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283 lines (240 loc) · 7.72 KB
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from collections import deque
if __name__ == "__main__":
# Accept No. of Nodes and edges
n, m = map(int, input().split(" "))
# Initialising Dictionary of edges
g = {}
for i in range(n):
g[i + 1] = []
"""
----------------------------------------------------------------------------
Accepting edges of Unweighted Directed Graphs
----------------------------------------------------------------------------
"""
for _ in range(m):
x, y = map(int, input().strip().split(" "))
g[x].append(y)
"""
----------------------------------------------------------------------------
Accepting edges of Unweighted Undirected Graphs
----------------------------------------------------------------------------
"""
for _ in range(m):
x, y = map(int, input().strip().split(" "))
g[x].append(y)
g[y].append(x)
"""
----------------------------------------------------------------------------
Accepting edges of Weighted Undirected Graphs
----------------------------------------------------------------------------
"""
for _ in range(m):
x, y, r = map(int, input().strip().split(" "))
g[x].append([y, r])
g[y].append([x, r])
"""
--------------------------------------------------------------------------------
Depth First Search.
Args : G - Dictionary of edges
s - Starting Node
Vars : vis - Set of visited nodes
S - Traversal Stack
--------------------------------------------------------------------------------
"""
def dfs(G, s):
vis, S = {s}, [s]
print(s)
while S:
flag = 0
for i in G[S[-1]]:
if i not in vis:
S.append(i)
vis.add(i)
flag = 1
print(i)
break
if not flag:
S.pop()
"""
--------------------------------------------------------------------------------
Breadth First Search.
Args : G - Dictionary of edges
s - Starting Node
Vars : vis - Set of visited nodes
Q - Traversal Stack
--------------------------------------------------------------------------------
"""
def bfs(G, s):
vis, Q = {s}, deque([s])
print(s)
while Q:
u = Q.popleft()
for v in G[u]:
if v not in vis:
vis.add(v)
Q.append(v)
print(v)
"""
--------------------------------------------------------------------------------
Dijkstra's shortest path Algorithm
Args : G - Dictionary of edges
s - Starting Node
Vars : dist - Dictionary storing shortest distance from s to every other node
known - Set of knows nodes
path - Preceding node in path
--------------------------------------------------------------------------------
"""
def dijk(G, s):
dist, known, path = {s: 0}, set(), {s: 0}
while True:
if len(known) == len(G) - 1:
break
mini = 100000
for i in dist:
if i not in known and dist[i] < mini:
mini = dist[i]
u = i
known.add(u)
for v in G[u]:
if v[0] not in known:
if dist[u] + v[1] < dist.get(v[0], 100000):
dist[v[0]] = dist[u] + v[1]
path[v[0]] = u
for i in dist:
if i != s:
print(dist[i])
"""
--------------------------------------------------------------------------------
Topological Sort
--------------------------------------------------------------------------------
"""
def topo(G, ind=None, Q=None):
if Q is None:
Q = [1]
if ind is None:
ind = [0] * (len(G) + 1) # SInce oth Index is ignored
for u in G:
for v in G[u]:
ind[v] += 1
Q = deque()
for i in G:
if ind[i] == 0:
Q.append(i)
if len(Q) == 0:
return
v = Q.popleft()
print(v)
for w in G[v]:
ind[w] -= 1
if ind[w] == 0:
Q.append(w)
topo(G, ind, Q)
"""
--------------------------------------------------------------------------------
Reading an Adjacency matrix
--------------------------------------------------------------------------------
"""
def adjm():
n = input().strip()
a = []
for i in range(n):
a.append(map(int, input().strip().split()))
return a, n
"""
--------------------------------------------------------------------------------
Floyd Warshall's algorithm
Args : G - Dictionary of edges
s - Starting Node
Vars : dist - Dictionary storing shortest distance from s to every other node
known - Set of knows nodes
path - Preceding node in path
--------------------------------------------------------------------------------
"""
def floy(A_and_n):
(A, n) = A_and_n
dist = list(A)
path = [[0] * n for i in range(n)]
for k in range(n):
for i in range(n):
for j in range(n):
if dist[i][j] > dist[i][k] + dist[k][j]:
dist[i][j] = dist[i][k] + dist[k][j]
path[i][k] = k
print(dist)
"""
--------------------------------------------------------------------------------
Prim's MST Algorithm
Args : G - Dictionary of edges
s - Starting Node
Vars : dist - Dictionary storing shortest distance from s to nearest node
known - Set of knows nodes
path - Preceding node in path
--------------------------------------------------------------------------------
"""
def prim(G, s):
dist, known, path = {s: 0}, set(), {s: 0}
while True:
if len(known) == len(G) - 1:
break
mini = 100000
for i in dist:
if i not in known and dist[i] < mini:
mini = dist[i]
u = i
known.add(u)
for v in G[u]:
if v[0] not in known:
if v[1] < dist.get(v[0], 100000):
dist[v[0]] = v[1]
path[v[0]] = u
return dist
"""
--------------------------------------------------------------------------------
Accepting Edge list
Vars : n - Number of nodes
m - Number of edges
Returns : l - Edge list
n - Number of Nodes
--------------------------------------------------------------------------------
"""
def edglist():
n, m = map(int, input().split(" "))
edges = []
for i in range(m):
edges.append(map(int, input().split(" ")))
return edges, n
"""
--------------------------------------------------------------------------------
Kruskal's MST Algorithm
Args : E - Edge list
n - Number of Nodes
Vars : s - Set of all nodes as unique disjoint sets (initially)
--------------------------------------------------------------------------------
"""
def krusk(E_and_n):
# Sort edges on the basis of distance
(E, n) = E_and_n
E.sort(reverse=True, key=lambda x: x[2])
s = [{i} for i in range(1, n + 1)]
while True:
if len(s) == 1:
break
print(s)
x = E.pop()
for i in range(len(s)):
if x[0] in s[i]:
break
for j in range(len(s)):
if x[1] in s[j]:
if i == j:
break
s[j].update(s[i])
s.pop(i)
break
# find the isolated node in the graph
def find_isolated_nodes(graph):
isolated = []
for node in graph:
if not graph[node]:
isolated.append(node)
return isolated
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