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)