def fisher_discriminant(X, Y): model = LFDA() model.fit(X, Y) return model.transform(X), model.metric()
def test_iris(self):
lfda = LFDA(k=2, num_dims=2)
lfda.fit(self.iris_points, self.iris_labels)
csep = class_separation(lfda.transform(), self.iris_labels)
self.assertLess(csep, 0.15)
# Sanity checks for learned matrices.
self.assertEqual(lfda.metric().shape, (4, 4))
self.assertEqual(lfda.transformer().shape, (2, 4))
def test_iris(self):
lfda = LFDA(k=2, num_dims=2)
lfda.fit(self.iris_points, self.iris_labels)
csep = class_separation(lfda.transform(), self.iris_labels)
self.assertLess(csep, 0.15)
# Sanity checks for learned matrices.
self.assertEqual(lfda.metric().shape, (4, 4))
self.assertEqual(lfda.transformer().shape, (2, 4))
def test_lfda(self): lfda = LFDA(k=2, num_dims=2) lfda.fit(self.X, self.y) L = lfda.transformer_ assert_array_almost_equal(L.T.dot(L), lfda.metric())
def test_lfda(self): lfda = LFDA(k=2, num_dims=2) lfda.fit(self.X, self.y) L = lfda.transformer() assert_array_almost_equal(L.T.dot(L), lfda.metric())
