mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-27 23:11:09 +00:00
b3a15175bd
Added topological sort, cycle detection and a function to report the nodes participating in cycles in graph(for a use case I myself needed ).
439 lines
10 KiB
Plaintext
439 lines
10 KiB
Plaintext
from collections import deque
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import random as rand
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import math as math
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# the dfault weight is 1 if not assigend but all the implementation is weighted
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class DirectedGraph:
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def __init__(self):
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self.graph = {}
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# adding vertices and edges
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# adding the weight is optional
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# handels repetition
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def add_pair(self, u, v, w = 1):
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if self.graph.get(u):
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if self.graph[u].count([w,v]) == 0:
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self.graph[u].append([w, v])
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else:
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self.graph[u] = [[w, v]]
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if not self.graph.get(v):
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self.graph[v] = []
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# handels if the input does not exist
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def remove_pair(self, u, v):
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if self.graph.get(u):
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for _ in self.graph[u]:
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if _[1] == v:
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self.graph[u].remove(_)
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# if no destination is meant the defaut value is -1
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def dfs(self, s = -2, d = -1):
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if s == d:
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return []
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stack = []
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visited = []
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if s == -2:
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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ss = s
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) < 1:
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if __[1] == d:
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visited.append(d)
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return visited
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else:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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stack.pop()
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return visited
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# c is the count of nodes you want and if you leave it or pass -1 to the funtion the count
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# will be random from 10 to 10000
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def fill_graph_randomly(self, c = -1):
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if c == -1:
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c = (math.floor(rand.random() * 10000)) + 10
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for _ in range(c):
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# every vertex has max 100 edges
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e = math.floor(rand.random() * 102) + 1
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for __ in range(e):
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n = math.floor(rand.random() * (c)) + 1
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if n == _:
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continue
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self.add_pair(_, n, 1)
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def bfs(self, s = -2):
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d = deque()
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visited = []
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if s == -2:
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s = list(self.graph.keys())[0]
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d.append(s)
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visited.append(s)
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while d:
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s = d.popleft()
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if len(self.graph[s]) != 0:
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for __ in self.graph[s]:
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if visited.count(__[1]) < 1:
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d.append(__[1])
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visited.append(__[1])
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return visited
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def in_degree(self, u):
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count = 0
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for _ in self.graph:
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for __ in self.graph[_]:
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if __[1] == u:
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count += 1
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return count
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def out_degree(self, u):
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return len(self.graph[u])
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def topological_sort(self, s = -2):
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stack = []
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visited = []
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if s == -2:
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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ss = s
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sorted_nodes = []
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) < 1:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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sorted_nodes.append(stack.pop())
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return sorted_nodes
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def cycle_nodes(self):
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stack = []
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visited = []
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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parent = -2
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indirect_parents = []
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ss = s
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anticipating_nodes = set()
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
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l = len(stack) - 1
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while True and l >= 0:
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if stack[l] == __[1]:
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anticipating_nodes.add(__[1])
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break
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else:
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anticipating_nodes.add(stack[l])
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l -= 1
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if visited.count(__[1]) < 1:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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stack.pop()
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on_the_way_back = True
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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on_the_way_back = False
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indirect_parents.append(parent)
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parent = s
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return list(anticipating_nodes)
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def has_cycle(self):
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stack = []
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visited = []
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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parent = -2
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indirect_parents = []
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ss = s
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anticipating_nodes = set()
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
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l = len(stack) - 1
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while True and l >= 0:
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if stack[l] == __[1]:
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anticipating_nodes.add(__[1])
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break
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else:
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return True
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anticipating_nodes.add(stack[l])
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l -= 1
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if visited.count(__[1]) < 1:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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stack.pop()
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on_the_way_back = True
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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on_the_way_back = False
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indirect_parents.append(parent)
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parent = s
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return False
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class Graph:
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def __init__(self):
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self.graph = {}
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# adding vertices and edges
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# adding the weight is optional
