Python/graphs/basic_graphs.py
Christian Clauss 64543faa98
Make some ruff fixes (#8154)
* Make some ruff fixes

* Undo manual fix

* Undo manual fix

* Updates from ruff=0.0.251
2023-03-01 17:23:33 +01:00

309 lines
8.1 KiB
Python

from collections import deque
def _input(message):
return input(message).strip().split(" ")
def initialize_unweighted_directed_graph(
node_count: int, edge_count: int
) -> dict[int, list[int]]:
graph: dict[int, list[int]] = {}
for i in range(node_count):
graph[i + 1] = []
for e in range(edge_count):
x, y = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> "))
graph[x].append(y)
return graph
def initialize_unweighted_undirected_graph(
node_count: int, edge_count: int
) -> dict[int, list[int]]:
graph: dict[int, list[int]] = {}
for i in range(node_count):
graph[i + 1] = []
for e in range(edge_count):
x, y = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> "))
graph[x].append(y)
graph[y].append(x)
return graph
def initialize_weighted_undirected_graph(
node_count: int, edge_count: int
) -> dict[int, list[tuple[int, int]]]:
graph: dict[int, list[tuple[int, int]]] = {}
for i in range(node_count):
graph[i + 1] = []
for e in range(edge_count):
x, y, w = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> <weight> "))
graph[x].append((y, w))
graph[y].append((x, w))
return graph
if __name__ == "__main__":
n, m = (int(i) for i in _input("Number of nodes and edges: "))
graph_choice = int(
_input(
"Press 1 or 2 or 3 \n"
"1. Unweighted directed \n"
"2. Unweighted undirected \n"
"3. Weighted undirected \n"
)[0]
)
g = {
1: initialize_unweighted_directed_graph,
2: initialize_unweighted_undirected_graph,
3: initialize_weighted_undirected_graph,
}[graph_choice](n, m)
"""
--------------------------------------------------------------------------------
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 and 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 _ 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 and 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 _ 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