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* Pyupgrade to Python 3.9
* updating DIRECTORY.md
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311 lines
8.2 KiB
Python
311 lines
8.2 KiB
Python
from collections import deque
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def _input(message):
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return input(message).strip().split(" ")
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def initialize_unweighted_directed_graph(
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node_count: int, edge_count: int
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) -> dict[int, list[int]]:
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graph: dict[int, list[int]] = {}
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for i in range(node_count):
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graph[i + 1] = []
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for e in range(edge_count):
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x, y = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> "))
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graph[x].append(y)
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return graph
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def initialize_unweighted_undirected_graph(
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node_count: int, edge_count: int
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) -> dict[int, list[int]]:
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graph: dict[int, list[int]] = {}
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for i in range(node_count):
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graph[i + 1] = []
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for e in range(edge_count):
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x, y = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> "))
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graph[x].append(y)
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graph[y].append(x)
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return graph
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def initialize_weighted_undirected_graph(
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node_count: int, edge_count: int
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) -> dict[int, list[tuple[int, int]]]:
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graph: dict[int, list[tuple[int, int]]] = {}
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for i in range(node_count):
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graph[i + 1] = []
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for e in range(edge_count):
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x, y, w = (int(i) for i in _input(f"Edge {e + 1}: <node1> <node2> <weight> "))
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graph[x].append((y, w))
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graph[y].append((x, w))
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return graph
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if __name__ == "__main__":
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n, m = (int(i) for i in _input("Number of nodes and edges: "))
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graph_choice = int(
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_input(
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"Press 1 or 2 or 3 \n"
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"1. Unweighted directed \n"
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"2. Unweighted undirected \n"
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"3. Weighted undirected \n"
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)[0]
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)
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g = {
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1: initialize_unweighted_directed_graph,
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2: initialize_unweighted_undirected_graph,
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3: initialize_weighted_undirected_graph,
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}[graph_choice](n, m)
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"""
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--------------------------------------------------------------------------------
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Depth First Search.
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Args : G - Dictionary of edges
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s - Starting Node
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Vars : vis - Set of visited nodes
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S - Traversal Stack
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--------------------------------------------------------------------------------
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"""
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def dfs(G, s):
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vis, S = {s}, [s]
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print(s)
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while S:
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flag = 0
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for i in G[S[-1]]:
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if i not in vis:
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S.append(i)
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vis.add(i)
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flag = 1
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print(i)
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break
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if not flag:
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S.pop()
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"""
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--------------------------------------------------------------------------------
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Breadth First Search.
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Args : G - Dictionary of edges
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s - Starting Node
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Vars : vis - Set of visited nodes
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Q - Traversal Stack
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--------------------------------------------------------------------------------
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"""
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def bfs(G, s):
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vis, Q = {s}, deque([s])
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print(s)
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while Q:
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u = Q.popleft()
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for v in G[u]:
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if v not in vis:
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vis.add(v)
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Q.append(v)
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print(v)
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"""
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--------------------------------------------------------------------------------
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Dijkstra's shortest path Algorithm
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Args : G - Dictionary of edges
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s - Starting Node
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Vars : dist - Dictionary storing shortest distance from s to every other node
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known - Set of knows nodes
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path - Preceding node in path
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--------------------------------------------------------------------------------
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"""
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def dijk(G, s):
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dist, known, path = {s: 0}, set(), {s: 0}
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while True:
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if len(known) == len(G) - 1:
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break
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mini = 100000
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for i in dist:
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if i not in known and dist[i] < mini:
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mini = dist[i]
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u = i
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known.add(u)
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for v in G[u]:
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if v[0] not in known:
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if dist[u] + v[1] < dist.get(v[0], 100000):
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dist[v[0]] = dist[u] + v[1]
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path[v[0]] = u
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for i in dist:
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if i != s:
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print(dist[i])
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"""
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--------------------------------------------------------------------------------
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Topological Sort
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--------------------------------------------------------------------------------
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"""
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def topo(G, ind=None, Q=None):
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if Q is None:
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Q = [1]
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if ind is None:
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ind = [0] * (len(G) + 1) # SInce oth Index is ignored
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for u in G:
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for v in G[u]:
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ind[v] += 1
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Q = deque()
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for i in G:
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if ind[i] == 0:
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Q.append(i)
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if len(Q) == 0:
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return
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v = Q.popleft()
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print(v)
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for w in G[v]:
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ind[w] -= 1
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if ind[w] == 0:
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Q.append(w)
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topo(G, ind, Q)
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"""
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--------------------------------------------------------------------------------
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Reading an Adjacency matrix
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--------------------------------------------------------------------------------
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"""
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def adjm():
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n = input().strip()
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a = []
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for i in range(n):
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a.append(map(int, input().strip().split()))
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return a, n
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"""
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--------------------------------------------------------------------------------
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Floyd Warshall's algorithm
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Args : G - Dictionary of edges
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s - Starting Node
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Vars : dist - Dictionary storing shortest distance from s to every other node
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known - Set of knows nodes
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path - Preceding node in path
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--------------------------------------------------------------------------------
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"""
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def floy(A_and_n):
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(A, n) = A_and_n
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dist = list(A)
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path = [[0] * n for i in range(n)]
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for k in range(n):
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for i in range(n):
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for j in range(n):
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if dist[i][j] > dist[i][k] + dist[k][j]:
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dist[i][j] = dist[i][k] + dist[k][j]
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path[i][k] = k
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print(dist)
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"""
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--------------------------------------------------------------------------------
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Prim's MST Algorithm
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Args : G - Dictionary of edges
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s - Starting Node
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Vars : dist - Dictionary storing shortest distance from s to nearest node
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known - Set of knows nodes
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path - Preceding node in path
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--------------------------------------------------------------------------------
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"""
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def prim(G, s):
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dist, known, path = {s: 0}, set(), {s: 0}
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while True:
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if len(known) == len(G) - 1:
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break
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mini = 100000
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for i in dist:
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if i not in known and dist[i] < mini:
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mini = dist[i]
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u = i
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known.add(u)
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for v in G[u]:
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if v[0] not in known:
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if v[1] < dist.get(v[0], 100000):
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dist[v[0]] = v[1]
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path[v[0]] = u
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return dist
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"""
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--------------------------------------------------------------------------------
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Accepting Edge list
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Vars : n - Number of nodes
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m - Number of edges
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Returns : l - Edge list
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n - Number of Nodes
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--------------------------------------------------------------------------------
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"""
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def edglist():
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n, m = map(int, input().split(" "))
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edges = []
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for i in range(m):
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edges.append(map(int, input().split(" ")))
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return edges, n
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"""
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--------------------------------------------------------------------------------
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Kruskal's MST Algorithm
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Args : E - Edge list
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n - Number of Nodes
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Vars : s - Set of all nodes as unique disjoint sets (initially)
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--------------------------------------------------------------------------------
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"""
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def krusk(E_and_n):
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# Sort edges on the basis of distance
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(E, n) = E_and_n
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E.sort(reverse=True, key=lambda x: x[2])
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s = [{i} for i in range(1, n + 1)]
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while True:
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if len(s) == 1:
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break
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print(s)
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x = E.pop()
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for i in range(len(s)):
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if x[0] in s[i]:
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break
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for j in range(len(s)):
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if x[1] in s[j]:
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if i == j:
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break
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s[j].update(s[i])
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s.pop(i)
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break
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# find the isolated node in the graph
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def find_isolated_nodes(graph):
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isolated = []
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for node in graph:
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if not graph[node]:
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isolated.append(node)
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return isolated
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