.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_exercises_plot_iris_exercise.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_exercises_plot_iris_exercise.py:


================================
SVM Exercise
================================

A tutorial exercise for using different SVM kernels.

This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_001.png
          :alt: linear
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_002.png
          :alt: rbf
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/exercises/images/sphx_glr_plot_iris_exercise_003.png
          :alt: poly
          :class: sphx-glr-multi-img






.. code-block:: default

    print(__doc__)


    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import datasets, svm

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    X = X[y != 0, :2]
    y = y[y != 0]

    n_sample = len(X)

    np.random.seed(0)
    order = np.random.permutation(n_sample)
    X = X[order]
    y = y[order].astype(np.float)

    X_train = X[:int(.9 * n_sample)]
    y_train = y[:int(.9 * n_sample)]
    X_test = X[int(.9 * n_sample):]
    y_test = y[int(.9 * n_sample):]

    # fit the model
    for kernel in ('linear', 'rbf', 'poly'):
        clf = svm.SVC(kernel=kernel, gamma=10)
        clf.fit(X_train, y_train)

        plt.figure()
        plt.clf()
        plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired,
                    edgecolor='k', s=20)

        # Circle out the test data
        plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none',
                    zorder=10, edgecolor='k')

        plt.axis('tight')
        x_min = X[:, 0].min()
        x_max = X[:, 0].max()
        y_min = X[:, 1].min()
        y_max = X[:, 1].max()

        XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
        Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])

        # Put the result into a color plot
        Z = Z.reshape(XX.shape)
        plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
        plt.contour(XX, YY, Z, colors=['k', 'k', 'k'],
                    linestyles=['--', '-', '--'], levels=[-.5, 0, .5])

        plt.title(kernel)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  5.440 seconds)


.. _sphx_glr_download_auto_examples_exercises_plot_iris_exercise.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/exercises/plot_iris_exercise.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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