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<!DOCTYPE html>
<html lang="en">
<title>HungarianVisualizer-Python</title>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Lato">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<style>
@import url(https://fonts.googleapis.com/css?family=Righteous);
@import url('https://fonts.googleapis.com/css2?family=Fjalla+One&family=Open+Sans&display=swap');
@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@700&display=swap');
body {font-family: 'Righteous', serif;
background-color:#f7d596;}
.mySlides {display: none}
.inTable {
max-width: 50px;
text-align: center;
}
td {
border: 1px solid black;
padding: 1vw;
text-align: center;
}
h1 {
text-align: center;
color: #E8CEBF;
}
h1 {
font-family: 'Righteous';
color: #202020;
text-transform: uppercase;
letter-spacing: -2vw;
}
h1 span {
display: block;
margin: 11px 0 17px 0;
font-size: 6vw;
line-height: 8vw;
color: #111;
text-shadow: 0 5.36px 8.896px white,0 -2px 1px #fff;
letter-spacing: -1px;
}
@media (max-width: 1200px) {
}
input[type="button"] {
background-color: #E8CEBF;
border: 2px solid black;
color: black;
padding: 16px 32px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: 1.2vw;
margin: 4px 2px;
transition-duration: 0.4s;
cursor: pointer;
}
input[type="number"],#problemType,.inTable
{
font-size: 2vw;
}
input[type="button"]:hover, input[type="button"]:focus, #printAns:hover, #printAns:focus {
background-color: white;
color: black;
border: 2px solid black;
}
#anotherQue {
background-color: #d70c38;
border: 2px solid #d70c28;
}
#anotherQue:hover, #anotherQue:focus{
background-color: #E8CEBF;
color: black;
border: 2px solid #d70c28;
}
.result {
color: #ee1139;
}
</style>
<body>
<div class="w3-top">
<div class="w3-bar w3-black w3-card">
<a class="w3-bar-item w3-button w3-padding-large w3-hide-medium w3-hide-large w3-right" href="javascript:void(0)" onclick="myFunction()" title="Toggle Navigation Menu"><i class="fa fa-bars"></i></a>
<a href="index.html" class="w3-bar-item w3-button w3-padding-large">HOME</a>
<div class="w3-dropdown-hover w3-hide-small">
<button class="w3-padding-large w3-button" title="More">CODE <i class="fa fa-caret-down"></i></button>
<div class="w3-dropdown-content w3-bar-block w3-card-4">
<a href="c_hung.html" class="w3-bar-item w3-button">C</a>
<a href="cpp_hung.html" class="w3-bar-item w3-button">C++</a>
<a href="py_hung.html" class="w3-bar-item w3-button">Python</a>
</div>
</div>
</div>
</div>
<div id="navDemo" class="w3-bar-block w3-black w3-hide w3-hide-large w3-hide-medium w3-top" style="margin-top:46px">
<a href="#" class="w3-bar-item w3-button">C</a>
<a href="#" class="w3-bar-item w3-button">C++</a>
<a href="#" class="w3-bar-item w3-button">Python</a>
</div>
<div class="w3-content" style="max-width:2000px;margin-top:46px">
<div>
<h1> <span>PYTHON CODE</span> </h1><br/>
</div>
</div>
<div >
<div style="background: #4CAF60;
color: white;
font-size: 1vw;
padding: 3vw;
width: 88vw;
height: 90vw;
overflow: scroll;
border: 1px solid #ccc;
margin-left: 5vw; ">
<pre><xmp style="">
#!/usr/bin/python
"""
Implementation of the Hungarian (Munkres) Algorithm using Python and NumPy
References: http://www.ams.jhu.edu/~castello/362/Handouts/hungarian.pdf
http://weber.ucsd.edu/~vcrawfor/hungar.pdf
http://en.wikipedia.org/wiki/Hungarian_algorithm
http://www.public.iastate.edu/~ddoty/HungarianAlgorithm.html
http://www.clapper.org/software/python/munkres/
"""
# Module Information.
__version__ = "1.1.1"
__author__ = "Thom Dedecko"
__url__ = "http://github.com/tdedecko/hungarian-algorithm"
__copyright__ = "(c) 2010 Thom Dedecko"
__license__ = "MIT License"
class HungarianError(Exception):
pass
# Import numpy. Error if fails
try:
import numpy as np
except ImportError:
raise HungarianError("NumPy is not installed.")
class Hungarian:
"""
Implementation of the Hungarian (Munkres) Algorithm using np.
