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

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

.. _sphx_glr_auto_examples__general_plot_rim.py:


=================================================================
Simple logistic regression with RIM
=================================================================

RIM (regularised mutual information) is a proposal of model by Krause et al. (2010) which consists in
maximising for a linear model under :math:`\ell_2` penalty. In this example, we show how to do clustering of a
Gaussian mixture using RIM.

.. GENERATED FROM PYTHON SOURCE LINES 10-17

.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt
    from sklearn import datasets

    from gemclus.linear import RIM








.. GENERATED FROM PYTHON SOURCE LINES 18-20

Load a simple synthetic dataset
--------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 22-26

.. code-block:: Python


    # Generate samples on that are simple to separate
    X, y = datasets.make_blobs(centers=3, cluster_std=0.5, n_samples=200, random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 27-30

Train the model
--------------------------------------------------------------
Create the RIM clustering model (just a logistic regression) and fit it to the data.

.. GENERATED FROM PYTHON SOURCE LINES 32-36

.. code-block:: Python


    clf = RIM(n_clusters=3, reg=1e-1, random_state=0)
    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(


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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    #sk-container-id-2 div.sk-serial {
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      padding-right: 1em;
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    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RIM(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-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RIM</div></div><div><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>RIM(random_state=0)</pre></div> </div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 37-40

Final Clustering
-----------------
Let us take a look at the decision boundaries according to the probabilities

.. GENERATED FROM PYTHON SOURCE LINES 42-77

.. code-block:: Python


    # Predict a grad of inputs
    x_vals = np.linspace(X[:, 0].min() - 0.5, X[:, 0].max() + 0.5, num=50)
    y_vals = np.linspace(X[:, 1].min() - 0.5, X[:, 1].max() + 0.5, num=50)
    xx, yy = np.meshgrid(x_vals, y_vals)
    grid_inputs = np.c_[xx.ravel(), yy.ravel()]
    grid_pred = clf.predict_proba(grid_inputs)

    # Isolate probability of the argmax
    zz = grid_pred.max(1)
    zz = zz.reshape((50, 50))

    plt.contourf(xx, yy, zz, alpha=0.3, levels=10)

    # Now, show the cluster predictions
    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()

    print(clf.score(X))



.. image-sg:: /auto_examples/_general/images/sphx_glr_plot_rim_001.png
   :alt: plot rim
   :srcset: /auto_examples/_general/images/sphx_glr_plot_rim_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    0.44865384782317197





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

   **Total running time of the script:** (0 minutes 0.349 seconds)


.. _sphx_glr_download_auto_examples__general_plot_rim.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_rim.zip <plot_rim.zip>`


.. only:: html

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

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