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A tutorial exercise for using different SVM kernels.
This exercise is used in the Using kernels part of the Supervised learning: predicting an output variable from high-dimensional observations section of the A tutorial on statistical-learning for scientific data processing.
print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, svm iris = datasets.load_iris() X = iris.data y = iris.target X = X[y != 0, :2] y = y[y != 0] n_sample = len(X) np.random.seed(0) order = np.random.permutation(n_sample) X = X[order] y = y[order].astype(np.float) X_train = X[:int(.9 * n_sample)] y_train = y[:int(.9 * n_sample)] X_test = X[int(.9 * n_sample):] y_test = y[int(.9 * n_sample):] # fit the model for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')): clf = svm.SVC(kernel=kernel, gamma=10) clf.fit(X_train, y_train) plt.figure(fig_num) plt.clf() plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired, edgecolor='k', s=20) # Circle out the test data plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10, edgecolor='k') plt.axis('tight') x_min = X[:, 0].min() x_max = X[:, 0].max() y_min = X[:, 1].min() y_max = X[:, 1].max() XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # Put the result into a color plot Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5]) plt.title(kernel) plt.show()
Total running time of the script: ( 0 minutes 6.726 seconds)
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http://scikit-learn.org/stable/auto_examples/exercises/plot_iris_exercise.html