45 lines
1.0 KiB
Python
45 lines
1.0 KiB
Python
"""
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==============
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Non-linear SVM
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==============
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Perform binary classification using non-linear SVC
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with RBF kernel. The target to predict is a XOR of the
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inputs.
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The color map illustrates the decision function learned by the SVC.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import svm
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xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
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np.random.seed(0)
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X = np.random.randn(300, 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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clf = svm.NuSVC(gamma="auto")
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clf.fit(X, Y)
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# plot the decision function for each datapoint on the grid
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Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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plt.imshow(
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Z,
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interpolation="nearest",
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extent=(xx.min(), xx.max(), yy.min(), yy.max()),
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aspect="auto",
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origin="lower",
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cmap=plt.cm.PuOr_r,
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)
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contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed")
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plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors="k")
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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.show()
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