def test_lfda(self):
lfda = LFDA(k=2, n_components=2)
lfda.fit(self.X, self.y)
res_1 = lfda.transform(self.X)
lfda = LFDA(k=2, n_components=2)
res_2 = lfda.fit_transform(self.X, self.y)
# signs may be flipped, that's okay
assert_array_almost_equal(abs(res_1), abs(res_2))
def test_lfda(self):
lfda = LFDA(k=2, num_dims=2)
lfda.fit(self.X, self.y)
res_1 = lfda.transform()
lfda = LFDA(k=2, num_dims=2)
res_2 = lfda.fit_transform(self.X, self.y)
# signs may be flipped, that's okay
if np.sign(res_1[0, 0]) != np.sign(res_2[0, 0]):
res_2 *= -1
assert_array_almost_equal(res_1, res_2)
def test_lfda(self):
lfda = LFDA(k=2, num_dims=2)
lfda.fit(self.X, self.y)
res_1 = lfda.transform(self.X)
lfda = LFDA(k=2, num_dims=2)
res_2 = lfda.fit_transform(self.X, self.y)
# signs may be flipped, that's okay
if np.sign(res_1[0,0]) != np.sign(res_2[0,0]):
res_2 *= -1
assert_array_almost_equal(res_1, res_2)
