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directed_and_undirected_(weighted)_graph.py
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from collections import deque
import random as rand
import math as math
import time
# the dfault weight is 1 if not assigend but all the implementation is weighted
class DirectedGraph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handels repetition
def add_pair(self, u, v, w = 1):
if self.graph.get(u):
if self.graph[u].count([w,v]) == 0:
self.graph[u].append([w, v])
else:
self.graph[u] = [[w, v]]
if not self.graph.get(v):
self.graph[v] = []
def all_nodes(self):
return list(self.graph)
# handels if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# if no destination is meant the defaut value is -1
def dfs(self, s = -2, d = -1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) < 1:
if __[1] == d:
visited.append(d)
return visited
else:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the funtion the count
# will be random from 10 to 10000
def fill_graph_randomly(self, c = -1):
if c == -1:
c = (math.floor(rand.random() * 10000)) + 10
for _ in range(c):
# every vertex has max 100 edges
e = math.floor(rand.random() * 102) + 1
for __ in range(e):
n = math.floor(rand.random() * (c)) + 1
if n == _:
continue
self.add_pair(_, n, 1)
def bfs(self, s = -2):
d = deque()
visited = []
if s == -2:
s = list(self.graph.keys())[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for __ in self.graph[s]:
if visited.count(__[1]) < 1:
d.append(__[1])
visited.append(__[1])
return visited
def in_degree(self, u):
count = 0
for _ in self.graph:
for __ in self.graph[_]:
if __[1] == u:
count += 1
return count
def out_degree(self, u):
return len(self.graph[u])
def topological_sort(self, s = -2):
stack = []
visited = []
if s == -2:
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
ss = s
sorted_nodes = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
sorted_nodes.append(stack.pop())
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return sorted_nodes
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
anticipating_nodes.add(__[1])
break
else:
anticipating_nodes.add(stack[l])
l -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
anticipating_nodes.add(__[1])
break
else:
return True
anticipating_nodes.add(stack[l])
l -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def dfs_time(self, s = -2, e = -1):
begin = time.time()
self.dfs(s,e)
end = time.time()
return end - begin
def bfs_time(self, s = -2):
begin = time.time()
self.bfs(s)
end = time.time()
return end - begin
class Graph:
def __init__(self):
self.graph = {}
# adding vertices and edges
# adding the weight is optional
# handels repetition
def add_pair(self, u, v, w = 1):
# check if the u exists
if self.graph.get(u):
# if there already is a edge
if self.graph[u].count([w,v]) == 0:
self.graph[u].append([w, v])
else:
# if u does not exist
self.graph[u] = [[w, v]]
# add the other way
if self.graph.get(v):
# if there already is a edge
if self.graph[v].count([w,u]) == 0:
self.graph[v].append([w, u])
else:
# if u does not exist
self.graph[v] = [[w, u]]
# handels if the input does not exist
def remove_pair(self, u, v):
if self.graph.get(u):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_)
# the other way round
if self.graph.get(v):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_)
# if no destination is meant the defaut value is -1
def dfs(self, s = -2, d = -1):
if s == d:
return []
stack = []
visited = []
if s == -2:
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
ss = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) < 1:
if __[1] == d:
visited.append(d)
return visited
else:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
stack.pop()
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the funtion the count
# will be random from 10 to 10000
def fill_graph_randomly(self, c = -1):
if c == -1:
c = (math.floor(rand.random() * 10000)) + 10
for _ in range(c):
# every vertex has max 100 edges
e = math.floor(rand.random() * 102) + 1
for __ in range(e):
n = math.floor(rand.random() * (c)) + 1
if n == _:
continue
self.add_pair(_, n, 1)
def bfs(self, s = -2):
d = deque()
visited = []
if s == -2:
s = list(self.graph.keys())[0]
d.append(s)
visited.append(s)
while d:
s = d.popleft()
if len(self.graph[s]) != 0:
for __ in self.graph[s]:
if visited.count(__[1]) < 1:
d.append(__[1])
visited.append(__[1])
return visited
def degree(self, u):
return len(self.graph[u])
def cycle_nodes(self):
stack = []
visited = []
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
anticipating_nodes.add(__[1])
break
else:
anticipating_nodes.add(stack[l])
l -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return list(anticipating_nodes)
def has_cycle(self):
stack = []
visited = []
s = list(self.graph.keys())[0]
stack.append(s)
visited.append(s)
parent = -2
indirect_parents = []
ss = s
on_the_way_back = False
anticipating_nodes = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s]) != 0:
ss = s
for __ in self.graph[s]:
if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
anticipating_nodes.add(__[1])
break
else:
return True
anticipating_nodes.add(stack[l])
l -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
ss =__[1]
break
# check if all the children are visited
if s == ss :
stack.pop()
on_the_way_back = True
if len(stack) != 0:
s = stack[len(stack) - 1]
else:
on_the_way_back = False
indirect_parents.append(parent)
parent = s
s = ss
# check if se have reached the starting point
if len(stack) == 0:
return False
def all_nodes(self):
return list(self.graph)
def dfs_time(self, s = -2, e = -1):
begin = time.time()
self.dfs(s,e)
end = time.time()
return end - begin
def bfs_time(self, s = -2):
begin = time.time()
self.bfs(s)
end = time.time()
return end - begin