forked from 170010011/fr
106 lines
3.9 KiB
Python
106 lines
3.9 KiB
Python
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# Wrapper for the shortest augmenting path algorithm for solving the
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# rectangular linear sum assignment problem. The original code was an
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# implementation of the Hungarian algorithm (Kuhn-Munkres) taken from
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# scikit-learn, based on original code by Brian Clapper and adapted to NumPy
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# by Gael Varoquaux. Further improvements by Ben Root, Vlad Niculae, Lars
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# Buitinck, and Peter Larsen.
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#
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# Copyright (c) 2008 Brian M. Clapper <bmc@clapper.org>, Gael Varoquaux
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# Author: Brian M. Clapper, Gael Varoquaux
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# License: 3-clause BSD
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import numpy as np
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from . import _lsap_module
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def linear_sum_assignment(cost_matrix, maximize=False):
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"""Solve the linear sum assignment problem.
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The linear sum assignment problem is also known as minimum weight matching
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in bipartite graphs. A problem instance is described by a matrix C, where
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each C[i,j] is the cost of matching vertex i of the first partite set
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(a "worker") and vertex j of the second set (a "job"). The goal is to find
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a complete assignment of workers to jobs of minimal cost.
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Formally, let X be a boolean matrix where :math:`X[i,j] = 1` iff row i is
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assigned to column j. Then the optimal assignment has cost
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.. math::
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\\min \\sum_i \\sum_j C_{i,j} X_{i,j}
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where, in the case where the matrix X is square, each row is assigned to
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exactly one column, and each column to exactly one row.
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This function can also solve a generalization of the classic assignment
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problem where the cost matrix is rectangular. If it has more rows than
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columns, then not every row needs to be assigned to a column, and vice
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versa.
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The problem is also solved for sparse inputs in
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:func:`scipy.sparse.csgraph.min_weight_full_bipartite_matching` which
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may perform better if the input is sparse, or for certain classes of
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problems, such as uniformly distributed costs.
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Parameters
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----------
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cost_matrix : array
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The cost matrix of the bipartite graph.
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maximize : bool (default: False)
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Calculates a maximum weight matching if true.
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Returns
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-------
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row_ind, col_ind : array
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An array of row indices and one of corresponding column indices giving
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the optimal assignment. The cost of the assignment can be computed
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as ``cost_matrix[row_ind, col_ind].sum()``. The row indices will be
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sorted; in the case of a square cost matrix they will be equal to
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``numpy.arange(cost_matrix.shape[0])``.
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Notes
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-----
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.. versionadded:: 0.17.0
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References
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----------
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1. https://en.wikipedia.org/wiki/Assignment_problem
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2. DF Crouse. On implementing 2D rectangular assignment algorithms.
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*IEEE Transactions on Aerospace and Electronic Systems*,
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52(4):1679-1696, August 2016, :doi:`10.1109/TAES.2016.140952`
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Examples
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--------
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>>> cost = np.array([[4, 1, 3], [2, 0, 5], [3, 2, 2]])
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>>> from scipy.optimize import linear_sum_assignment
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>>> row_ind, col_ind = linear_sum_assignment(cost)
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>>> col_ind
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array([1, 0, 2])
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>>> cost[row_ind, col_ind].sum()
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5
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"""
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cost_matrix = np.asarray(cost_matrix)
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if cost_matrix.ndim != 2:
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raise ValueError("expected a matrix (2-D array), got a %r array"
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% (cost_matrix.shape,))
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if not (np.issubdtype(cost_matrix.dtype, np.number) or
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cost_matrix.dtype == np.dtype(np.bool_)):
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raise ValueError("expected a matrix containing numerical entries, got %s"
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% (cost_matrix.dtype,))
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if maximize:
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cost_matrix = -cost_matrix
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cost_matrix = cost_matrix.astype(np.double)
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# The algorithm expects more columns than rows in the cost matrix.
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if cost_matrix.shape[1] < cost_matrix.shape[0]:
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a, b = _lsap_module.calculate_assignment(cost_matrix.T)
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indices = np.argsort(b)
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return (b[indices], a[indices])
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else:
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return _lsap_module.calculate_assignment(cost_matrix)
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