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))