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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cluster</span></code>.AffinityPropagation</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-affinitypropagation">Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.AffinityPropagation</span></code></a></li>
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<div class="section" id="sklearn-cluster-affinitypropagation">
<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>.AffinityPropagation<a class="headerlink" href="#sklearn-cluster-affinitypropagation" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.AffinityPropagation">
<em class="property">class </em><code class="descclassname">sklearn.cluster.</code><code class="descname">AffinityPropagation</code><span class="sig-paren">(</span><em>damping=0.5</em>, <em>max_iter=200</em>, <em>convergence_iter=15</em>, <em>copy=True</em>, <em>preference=None</em>, <em>affinity='euclidean'</em>, <em>verbose=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/affinity_propagation_.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation" 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>
<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>damping</strong> : float, optional, default: 0.5</p>
<blockquote>
<div><p>Damping factor (between 0.5 and 1) is the extent to
which the current value is maintained relative to
incoming values (weighted 1 - damping). This in order
to avoid numerical oscillations when updating these
values (messages).</p>
</div></blockquote>
<p><strong>max_iter</strong> : int, optional, default: 200</p>
<blockquote>
<div><p>Maximum number of iterations.</p>
</div></blockquote>
<p><strong>convergence_iter</strong> : int, optional, default: 15</p>
<blockquote>
<div><p>Number of iterations with no change in the number
of estimated clusters that stops the convergence.</p>
</div></blockquote>
<p><strong>copy</strong> : boolean, optional, default: True</p>
<blockquote>
<div><p>Make a copy of input data.</p>
</div></blockquote>
<p><strong>preference</strong> : array-like, shape (n_samples,) or float, optional</p>
<blockquote>
<div><p>Preferences for each point - points with larger values of
preferences are more likely to be chosen as exemplars. The number
of exemplars, ie 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.</p>
</div></blockquote>
<p><strong>affinity</strong> : string, optional, default=``euclidean``</p>
<blockquote>
<div><p>Which affinity to use. At the moment <code class="docutils literal"><span class="pre">precomputed</span></code> and
<code class="docutils literal"><span class="pre">euclidean</span></code> are supported. <code class="docutils literal"><span class="pre">euclidean</span></code> uses the
negative squared euclidean distance between points.</p>
</div></blockquote>
<p><strong>verbose</strong> : boolean, optional, default: False</p>
<blockquote class="last">
<div><p>Whether to be verbose.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="24%" />
<col width="76%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>cluster_centers_indices_</strong></td>
<td>(array, shape (n_clusters,)) Indices of cluster centers</td>
</tr>
<tr class="row-even"><td><strong>cluster_centers_</strong></td>
<td>(array, shape (n_clusters, n_features)) Cluster centers (if affinity != <code class="docutils literal"><span class="pre">precomputed</span></code>).</td>
</tr>
<tr class="row-odd"><td><strong>labels_</strong></td>
<td>(array, shape (n_samples,)) Labels of each point</td>
</tr>
<tr class="row-even"><td><strong>affinity_matrix_</strong></td>
<td>(array, shape (n_samples, n_samples)) Stores the affinity matrix used in <code class="docutils literal"><span class="pre">fit</span></code>.</td>
</tr>
<tr class="row-odd"><td><strong>n_iter_</strong></td>
<td>(int) Number of iterations taken to converge.</td>
</tr>
</tbody>
</table>
<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>The algorithmic complexity of affinity propagation is quadratic
in the number of points.</p>
<p>When <code class="docutils literal"><span class="pre">fit</span></code> does not converge, <code class="docutils literal"><span class="pre">cluster_centers_</span></code> becomes an empty
array and all training samples will be labelled as <code class="docutils literal"><span class="pre">-1</span></code>. In addition,
<code class="docutils literal"><span class="pre">predict</span></code> will then label every sample as <code class="docutils literal"><span class="pre">-1</span></code>.</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, <code class="docutils literal"><span class="pre">fit</span></code> will result in
a single cluster center and label <code class="docutils literal"><span class="pre">0</span></code> for every sample. 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>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.fit" title="sklearn.cluster.AffinityPropagation.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y])</td>
<td>Create affinity matrix from negative euclidean distances, then apply affinity propagation clustering.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.fit_predict" title="sklearn.cluster.AffinityPropagation.fit_predict"><code class="xref py py-obj docutils literal"><span class="pre">fit_predict</span></code></a>(X[, y])</td>
<td>Performs clustering on X and returns cluster labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.get_params" title="sklearn.cluster.AffinityPropagation.get_params"><code class="xref py py-obj docutils literal"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.predict" title="sklearn.cluster.AffinityPropagation.predict"><code class="xref py py-obj docutils literal"><span class="pre">predict</span></code></a>(X)</td>
<td>Predict the closest cluster each sample in X belongs to.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.set_params" title="sklearn.cluster.AffinityPropagation.set_params"><code class="xref py py-obj docutils literal"><span class="pre">set_params</span></code></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.AffinityPropagation.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>damping=0.5</em>, <em>max_iter=200</em>, <em>convergence_iter=15</em>, <em>copy=True</em>, <em>preference=None</em>, <em>affinity='euclidean'</em>, <em>verbose=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/affinity_propagation_.py#L319"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AffinityPropagation.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/affinity_propagation_.py#L335"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Create affinity matrix from negative euclidean distances, then
apply affinity propagation clustering.</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) or (n_samples, n_samples)</p>
<blockquote>
<div><p>Data matrix or, if affinity is <code class="docutils literal"><span class="pre">precomputed</span></code>, matrix of
similarities / affinities.</p>
</div></blockquote>
<p class="last"><strong>y</strong> : Ignored</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AffinityPropagation.fit_predict">
<code class="descname">fit_predict</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L384"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs clustering on X and returns cluster labels.</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> : ndarray, shape (n_samples, n_features)</p>
<blockquote>
<div><p>Input data.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>y</strong> : ndarray, shape (n_samples,)</p>
<blockquote class="last">
<div><p>cluster labels</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AffinityPropagation.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L213"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</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>deep</strong> : boolean, optional</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AffinityPropagation.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/cluster/affinity_propagation_.py#L370"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the closest cluster each sample in X belongs to.</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, sparse matrix}, shape (n_samples, n_features)</p>
<blockquote>
<div><p>New data to predict.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>labels</strong> : array, shape (n_samples,)</p>
<blockquote class="last">
<div><p>Index of the cluster each sample belongs to.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.cluster.AffinityPropagation.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/2d7cd0c/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal"><span class="pre"><component>__<parameter></span></code> so that it’s possible to update each
component of a nested object.</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">Returns:</th><td class="field-body">self</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-affinitypropagation">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.cluster.AffinityPropagation</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-affinitypropagation" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Poin..."><div class="figure" id="id1">
<img alt="../../_images/sphx_glr_plot_affinity_propagation_thumb.png" src="../../_images/sphx_glr_plot_affinity_propagation_thumb.png" />
<p class="caption"><span class="caption-text"><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">Demo of affinity propagation clustering algorithm</span></a></span></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" id="id2">
<img alt="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" 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></p>
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