Created
October 11, 2012 21:46
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LinearSVM
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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn import svm | |
| x1 = np.genfromtxt("class1.csv", delimiter = ",") | |
| x2 = np.genfromtxt("class2.csv", delimiter = ",") | |
| x3 = np.genfromtxt("class3.csv", delimiter = ",") | |
| y1 = np.zeros(x1.shape[0]) | |
| y2 = np.ones(x2.shape[0]) | |
| y3 = np.arange(x3.shape[0]) | |
| y3.fill(2) | |
| x = np.concatenate((x1, x2, x3), axis = 0) | |
| y = np.concatenate((y1, y2, y3)) | |
| xmin, xmax = x[:, 0].min() - 0.1, x[:, 0].max() + 0.1 | |
| ymin, ymax = x[:, 1].min() - 0.1, x[:, 1].max() + 0.1 | |
| clf = svm.LinearSVC() | |
| y_pred = clf.fit(x, y).predict(x) | |
| print "Number of mislabeled points: %d" % (y != y_pred).sum() | |
| xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.01), np.arange(ymin, ymax, 0.01)) | |
| xnew = np.c_[xx.ravel(), yy.ravel()] | |
| ynew = clf.fit(x, y).predict(xnew).reshape(xx.shape) | |
| fig = plt.figure(1) | |
| plt.set_cmap(plt.cm.Paired) | |
| plt.pcolormesh(xx, yy, ynew) | |
| plt.plot(x1[:, 0], x1[:, 1], 'ob', x2[:, 0], x2[:, 1], 'or', x3[:, 0], x3[:, 1], 'og') | |
| plt.savefig("linearsvm_simple.png") |
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