Usage:
hungarian = Hungarian(cost_matrix)
hungarian.calculate()
or
hungarian = Hungarian()
hungarian.calculate(cost_matrix)
Handle Profit matrix:
hungarian = Hungarian(profit_matrix, is_profit_matrix=True)
or
cost_matrix = Hungarian.make_cost_matrix(profit_matrix)
The matrix will be automatically padded if it is not square.
For that numpy's resize function is used, which automatically adds 0's to any row/column that is added
Get results and total potential after calculation:
hungarian.get_results()
hungarian.get_total_potential()
"""
def __init__(self, input_matrix=None, is_profit_matrix=False):
"""
input_matrix is a List of Lists.
input_matrix is assumed to be a cost matrix unless is_profit_matrix is True.
"""
if input_matrix is not None:
# Save input
my_matrix = np.array(input_matrix)
self._input_matrix = np.array(input_matrix)
self._maxColumn = my_matrix.shape[1]
self._maxRow = my_matrix.shape[0]
# Adds 0s if any columns/rows are added. Otherwise stays unaltered
matrix_size = max(self._maxColumn, self._maxRow)
pad_columns = matrix_size - self._maxRow
pad_rows = matrix_size - self._maxColumn
my_matrix = np.pad(my_matrix, ((0,pad_columns),(0,pad_rows)), 'constant', constant_values=(0))
# Convert matrix to profit matrix if necessary
if is_profit_matrix:
my_matrix = self.make_cost_matrix(my_matrix)
self._cost_matrix = my_matrix
self._size = len(my_matrix)
self._shape = my_matrix.shape
# Results from algorithm.
self._results = []
self._totalPotential = 0
else:
self._cost_matrix = None
def get_results(self):
"""Get results after calculation."""
return self._results
def get_total_potential(self):
"""Returns expected value after calculation."""
return self._totalPotential
def calculate(self, input_matrix=None, is_profit_matrix=False):
"""
Implementation of the Hungarian (Munkres) Algorithm.
input_matrix is a List of Lists.
input_matrix is assumed to be a cost matrix unless is_profit_matrix is True.
"""
# Handle invalid and new matrix inputs.
if input_matrix is None and self._cost_matrix is None:
raise HungarianError("Invalid input")
elif input_matrix is not None:
self.__init__(input_matrix, is_profit_matrix)
result_matrix = self._cost_matrix.copy()
# Step 1: Subtract row mins from each row.
for index, row in enumerate(result_matrix):
result_matrix[index] -= row.min()
# Step 2: Subtract column mins from each column.
for index, column in enumerate(result_matrix.T):
result_matrix[:, index] -= column.min()
# Step 3: Use minimum number of lines to cover all zeros in the matrix.
# If the total covered rows+columns is not equal to the matrix size then adjust matrix and repeat.
total_covered = 0
while total_covered < self._size:
# Find minimum number of lines to cover all zeros in the matrix and find total covered rows and columns.
cover_zeros = CoverZeros(result_matrix)
covered_rows = cover_zeros.get_covered_rows()
covered_columns = cover_zeros.get_covered_columns()
total_covered = len(covered_rows) + len(covered_columns)
# if the total covered rows+columns is not equal to the matrix size then adjust it by min uncovered num (m).
if total_covered < self._size:
result_matrix = self._adjust_matrix_by_min_uncovered_num(result_matrix, covered_rows, covered_columns)
# Step 4: Starting with the top row, work your way downwards as you make assignments.
# Find single zeros in rows or columns.
# Add them to final result and remove them and their associated row/column from the matrix.
expected_results = min(self._maxColumn, self._maxRow)
zero_locations = (result_matrix == 0)
while len(self._results) != expected_results:
# If number of zeros in the matrix is zero before finding all the results then an error has occurred.
if not zero_locations.any():
raise HungarianError("Unable to find results. Algorithm has failed.")
