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<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="#examples-using-sklearn-cluster-estimate-bandwidth">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.estimate_bandwidth</span></code></a></li>
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<div class="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 headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.cluster.estimate_bandwidth">
<code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">estimate_bandwidth</code><span class="sig-paren">(</span><em class="sig-param">X</em>, <em class="sig-param">*</em>, <em class="sig-param">quantile=0.3</em>, <em class="sig-param">n_samples=None</em>, <em class="sig-param">random_state=0</em>, <em class="sig-param">n_jobs=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/cluster/_mean_shift.py#L29"><span class="viewcode-link">[source]</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>That this function takes time at least quadratic in n_samples. For large
datasets, it’s wise to set that parameter to a small value.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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/parallel.html#joblib.parallel_backend" title="(in joblib v0.17.0.dev0)"><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</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>
<div class="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 headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Reference:"><div class="figure align-default" id="id1">
<img alt="A demo of the mean-shift clustering algorithm" src="../../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p class="caption"><span class="caption-text"><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></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><div class="figure align-default" id="id2">
<img alt="Comparing different clustering algorithms on toy datasets" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><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></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
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