The Hungarian Algorithm solves assignment problems where $n$ items must be assigned to $n$ elements.

This notebook will present a basic implementation of the Hungarian Algorithm and solve randomised problem instances.

`numpy`

and generating an initial random problem instance of size $n \times n$. `max_int`

defines the maximum values created by NumPy's built-in random number generator (RNG).

In [1]:

```
import numpy as np
import copy
max_int = 100
n = 5
cost_matrix = np.random.randint(max_int, size=(n,n))
print(cost_matrix)
```

[[79 46 17 16 58] [25 23 26 52 23] [ 6 0 17 33 81] [ 0 13 43 33 20] [15 44 10 12 80]]

`scipy`

¶Perhaps the simplest solution is to use the `scipy`

package to solve the assignment problem. The function `linear_sum_assignment`

also uses the Hungarian algorithm. Let's see if the results are equal.

In [2]:

```
# import scipy's linear_sum_assignment
from scipy.optimize import linear_sum_assignment
# execute the assignment
scp_assignment = linear_sum_assignment(cost_matrix)
# find the total cost
scp_total = 0
for i in range(len(scp_assignment[0])):
scp_total += cost_matrix[scp_assignment[0][i], scp_assignment[1][i]]
print(scp_total)
```

49

`scipy`

implementation and figure¶In this section, we implement a neater version of Part 1. We still use `scipy`

to find the assignment, but we also produce readable results and a figure to accompany the final solution. The structure is separated into 4 functions:

`run_assignment`

- the master function - it runs the`linear_sum_assignment`

code and calls the suplementary functions to make the results more readable.`clean_assignment`

- scipy returns two arrays as a solution, here we combine then into a single array containing each assignment pair.`calc_costs`

- calculates the total assignment costs.`draw_network`

- draws a bipartite graph with the final assignment highlighted in red.

In [3]:

```
def draw_network(cost_matrix, assignment):
import networkx as nx
x_diff = 10
y_min = 0
y_max = 5
G = nx.Graph()
for i in range(len(assignment)):
G.add_node(f"r_{i}", pos=(y_min, i*x_diff))
G.add_node(f"c_{i}", pos=(y_max, i*x_diff))
pos=nx.get_node_attributes(G,'pos')
for i in range(len(assignment)):
for j in range(len(assignment)):
val = [i, j]
if val in assignment:
c = 'r'
w = 4
else:
c = 'k'
w = 2
G.add_edge(f"r_{i}", f"c_{j}", color=c, weight=w)
edges = G.edges()
colors = [G[u][v]['color'] for u,v in edges]
weights = [G[u][v]['weight'] for u,v in edges]
nx.draw(G,pos,with_labels=True, node_size=600, font_color='w', edge_color=colors, width=weights)
def calc_costs(cost_matrix, assignment):
total = 0
for a in assignment:
total += cost_matrix[a[0], a[1]]
return total
def clean_assignment(row, columns):
assignments = []
# create pairs
text = "The final assignment is "
for i in range(len(row)):
assignments.append([row[i], columns[i]])
if i > 0:
text += ", "
text += f"({row[i]}, {columns[i]})"
print(text)
return assignments
def run_assignment(cost_matrix):
row,columns = linear_sum_assignment(cost_matrix)
assignments = clean_assignment(row, columns)
total_cost = calc_costs(cost_matrix, assignments)
print(f"The total cost of the assignment is {total_cost}.")
draw_network(cost_matrix, assignments)
```

In [4]:

```
run_assignment(cost_matrix)
```

This section presents a step-by-step implementation of the algorithm.

`hungarian_step`

that has the cost_matrix as input.

In [5]:

```
def hungarian_step(mat):
#The for-loop iterates through every column in the matrix so we subtract this value to every element of the row
for row_num in range(mat.shape[0]):
mat[row_num] = mat[row_num] - np.min(mat[row_num])
#We repeat the process for the columns
for col_num in range(mat.shape[1]):
mat[:,col_num] = mat[:,col_num] - np.min(mat[:,col_num])
return mat
```

The next step, while easy to carry out visually, becomes more difficult to code. We need to find the row containing the least number of zeros first.

