Source code for mindspore.scipy.optimize.linear_sum_assignment

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"""Linear Sum Assignment"""
import sys
from ..ops import LinearSumAssignment
from ... import Tensor


[docs]def linear_sum_assignment(cost_matrix, maximize, dimension_limit=Tensor(sys.maxsize)): r""" Solve the linear sum assignment problem. The assignment problem is represented as follows: .. math:: min\sum_{i}^{} \sum_{j}^{} C_{i,j} X_{i,j} where :math:`C` is cost matrix, :math:`X_{i,j} = 1` means column :math:`j` is assigned to row :math:`i` . Args: cost_matrix (Tensor): 2-D cost matrix. Tensor of shape :math:`(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. 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 :math:`(N, )` , where :math:`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 :math:`(N, )` , where :math:`N` 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] """ solve = LinearSumAssignment() return solve(cost_matrix, dimension_limit, maximize)