def test_rq_decomposition(dtype, R, R1, num_charges):
np.random.seed(10)
D = 30
charges = [
BaseCharge(np.random.randint(-5, 6, (num_charges, D)),
charge_types=[U1Charge] * num_charges) for n in range(R)
]
flows = [True] * R
A = BlockSparseTensor.random(
[Index(charges[n], flows[n]) for n in range(R)], dtype=dtype)
r, q = decompositions.rq_decomposition(bs, A, R1)
res = bs.tensordot(r, q, 1)
r_dense, q_dense = np_decompositions.rq_decomposition(np, A.todense(), R1)
res2 = np.tensordot(r_dense, q_dense, 1)
np.testing.assert_almost_equal(res.todense(), res2)
def test_rq_decomposition(self):
random_matrix = np.random.rand(10, 10)
r, q = decompositions.rq_decomposition(np, random_matrix, 1)
self.assertAllClose(r.dot(q), random_matrix)
def test_expected_shapes_rq(self):
val = np.zeros((2, 3, 4, 5))
r, q = decompositions.rq_decomposition(np, val, 2)
self.assertEqual(r.shape_tensor, (2, 3, 6))
self.assertEqual(q.shape_tensor, (6, 4, 5))
def rq_decomposition(
self,
tensor: Tensor,
split_axis: int,
) -> Tuple[Tensor, Tensor]:
return decompositions.rq_decomposition(np, tensor, split_axis)