# Find results and mark rows and columns for deletion
matched_rows, matched_columns = self.__find_matches(zero_locations)
# Make arbitrary selection
total_matched = len(matched_rows) + len(matched_columns)
if total_matched == 0:
matched_rows, matched_columns = self.select_arbitrary_match(zero_locations)
# Delete rows and columns
for row in matched_rows:
zero_locations[row] = False
for column in matched_columns:
zero_locations[:, column] = False
# Save Results
self.__set_results(zip(matched_rows, matched_columns))
# Calculate total potential
value = 0
for row, column in self._results:
value += self._input_matrix[row, column]
self._totalPotential = value
@staticmethod
def make_cost_matrix(profit_matrix):
"""
Converts a profit matrix into a cost matrix.
Expects NumPy objects as input.
"""
# subtract profit matrix from a matrix made of the max value of the profit matrix
matrix_shape = profit_matrix.shape
offset_matrix = np.ones(matrix_shape, dtype=int) * profit_matrix.max()
cost_matrix = offset_matrix - profit_matrix
return cost_matrix
def _adjust_matrix_by_min_uncovered_num(self, result_matrix, covered_rows, covered_columns):
"""Subtract m from every uncovered number and add m to every element covered with two lines."""
# Calculate minimum uncovered number (m)
elements = []
for row_index, row in enumerate(result_matrix):
if row_index not in covered_rows:
for index, element in enumerate(row):
if index not in covered_columns:
elements.append(element)
min_uncovered_num = min(elements)
# Add m to every covered element
adjusted_matrix = result_matrix
for row in covered_rows:
adjusted_matrix[row] += min_uncovered_num
for column in covered_columns:
adjusted_matrix[:, column] += min_uncovered_num
# Subtract m from every element
m_matrix = np.ones(self._shape, dtype=int) * min_uncovered_num
adjusted_matrix -= m_matrix
return adjusted_matrix
def __find_matches(self, zero_locations):
"""Returns rows and columns with matches in them."""
marked_rows = np.array([], dtype=int)
marked_columns = np.array([], dtype=int)
# Mark rows and columns with matches
# Iterate over rows
for index, row in enumerate(zero_locations):
row_index = np.array([index])
if np.sum(row) == 1:
column_index, = np.where(row)
marked_rows, marked_columns = self.__mark_rows_and_columns(marked_rows, marked_columns, row_index,
column_index)
# Iterate over columns
for index, column in enumerate(zero_locations.T):
column_index = np.array([index])
if np.sum(column) == 1:
row_index, = np.where(column)
marked_rows, marked_columns = self.__mark_rows_and_columns(marked_rows, marked_columns, row_index,
column_index)
return marked_rows, marked_columns
@staticmethod
def __mark_rows_and_columns(marked_rows, marked_columns, row_index, column_index):
"""Check if column or row is marked. If not marked then mark it."""
new_marked_rows = marked_rows
new_marked_columns = marked_columns
if not (marked_rows == row_index).any() and not (marked_columns == column_index).any():
new_marked_rows = np.insert(marked_rows, len(marked_rows), row_index)
new_marked_columns = np.insert(marked_columns, len(marked_columns), column_index)
return new_marked_rows, new_marked_columns
@staticmethod
def select_arbitrary_match(zero_locations):
"""Selects row column combination with minimum number of zeros in it."""
# Count number of zeros in row and column combinations
rows, columns = np.where(zero_locations)
zero_count = []
for index, row in enumerate(rows):
total_zeros = np.sum(zero_locations[row]) + np.sum(zero_locations[:, columns[index]])
zero_count.append(total_zeros)
# Get the row column combination with the minimum number of zeros.
indices = zero_count.index(min(zero_count))
row = np.array([rows[indices]])
column = np.array([columns[indices]])
return row, column
def __set_results(self, result_lists):
"""Set results during calculation."""
