46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
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"""
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=========================================
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SGD: 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 linear Support Vector Machines classifier
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trained using SGD.
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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 scikits.learn.linear_model.stochastic_gradient import SGDClassifier
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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 = SGDClassifier(loss="hinge", alpha = 0.01, n_iter=50,
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fit_intercept=True)
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clf.fit(X, Y)
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# plot the line, the points, and the nearest vectors to the plane
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xx = np.linspace(-5, 5, 10)
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yy = np.linspace(-5, 5, 10)
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X1, X2 = np.meshgrid(xx, yy)
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Z = np.empty(X1.shape)
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for (i,j), val in np.ndenumerate(X1):
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x1 = val
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x2 = X2[i,j]
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p = clf.decision_function([x1, x2])
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Z[i,j] = p[0]
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levels = [-1.0, 0.0, 1.0]
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linestyles = ['dashed','solid', 'dashed']
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colors = 'k'
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pl.set_cmap(pl.cm.Paired)
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pl.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
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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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