89 lines
2.0 KiB
Python
89 lines
2.0 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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SVM-Kernels
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=========================================================
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Three different types of SVM-Kernels are displayed below.
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The polynomial and RBF are especially useful when the
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data-points are not linearly seperable.
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"""
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print __doc__
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# Code source: Gael Varoqueux
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# License: BSD
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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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# Our dataset and targets
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X = np.c_[(.4, -.7),
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(-1.5, -1),
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(-1.4, -.9),
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(-1.3, -1.2),
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(-1.1, -.2),
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(-1.2, -.4),
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( -.5, 1.2),
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( -1.5, 2.1),
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( 1, 1),
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# --
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( 1.3, .8),
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( 1.2, .5),
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( .2, -2),
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( .5, -2.4),
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( .2, -2.3),
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( 0, -2.7),
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( 1.3, 2.1),
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].T
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Y = [0]*8 + [1]*8
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# figure number
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fignum = 1
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# fit the model
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for kernel in ('linear', 'poly', 'rbf'):
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clf = svm.SVC(kernel=kernel, gamma=2)
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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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pl.figure(fignum, figsize=(4, 3))
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pl.clf()
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pl.set_cmap(pl.cm.Paired)
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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 = -3
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x_max = 3
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y_min = -3
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y_max = 3
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XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
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Z = clf.decision_function(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(fignum, figsize=(4, 3))
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pl.set_cmap(pl.cm.Paired)
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pl.pcolormesh(XX, YY, Z > 0)
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pl.contour(XX, YY, Z, colors=['k', 'k', 'k'],
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linestyles=['--', '-', '--'],
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levels=[-.5, 0, .5])
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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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fignum = fignum + 1
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pl.show()
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