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# handels repetition
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def add_pair(self, u, v, w = 1):
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# check if the u exists
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if self.graph.get(u):
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# if there already is a edge
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if self.graph[u].count([w,v]) == 0:
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self.graph[u].append([w, v])
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else:
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# if u does not exist
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self.graph[u] = [[w, v]]
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# add the other way
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if self.graph.get(v):
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# if there already is a edge
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if self.graph[v].count([w,u]) == 0:
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self.graph[v].append([w, u])
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else:
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# if u does not exist
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self.graph[v] = [[w, u]]
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# handels if the input does not exist
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def remove_pair(self, u, v):
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if self.graph.get(u):
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for _ in self.graph[u]:
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if _[1] == v:
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self.graph[u].remove(_)
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# the other way round
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if self.graph.get(v):
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for _ in self.graph[v]:
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if _[1] == u:
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self.graph[v].remove(_)
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# if no destination is meant the defaut value is -1
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def dfs(self, s = -2, d = -1):
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if s == d:
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return []
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stack = []
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visited = []
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if s == -2:
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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ss = s
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) < 1:
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if __[1] == d:
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visited.append(d)
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return visited
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else:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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stack.pop()
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return visited
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# c is the count of nodes you want and if you leave it or pass -1 to the funtion the count
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# will be random from 10 to 10000
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def fill_graph_randomly(self, c = -1):
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if c == -1:
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c = (math.floor(rand.random() * 10000)) + 10
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for _ in range(c):
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# every vertex has max 100 edges
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e = math.floor(rand.random() * 102) + 1
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for __ in range(e):
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n = math.floor(rand.random() * (c)) + 1
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if n == _:
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continue
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self.add_pair(_, n, 1)
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def bfs(self, s = -2):
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d = deque()
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visited = []
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if s == -2:
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s = list(self.graph.keys())[0]
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d.append(s)
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visited.append(s)
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while d:
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s = d.popleft()
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if len(self.graph[s]) != 0:
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for __ in self.graph[s]:
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if visited.count(__[1]) < 1:
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d.append(__[1])
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visited.append(__[1])
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return visited
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def degree(self, u):
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return len(self.graph[u])
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def cycle_nodes(self):
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stack = []
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visited = []
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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parent = -2
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indirect_parents = []
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ss = s
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anticipating_nodes = set()
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
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l = len(stack) - 1
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while True and l >= 0:
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if stack[l] == __[1]:
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anticipating_nodes.add(__[1])
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break
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else:
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anticipating_nodes.add(stack[l])
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l -= 1
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if visited.count(__[1]) < 1:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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stack.pop()
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on_the_way_back = True
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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on_the_way_back = False
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indirect_parents.append(parent)
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parent = s
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return list(anticipating_nodes)
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def has_cycle(self):
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stack = []
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visited = []
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s = list(self.graph.keys())[0]
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stack.append(s)
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visited.append(s)
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parent = -2
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indirect_parents = []
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ss = s
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anticipating_nodes = set()
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while True:
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# check if there is any non isolated nodes
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if len(self.graph[s]) != 0:
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ss = s
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for __ in self.graph[s]:
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if visited.count(__[1]) > 0 and __[1] != parent and indirect_parents.count(__[1]) > 0 and not on_the_way_back:
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l = len(stack) - 1
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while True and l >= 0:
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if stack[l] == __[1]:
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anticipating_nodes.add(__[1])
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break
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else:
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return True
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anticipating_nodes.add(stack[l])
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l -= 1
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if visited.count(__[1]) < 1:
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stack.append(__[1])
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visited.append(__[1])
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ss =__[1]
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break
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# check if all the children are visited
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if s == ss :
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stack.pop()
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on_the_way_back = True
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if len(stack) != 0:
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s = stack[len(stack) - 1]
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else:
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on_the_way_back = False
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indirect_parents.append(parent)
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parent = s
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s = ss
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# check if se have reached the starting point
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if len(stack) == 0:
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return False
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