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<section id="estimate-bandwidth">
<h1>estimate_bandwidth<a class="headerlink" href="#estimate-bandwidth" title="Link 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/4ee3afa55/sklearn/cluster/_mean_shift.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.estimate_bandwidth" title="Link 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 v1.5.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<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>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">estimate_bandwidth</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</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="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">estimate_bandwidth</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="go">np.float64(1.61...)</span>
</pre></div>
</div>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link 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 "interesting" but still in 2D. With the exception of the last dataset, the parameters of each of these dataset-algorithm pairs has been tuned to produce good clustering results. Some algorithms are more sensitive to parameter values than others."><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>
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