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

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

.. _sphx_glr_auto_examples_plot_gstm.py:


================================================================================================
Example of decision boundary map for a mixture of Gaussian and low-degree Student distributions
================================================================================================

This example is a retake from the experiment in the original GEMINI paper where we want to find the true clusters
in a mixture of Gaussian that incorporates a low-degree-of-freedom student t-distribution. Consequently, this
distribution generates sample that may seem like outliers if we are to expect only Gaussian distribution.

Unlike the paper, this example here is done with the `gemclus.linear.LinearWasserstein` instead of an MLP.

.. GENERATED FROM PYTHON SOURCE LINES 12-19

.. code-block:: default


    import numpy as np
    from matplotlib import pyplot as plt

    from gemclus.data import gstm
    from gemclus.linear import LinearWasserstein








.. GENERATED FROM PYTHON SOURCE LINES 20-23

Generate the data
-----------------


.. GENERATED FROM PYTHON SOURCE LINES 25-26

Taking 200 samples, 1 degree of freedom and not-so-far apart means

.. GENERATED FROM PYTHON SOURCE LINES 26-28

.. code-block:: default

    X, y = gstm(n=200, alpha=3, df=1, random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 29-32

Train the model for clustering
-------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 34-38

.. code-block:: default


    clf = LinearWasserstein(n_clusters=4, random_state=0, batch_size=50)
    y_pred = clf.fit_predict(X)








.. GENERATED FROM PYTHON SOURCE LINES 39-41

Final Clustering
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 43-62

.. code-block:: default


    # Now, generate as well grid inputs to help drawing the decision boundary
    x_vals = np.linspace(-10, 10, num=50)
    y_vals = np.linspace(-10, 10, num=50)
    xx, yy = np.meshgrid(x_vals, y_vals)
    grid_inputs = np.c_[xx.ravel(), yy.ravel()]
    zz = clf.predict(grid_inputs).reshape((50, 50))

    # Plot decision boundary with predictions on top
    plt.contourf(xx, yy, zz, alpha=0.5, cmap="Blues")
    plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap="Reds_r")

    plt.xlim(-10, 10)
    plt.ylim(-10, 10)

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

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_gstm_001.png
   :alt: plot gstm
   :srcset: /auto_examples/images/sphx_glr_plot_gstm_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 8.913 seconds)


.. _sphx_glr_download_auto_examples_plot_gstm.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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