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

    Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_cluster_plot_dict_face_patches.py:


Online learning of a dictionary of parts of faces
==================================================

This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.

From the programming standpoint, it is interesting because it shows how
to use the online API of the scikit-learn to process a very large
dataset by chunks. The way we proceed is that we load an image at a time
and extract randomly 50 patches from this image. Once we have accumulated
500 of these patches (using 10 images), we run the `partial_fit` method
of the online KMeans object, MiniBatchKMeans.

The verbose setting on the MiniBatchKMeans enables us to see that some
clusters are reassigned during the successive calls to
partial-fit. This is because the number of patches that they represent
has become too low, and it is better to choose a random new
cluster.



.. code-block:: python

    print(__doc__)

    import time

    import matplotlib.pyplot as plt
    import numpy as np


    from sklearn import datasets
    from sklearn.cluster import MiniBatchKMeans
    from sklearn.feature_extraction.image import extract_patches_2d

    faces = datasets.fetch_olivetti_faces()

    # #############################################################################
    # Learn the dictionary of images

    print('Learning the dictionary... ')
    rng = np.random.RandomState(0)
    kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)
    patch_size = (20, 20)

    buffer = []
    t0 = time.time()

    # The online learning part: cycle over the whole dataset 6 times
    index = 0
    for _ in range(6):
        for img in faces.images:
            data = extract_patches_2d(img, patch_size, max_patches=50,
                                      random_state=rng)
            data = np.reshape(data, (len(data), -1))
            buffer.append(data)
            index += 1
            if index % 10 == 0:
                data = np.concatenate(buffer, axis=0)
                data -= np.mean(data, axis=0)
                data /= np.std(data, axis=0)
                kmeans.partial_fit(data)
                buffer = []
            if index % 100 == 0:
                print('Partial fit of %4i out of %i'
                      % (index, 6 * len(faces.images)))

    dt = time.time() - t0
    print('done in %.2fs.' % dt)

    # #############################################################################
    # Plot the results
    plt.figure(figsize=(4.2, 4))
    for i, patch in enumerate(kmeans.cluster_centers_):
        plt.subplot(9, 9, i + 1)
        plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray,
                   interpolation='nearest')
        plt.xticks(())
        plt.yticks(())


    plt.suptitle('Patches of faces\nTrain time %.1fs on %d patches' %
                 (dt, 8 * len(faces.images)), fontsize=16)
    plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)

    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_dict_face_patches.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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