62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""
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==================================================
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Plot different SVM classifiers in the iris dataset
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==================================================
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Comparison of different linear SVM classifiers on the iris dataset. It
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will plot the decision surface for four different SVM classifiers.
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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 sklearn import svm, datasets
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features. We could
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# avoid this ugly slicing by using a two-dim dataset
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Y = iris.target
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h = .02 # step size in the mesh
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# we create an instance of SVM and fit out data. We do not scale our
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# data since we want to plot the support vectors
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C = 1.0 # SVM regularization parameter
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svc = svm.SVC(kernel='linear', C=C).fit(X, Y)
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rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, Y)
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poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, Y)
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lin_svc = svm.LinearSVC(C=C).fit(X, Y)
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# create a mesh to plot in
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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# title for the plots
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titles = ['SVC with linear kernel',
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'SVC with RBF kernel',
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'SVC with polynomial (degree 3) kernel',
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'LinearSVC (linear kernel)']
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for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
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# Plot the decision boundary. For that, we will asign a color to each
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# point in the mesh [x_min, m_max]x[y_min, y_max].
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pl.subplot(2, 2, i + 1)
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Z = clf.predict(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.contourf(xx, yy, Z, cmap=pl.cm.Paired)
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pl.axis('off')
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# Plot also the training points
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pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
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pl.title(titles[i])
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pl.show()
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