
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_mean_shift.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_mean_shift.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_mean_shift.py:


=============================================
A demo of the mean-shift clustering algorithm
=============================================

Reference:

Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
feature space analysis". IEEE Transactions on Pattern Analysis and
Machine Intelligence. 2002. pp. 603-619.

.. GENERATED FROM PYTHON SOURCE LINES 13-19

.. code-block:: default


    import numpy as np

    from sklearn.cluster import MeanShift, estimate_bandwidth
    from sklearn.datasets import make_blobs








.. GENERATED FROM PYTHON SOURCE LINES 20-22

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

.. GENERATED FROM PYTHON SOURCE LINES 22-25

.. code-block:: default

    centers = [[1, 1], [-1, -1], [1, -1]]
    X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)








.. GENERATED FROM PYTHON SOURCE LINES 26-28

Compute clustering with MeanShift
---------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 28-42

.. code-block:: default


    # The following bandwidth can be automatically detected using
    bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)

    ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
    ms.fit(X)
    labels = ms.labels_
    cluster_centers = ms.cluster_centers_

    labels_unique = np.unique(labels)
    n_clusters_ = len(labels_unique)

    print("number of estimated clusters : %d" % n_clusters_)





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

 .. code-block:: none

    number of estimated clusters : 3




.. GENERATED FROM PYTHON SOURCE LINES 43-45

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

.. GENERATED FROM PYTHON SOURCE LINES 45-67

.. code-block:: default

    import matplotlib.pyplot as plt

    plt.figure(1)
    plt.clf()

    colors = ["#dede00", "#377eb8", "#f781bf"]
    markers = ["x", "o", "^"]

    for k, col in zip(range(n_clusters_), colors):
        my_members = labels == k
        cluster_center = cluster_centers[k]
        plt.plot(X[my_members, 0], X[my_members, 1], markers[k], color=col)
        plt.plot(
            cluster_center[0],
            cluster_center[1],
            markers[k],
            markerfacecolor=col,
            markeredgecolor="k",
            markersize=14,
        )
    plt.title("Estimated number of clusters: %d" % n_clusters_)
    plt.show()



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_mean_shift_001.png
   :alt: Estimated number of clusters: 3
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_mean_shift_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.263 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_mean_shift.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/1.3.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_mean_shift.ipynb
        :alt: Launch binder
        :width: 150 px



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

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

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

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


.. only:: html

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

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