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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.mean_shift</a></li>
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<div class="section" id="sklearn-cluster-mean-shift">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></code></a>.mean_shift<a class="headerlink" href="#sklearn-cluster-mean-shift" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.cluster.mean_shift">
<code class="descclassname">sklearn.cluster.</code><code class="descname">mean_shift</code><span class="sig-paren">(</span><em>X</em>, <em>bandwidth=None</em>, <em>seeds=None</em>, <em>bin_seeding=False</em>, <em>min_bin_freq=1</em>, <em>cluster_all=True</em>, <em>max_iter=300</em>, <em>max_iterations=None</em>, <em>n_jobs=1</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cluster/mean_shift_.py#L94"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.mean_shift" title="Permalink 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>User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape=[n_samples, n_features]</p>
<blockquote>
<div><p>Input data.</p>
</div></blockquote>
<p><strong>bandwidth</strong> : float, optional</p>
<blockquote>
<div><p>Kernel bandwidth.</p>
<p>If bandwidth is not given, it 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>
</div></blockquote>
<p><strong>seeds</strong> : array-like, shape=[n_seeds, n_features] or None</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>bin_seeding</strong> : boolean, default=False</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>min_bin_freq</strong> : int, default=1</p>
<blockquote>
<div><p>To speed up the algorithm, accept only those bins with at least
min_bin_freq points as seeds.</p>
</div></blockquote>
<p><strong>cluster_all</strong> : boolean, default True</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>max_iter</strong> : int, default 300</p>
<blockquote>
<div><p>Maximum number of iterations, per seed point before the clustering
operation terminates (for that seed point), if has not converged yet.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : int</p>
<blockquote>
<div><p>The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.</p>
<p>If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.17: </span>Parallel Execution using <em>n_jobs</em>.</p>
</div>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>cluster_centers</strong> : array, shape=[n_clusters, n_features]</p>
<blockquote>
<div><p>Coordinates of cluster centers.</p>
</div></blockquote>
<p><strong>labels</strong> : array, shape=[n_samples]</p>
<blockquote class="last">
<div><p>Cluster labels for each point.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>See examples/cluster/plot_meanshift.py for an example.</p>
</dd></dl>
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