def qr(
self,
tensor: Tensor,
pivot_axis: int = -1,
non_negative_diagonal: bool = False
) -> Tuple[Tensor, Tensor]:
return decompositions.qr(jnp, tensor, pivot_axis, non_negative_diagonal)
def qr(
self,
tensor: Tensor,
pivot_axis: int = -1,
non_negative_diagonal: bool = False
) -> Tuple[Tensor, Tensor]:
#pylint: disable=too-many-function-args
return decompositions.qr(np, tensor, pivot_axis, non_negative_diagonal)
def test_qr(dtype, R, R1):
np.random.seed(10)
D = 30
charges = [
U1Charge.random(dimension=D, minval=-5, maxval=5) for n in range(R)
]
flows = [True] * R
A = BlockSparseTensor.random(
[Index(charges[n], flows[n]) for n in range(R)], dtype=dtype)
q, r = decompositions.qr(bs, A, R1)
res = bs.tensordot(q, r, 1)
q_dense, r_dense = np_decompositions.qr(np, A.todense(), R1, False)
res2 = np.tensordot(q_dense, r_dense, 1)
np.testing.assert_almost_equal(res.todense(), res2)
def test_qr(self):
random_matrix = np.random.rand(10, 10)
for non_negative_diagonal in [True, False]:
q, r = decompositions.qr(np, random_matrix, 1,
non_negative_diagonal)
self.assertAllClose(q.dot(r), random_matrix)
def test_expected_shapes_qr(self):
val = np.zeros((2, 3, 4, 5))
q, r = decompositions.qr(np, val, 2, False)
self.assertEqual(q.shape, (2, 3, 6))
self.assertEqual(r.shape, (6, 4, 5))