The first step to do this is to define a function that finds the minimum number of rows to mark that contain a zero value, let's call this `min_zeros`

. Let's assume that the matrix being input is boolean with True where 0 existed and False where non-zero.

Now, mark the column and row as False and repeat, saving the information where the last zero value was retrieved.

By repeating this process, we collect all zeros in the matrix.

In [6]:

```
def min_zeros(zero_mat, mark_zero):
# min_row = [number of zeros, row index number]
min_row = [99999, -1]
for row_num in range(zero_mat.shape[0]):
if np.sum(zero_mat[row_num] == True) > 0 and min_row[0] > np.sum(zero_mat[row_num] == True):
min_row = [np.sum(zero_mat[row_num] == True), row_num]
# Marked the specific row and column as False
zero_index = np.where(zero_mat[min_row[1]] == True)[0][0]
mark_zero.append((min_row[1], zero_index))
zero_mat[min_row[1], :] = False
zero_mat[:, zero_index] = False
```

In [7]:

```
def mark_matrix(mat):
#Transform the matrix to boolean matrix(0 = True, others = False)
cur_mat = mat
zero_bool_mat = (cur_mat == 0)
zero_bool_mat_copy = zero_bool_mat.copy()
#Recording possible answer positions by marked_zero
marked_zero = []
while (True in zero_bool_mat_copy):
min_zeros(zero_bool_mat_copy, marked_zero)
#Recording the row and column indexes seperately.
marked_zero_row = []
marked_zero_col = []
for i in range(len(marked_zero)):
marked_zero_row.append(marked_zero[i][0])
marked_zero_col.append(marked_zero[i][1])
# mark rows not containing zeros
non_marked_row = list(set(range(cur_mat.shape[0])) - set(marked_zero_row))
# mark columns with zeros
marked_cols = []
check_switch = True
while check_switch:
check_switch = False
for i in range(len(non_marked_row)):
row_array = zero_bool_mat[non_marked_row[i], :]
for j in range(row_array.shape[0]):
if row_array[j] == True and j not in marked_cols:
marked_cols.append(j)
check_switch = True
for row_num, col_num in marked_zero:
if row_num not in non_marked_row and col_num in marked_cols:
non_marked_row.append(row_num)
check_switch = True
# mark rows with zeros
marked_rows = list(set(range(mat.shape[0])) - set(non_marked_row))
return(marked_zero, marked_rows, marked_cols)
```

In [8]:

```
def adjust_matrix(mat, cover_rows, cover_cols):
cur_mat = mat
non_zero_element = []
# find the minimum value of an element not in a marked column/row
for row in range(len(cur_mat)):
if row not in cover_rows:
for i in range(len(cur_mat[row])):
if i not in cover_cols:
non_zero_element.append(cur_mat[row][i])
min_num = min(non_zero_element)
# substract to all values not in a marked row/column
for row in range(len(cur_mat)):
if row not in cover_rows:
for i in range(len(cur_mat[row])):
if i not in cover_cols:
cur_mat[row, i] = cur_mat[row, i] - min_num
# add to all values in marked rows/column
for row in range(len(cover_rows)):
for col in range(len(cover_cols)):
cur_mat[cover_rows[row], cover_cols[col]] = cur_mat[cover_rows[row], cover_cols[col]] + min_num
return cur_mat
```

We can now put it all together into a single function:

In [9]:

```
def hungarian_algorithm(cost_matrix):
n = cost_matrix.shape[0]
cur_mat = copy.deepcopy(cost_matrix)
cur_mat = hungarian_step(cur_mat)
count_zero_lines = 0
while count_zero_lines < n:
ans_pos, marked_rows, marked_cols = mark_matrix(cur_mat)
count_zero_lines = len(marked_rows) + len(marked_cols)
if count_zero_lines < n:
cur_mat = adjust_matrix(cur_mat, marked_rows, marked_cols)
return ans_pos
```

In [10]:

```
assignment = hungarian_algorithm(cost_matrix)
print(f"The final assignment is: {assignment}")
print(cost_matrix)
```

We can now calculate the final cost of assignment.

In [11]:

```
total = 0
for i in range(len(assignment)):
total += cost_matrix[assignment[i][0], assignment[i][1]]
print(f"The total cost of the assignment is {total}")
```

The total cost of the assignment is 49