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#!/usr/bin/env python
# -*- coding: iso-8859-1 -*-
# Documentation is intended to be processed by Epydoc.
"""
Introduction
============
The Munkres module provides an implementation of the Munkres algorithm
(also called the Hungarian algorithm or the Kuhn-Munkres algorithm),
useful for solving the Assignment Problem.
Assignment Problem
==================
Let *C* be an *n* by *n* matrix representing the costs of each of *n* workers
to perform any of *n* jobs. The assignment problem is to assign jobs to
workers in a way that minimizes the total cost. Since each worker can perform
only one job and each job can be assigned to only one worker the assignments
represent an independent set of the matrix *C*.
One way to generate the optimal set is to create all permutations of
the indexes necessary to traverse the matrix so that no row and column
are used more than once. For instance, given this matrix (expressed in
Python):
matrix = [[5, 9, 1],
[10, 3, 2],
[8, 7, 4]]
You could use this code to generate the traversal indexes:
def permute(a, results):
if len(a) == 1:
results.insert(len(results), a)
else:
for i in range(0, len(a)):
element = a[i]
a_copy = [a[j] for j in range(0, len(a)) if j != i]
subresults = []
permute(a_copy, subresults)
for subresult in subresults:
result = [element] + subresult
results.insert(len(results), result)
results = []
permute(range(len(matrix)), results) # [0, 1, 2] for a 3x3 matrix
After the call to permute(), the results matrix would look like this:
[[0, 1, 2],
[0, 2, 1],
[1, 0, 2],
[1, 2, 0],
[2, 0, 1],
[2, 1, 0]]
You could then use that index matrix to loop over the original cost matrix
and calculate the smallest cost of the combinations:
minval = sys.maxsize
for indexes in results:
cost = 0
for row, col in enumerate(indexes):
cost += matrix[row][col]
minval = min(cost, minval)
print minval
While this approach works fine for small matrices, it does not scale. It
executes in O(*n*!) time: Calculating the permutations for an *n*\ x\ *n*
matrix requires *n*! operations. For a 12x12 matrix, that's 479,001,600
traversals. Even if you could manage to perform each traversal in just one
millisecond, it would still take more than 133 hours to perform the entire
traversal. A 20x20 matrix would take 2,432,902,008,176,640,000 operations. At
an optimistic millisecond per operation, that's more than 77 million years.
The Munkres algorithm runs in O(*n*\ ^3) time, rather than O(*n*!). This
package provides an implementation of that algorithm.
This version is based on
http://csclab.murraystate.edu/~bob.pilgrim/445/munkres.html
This version was written for Python by Brian Clapper from the algorithm
at the above web site. (The ``Algorithm:Munkres`` Perl version, in CPAN, was
clearly adapted from the same web site.)
Usage
=====
Construct a Munkres object:
from munkres import Munkres
m = Munkres()
Then use it to compute the lowest cost assignment from a cost matrix. Here's
a sample program:
from munkres import Munkres, print_matrix
matrix = [[5, 9, 1],
[10, 3, 2],
[8, 7, 4]]
m = Munkres()
indexes = m.compute(matrix)
print_matrix(matrix, msg='Lowest cost through this matrix:')
total = 0
for row, column in indexes:
value = matrix[row][column]
total += value
print '(%d, %d) -> %d' % (row, column, value)
print 'total cost: %d' % total
Running that program produces:
Lowest cost through this matrix:
[5, 9, 1]
[10, 3, 2]
[8, 7, 4]
(0, 0) -> 5
(1, 1) -> 3
(2, 2) -> 4
total cost=12
The instantiated Munkres object can be used multiple times on different
matrices.
Non-square Cost Matrices
========================
The Munkres algorithm assumes that the cost matrix is square. However, it's
possible to use a rectangular matrix if you first pad it with 0 values to make
it square. This module automatically pads rectangular cost matrices to make
them square.
Notes:
- The module operates on a *copy* of the caller's matrix, so any padding will
not be seen by the caller.
- The cost matrix must be rectangular or square. An irregular matrix will
*not* work.
