mindspore.ops.lu_unpack
- mindspore.ops.lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True)[source]
Converts LU_data and LU_pivots back into P, L and U matrices, where P is a permutation matrix, L is a lower triangular matrix, and U is an upper triangular matrix. Typically, LU_data and LU_pivots are generated from the LU decomposition of a matrix.
- Parameters
LU_data (Tensor) – The packed LU factorization data. A Tensor of shape \((*, M, N)\), where \(*\) is batch dimensions. The dim of LU_data must be equal to or greater than 2.
LU_pivots (Tensor) – The packed LU factorization pivots. A Tensor of shape \((*, min(M, N))\), where \(*\) is batch dimensions, with data type int8, uint8, int16, int32, int64.
unpack_data (bool, optional) – A flag indicating if the LU_data should be unpacked. If
False
, then the returned L and U are None. Default:True
.unpack_pivots (bool, optional) – A flag indicating if the LU_pivots should be unpacked into a permutation matrix P. If
False
, then the returned P is None. Default:True
.
- Returns
pivots(Tensor) - The permutation matrix of LU factorization. The shape is \((*, M, M)\), the dtype is same as LU_data.
L (Tensor) - The L matrix of LU factorization. The dtype is same as LU_data.
U (Tensor) - The U matrix of LU factorization. The dtype is same as LU_data.
- Raises
TypeError – If the dtype of LU_data is int, uint or float.
TypeError – If the dtype of LU_pivots is not one of the following: int8, uint8, int16, int32, int64.
ValueError – If the dimension of LU_data is less than 2.
ValueError – If the dimension of LU_pivots is less than 1.
ValueError – If the size of the last dimension of LU_pivots is not equal to the minimum of the sizes of the last two dimensions of LU_data.
ValueError – If the batch dimensions of LU_data's does not match LU_pivots's batch dimensions.
ValueError – On the CPU platform, if the value of LU_pivots are out of range \([1, LU\_data.shape[-2])\).
RuntimeError – On the Ascend platform, if the value of LU_pivots are out of range \([1, LU\_data.shape[-2])\).
- Supported Platforms:
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor, ops >>> from mindspore import dtype as mstype >>> LU_data = Tensor(np.array([[[-0.3806, -0.4872, 0.5536], ... [-0.1287, 0.6508, -0.2396], ... [ 0.2583, 0.5239, 0.6902]], ... [[ 0.6706, -1.1782, 0.4574], ... [-0.6401, -0.4779, 0.6701], ... [ 0.1015, -0.5363, 0.6165]]]), mstype.float64) >>> LU_pivots = Tensor(np.array([[1, 3, 3], ... [2, 3, 3]]), mstype.int32) >>> pivots, L, U = ops.lu_unpack(LU_data, LU_pivots) >>> print(pivots) [[[1. 0. 0.] [0. 0. 1.] [0. 1. 0.]] [[0. 0. 1.] [1. 0. 0.] [0. 1. 0.]]] >>> print(L) [[[ 1. 0. 0.] [-0.1287 1. 0.] [ 0.2583 0.5239 1.]] [[ 1.0000 0. 0.] [-0.6401 1. 0.] [ 0.1015 -0.5363 1.]]] >>> print(U) [[[-0.3806 -0.4872 0.5536] [ 0. 0.6508 -0.2396] [ 0. 0. 0.6902]] [[ 0.6706 -1.1782 0.4574] [ 0. -0.4779 0.6701] [ 0. 0. 0.6165]]]