.. 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_digits_agglomeration.py:
=========================================================
Feature agglomeration
=========================================================
These images how similar features are merged together using
feature agglomeration.
.. image:: /auto_examples/cluster/images/sphx_glr_plot_digits_agglomeration_001.png
:alt: Original data, Agglomerated data, Labels
:class: sphx-glr-single-img
.. code-block:: default
print(__doc__)
# Code source: Gaƫl Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, cluster
from sklearn.feature_extraction.image import grid_to_graph
digits = datasets.load_digits()
images = digits.images
X = np.reshape(images, (len(images), -1))
connectivity = grid_to_graph(*images[0].shape)
agglo = cluster.FeatureAgglomeration(connectivity=connectivity,
n_clusters=32)
agglo.fit(X)
X_reduced = agglo.transform(X)
X_restored = agglo.inverse_transform(X_reduced)
images_restored = np.reshape(X_restored, images.shape)
plt.figure(1, figsize=(4, 3.5))
plt.clf()
plt.subplots_adjust(left=.01, right=.99, bottom=.01, top=.91)
for i in range(4):
plt.subplot(3, 4, i + 1)
plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation='nearest')
plt.xticks(())
plt.yticks(())
if i == 1:
plt.title('Original data')
plt.subplot(3, 4, 4 + i + 1)
plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16,
interpolation='nearest')
if i == 1:
plt.title('Agglomerated data')
plt.xticks(())
plt.yticks(())
plt.subplot(3, 4, 10)
plt.imshow(np.reshape(agglo.labels_, images[0].shape),
interpolation='nearest', cmap=plt.cm.nipy_spectral)
plt.xticks(())
plt.yticks(())
plt.title('Labels')
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.243 seconds)
.. _sphx_glr_download_auto_examples_cluster_plot_digits_agglomeration.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_digits_agglomeration.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_digits_agglomeration.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_digits_agglomeration.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_