Calculating Profit, Rather than Cost
====================================
The cost matrix is just that: A cost matrix. The Munkres algorithm finds
the combination of elements (one from each row and column) that results in
the smallest cost. It's also possible to use the algorithm to maximize
profit. To do that, however, you have to convert your profit matrix to a
cost matrix. The simplest way to do that is to subtract all elements from a
large value. For example:
from munkres import Munkres, print_matrix
matrix = [[5, 9, 1],
[10, 3, 2],
[8, 7, 4]]
cost_matrix = []
for row in matrix:
cost_row = []
for col in row:
cost_row += [sys.maxsize - col]
cost_matrix += [cost_row]
m = Munkres()
indexes = m.compute(cost_matrix)
print_matrix(matrix, msg='Highest profit through this matrix:')
total = 0
for row, column in indexes:
value = matrix[row][column]
total += value
print '(%d, %d) -> %d' % (row, column, value)
print 'total profit=%d' % total
Running that program produces:
Highest profit through this matrix:
[5, 9, 1]
[10, 3, 2]
[8, 7, 4]
(0, 1) -> 9
(1, 0) -> 10
(2, 2) -> 4
total profit=23
The ``munkres`` module provides a convenience method for creating a cost
matrix from a profit matrix. By default, it calculates the maximum profit
and subtracts every profit from it to obtain a cost. If, however, you
need a more general function, you can provide the
conversion function; but the convenience method takes care of the actual
creation of the matrix:
import munkres
cost_matrix = munkres.make_cost_matrix(
matrix,
lambda profit: 1000.0 - math.sqrt(profit))
So, the above profit-calculation program can be recast as:
from munkres import Munkres, print_matrix, make_cost_matrix
matrix = [[5, 9, 1],
[10, 3, 2],
[8, 7, 4]]
cost_matrix = make_cost_matrix(matrix)
# cost_matrix == [[5, 1, 9],
# [0, 7, 8],
# [2, 3, 6]]
m = Munkres()
indexes = m.compute(cost_matrix)
print_matrix(matrix, msg='Highest profits through this matrix:')
total = 0
for row, column in indexes:
value = matrix[row][column]
total += value
print '(%d, %d) -> %d' % (row, column, value)
print 'total profit=%d' % total
Disallowed Assignments
======================
You can also mark assignments in your cost or profit matrix as disallowed.
Simply use the munkres.DISALLOWED constant.
from munkres import Munkres, print_matrix, make_cost_matrix, DISALLOWED
matrix = [[5, 9, DISALLOWED],
[10, DISALLOWED, 2],
[8, 7, 4]]
cost_matrix = make_cost_matrix(matrix, lambda cost: (sys.maxsize - cost) if
(cost != DISALLOWED) else DISALLOWED)
m = Munkres()
indexes = m.compute(cost_matrix)
print_matrix(matrix, msg='Highest profit through this matrix:')
total = 0
for row, column in indexes:
value = matrix[row][column]
total += value
print '(%d, %d) -> %d' % (row, column, value)
print 'total profit=%d' % total
Running this program produces:
Lowest cost through this matrix:
[ 5, 9, D]
[10, D, 2]
[ 8, 7, 4]
(0, 1) -> 9
(1, 0) -> 10
(2, 2) -> 4
total profit=23
References
==========
1. http://www.public.iastate.edu/~ddoty/HungarianAlgorithm.html
2. Harold W. Kuhn. The Hungarian Method for the assignment problem.
*Naval Research Logistics Quarterly*, 2:83-97, 1955.
3. Harold W. Kuhn. Variants of the Hungarian method for assignment
problems. *Naval Research Logistics Quarterly*, 3: 253-258, 1956.
4. Munkres, J. Algorithms for the Assignment and Transportation Problems.
*Journal of the Society of Industrial and Applied Mathematics*,
5(1):32-38, March, 1957.
5. http://en.wikipedia.org/wiki/Hungarian_algorithm
Copyright and License
=====================
Copyright 2008-2016 Brian M. Clapper
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
__docformat__ = 'restructuredtext'
# ---------------------------------------------------------------------------
# Imports
# ---------------------------------------------------------------------------
import sys
import copy
# ---------------------------------------------------------------------------
# Exports
# ---------------------------------------------------------------------------
__all__ = ['Munkres', 'make_cost_matrix', 'DISALLOWED']
# ---------------------------------------------------------------------------
# Globals
# ---------------------------------------------------------------------------
# Info about the module
__version__ = "1.0.12"
__author__ = "Brian Clapper, bmc@clapper.org"
__url__ = "http://software.clapper.org/munkres/"
__copyright__ = "(c) 2008-2017 Brian M. Clapper"
__license__ = "Apache Software License"
# Constants
class DISALLOWED_OBJ(object):
pass
DISALLOWED = DISALLOWED_OBJ()
DISALLOWED_PRINTVAL = "D"
# ---------------------------------------------------------------------------
# Exceptions
# ---------------------------------------------------------------------------
class UnsolvableMatrix(Exception):
"""
Exception raised for unsolvable matrices
"""
pass
# ---------------------------------------------------------------------------
# Classes
# ---------------------------------------------------------------------------
class Munkres:
"""
Calculate the Munkres solution to the classical assignment problem.
