49 lines
1.2 KiB
Python
49 lines
1.2 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 Machines 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 pylab as pl
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from sklearn import svm
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# we create 40 separable points
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np.random.seed(0)
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X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
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Y = [0] * 20 + [1] * 20
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# fit the model
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clf = svm.SVC(kernel='linear')
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clf.fit(X, Y)
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# get the separating hyperplane
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w = clf.coef_[0]
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a = -w[0] / w[1]
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xx = np.linspace(-5, 5)
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yy = a * xx - (clf.intercept_[0]) / w[1]
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# plot the parallels to the separating hyperplane that pass through the
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# support vectors
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b = clf.support_vectors_[0]
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yy_down = a * xx + (b[1] - a * b[0])
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b = clf.support_vectors_[-1]
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yy_up = a * xx + (b[1] - a * b[0])
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# plot the line, the points, and the nearest vectors to the plane
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pl.plot(xx, yy, 'k-')
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pl.plot(xx, yy_down, 'k--')
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pl.plot(xx, yy_up, 'k--')
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pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
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s=80, facecolors='none')
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pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
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pl.axis('tight')
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pl.show()
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