# Check if results values are out of bound from input matrix (because of matrix being padded).
# Add results to results list.
for result in result_lists:
row, column = result
if row < self._maxRow and column < self._maxColumn:
new_result = (int(row), int(column))
self._results.append(new_result)
class CoverZeros:
"""
Use minimum number of lines to cover all zeros in the matrix.
Algorithm based on: http://weber.ucsd.edu/~vcrawfor/hungar.pdf
"""
def __init__(self, matrix):
"""
Input a matrix and save it as a boolean matrix to designate zero locations.
Run calculation procedure to generate results.
"""
# Find zeros in matrix
self._zero_locations = (matrix == 0)
self._shape = matrix.shape
# Choices starts without any choices made.
self._choices = np.zeros(self._shape, dtype=bool)
self._marked_rows = []
self._marked_columns = []
# marks rows and columns
self.__calculate()
# Draw lines through all unmarked rows and all marked columns.
self._covered_rows = list(set(range(self._shape[0])) - set(self._marked_rows))
self._covered_columns = self._marked_columns
def get_covered_rows(self):
"""Return list of covered rows."""
return self._covered_rows
def get_covered_columns(self):
"""Return list of covered columns."""
return self._covered_columns
def __calculate(self):
"""
Calculates minimum number of lines necessary to cover all zeros in a matrix.
Algorithm based on: http://weber.ucsd.edu/~vcrawfor/hungar.pdf
"""
while True:
# Erase all marks.
self._marked_rows = []
self._marked_columns = []
# Mark all rows in which no choice has been made.
for index, row in enumerate(self._choices):
if not row.any():
self._marked_rows.append(index)
# If no marked rows then finish.
if not self._marked_rows:
return True
# Mark all columns not already marked which have zeros in marked rows.
num_marked_columns = self.__mark_new_columns_with_zeros_in_marked_rows()
# If no new marked columns then finish.
if num_marked_columns == 0:
return True
# While there is some choice in every marked column.
while self.__choice_in_all_marked_columns():
# Some Choice in every marked column.
# Mark all rows not already marked which have choices in marked columns.
num_marked_rows = self.__mark_new_rows_with_choices_in_marked_columns()
# If no new marks then Finish.
if num_marked_rows == 0:
return True
# Mark all columns not already marked which have zeros in marked rows.
num_marked_columns = self.__mark_new_columns_with_zeros_in_marked_rows()
# If no new marked columns then finish.
if num_marked_columns == 0:
return True
# No choice in one or more marked columns.
# Find a marked column that does not have a choice.
choice_column_index = self.__find_marked_column_without_choice()
while choice_column_index is not None:
# Find a zero in the column indexed that does not have a row with a choice.
choice_row_index = self.__find_row_without_choice(choice_column_index)
# Check if an available row was found.
new_choice_column_index = None
if choice_row_index is None:
# Find a good row to accomodate swap. Find its column pair.
choice_row_index, new_choice_column_index = \
self.__find_best_choice_row_and_new_column(choice_column_index)
# Delete old choice.
self._choices[choice_row_index, new_choice_column_index] = False
# Set zero to choice.
self._choices[choice_row_index, choice_column_index] = True
# Loop again if choice is added to a row with a choice already in it.
choice_column_index = new_choice_column_index
def __mark_new_columns_with_zeros_in_marked_rows(self):
"""Mark all columns not already marked which have zeros in marked rows."""
num_marked_columns = 0
for index, column in enumerate(self._zero_locations.T):
if index not in self._marked_columns:
if column.any():
row_indices, = np.where(column)
zeros_in_marked_rows = (set(self._marked_rows) & set(row_indices)) != set([])
if zeros_in_marked_rows:
self._marked_columns.append(index)
num_marked_columns += 1
return num_marked_columns
def __mark_new_rows_with_choices_in_marked_columns(self):
"""Mark all rows not already marked which have choices in marked columns."""
