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)
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)
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)
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)