43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
"""Gaussian process classification (GPC)
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This example illustrates GPC on XOR data.
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"""
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print __doc__
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# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
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#
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# License: BSD 3 clause
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.gaussian_process.kernels import RBF
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xx, yy = np.meshgrid(np.linspace(-3, 3, 50),
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np.linspace(-3, 3, 50))
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rng = np.random.RandomState(0)
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X = rng.randn(200, 2)
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Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
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# fit the model
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kernel = 1.0 * RBF(1.0)
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clf = GaussianProcessClassifier(kernel=kernel).fit(X, Y)
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# plot the decision function for each datapoint on the grid
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Z = clf.predict_proba(np.vstack((xx.ravel(), yy.ravel())).T)[:, 1]
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Z = Z.reshape(xx.shape)
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image = plt.imshow(Z, interpolation='nearest',
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extent=(xx.min(), xx.max(), yy.min(), yy.max()),
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aspect='auto', origin='lower', cmap=plt.cm.PuOr_r)
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contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
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linetypes='--')
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plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired)
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plt.xticks(())
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plt.yticks(())
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plt.axis([-3, 3, -3, 3])
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plt.colorbar(image)
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plt.show()
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