gmm_pdf.html#example-mixture-plot-gmm-pdf-py"><em>Density Estimation for a mixture of Gaussians</em></a> for an example on plotting the
density estimation.</li>
</ul>
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