scikit-learn/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.

"""
print __doc__

import numpy as np
import pylab as pl
from sklearn import svm

xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 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)

pl.set_cmap(pl.cm.jet)
pl.pcolormesh(xx, yy, Z)
pl.scatter(X[:, 0], X[:, 1], c=Y)
pl.xticks(())
pl.yticks(())

pl.axis('tight')
pl.show()