.. _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.



.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_nonlinear_001.png
    :align: center





.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    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()
    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,
                           linetypes='--')
    plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired)
    plt.xticks(())
    plt.yticks(())
    plt.axis([-3, 3, -3, 3])
    plt.show()

**Total running time of the script:**
(0 minutes 1.229 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_svm_nonlinear.ipynb <plot_svm_nonlinear.ipynb>`