mindspore.scipy.optimize.linear_sum_assignment
- mindspore.scipy.optimize.linear_sum_assignment(cost_matrix, maximize, dimension_limit=Tensor(sys.maxsize))[source]
Solve the linear sum assignment problem.
The assignment problem is represented as follows:
where
is cost matrix, means column is assigned to row .- Parameters
cost_matrix (Tensor) – 2-D cost matrix. Tensor of shape
.maximize (bool) – Calculate a maximum weight matching if true, otherwise calculate a minimum weight matching.
dimension_limit (Tensor, optional) – A scalar used to limit the actual size of the 2nd dimension of
cost_matrix
. Default isTensor(sys.maxsize)
, which means no limitation. The type is 0-D int64 Tensor.
- Returns
A tuple of tensors containing 'row_idx' and 'col_idx'.
row_idx (Tensor) - Row indices of the problem. If dimension_limit is given, -1 would be padded at the end. The shape is
, where is the minimum value of cost_matrix dimension.col_idx (Tensor) - Column indices of the problem. If dimension_limit is given, -1 would be padded at the end. The shape is
, where is the minimum value of cost_matrix dimension.
- Raises
TypeError – If the data type of cost_matrix is not the type in [float16, float32, float64, int8, int16, int32, int64, uint8, uint16, uint32, uint64, bool]
TypeError – If the type of maximize is not bool.
TypeError – If the data type of dimension_limit is not int64.
ValueError – If the rank of cost_matrix is not 2.
- Supported Platforms:
Ascend
CPU
Examples
>>> import mindspore as ms >>> import numpy as np >>> from mindspore import Tensor >>> import mindspore.scipy.optimize.linear_sum_assignment as lsap >>> cost_matrix = Tensor(np.array([[2, 3, 3], [3, 2, 3], [3, 3, 2]])).astype(ms.float64) >>> dimension_limit = Tensor(2) >>> maximize = False >>> a, b = lsap(cost_matrix, maximize, dimension_limit) >>> print(a) [0 1 -1] >>> print(b) [0 1 -1] >>> a, b = lsap(cost_matrix, maximize) >>> print(a) [0 1 2] >>> print(b) [0 1 2]