.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/svm/plot_separating_hyperplane.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_svm_plot_separating_hyperplane.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_svm_plot_separating_hyperplane.py:


=========================================
SVM: Maximum margin separating hyperplane
=========================================

Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machine classifier with
linear kernel.

.. GENERATED FROM PYTHON SOURCE LINES 10-46



.. image:: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_001.png
    :alt: plot separating hyperplane
    :class: sphx-glr-single-img





.. code-block:: default

    print(__doc__)

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


    # we create 40 separable points
    X, y = make_blobs(n_samples=40, centers=2, random_state=6)

    # fit the model, don't regularize for illustration purposes
    clf = svm.SVC(kernel='linear', C=1000)
    clf.fit(X, y)

    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)

    # plot the decision function
    ax = plt.gca()
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()

    # create grid to evaluate model
    xx = np.linspace(xlim[0], xlim[1], 30)
    yy = np.linspace(ylim[0], ylim[1], 30)
    YY, XX = np.meshgrid(yy, xx)
    xy = np.vstack([XX.ravel(), YY.ravel()]).T
    Z = clf.decision_function(xy).reshape(XX.shape)

    # plot decision boundary and margins
    ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
               linestyles=['--', '-', '--'])
    # plot support vectors
    ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,
               linewidth=1, facecolors='none', edgecolors='k')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.104 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_separating_hyperplane.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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