.. 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 Click :ref:`here ` 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 13-57 .. image:: /auto_examples/cluster/images/sphx_glr_plot_mean_shift_001.png :alt: Estimated number of clusters: 3 :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none number of estimated clusters : 3 | .. code-block:: default print(__doc__) import numpy as np from sklearn.cluster import MeanShift, estimate_bandwidth from sklearn.datasets import make_blobs # ############################################################################# # Generate sample data centers = [[1, 1], [-1, -1], [1, -1]] X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6) # ############################################################################# # Compute clustering with MeanShift # 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_) # ############################################################################# # Plot result import matplotlib.pyplot as plt from itertools import cycle plt.figure(1) plt.clf() colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk') 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], col + '.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) plt.title('Estimated number of clusters: %d' % n_clusters_) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.611 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_mean_shift.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.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 ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_mean_shift.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_