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              <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.estimate_bandwidth</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.estimate_bandwidth"><code class="docutils literal notranslate"><span class="pre">estimate_bandwidth</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-estimate-bandwidth">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.estimate_bandwidth</span></code></a></li>
</ul>
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  <section id="sklearn-cluster-estimate-bandwidth">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.estimate_bandwidth<a class="headerlink" href="#sklearn-cluster-estimate-bandwidth" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.cluster.estimate_bandwidth">
<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">estimate_bandwidth</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">quantile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/cluster/_mean_shift.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.estimate_bandwidth" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate the bandwidth to use with the mean-shift algorithm.</p>
<p>This function takes time at least quadratic in <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>. For large
datasets, it is wise to subsample by setting <code class="docutils literal notranslate"><span class="pre">n_samples</span></code>. Alternatively,
the parameter <code class="docutils literal notranslate"><span class="pre">bandwidth</span></code> can be set to a small value without estimating
it.</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">array-like of shape (n_samples, n_features)</span></dt><dd><p>Input points.</p>
</dd>
<dt><strong>quantile</strong><span class="classifier">float, default=0.3</span></dt><dd><p>Should be between [0, 1]
0.5 means that the median of all pairwise distances is used.</p>
</dd>
<dt><strong>n_samples</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of samples to use. If not given, all samples are used.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, default=None</span></dt><dd><p>The generator used to randomly select the samples from input points
for bandwidth estimation. Use an int to make the randomness
deterministic.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.3.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>bandwidth</strong><span class="classifier">float</span></dt><dd><p>The bandwidth parameter.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<section id="examples-using-sklearn-cluster-estimate-bandwidth">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.estimate_bandwidth</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-estimate-bandwidth" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Reference:"><img alt="" src="../../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py"><span class="std std-ref">A demo of the mean-shift clustering algorithm</span></a></p>
  <div class="sphx-glr-thumbnail-title">A demo of the mean-shift clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are &quot;int..."><img alt="" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></p>
  <div class="sphx-glr-thumbnail-title">Comparing different clustering algorithms on toy datasets</div>
</div></div><div class="clearer"></div></section>
</section>


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