mindspore.scipy.optimize.linear_sum_assignment

mindspore.scipy.optimize.linear_sum_assignment(cost_matrix, maximize, dimension_limit=Tensor(sys.maxsize))[源代码]

Solve the linear sum assignment problem.

The assignment problem is represented as follows:

\[min\sum_{i}^{} \sum_{j}^{} C_{i,j} X_{i,j}\]

where \(C\) is cost matrix, \(X_{i,j} = 1\) means column \(j\) is assigned to row \(i\) .

参数

  • cost_matrix (Tensor) – 2-D cost matrix. Tensor of shape \((M, N)\) .

  • 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 is Tensor(sys.maxsize), which means no limitation. The type is 0-D int64 Tensor.

返回

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 \((N, )\) , where \(N\) 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 \((N, )\) , where \(N\) is the minimum value of cost_matrix dimension.

异常

  • 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

样例

>>> 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]