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1 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- 2 # vi: set ft=python sts=4 ts=4 sw=4 et: 3 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 4 # 5 # See COPYING file distributed along with the PyMVPA package for the 6 # copyright and license terms. 7 # 8 ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 9 """Unit tests for PyMVPA kNN classifier""" 10 11 from mvpa.clfs.knn import kNN 12 from tests_warehouse import * 13 from tests_warehouse import pureMultivariateSignal 14 from mvpa.clfs.distance import oneMinusCorrelation 151756 5719 20 mv_perf = [] 21 uv_perf = [] 22 23 clf = kNN(k=10) 24 for i in xrange(20): 25 train = pureMultivariateSignal( 20, 3 ) 26 test = pureMultivariateSignal( 20, 3 ) 27 clf.train(train) 28 p_mv = clf.predict( test.samples ) 29 mv_perf.append( N.mean(p_mv==test.labels) ) 30 31 clf.train(train.selectFeatures([0])) 32 p_uv = clf.predict( test.selectFeatures([0]).samples ) 33 uv_perf.append( N.mean(p_uv==test.labels) ) 34 35 mean_mv_perf = N.mean(mv_perf) 36 mean_uv_perf = N.mean(uv_perf) 37 38 self.failUnless( mean_mv_perf > 0.9 ) 39 self.failUnless( mean_uv_perf < mean_mv_perf )40 4143 train = pureMultivariateSignal( 20, 3 ) 44 test = pureMultivariateSignal( 20, 3 ) 45 46 clf = kNN(k=10) 47 clf.train(train) 48 49 clf.states.enable('values') 50 clf.states.enable('predictions') 51 52 p = clf.predict(test.samples) 53 54 self.failUnless(p == clf.predictions) 55 self.failUnless(N.array(clf.values).shape == (80,2))59 return unittest.makeSuite(KNNTests)60 61 62 if __name__ == '__main__': 63 import runner 64
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