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

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

.. _sphx_glr_auto_examples_plot_intro_mlp_mmd.py:


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

An example plot of :class:`gemclus.base_gemini.DenseMMDOvO`

.. GENERATED FROM PYTHON SOURCE LINES 8-14

.. code-block:: default


    from matplotlib import pyplot as plt
    from sklearn import datasets

    from gemclus.mlp import MLPMMD








.. GENERATED FROM PYTHON SOURCE LINES 15-17

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

.. GENERATED FROM PYTHON SOURCE LINES 19-22

.. code-block:: default


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








.. GENERATED FROM PYTHON SOURCE LINES 23-25

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

.. GENERATED FROM PYTHON SOURCE LINES 27-30

.. code-block:: default

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






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow">MLPMMD</label><div class="sk-toggleable__content"><pre>MLPMMD(ovo=True, random_state=0)</pre></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 31-33

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

.. GENERATED FROM PYTHON SOURCE LINES 35-54

.. code-block:: default


    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/images/sphx_glr_plot_intro_mlp_mmd_001.png
   :alt: plot intro mlp mmd
   :srcset: /auto_examples/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 0.768 seconds)


.. _sphx_glr_download_auto_examples_plot_intro_mlp_mmd.py:

.. only:: html

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

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

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


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