46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
"""
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=========================================
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SVM: Maximum margin separating hyperplane
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=========================================
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Plot the maximum margin separating hyperplane within a two-class
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separable dataset using a Support Vector Machine classifier with
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linear kernel.
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"""
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print(__doc__)
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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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from sklearn.datasets import make_blobs
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# we create 40 separable points
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X, y = make_blobs(n_samples=40, centers=2, random_state=6)
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# fit the model, don't regularize for illustration purposes
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clf = svm.SVC(kernel='linear', C=1000)
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clf.fit(X, y)
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plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
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# plot the decision function
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ax = plt.gca()
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xlim = ax.get_xlim()
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ylim = ax.get_ylim()
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# create grid to evaluate model
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xx = np.linspace(xlim[0], xlim[1], 30)
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yy = np.linspace(ylim[0], ylim[1], 30)
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YY, XX = np.meshgrid(yy, xx)
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xy = np.vstack([XX.ravel(), YY.ravel()]).T
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Z = clf.decision_function(xy).reshape(XX.shape)
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# plot decision boundary and margins
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ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
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linestyles=['--', '-', '--'])
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# plot support vectors
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ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,
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linewidth=1, facecolors='none', edgecolors='k')
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plt.show()
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