61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
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import numpy as np
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import pylab as pl
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from scikits.learn 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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for name, penality in (('', 1), ('_no_penalty', .2e-2)):
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clf = svm.SVC(kernel='linear', C=penality)
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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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margin = 1/np.sqrt(np.sum(clf.coef_**2))
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yy_down = yy + a*margin
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yy_up = yy - a*margin
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# plot the line, the points, and the nearest vectors to the plane
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pl.figure(1, figsize=(4, 3))
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pl.clf()
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pl.set_cmap(pl.cm.Paired)
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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', zorder=10)
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pl.scatter(X[:,0], X[:,1], c=Y, zorder=10)
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pl.axis('tight')
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x_min = -4.8
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x_max = 4.2
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y_min = -6
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y_max = 6
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XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
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Z = clf.predict(np.c_[XX.ravel(), YY.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(XX.shape)
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pl.figure(1, figsize=(4, 3))
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pl.set_cmap(pl.cm.Paired)
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pl.pcolormesh(XX, YY, Z)
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pl.xlim(x_min, x_max)
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pl.ylim(y_min, y_max)
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pl.xticks(())
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pl.yticks(())
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pl.savefig('svm_margin%s.png' % name)
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