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

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

.. _sphx_glr_auto_examples_svm_plot_svm_nonlinear.py:


==============
Non-linear SVM
==============

Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.

The color map illustrates the decision function learned by the SVC.

.. GENERATED FROM PYTHON SOURCE LINES 13-46



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_svm_nonlinear_001.png
   :alt: plot svm nonlinear
   :srcset: /auto_examples/svm/images/sphx_glr_plot_svm_nonlinear_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import svm

    xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
    np.random.seed(0)
    X = np.random.randn(300, 2)
    Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)

    # fit the model
    clf = svm.NuSVC(gamma="auto")
    clf.fit(X, Y)

    # plot the decision function for each datapoint on the grid
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.imshow(
        Z,
        interpolation="nearest",
        extent=(xx.min(), xx.max(), yy.min(), yy.max()),
        aspect="auto",
        origin="lower",
        cmap=plt.cm.PuOr_r,
    )
    contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed")
    plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors="k")
    plt.xticks(())
    plt.yticks(())
    plt.axis([-3, 3, -3, 3])
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.172 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_svm_nonlinear.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/svm/plot_svm_nonlinear.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/svm/plot_svm_nonlinear.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_svm_nonlinear.recommendations


.. only:: html

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

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