mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-10-06 05:39:30 +00:00
6987614067
*added docstring and doctest for edglist *added docstring and doctest for adjm Co-authored-by: Ketan <ketanbmahajan@gmail.com>
378 lines
9.9 KiB
Python
378 lines
9.9 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 and 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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r"""
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Reading an Adjacency matrix
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Parameters:
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None
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Returns:
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tuple: A tuple containing a list of edges and number of edges
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Example:
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>>> # Simulate user input for 3 nodes
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>>> input_data = "4\n0 1 0 1\n1 0 1 0\n0 1 0 1\n1 0 1 0\n"
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>>> import sys,io
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>>> original_input = sys.stdin
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>>> sys.stdin = io.StringIO(input_data) # Redirect stdin for testing
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>>> adjm()
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([(0, 1, 0, 1), (1, 0, 1, 0), (0, 1, 0, 1), (1, 0, 1, 0)], 4)
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>>> sys.stdin = original_input # Restore original stdin
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"""
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n = int(input().strip())
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a = []
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for _ in range(n):
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a.append(tuple(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 and 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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r"""
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Get the edges and number of edges from the user
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Parameters:
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None
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Returns:
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tuple: A tuple containing a list of edges and number of edges
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Example:
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>>> # Simulate user input for 3 edges and 4 vertices: (1, 2), (2, 3), (3, 4)
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>>> input_data = "4 3\n1 2\n2 3\n3 4\n"
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>>> import sys,io
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>>> original_input = sys.stdin
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>>> sys.stdin = io.StringIO(input_data) # Redirect stdin for testing
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>>> edglist()
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([(1, 2), (2, 3), (3, 4)], 4)
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>>> sys.stdin = original_input # Restore original stdin
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"""
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n, m = tuple(map(int, input().split(" ")))
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edges = []
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for _ in range(m):
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edges.append(tuple(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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"""
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Sort edges on the basis of distance
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"""
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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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def find_isolated_nodes(graph):
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"""
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Find the isolated node in the graph
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Parameters:
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graph (dict): A dictionary representing a graph.
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Returns:
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list: A list of isolated nodes.
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Examples:
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>>> graph1 = {1: [2, 3], 2: [1, 3], 3: [1, 2], 4: []}
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>>> find_isolated_nodes(graph1)
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[4]
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>>> graph2 = {'A': ['B', 'C'], 'B': ['A'], 'C': ['A'], 'D': []}
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>>> find_isolated_nodes(graph2)
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['D']
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>>> graph3 = {'X': [], 'Y': [], 'Z': []}
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>>> find_isolated_nodes(graph3)
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['X', 'Y', 'Z']
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>>> graph4 = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
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>>> find_isolated_nodes(graph4)
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[]
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>>> graph5 = {}
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>>> find_isolated_nodes(graph5)
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[]
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"""
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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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