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maximum_flow_dfs.py
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maximum_flow_dfs.py
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"""
Given a n*n adjacency array.
it will give you a maximum flow.
This version use DFS to search path.
Assume the first is the source and the last is the sink.
Time complexity - O(Ef)
example
graph = [[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0]]
answer should be
23
"""
import copy
import math
def maximum_flow_dfs(adjacency_matrix):
"""
Get the maximum flow through a graph using a depth first search
"""
#initial setting
new_array = copy.deepcopy(adjacency_matrix)
total = 0
while True:
#setting min to max_value
min = math.inf
#save visited nodes
visited = [0]*len(new_array)
#save parent nodes
path = [0]*len(new_array)
#initialize stack for DFS
stack = []
#initial setting
visited[0] = 1
stack.append(0)
#DFS to find path
while len(stack) > 0:
#pop from queue
src = stack.pop()
for k in range(len(new_array)):
#checking capacity and visit
if new_array[src][k] > 0 and visited[k] == 0:
#if not, put into queue and chage to visit and save path
visited[k] = 1
stack.append(k)
path[k] = src
#if there is no path from src to sink
if visited[len(new_array) - 1] == 0:
break
#initial setting
tmp = len(new_array) - 1
#Get minimum flow
while tmp != 0:
#find minimum flow
if min > new_array[path[tmp]][tmp]:
min = new_array[path[tmp]][tmp]
tmp = path[tmp]
#initial setting
tmp = len(new_array) - 1
#reduce capacity
while tmp != 0:
new_array[path[tmp]][tmp] = new_array[path[tmp]][tmp] - min
tmp = path[tmp]
total = total + min
return total