.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_digits_agglomeration.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_digits_agglomeration.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py:


=========================================================
Feature agglomeration
=========================================================

These images show how similar features are merged together using
feature agglomeration.

.. GENERATED FROM PYTHON SOURCE LINES 10-60



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_digits_agglomeration_001.png
   :alt: Original data, Agglomerated data, Labels
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_digits_agglomeration_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import cluster, datasets
    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=0.01, right=0.99, bottom=0.01, top=0.91)
    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 0.135 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_digits_agglomeration.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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_digits_agglomeration.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/cluster/plot_digits_agglomeration.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_digits_agglomeration.recommendations


.. only:: html

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

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