46 lines
1000 B
Python
46 lines
1000 B
Python
"""
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================================================
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SVM: Separating hyperplane with weighted classes
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================================================
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"""
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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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nsamples_1 = 1000
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nsamples_2 = 100
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X = np.r_[1.5*np.random.randn(nsamples_1, 2), 0.5*np.random.randn(nsamples_2, 2) + [2, 2]]
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Y = [0]*(nsamples_1) + [1]*(nsamples_2)
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# fit the model and get the separating hyperplane
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clf = svm.SVC(kernel='linear')
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clf.fit(X, Y)
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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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# get the separating hyperplane using weighted classes
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wclf = svm.SVC(kernel='linear')
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wclf.fit(X, Y, {1: 10})
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ww = wclf.coef_[0]
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wa = -ww[0]/ww[1]
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wyy = wa*xx - (wclf.intercept_[0])/ww[1]
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# plot separating hyperplanes and samples
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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, wyy, 'k--')
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pl.scatter(X[:,0], X[:,1], c=Y)
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pl.axis('tight')
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pl.show()
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