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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><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 notranslate"><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 notranslate"><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="py class">
<dt id="sklearn.cluster.AffinityPropagation">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">AffinityPropagation</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">damping</span><span class="o">=</span><span class="default_value">0.5</span></em>, <em class="sig-param"><span class="n">max_iter</span><span class="o">=</span><span class="default_value">200</span></em>, <em class="sig-param"><span class="n">convergence_iter</span><span class="o">=</span><span class="default_value">15</span></em>, <em class="sig-param"><span class="n">copy</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">preference</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">affinity</span><span class="o">=</span><span class="default_value">'euclidean'</span></em>, <em class="sig-param"><span class="n">verbose</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">'warn'</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_affinity_propagation.py#L259"><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>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>damping</strong><span class="classifier">float, default=0.5</span></dt><dd><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>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=200</span></dt><dd><p>Maximum number of iterations.</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>copy</strong><span class="classifier">bool, default=True</span></dt><dd><p>Make a copy of input data.</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, 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>
</dd>
<dt><strong>affinity</strong><span class="classifier">{‘euclidean’, ‘precomputed’}, default=’euclidean’</span></dt><dd><p>Which affinity to use. At the moment ‘precomputed’ and
<code class="docutils literal notranslate"><span class="pre">euclidean</span></code> are supported. ‘euclidean’ uses the
negative squared euclidean distance between points.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">bool, default=False</span></dt><dd><p>Whether to be verbose.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=0</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">Attributes</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>Indices of cluster centers.</p>
</dd>
<dt><strong>cluster_centers_</strong><span class="classifier">ndarray of shape (n_clusters, n_features)</span></dt><dd><p>Cluster centers (if affinity != <code class="docutils literal notranslate"><span class="pre">precomputed</span></code>).</p>
</dd>
<dt><strong>labels_</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Labels of each point.</p>
</dd>
<dt><strong>affinity_matrix_</strong><span class="classifier">ndarray of shape (n_samples, n_samples)</span></dt><dd><p>Stores the affinity matrix used in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of iterations taken to converge.</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>The algorithmic complexity of affinity propagation is quadratic
in the number of points.</p>
<p>When <code class="docutils literal notranslate"><span class="pre">fit</span></code> does not converge, <code class="docutils literal notranslate"><span class="pre">cluster_centers_</span></code> becomes an empty
array and all training samples will be labelled as <code class="docutils literal notranslate"><span class="pre">-1</span></code>. In addition,
<code class="docutils literal notranslate"><span class="pre">predict</span></code> will then label every sample as <code class="docutils literal notranslate"><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 notranslate"><span class="pre">fit</span></code> will result in
a single cluster center and label <code class="docutils literal notranslate"><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">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">AffinityPropagation</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="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">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</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">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">clustering</span> <span class="o">=</span> <span class="n">AffinityPropagation</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clustering</span>
<span class="go">AffinityPropagation(random_state=5)</span>
<span class="gp">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">labels_</span>
<span class="go">array([0, 0, 0, 1, 1, 1])</span>
<span class="gp">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="go">array([0, 1])</span>
<span class="gp">>>> </span><span class="n">clustering</span><span class="o">.</span><span class="n">cluster_centers_</span>
<span class="go">array([[1, 2],</span>
<span class="go"> [4, 2]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.fit" title="sklearn.cluster.AffinityPropagation.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y])</p></td>
<td><p>Fit the clustering from features, or affinity matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.fit_predict" title="sklearn.cluster.AffinityPropagation.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(X[, y])</p></td>
<td><p>Fit the clustering from features or affinity matrix, and return cluster labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.get_params" title="sklearn.cluster.AffinityPropagation.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.predict" title="sklearn.cluster.AffinityPropagation.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
<td><p>Predict the closest cluster each sample in X belongs to.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.AffinityPropagation.set_params" title="sklearn.cluster.AffinityPropagation.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt id="sklearn.cluster.AffinityPropagation.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_affinity_propagation.py#L390"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AffinityPropagation.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the clustering from features, or affinity matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)</span></dt><dd><p>Training instances to cluster, or similarities / affinities between
instances if <code class="docutils literal notranslate"><span class="pre">affinity='precomputed'</span></code>. If a sparse feature matrix
is provided, it will be converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.AffinityPropagation.fit_predict">
<code class="sig-name descname">fit_predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_affinity_propagation.py#L465"><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>Fit the clustering from features or affinity matrix, and return
cluster labels.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)</span></dt><dd><p>Training instances to cluster, or similarities / affinities between
instances if <code class="docutils literal notranslate"><span class="pre">affinity='precomputed'</span></code>. If a sparse feature matrix
is provided, it will be converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Cluster labels.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.AffinityPropagation.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">deep</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L178"><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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.AffinityPropagation.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_affinity_propagation.py#L436"><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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>New data to predict. If a sparse matrix is provided, it will be
converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Cluster labels.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.AffinityPropagation.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L202"><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 <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-affinitypropagation">
<h2>Examples using <code class="docutils literal notranslate"><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 align-default" id="id1">
<img alt="Demo of affinity propagation clustering algorithm" 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><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></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 align-default" id="id2">
<img alt="Comparing different clustering algorithms on toy datasets" 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><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
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