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

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

.. _sphx_glr_auto_examples_simple_example_precomputed.py:


=========================================================
Simple example precomputed
=========================================================


.. code-block:: default

    print(__doc__)

    import numpy as np
    from sklearn.metrics.pairwise import pairwise_kernels

    from sklearn_rvm import EMRVC

    # General a toy dataset:s it's just a straight line with some Gaussian noise:
    n_samples = 100
    np.random.seed(0)
    X = np.random.normal(size=n_samples)
    y = (X > 0).astype(np.float)
    X[X > 0] *= 4
    X += .3 * np.random.normal(size=n_samples)

    X = X[:, np.newaxis]

    K = pairwise_kernels(X)
    # Fit the classifier
    clf = EMRVC(kernel="precomputed")
    clf.fit(K, y)

    print(clf.predict(K))
    print(clf.predict_proba(K))
    print(clf.score(K, y))


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_simple_example_precomputed.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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