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<li><a class="reference internal" href="#">Demo of affinity propagation clustering algorithm</a><ul>
<li><a class="reference internal" href="#generate-sample-data">Generate sample data</a></li>
<li><a class="reference internal" href="#compute-affinity-propagation">Compute Affinity Propagation</a></li>
<li><a class="reference internal" href="#plot-result">Plot result</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-cluster-plot-affinity-propagation-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<section class="sphx-glr-example-title" id="demo-of-affinity-propagation-clustering-algorithm">
<span id="sphx-glr-auto-examples-cluster-plot-affinity-propagation-py"></span><h1>Demo of affinity propagation clustering algorithm<a class="headerlink" href="#demo-of-affinity-propagation-clustering-algorithm" title="Link to this heading">¶</a></h1>
<p>Reference:
Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages
Between Data Points”, Science Feb. 2007</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" title="sklearn.cluster.AffinityPropagation" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AffinityPropagation</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
</pre></div>
</div>
<section id="generate-sample-data">
<h2>Generate sample data<a class="headerlink" href="#generate-sample-data" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">centers</span> <span class="o">=</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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</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="o">-</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">X</span><span class="p">,</span> <span class="n">labels_true</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span>
<span class="n">n_samples</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="n">centers</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="compute-affinity-propagation">
<h2>Compute Affinity Propagation<a class="headerlink" href="#compute-affinity-propagation" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">af</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" title="sklearn.cluster.AffinityPropagation" class="sphx-glr-backref-module-sklearn-cluster sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AffinityPropagation</span></a><span class="p">(</span><span class="n">preference</span><span class="o">=-</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</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="n">cluster_centers_indices</span> <span class="o">=</span> <span class="n">af</span><span class="o">.</span><span class="n">cluster_centers_indices_</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">af</span><span class="o">.</span><span class="n">labels_</span>
<span class="n">n_clusters_</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cluster_centers_indices</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Estimated number of clusters: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_clusters_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Homogeneity: </span><span class="si">%0.3f</span><span class="s2">"</span> <span class="o">%</span> <a href="../../modules/generated/sklearn.metrics.homogeneity_score.html#sklearn.metrics.homogeneity_score" title="sklearn.metrics.homogeneity_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">homogeneity_score</span></a><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Completeness: </span><span class="si">%0.3f</span><span class="s2">"</span> <span class="o">%</span> <a href="../../modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score" title="sklearn.metrics.completeness_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">completeness_score</span></a><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"V-measure: </span><span class="si">%0.3f</span><span class="s2">"</span> <span class="o">%</span> <a href="../../modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="sklearn.metrics.v_measure_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">v_measure_score</span></a><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Adjusted Rand Index: </span><span class="si">%0.3f</span><span class="s2">"</span> <span class="o">%</span> <a href="../../modules/generated/sklearn.metrics.adjusted_rand_score.html#sklearn.metrics.adjusted_rand_score" title="sklearn.metrics.adjusted_rand_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_rand_score</span></a><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span>
<span class="s2">"Adjusted Mutual Information: </span><span class="si">%0.3f</span><span class="s2">"</span>
<span class="o">%</span> <a href="../../modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score" title="sklearn.metrics.adjusted_mutual_info_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">adjusted_mutual_info_score</span></a><span class="p">(</span><span class="n">labels_true</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span>
<span class="s2">"Silhouette Coefficient: </span><span class="si">%0.3f</span><span class="s2">"</span>
<span class="o">%</span> <a href="../../modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score" title="sklearn.metrics.silhouette_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">metrics</span><span class="o">.</span><span class="n">silhouette_score</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">"sqeuclidean"</span><span class="p">)</span>
<span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Estimated number of clusters: 3
Homogeneity: 0.872
Completeness: 0.872
V-measure: 0.872
Adjusted Rand Index: 0.912
Adjusted Mutual Information: 0.871
Silhouette Coefficient: 0.753
</pre></div>
</div>
</section>
<section id="plot-result">
<h2>Plot result<a class="headerlink" href="#plot-result" title="Link to this heading">¶</a></h2>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.close.html#matplotlib.pyplot.close" title="matplotlib.pyplot.close" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">close</span></a><span class="p">(</span><span class="s2">"all"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.clf.html#matplotlib.pyplot.clf" title="matplotlib.pyplot.clf" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">clf</span></a><span class="p">()</span>
<span class="n">colors</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cycler</span><span class="p">(</span><span class="s2">"color"</span><span class="p">,</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</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="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_clusters_</span><span class="p">),</span> <span class="n">colors</span><span class="p">):</span>
<span class="n">class_members</span> <span class="o">=</span> <span class="n">labels</span> <span class="o">==</span> <span class="n">k</span>
<span class="n">cluster_center</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">cluster_centers_indices</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
<span class="n">X</span><span class="p">[</span><span class="n">class_members</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">class_members</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">col</span><span class="p">[</span><span class="s2">"color"</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"."</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
<span class="n">cluster_center</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cluster_center</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">col</span><span class="p">[</span><span class="s2">"color"</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">[</span><span class="n">class_members</span><span class="p">]:</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span>
<span class="p">[</span><span class="n">cluster_center</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[</span><span class="n">cluster_center</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span><span class="o">=</span><span class="n">col</span><span class="p">[</span><span class="s2">"color"</span><span class="p">]</span>
<span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"Estimated number of clusters: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_clusters_</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_affinity_propagation_001.png" srcset="../../_images/sphx_glr_plot_affinity_propagation_001.png" alt="Estimated number of clusters: 3" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.314 seconds)</p>
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<p class="rubric">Related examples</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core samples in regi..."><img alt="" src="../../_images/sphx_glr_plot_dbscan_thumb.png" />
<p><a class="reference internal" href="plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">Demo of DBSCAN clustering algorithm</span></a></p>
<div class="sphx-glr-thumbnail-title">Demo of DBSCAN clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Reference:"><img alt="" src="../../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p><a class="reference internal" href="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>
<div class="sphx-glr-thumbnail-title">A demo of the mean-shift clustering algorithm</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa..."><img alt="" src="../../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" />
<p><a class="reference internal" href="plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py"><span class="std std-ref">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="- a first experiment with fixed "ground truth labels" (and therefore fixed number of classes)..."><img alt="" src="../../_images/sphx_glr_plot_adjusted_for_chance_measures_thumb.png" />
<p><a class="reference internal" href="plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py"><span class="std std-ref">Adjustment for chance in clustering performance evaluation</span></a></p>
<div class="sphx-glr-thumbnail-title">Adjustment for chance in clustering performance evaluation</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we compare the various initialization strategies for K-means in terms of runtim..."><img alt="" src="../../_images/sphx_glr_plot_kmeans_digits_thumb.png" />
<p><a class="reference internal" href="plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py"><span class="std std-ref">A demo of K-Means clustering on the handwritten digits data</span></a></p>
<div class="sphx-glr-thumbnail-title">A demo of K-Means clustering on the handwritten digits data</div>
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