.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/_general/plot_intro_mlp_mmd.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__general_plot_intro_mlp_mmd.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples__general_plot_intro_mlp_mmd.py:


=====================================================================
An introducing example to clustering with an MLP and the MMD GEMINI
=====================================================================

We show in this example how to use the `gemclus.mlp.MLPMMD` to fit
a simple mixture of Gaussian distributions. The architecture here
is a two-layer neural network trained with the MMD GEMINI, an
objective equivalent to kernel KMeans.

.. GENERATED FROM PYTHON SOURCE LINES 11-17

.. code-block:: Python


    from matplotlib import pyplot as plt
    from sklearn import datasets

    from gemclus.mlp import MLPMMD








.. GENERATED FROM PYTHON SOURCE LINES 18-20

Generate data
-------------

.. GENERATED FROM PYTHON SOURCE LINES 22-25

.. code-block:: Python


    X, y = datasets.make_blobs(centers=3, cluster_std=0.5, n_samples=200, random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 26-28

Create the MLP clustering model and fit it
-------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 30-33

.. code-block:: Python

    clf = MLPMMD(random_state=0, ovo=True)
    clf.fit(X)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/circleci/.local/lib/python3.10/site-packages/sklearn/base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.
      warnings.warn(


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    </style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MLPMMD(ovo=True, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MLPMMD</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>MLPMMD(ovo=True, random_state=0)</pre></div> </div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 34-36

Plot the final clustering
-------------------------

.. GENERATED FROM PYTHON SOURCE LINES 38-57

.. code-block:: Python


    y_pred = clf.predict(X)
    X_0 = X[y_pred == 0]
    X_1 = X[y_pred == 1]
    X_2 = X[y_pred == 2]

    ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='crimson', s=50)
    ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=50)
    ax2 = plt.scatter(X_2[:, 0], X_2[:, 1], c='darkgreen', s=50)

    leg = plt.legend([ax0, ax1, ax2],
                     ['Cluster 0', 'Cluster 1', 'Cluster 2'],
                     loc='upper left', fancybox=True, scatterpoints=1)
    leg.get_frame().set_alpha(0.5)

    plt.xlabel('Feature 1')
    plt.ylabel('Feature 2')

    plt.show()



.. image-sg:: /auto_examples/_general/images/sphx_glr_plot_intro_mlp_mmd_001.png
   :alt: plot intro mlp mmd
   :srcset: /auto_examples/_general/images/sphx_glr_plot_intro_mlp_mmd_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.710 seconds)


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