num_marked_rows = 0
for index, row in enumerate(self._choices):
if index not in self._marked_rows:
if row.any():
column_index, = np.where(row)
if column_index in self._marked_columns:
self._marked_rows.append(index)
num_marked_rows += 1
return num_marked_rows
def __choice_in_all_marked_columns(self):
"""Return Boolean True if there is a choice in all marked columns. Returns boolean False otherwise."""
for column_index in self._marked_columns:
if not self._choices[:, column_index].any():
return False
return True
def __find_marked_column_without_choice(self):
"""Find a marked column that does not have a choice."""
for column_index in self._marked_columns:
if not self._choices[:, column_index].any():
return column_index
raise HungarianError(
"Could not find a column without a choice. Failed to cover matrix zeros. Algorithm has failed.")
def __find_row_without_choice(self, choice_column_index):
"""Find a row without a choice in it for the column indexed. If a row does not exist then return None."""
row_indices, = np.where(self._zero_locations[:, choice_column_index])
for row_index in row_indices:
if not self._choices[row_index].any():
return row_index
# All rows have choices. Return None.
return None
def __find_best_choice_row_and_new_column(self, choice_column_index):
"""
Find a row index to use for the choice so that the column that needs to be changed is optimal.
Return a random row and column if unable to find an optimal selection.
"""
row_indices, = np.where(self._zero_locations[:, choice_column_index])
for row_index in row_indices:
column_indices, = np.where(self._choices[row_index])
column_index = column_indices[0]
if self.__find_row_without_choice(column_index) is not None:
return row_index, column_index
# Cannot find optimal row and column. Return a random row and column.
from random import shuffle
shuffle(row_indices)
column_index, = np.where(self._choices[row_indices[0]])
return row_indices[0], column_index[0]
if __name__ == '__main__':
profit_matrix = [
[62, 75, 80, 93, 95, 97],
[75, 80, 82, 85, 71, 97],
[80, 75, 81, 98, 90, 97],
[78, 82, 84, 80, 50, 98],
[90, 85, 85, 80, 85, 99],
[65, 75, 80, 75, 68, 96]]
hungarian = Hungarian(profit_matrix, is_profit_matrix=True)
hungarian.calculate()
print("Expected value:\t\t543")
print("Calculated value:\t", hungarian.get_total_potential()) # = 543
print("Expected results:\n\t[(0, 4), (2, 3), (5, 5), (4, 0), (1, 1), (3, 2)]")
print("Results:\n\t", hungarian.get_results())
print("-" * 80)
cost_matrix = [
[4, 2, 8],
[4, 3, 7],
[3, 1, 6]]
hungarian = Hungarian(cost_matrix)
print('calculating...')
hungarian.calculate()
print("Expected value:\t\t12")
print("Calculated value:\t", hungarian.get_total_potential()) # = 12
print("Expected results:\n\t[(0, 1), (1, 0), (2, 2)]")
print("Results:\n\t", hungarian.get_results())
print("-" * 80)
profit_matrix = [
[62, 75, 80, 93, 0, 97],
[75, 0, 82, 85, 71, 97],
[80, 75, 81, 0, 90, 97],
[78, 82, 0, 80, 50, 98],
[0, 85, 85, 80, 85, 99],
[65, 75, 80, 75, 68, 0]]
hungarian = Hungarian()
hungarian.calculate(profit_matrix, is_profit_matrix=True)
print("Expected value:\t\t523")
print("Calculated value:\t", hungarian.get_total_potential()) # = 523
print("Expected results:\n\t[(0, 3), (2, 4), (3, 0), (5, 2), (1, 5), (4, 1)]")
print("Results:\n\t", hungarian.get_results())
print("-" * 80)
</xmp></pre>
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<p style="font-size: 1vw;margin-left: 6vw; ">Reference: <a href="http://github.com/tdedecko/hungarian-algorithm" target="_blank">http://github.com</a></p></div>
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