.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/gaussian_process/plot_gpc_iris.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_gaussian_process_plot_gpc_iris.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_gaussian_process_plot_gpc_iris.py:


=====================================================
Gaussian process classification (GPC) on iris dataset
=====================================================

This example illustrates the predicted probability of GPC for an isotropic
and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by
assigning different length-scales to the two feature dimensions.

.. GENERATED FROM PYTHON SOURCE LINES 12-67



.. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpc_iris_001.png
   :alt: Isotropic RBF, LML: -48.316, Anisotropic RBF, LML: -47.888
   :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpc_iris_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import datasets
    from sklearn.gaussian_process import GaussianProcessClassifier
    from sklearn.gaussian_process.kernels import RBF

    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    y = np.array(iris.target, dtype=int)

    h = 0.02  # step size in the mesh

    kernel = 1.0 * RBF([1.0])
    gpc_rbf_isotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
    kernel = 1.0 * RBF([1.0, 1.0])
    gpc_rbf_anisotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)

    # create a mesh to plot in
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

    titles = ["Isotropic RBF", "Anisotropic RBF"]
    plt.figure(figsize=(10, 5))
    for i, clf in enumerate((gpc_rbf_isotropic, gpc_rbf_anisotropic)):
        # Plot the predicted probabilities. For that, we will assign a color to
        # each point in the mesh [x_min, m_max]x[y_min, y_max].
        plt.subplot(1, 2, i + 1)

        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot
        Z = Z.reshape((xx.shape[0], xx.shape[1], 3))
        plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower")

        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y], edgecolors=(0, 0, 0))
        plt.xlabel("Sepal length")
        plt.ylabel("Sepal width")
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.xticks(())
        plt.yticks(())
        plt.title(
            "%s, LML: %.3f" % (titles[i], clf.log_marginal_likelihood(clf.kernel_.theta))
        )

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 7.117 seconds)


.. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_iris.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/gaussian_process/plot_gpc_iris.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_gpc_iris.recommendations


.. only:: html

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

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