See the module documentation for usage.
"""
def __init__(self):
"""Create a new instance"""
self.C = None
self.row_covered = []
self.col_covered = []
self.n = 0
self.Z0_r = 0
self.Z0_c = 0
self.marked = None
self.path = None
def make_cost_matrix(profit_matrix, inversion_function):
"""
**DEPRECATED**
Please use the module function ``make_cost_matrix()``.
"""
import munkres
return munkres.make_cost_matrix(profit_matrix, inversion_function)
make_cost_matrix = staticmethod(make_cost_matrix)
def pad_matrix(self, matrix, pad_value=0):
"""
Pad a possibly non-square matrix to make it square.
:Parameters:
matrix : list of lists
matrix to pad
pad_value : int
value to use to pad the matrix
:rtype: list of lists
:return: a new, possibly padded, matrix
"""
max_columns = 0
total_rows = len(matrix)
for row in matrix:
max_columns = max(max_columns, len(row))
total_rows = max(max_columns, total_rows)
new_matrix = []
for row in matrix:
row_len = len(row)
new_row = row[:]
if total_rows > row_len:
# Row too short. Pad it.
new_row += [pad_value] * (total_rows - row_len)
new_matrix += [new_row]
while len(new_matrix) < total_rows:
new_matrix += [[pad_value] * total_rows]
return new_matrix
def compute(self, cost_matrix):
"""
Compute the indexes for the lowest-cost pairings between rows and
columns in the database. Returns a list of (row, column) tuples
that can be used to traverse the matrix.
:Parameters:
cost_matrix : list of lists
The cost matrix. If this cost matrix is not square, it
will be padded with zeros, via a call to ``pad_matrix()``.
(This method does *not* modify the caller's matrix. It
operates on a copy of the matrix.)
**WARNING**: This code handles square and rectangular
matrices. It does *not* handle irregular matrices.
:rtype: list
:return: A list of ``(row, column)`` tuples that describe the lowest
cost path through the matrix
"""
self.C = self.pad_matrix(cost_matrix)
self.n = len(self.C)
self.original_length = len(cost_matrix)
self.original_width = len(cost_matrix[0])
self.row_covered = [False for i in range(self.n)]
self.col_covered = [False for i in range(self.n)]
self.Z0_r = 0
self.Z0_c = 0
self.path = self.__make_matrix(self.n * 2, 0)
self.marked = self.__make_matrix(self.n, 0)
done = False
step = 1
steps = { 1 : self.__step1,
2 : self.__step2,
3 : self.__step3,
4 : self.__step4,
5 : self.__step5,
6 : self.__step6 }
while not done:
try:
func = steps[step]
step = func()
except KeyError:
done = True
# Look for the starred columns
results = []
for i in range(self.original_length):
for j in range(self.original_width):
if self.marked[i][j] == 1:
results += [(i, j)]
return results
def __copy_matrix(self, matrix):
"""Return an exact copy of the supplied matrix"""
return copy.deepcopy(matrix)
def __make_matrix(self, n, val):
"""Create an *n*x*n* matrix, populating it with the specific value."""
matrix = []
for i in range(n):
matrix += [[val for j in range(n)]]
return matrix
def __step1(self):
"""
For each row of the matrix, find the smallest element and
subtract it from every element in its row. Go to Step 2.
"""
C = self.C
n = self.n
for i in range(n):
vals = [x for x in self.C[i] if x is not DISALLOWED]
if len(vals) == 0:
# All values in this row are DISALLOWED. This matrix is
# unsolvable.
raise UnsolvableMatrix(
"Row {0} is entirely DISALLOWED.".format(i)
)
minval = min(vals)
# Find the minimum value for this row and subtract that minimum
# from every element in the row.
for j in range(n):
if self.C[i][j] is not DISALLOWED:
self.C[i][j] -= minval
return 2
def __step2(self):
"""
Find a zero (Z) in the resulting matrix. If there is no starred
zero in its row or column, star Z. Repeat for each element in the
matrix. Go to Step 3.
