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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>.affinity_propagation</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.affinity_propagation"><code class="docutils literal notranslate"><span class="pre">affinity_propagation</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-affinity-propagation">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.affinity_propagation</span></code></a></li>
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<section id="sklearn-cluster-affinity-propagation">
<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>.affinity_propagation<a class="headerlink" href="#sklearn-cluster-affinity-propagation" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.cluster.affinity_propagation">
<span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">affinity_propagation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">S</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">preference</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">convergence_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</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">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</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">verbose</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">return_n_iter</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">random_state</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/_affinity_propagation.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.affinity_propagation" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform Affinity Propagation Clustering of data.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#affinity-propagation"><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>S</strong><span class="classifier">array-like of shape (n_samples, n_samples)</span></dt><dd><p>Matrix of similarities between points.</p>
</dd>
<dt><strong>preference</strong><span class="classifier">array-like of shape (n_samples,) or float, default=None</span></dt><dd><p>Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number of
exemplars, i.e. of clusters, is influenced by the input preferences
value. If the preferences are not passed as arguments, they will be
set to the median of the input similarities (resulting in a moderate
number of clusters). For a smaller amount of clusters, this can be set
to the minimum value of the similarities.</p>
</dd>
<dt><strong>convergence_iter</strong><span class="classifier">int, default=15</span></dt><dd><p>Number of iterations with no change in the number
of estimated clusters that stops the convergence.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=200</span></dt><dd><p>Maximum number of iterations.</p>
</dd>
<dt><strong>damping</strong><span class="classifier">float, default=0.5</span></dt><dd><p>Damping factor between 0.5 and 1.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, default=True</span></dt><dd><p>If copy is False, the affinity matrix is modified inplace by the
algorithm, for memory efficiency.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, default=False</span></dt><dd><p>The verbosity level.</p>
</dd>
<dt><strong>return_n_iter</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether or not to return the number of iterations.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Pseudo-random number generator to control the starting state.
Use an int for reproducible results across function calls.
See the <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.23: </span>this parameter was previously hardcoded as 0.</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_indices</strong><span class="classifier">ndarray of shape (n_clusters,)</span></dt><dd><p>Index of clusters 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>
<dt><strong>n_iter</strong><span class="classifier">int</span></dt><dd><p>Number of iterations run. Returned only if <code class="docutils literal notranslate"><span class="pre">return_n_iter</span></code> is
set to True.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>For an example, see <a class="reference internal" href="../../auto_examples/cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py"><span class="std std-ref">examples/cluster/plot_affinity_propagation.py</span></a>.</p>
<p>When the algorithm does not converge, it will still return a arrays of
<code class="docutils literal notranslate"><span class="pre">cluster_center_indices</span></code> and labels if there are any exemplars/clusters,
however they may be degenerate and should be used with caution.</p>
<p>When all training samples have equal similarities and equal preferences,
the assignment of cluster centers and labels depends on the preference.
If the preference is smaller than the similarities, a single cluster center
and label <code class="docutils literal notranslate"><span class="pre">0</span></code> for every sample will be returned. Otherwise, every
training sample becomes its own cluster center and is assigned a unique
label.</p>
<p class="rubric">References</p>
<p>Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages
Between Data Points”, Science Feb. 2007</p>
</dd></dl>
<section id="examples-using-sklearn-cluster-affinity-propagation">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.affinity_propagation</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-affinity-propagation" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example employs several unsupervised learning techniques to extract the stock market struc..."><img alt="" src="../../_images/sphx_glr_plot_stock_market_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py"><span class="std std-ref">Visualizing the stock market structure</span></a></p>
<div class="sphx-glr-thumbnail-title">Visualizing the stock market structure</div>
</div></div><div class="clearer"></div></section>
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