Exemple #1
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 def test_lda_covw_whitened(self):
     '''cov_w should be whitened in the transformed space.'''
     classes = spy.create_training_classes(self.data, self.classes)
     fld = spy.linear_discriminant(classes)
     xdata = fld.transform(self.data)
     classes.transform(fld.transform)
     fld2 = spy.linear_discriminant(classes)
     assert_allclose(np.eye(fld2.cov_w.shape[0]), fld2.cov_w, atol=1e-8)
Exemple #2
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 def test_lda_covw_whitened(self):
     '''cov_w should be whitened in the transformed space.'''
     import spectral as spy
     classes = spy.create_training_classes(self.data, self.classes)
     fld = spy.linear_discriminant(classes)
     xdata = fld.transform(self.data)
     classes.transform(fld.transform)
     fld2 = spy.linear_discriminant(classes)
     assert_allclose(np.eye(fld2.cov_w.shape[0]), fld2.cov_w, atol=1e-8)
Exemple #3
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 def test_perceptron_learns_image_classes(self):
     '''Test that perceptron can learn image class means.'''
     fld = spy.linear_discriminant(self.ts)
     xdata = fld.transform(self.data)
     classes = spy.create_training_classes(xdata, self.gt)
     nfeatures = xdata.shape[-1]
     nclasses = len(classes)
     for i in range(10):
         p = spy.PerceptronClassifier([nfeatures, 20, 8, nclasses])
         success = p.train(classes, 1, 5000, batch=1, momentum=0.3,
                           rate=0.3)
         if success is True:
             return
     assert(False)
Exemple #4
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 def test_perceptron_learns_image_classes(self):
     '''Test that perceptron can learn image class means.'''
     fld = spy.linear_discriminant(self.ts)
     xdata = fld.transform(self.data)
     classes = spy.create_training_classes(xdata, self.gt)
     nfeatures = xdata.shape[-1]
     nclasses = len(classes)
     for i in range(10):
         p = spy.PerceptronClassifier([nfeatures, 20, 8, nclasses])
         success = p.train(classes,
                           1,
                           5000,
                           batch=1,
                           momentum=0.3,
                           rate=0.3)
         if success is True:
             return
     assert (False)