34 lines
971 B
Python
34 lines
971 B
Python
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD Style.
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# $Id$
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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 import datasets
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# Set regularization parameter
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C = 0.1
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classifier_l1_LR = LogisticRegression(C=C, penalty='l1')
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classifier_l2_LR = LogisticRegression(C=C, penalty='l2')
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classifier_l1_LR.fit(X, y)
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classifier_l2_LR.fit(X, y)
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hyperplane_coefficients_l1_LR = classifier_l1_LR.coef_[:]
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hyperplane_coefficients_l2_LR = classifier_l2_LR.coef_[:]
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# hyperplane_coefficients_l1_LR contains zeros due to the
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# L1 sparsity inducing norm
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pct_non_zeros_l1_LR = np.mean(hyperplane_coefficients_l1_LR != 0) * 100
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pct_non_zeros_l2_LR = np.mean(hyperplane_coefficients_l2_LR != 0) * 100
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print "Percentage of non zeros coefficients (L1) : %f" % pct_non_zeros_l1_LR
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print "Percentage of non zeros coefficients (L2) : %f" % pct_non_zeros_l2_LR
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