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)