.. 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

        :ref:`Go to the end <sphx_glr_download_auto_examples_cluster_plot_affinity_propagation.py>`
        to download the full example code. or to run this example in your browser via Binder

.. 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 11-21

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import numpy as np

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








.. GENERATED FROM PYTHON SOURCE LINES 22-24

Generate sample data
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 24-29

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 30-32

Compute Affinity Propagation
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 32-52

.. code-block:: Python

    af = AffinityPropagation(preference=-50, random_state=0).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")
    )





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

 .. code-block:: none

    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




.. GENERATED FROM PYTHON SOURCE LINES 53-55

Plot result
-----------

.. GENERATED FROM PYTHON SOURCE LINES 55-79

.. code-block:: Python

    import matplotlib.pyplot as plt

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

    colors = plt.cycler("color", plt.cm.viridis(np.linspace(0, 1, 4)))

    for k, col in zip(range(n_clusters_), colors):
        class_members = labels == k
        cluster_center = X[cluster_centers_indices[k]]
        plt.scatter(
            X[class_members, 0], X[class_members, 1], color=col["color"], marker="."
        )
        plt.scatter(
            cluster_center[0], cluster_center[1], s=14, color=col["color"], marker="o"
        )
        for x in X[class_members]:
            plt.plot(
                [cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col["color"]
            )

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



.. 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-timing

   **Total running time of the script:** (0 minutes 0.250 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_affinity_propagation.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_affinity_propagation.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

    .. 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-zip

      :download:`Download zipped: plot_affinity_propagation.zip <plot_affinity_propagation.zip>`


.. include:: plot_affinity_propagation.recommendations


.. only:: html

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

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