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  <section id="mean-shift">
<h1>mean_shift<a class="headerlink" href="#mean-shift" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.cluster.mean_shift">
<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">mean_shift</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">bandwidth</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">seeds</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">bin_seeding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_bin_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</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/86d099ec1/sklearn/cluster/_mean_shift.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.mean_shift" title="Link to this definition">#</a></dt>
<dd><p>Perform mean shift clustering of data using a flat kernel.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#mean-shift"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Input data.</p>
</dd>
<dt><strong>bandwidth</strong><span class="classifier">float, default=None</span></dt><dd><p>Kernel bandwidth. If not None, must be in the range [0, +inf).</p>
<p>If None, the bandwidth is determined using a heuristic based on
the median of all pairwise distances. This will take quadratic time in
the number of samples. The sklearn.cluster.estimate_bandwidth function
can be used to do this more efficiently.</p>
</dd>
<dt><strong>seeds</strong><span class="classifier">array-like of shape (n_seeds, n_features) or None</span></dt><dd><p>Point used as initial kernel locations. If None and bin_seeding=False,
each data point is used as a seed. If None and bin_seeding=True,
see bin_seeding.</p>
</dd>
<dt><strong>bin_seeding</strong><span class="classifier">bool, default=False</span></dt><dd><p>If true, initial kernel locations are not locations of all
points, but rather the location of the discretized version of
points, where points are binned onto a grid whose coarseness
corresponds to the bandwidth. Setting this option to True will speed
up the algorithm because fewer seeds will be initialized.
Ignored if seeds argument is not None.</p>
</dd>
<dt><strong>min_bin_freq</strong><span class="classifier">int, default=1</span></dt><dd><p>To speed up the algorithm, accept only those bins with at least
min_bin_freq points as seeds.</p>
</dd>
<dt><strong>cluster_all</strong><span class="classifier">bool, default=True</span></dt><dd><p>If true, then all points are clustered, even those orphans that are
not within any kernel. Orphans are assigned to the nearest kernel.
If false, then orphans are given cluster label -1.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=300</span></dt><dd><p>Maximum number of iterations, per seed point before the clustering
operation terminates (for that seed point), if has not converged yet.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to use for the computation. The following tasks benefit
from the parallelization:</p>
<ul class="simple">
<li><p>The search of nearest neighbors for bandwidth estimation and label
assignments. See the details in the docstring of the
<code class="docutils literal notranslate"><span class="pre">NearestNeighbors</span></code> class.</p></li>
<li><p>Hill-climbing optimization for all seeds.</p></li>
</ul>
<p>See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
<p><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>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.17: </span>Parallel Execution using <em>n_jobs</em>.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>cluster_centers</strong><span class="classifier">ndarray of shape (n_clusters, n_features)</span></dt><dd><p>Coordinates of cluster centers.</p>
</dd>
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Cluster labels for each point.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>For a usage example, see
<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>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_shift</span>
<span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="n">cluster_centers</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">mean_shift</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cluster_centers</span>
<span class="go">array([[3.33..., 6.     ],</span>
<span class="go">       [1.33..., 0.66...]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">labels</span>
<span class="go">array([1, 1, 1, 0, 0, 0])</span>
</pre></div>
</div>
</dd></dl>

</section>


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