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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code>.empirical_covariance</a><ul>
<li><a class="reference internal" href="#sklearn.covariance.empirical_covariance"><code class="docutils literal notranslate"><span class="pre">empirical_covariance</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-covariance-empirical-covariance">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.covariance.empirical_covariance</span></code></a></li>
</ul>
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<section id="sklearn-covariance-empirical-covariance">
<h1><a class="reference internal" href="../classes.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.covariance</span></code></a>.empirical_covariance<a class="headerlink" href="#sklearn-covariance-empirical-covariance" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.covariance.empirical_covariance">
<span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">empirical_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/covariance/_empirical_covariance.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.empirical_covariance" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the Maximum likelihood covariance estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Data from which to compute the covariance estimate.</p>
</dd>
<dt><strong>assume_centered</strong><span class="classifier">bool, default=False</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, data will not be centered before computation.
Useful when working with data whose mean is almost, but not exactly
zero.
If <code class="docutils literal notranslate"><span class="pre">False</span></code>, data will be centered before computation.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>covariance</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>Empirical covariance (Maximum Likelihood Estimator).</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.covariance</span> <span class="kn">import</span> <span class="n">empirical_covariance</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">empirical_covariance</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([[0.25, 0.25, 0.25],</span>
<span class="go"> [0.25, 0.25, 0.25],</span>
<span class="go"> [0.25, 0.25, 0.25]])</span>
</pre></div>
</div>
</dd></dl>
<section id="examples-using-sklearn-covariance-empirical-covariance">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.covariance.empirical_covariance</span></code><a class="headerlink" href="#examples-using-sklearn-covariance-empirical-covariance" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="When working with covariance estimation, the usual approach is to use a maximum likelihood esti..."><img alt="" src="../../_images/sphx_glr_plot_covariance_estimation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py"><span class="std std-ref">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</span></a></p>
<div class="sphx-glr-thumbnail-title">Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood</div>
</div></div><div class="clearer"></div></section>
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