64 lines
1.6 KiB
Python
64 lines
1.6 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-SVC (Support Vector Classification)
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=========================================================
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The classification application of the SVM is used below. The
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`Iris <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_
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dataset has been used for this example
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The decision boundaries, are shown with all the points in
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the training-set.
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"""
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print __doc__
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# Code source: Gael Varoqueux
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# Modified for Documentation merge by Jaques Grobler
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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, 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.
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Y = iris.target
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h = .02 # step size in the mesh
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clf=svm.SVC(C=20, kernel='linear')
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# we create an instance of SVM Classifier and fit the data.
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clf.fit(X, Y)
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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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x_min, x_max = X[:,0].min() - .5, X[:,0].max() + .5
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y_min, y_max = X[:,1].min() - .5, X[:,1].max() + .5
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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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.figure(1, figsize=(4, 3))
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pl.set_cmap(pl.cm.Paired)
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pl.pcolormesh(xx, yy, Z)
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# Plot also the training points
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pl.scatter(X[:,0],X[:,1],c=Y )
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pl.xlabel('Sepal length')
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pl.ylabel('Sepal width')
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pl.xlim(xx.min(), xx.max())
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pl.ylim(yy.min(), yy.max())
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pl.xticks(())
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pl.yticks(())
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pl.show()
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