.. _example_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 15 patches from this image. Once we have accumulated
750 of these patches (using 50 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.


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

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