37 lines
957 B
Python
37 lines
957 B
Python
import numpy as np
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from scikits.learn import cross_val, datasets, svm
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digits = datasets.load_digits()
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X = digits.data
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y = digits.target
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svc = svm.SVC()
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gammas = np.logspace(-6, -1, 10)
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scores = list()
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scores_std = list()
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for gamma in gammas:
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svc.gamma = gamma
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this_scores = cross_val.cross_val_score(svc, X, y, n_jobs=-1)
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scores.append(np.mean(this_scores))
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scores_std.append(np.std(this_scores))
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import pylab as pl
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pl.figure(1, figsize=(2.5, 2))
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pl.clf()
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pl.axes([.1, .25, .8, .7])
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pl.semilogx(gammas, scores)
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pl.semilogx(gammas, np.array(scores) + np.array(scores_std), 'b--')
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pl.semilogx(gammas, np.array(scores) - np.array(scores_std), 'b--')
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pl.yticks(())
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pl.ylabel('CV score')
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pl.xlabel('gamma')
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pl.ylim(0, 1.1)
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#pl.axhline(np.max(scores), linestyle='--', color='.5')
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pl.text(gammas[np.argmax(scores)], .9*np.max(scores), '%.3f' % np.max(scores),
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verticalalignment='top',
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horizontalalignment='center',
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)
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