.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_plot_rvm_for_classification.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_rvm_for_classification.py:


=========================================================
RVM for classification
=========================================================
Based on https://github.com/ctgk/PRML/blob/master/notebooks/ch07_Sparse_Kernel_Machines.ipynb



.. image:: /auto_examples/images/sphx_glr_plot_rvm_for_classification_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    /home/docs/checkouts/readthedocs.org/user_builds/sklearn-rvm/envs/latest/lib/python3.7/site-packages/sklearn_rvm/em_rvm.py:679: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.
      "avoid this warning.", FutureWarning)






|


.. code-block:: default

    print(__doc__)

    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn_rvm import EMRVC


    def create_toy_data():
        x0 = np.random.normal(size=100).reshape(-1, 2) - 1.
        x1 = np.random.normal(size=100).reshape(-1, 2) + 1.
        x = np.concatenate([x0, x1])
        y = np.concatenate([np.zeros(50), np.ones(50)]).astype(np.int)
        return x, y


    x_train, y_train = create_toy_data()

    model = EMRVC(kernel="rbf")
    model.fit(x_train, y_train)

    x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))
    x = np.array([x0, x1]).reshape(2, -1).T
    plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker="x")
    plt.scatter(model.relevance_vectors_[:, 0], model.relevance_vectors_[:, 1], s=100, facecolor="none", edgecolor="g")
    plt.contourf(x0, x1, model.predict_proba(x)[:, 1].reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)
    plt.colorbar()
    plt.xlim(-4, 4)
    plt.ylim(-4, 4)
    plt.gca().set_aspect("equal", adjustable="box")


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

   **Total running time of the script:** ( 0 minutes  7.893 seconds)


.. _sphx_glr_download_auto_examples_plot_rvm_for_classification.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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