"""
n = self.n
for i in range(n):
for j in range(n):
if (self.C[i][j] == 0) and \
(not self.col_covered[j]) and \
(not self.row_covered[i]):
self.marked[i][j] = 1
self.col_covered[j] = True
self.row_covered[i] = True
break
self.__clear_covers()
return 3
def __step3(self):
"""
Cover each column containing a starred zero. If K columns are
covered, the starred zeros describe a complete set of unique
assignments. In this case, Go to DONE, otherwise, Go to Step 4.
"""
n = self.n
count = 0
for i in range(n):
for j in range(n):
if self.marked[i][j] == 1 and not self.col_covered[j]:
self.col_covered[j] = True
count += 1
if count >= n:
step = 7 # done
else:
step = 4
return step
def __step4(self):
"""
Find a noncovered zero and prime it. If there is no starred zero
in the row containing this primed zero, Go to Step 5. Otherwise,
cover this row and uncover the column containing the starred
zero. Continue in this manner until there are no uncovered zeros
left. Save the smallest uncovered value and Go to Step 6.
"""
step = 0
done = False
row = 0
col = 0
star_col = -1
while not done:
(row, col) = self.__find_a_zero(row, col)
if row < 0:
done = True
step = 6
else:
self.marked[row][col] = 2
star_col = self.__find_star_in_row(row)
if star_col >= 0:
col = star_col
self.row_covered[row] = True
self.col_covered[col] = False
else:
done = True
self.Z0_r = row
self.Z0_c = col
step = 5
return step
def __step5(self):
"""
Construct a series of alternating primed and starred zeros as
follows. Let Z0 represent the uncovered primed zero found in Step 4.
Let Z1 denote the starred zero in the column of Z0 (if any).
Let Z2 denote the primed zero in the row of Z1 (there will always
be one). Continue until the series terminates at a primed zero
that has no starred zero in its column. Unstar each starred zero
of the series, star each primed zero of the series, erase all
primes and uncover every line in the matrix. Return to Step 3
"""
count = 0
path = self.path
path[count][0] = self.Z0_r
path[count][1] = self.Z0_c
done = False
while not done:
row = self.__find_star_in_col(path[count][1])
if row >= 0:
count += 1
path[count][0] = row
path[count][1] = path[count-1][1]
else:
done = True
if not done:
col = self.__find_prime_in_row(path[count][0])
count += 1
path[count][0] = path[count-1][0]
path[count][1] = col
self.__convert_path(path, count)
self.__clear_covers()
self.__erase_primes()
return 3
def __step6(self):
"""
Add the value found in Step 4 to every element of each covered
row, and subtract it from every element of each uncovered column.
Return to Step 4 without altering any stars, primes, or covered
lines.
"""
minval = self.__find_smallest()
events = 0 # track actual changes to matrix
for i in range(self.n):
for j in range(self.n):
if self.C[i][j] is DISALLOWED:
continue
if self.row_covered[i]:
self.C[i][j] += minval
events += 1
if not self.col_covered[j]:
self.C[i][j] -= minval
events += 1
if self.row_covered[i] and not self.col_covered[j]:
events -= 2 # change reversed, no real difference
if (events == 0):
raise UnsolvableMatrix("Matrix cannot be solved!")
return 4
def __find_smallest(self):
"""Find the smallest uncovered value in the matrix."""
minval = sys.maxsize
for i in range(self.n):
for j in range(self.n):
if (not self.row_covered[i]) and (not self.col_covered[j]):
if self.C[i][j] is not DISALLOWED and minval > self.C[i][j]:
minval = self.C[i][j]
return minval
def __find_a_zero(self, i0=0, j0=0):
"""Find the first uncovered element with value 0"""
row = -1
col = -1
i = i0
n = self.n
done = False
while not done:
j = j0
while True:
if (self.C[i][j] == 0) and \
(not self.row_covered[i]) and \
(not self.col_covered[j]):
row = i
col = j
done = True
j = (j + 1) % n
if j == j0:
break
i = (i + 1) % n
if i == i0:
done = True
return (row, col)
def __find_star_in_row(self, row):
"""
Find the first starred element in the specified row. Returns
the column index, or -1 if no starred element was found.
"""
col = -1
for j in range(self.n):
if self.marked[row][j] == 1:
col = j
break
return col
def __find_star_in_col(self, col):
"""
Find the first starred element in the specified row. Returns
the row index, or -1 if no starred element was found.
