
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_affinity_propagation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_affinity_propagation.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_cluster_plot_affinity_propagation.py:


=================================================
Demo of affinity propagation clustering algorithm
=================================================

Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007

.. GENERATED FROM PYTHON SOURCE LINES 11-63



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_affinity_propagation_001.png
   :alt: Estimated number of clusters: 3
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_affinity_propagation_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    /build/scikit-learn-ZSX7SD/scikit-learn-0.23.2/.pybuild/cpython3_3.10/build/sklearn/cluster/_affinity_propagation.py:146: FutureWarning: 'random_state' has been introduced in 0.23. It will be set to None starting from 0.25 which means that results will differ at every function call. Set 'random_state' to None to silence this warning, or to 0 to keep the behavior of versions <0.23.
      warnings.warn(("'random_state' has been introduced in 0.23. "
    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






|

.. code-block:: default

    print(__doc__)

    from sklearn.cluster import AffinityPropagation
    from sklearn import metrics
    from sklearn.datasets import make_blobs

    # #############################################################################
    # Generate sample data
    centers = [[1, 1], [-1, -1], [1, -1]]
    X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5,
                                random_state=0)

    # #############################################################################
    # Compute Affinity Propagation
    af = AffinityPropagation(preference=-50).fit(X)
    cluster_centers_indices = af.cluster_centers_indices_
    labels = af.labels_

    n_clusters_ = len(cluster_centers_indices)

    print('Estimated number of clusters: %d' % n_clusters_)
    print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
    print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
    print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
    print("Adjusted Rand Index: %0.3f"
          % metrics.adjusted_rand_score(labels_true, labels))
    print("Adjusted Mutual Information: %0.3f"
          % metrics.adjusted_mutual_info_score(labels_true, labels))
    print("Silhouette Coefficient: %0.3f"
          % metrics.silhouette_score(X, labels, metric='sqeuclidean'))

    # #############################################################################
    # Plot result
    import matplotlib.pyplot as plt
    from itertools import cycle

    plt.close('all')
    plt.figure(1)
    plt.clf()

    colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
    for k, col in zip(range(n_clusters_), colors):
        class_members = labels == k
        cluster_center = X[cluster_centers_indices[k]]
        plt.plot(X[class_members, 0], X[class_members, 1], col + '.')
        plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
                 markeredgecolor='k', markersize=14)
        for x in X[class_members]:
            plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)

    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.239 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_affinity_propagation.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_affinity_propagation.py <plot_affinity_propagation.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_affinity_propagation.ipynb <plot_affinity_propagation.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
