mindspore.ops.LuUnpack

class mindspore.ops.LuUnpack(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.

Warning

This is an experimental API that is subject to change or deletion.

Refer to mindspore.ops.lu_unpack() for more details.

Parameters

  • 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 .

Inputs:

  • LU_data (Tensor) - The packed LU factorization data. The shape of a tensor is \((*, M, N)\), where \(*\) is batch dimensions, with data type int8, uint8, int16, int32, int64, float16, float32, float64. The dims of LU_data must be equal to or greater than 2.

  • LU_pivots (Tensor) - The packed LU factorization pivots. The shape of a tensor is \((*, min(M, N))\), where \(*\) is batch dimensions, with data type int8, uint8, int16, int32, int64.

Outputs:

  • 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 the same as LU_data.

  • U (Tensor) - The U matrix of LU factorization. The dtype is the same as LU_data.

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.float32)
>>> LU_pivots = Tensor(np.array([[1, 3, 3],
...                              [2, 3, 3]]), mstype.int32)
>>> lu_unpack = ops.LuUnpack()
>>> pivots, L, U = 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.      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]]]