"""
row = -1
for i in range(self.n):
if self.marked[i][col] == 1:
row = i
break
return row
def __find_prime_in_row(self, row):
"""
Find the first prime element in the specified row. Returns
the column index, or -1 if no starred element was found.
"""
col = -1
for j in range(self.n):
if self.marked[row][j] == 2:
col = j
break
return col
def __convert_path(self, path, count):
for i in range(count+1):
if self.marked[path[i][0]][path[i][1]] == 1:
self.marked[path[i][0]][path[i][1]] = 0
else:
self.marked[path[i][0]][path[i][1]] = 1
def __clear_covers(self):
"""Clear all covered matrix cells"""
for i in range(self.n):
self.row_covered[i] = False
self.col_covered[i] = False
def __erase_primes(self):
"""Erase all prime markings"""
for i in range(self.n):
for j in range(self.n):
if self.marked[i][j] == 2:
self.marked[i][j] = 0
# ---------------------------------------------------------------------------
# Functions
# ---------------------------------------------------------------------------
def make_cost_matrix(profit_matrix, inversion_function=None):
"""
Create a cost matrix from a profit matrix by calling
'inversion_function' to invert each value. The inversion
function must take one numeric argument (of any type) and return
another numeric argument which is presumed to be the cost inverse
of the original profit. In case the inversion function is not provided,
calculate it as max(matrix) - matrix.
This is a static method. Call it like this:
.. python:
cost_matrix = Munkres.make_cost_matrix(matrix, inversion_func)
For example:
.. python:
cost_matrix = Munkres.make_cost_matrix(matrix, lambda x : sys.maxsize - x)
:Parameters:
profit_matrix : list of lists
The matrix to convert from a profit to a cost matrix
inversion_function : function
The function to use to invert each entry in the profit matrix.
In case it is not provided, calculate it as max(matrix) - matrix.
:rtype: list of lists
:return: The converted matrix
"""
if not inversion_function:
maximum = max(max(row) for row in profit_matrix)
inversion_function = lambda x: maximum - x
cost_matrix = []
for row in profit_matrix:
cost_matrix.append([inversion_function(value) for value in row])
return cost_matrix
def print_matrix(matrix, msg=None):
"""
Convenience function: Displays the contents of a matrix of integers.
:Parameters:
matrix : list of lists
Matrix to print
msg : str
Optional message to print before displaying the matrix
"""
import math
if msg is not None:
print(msg)
# Calculate the appropriate format width.
width = 0
for row in matrix:
for val in row:
if val is DISALLOWED:
val = DISALLOWED_PRINTVAL
width = max(width, len(str(val)))
# Make the format string
format = ('%%%d' % width)
# Print the matrix
for row in matrix:
sep = '['
for val in row:
if val is DISALLOWED:
formatted = ((format + 's') % DISALLOWED_PRINTVAL)
else: formatted = ((format + 'd') % val)
sys.stdout.write(sep + formatted)
sep = ', '
sys.stdout.write(']\n')
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
if __name__ == '__main__':
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
850), # expected cost
# Rectangular variant
([[400, 150, 400, 1],
[400, 450, 600, 2],
[300, 225, 300, 3]],
452), # expected cost
# Square
([[10, 10, 8],
[9, 8, 1],
[9, 7, 4]],
18),
# Rectangular variant
([[10, 10, 8, 11],
[9, 8, 1, 1],
[9, 7, 4, 10]],
15),
# Rectangular with DISALLOWED
([[4, 5, 6, DISALLOWED],
[1, 9, 12, 11],
[DISALLOWED, 5, 4, DISALLOWED],
[12, 12, 12, 10]],
20),
# DISALLOWED to force pairings
([[1, DISALLOWED, DISALLOWED, DISALLOWED],
[DISALLOWED, 2, DISALLOWED, DISALLOWED],
[DISALLOWED, DISALLOWED, 3, DISALLOWED],
[DISALLOWED, DISALLOWED, DISALLOWED, 4]],
10)]
m = Munkres()
for cost_matrix, expected_total in matrices:
print_matrix(cost_matrix, msg='cost matrix')
indexes = m.compute(cost_matrix)
total_cost = 0
for r, c in indexes:
x = cost_matrix[r][c]
total_cost += x
print('(%d, %d) -> %d' % (r, c, x))
print('lowest cost=%d' % total_cost)
assert expected_total == total_cost