55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD Style.
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# $Id$
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import pylab as pl
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import numpy as np
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from scikits.learn.logistic import LogisticRegression
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from scikits.learn.svm import SVC
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from scikits.learn import datasets
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features for visualization
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y = iris.target
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n_features = X.shape[1]
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C = 1.0
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# Create classifier (any of the following 3)
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classifier = LogisticRegression(C=C, penalty='l1')
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classifier = LogisticRegression(C=C, penalty='l2')
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classifier = SVC(kernel='linear', C=C, probability=True)
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classifier.fit(X, y)
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y_pred = classifier.predict(X)
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classif_rate = np.mean(y_pred.ravel() == y.ravel()) * 100
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print "classif_rate : %f " % classif_rate
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# ======================
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# = View probabilities =
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# ======================
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pl.figure()
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xx = np.linspace(3,9,100)
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yy = np.linspace(1,5,100).T
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xx, yy = np.meshgrid(xx, yy)
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Xfull = np.c_[xx.ravel(),yy.ravel()]
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probas = classifier.predict_proba(Xfull)
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n_classes = np.unique(y_pred).size
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for k in range(n_classes):
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pl.subplot(1, n_classes, k + 1)
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pl.title("Class %d" % k)
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imshow_handle = pl.imshow(probas[:,k].reshape((100, 100)), extent=(3, 9, 1, 5), origin='lower')
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pl.hold(True)
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idx = (y_pred == k)
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if idx.any(): pl.scatter(X[idx,0], X[idx,1], marker='o', c='k')
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ax = pl.axes([0.15,0.04,0.7,0.05])
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pl.title("Probability")
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pl.colorbar(imshow_handle, cax=ax, orientation='horizontal')
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pl.show()
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