Improved Bellman-Ford Algorithm : Added Path Reconstruction With Better Output Representation

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unknown 2024-10-11 03:34:07 +05:30
parent e9e7c96465
commit 2b50d97d3e

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from __future__ import annotations from typing import List, Tuple, Dict
from collections import namedtuple
Edge = namedtuple("Edge", ["src", "dst", "weight"])
def print_distance(distance: list[float], src): def print_distance_and_paths(distance: List[float], paths: List[List[int]], src: int):
print(f"Vertex\tShortest Distance from vertex {src}") """
for i, d in enumerate(distance): Prints the shortest distance and paths from the source vertex to each vertex.
print(f"{i}\t\t{d}") """
print(f"Vertex\tShortest Distance from Vertex {src}\tPath")
for vertex, (dist, path) in enumerate(zip(distance, paths)):
path_str = " -> ".join(map(str, path)) if path else "No path"
print(f"{vertex}\t\t{dist}\t\t\t\t{path_str}")
def check_negative_cycle( def check_negative_cycle(graph: List[Edge], distance: List[float], predecessor: List[int]) -> bool:
graph: list[dict[str, int]], distance: list[float], edge_count: int """
): Checks if there is a negative weight cycle reachable from the source vertex.
for j in range(edge_count): If found, return True, indicating a negative cycle.
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"]) """
if distance[u] != float("inf") and distance[u] + w < distance[v]: for edge in graph:
if distance[edge.src] != float("inf") and distance[edge.src] + edge.weight < distance[edge.dst]:
# Update predecessors to indicate a cycle for affected paths
predecessor[edge.dst] = -1 # Use -1 as a marker for negative cycle detection
return True return True
return False return False
def bellman_ford( def reconstruct_paths(predecessor: List[int], vertex_count: int, src: int) -> List[List[int]]:
graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
) -> list[float]:
""" """
Returns shortest paths from a vertex src to all Reconstructs the shortest paths from the source vertex to each vertex using the predecessor list.
other vertices. """
>>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)] paths = [[] for _ in range(vertex_count)]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges] for vertex in range(vertex_count):
>>> bellman_ford(g, 4, 4, 0) if predecessor[vertex] == -1:
[0.0, -2.0, 8.0, 5.0] paths[vertex] = ["Negative cycle detected"]
>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]] elif predecessor[vertex] is not None:
>>> bellman_ford(g, 4, 5, 0) path = []
Traceback (most recent call last): current = vertex
... while current is not None:
Exception: Negative cycle found path.insert(0, current)
if current == src:
break
current = predecessor[current]
paths[vertex] = path
return paths
def bellman_ford(graph: List[Edge], vertex_count: int, src: int) -> Tuple[List[float], List[List[int]]]:
"""
Returns the shortest paths from a vertex src to all other vertices, including path reconstruction.
""" """
distance = [float("inf")] * vertex_count distance = [float("inf")] * vertex_count
predecessor = [None] * vertex_count # Keeps track of the path predecessors
distance[src] = 0.0 distance[src] = 0.0
# Step 1: Relax edges repeatedly
for _ in range(vertex_count - 1): for _ in range(vertex_count - 1):
for j in range(edge_count): for edge in graph:
u, v, w = (graph[j][k] for k in ["src", "dst", "weight"]) if distance[edge.src] != float("inf") and distance[edge.src] + edge.weight < distance[edge.dst]:
distance[edge.dst] = distance[edge.src] + edge.weight
predecessor[edge.dst] = edge.src
if distance[u] != float("inf") and distance[u] + w < distance[v]: # Step 2: Check for negative weight cycles
distance[v] = distance[u] + w if check_negative_cycle(graph, distance, predecessor):
negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
if negative_cycle_exists:
raise Exception("Negative cycle found") raise Exception("Negative cycle found")
return distance # Step 3: Reconstruct paths from predecessor list
paths = reconstruct_paths(predecessor, vertex_count, src)
return distance, paths
if __name__ == "__main__": if __name__ == "__main__":
# Example graph input for testing purposes
import doctest import doctest
doctest.testmod() doctest.testmod()
try:
V = int(input("Enter number of vertices: ").strip()) V = int(input("Enter number of vertices: ").strip())
E = int(input("Enter number of edges: ").strip()) E = int(input("Enter number of edges: ").strip())
graph: list[dict[str, int]] = [{} for _ in range(E)] graph: List[Edge] = []
for i in range(E): for i in range(E):
print("Edge ", i + 1) print(f"Edge {i + 1}")
src, dest, weight = ( src, dest, weight = map(int, input("Enter source, destination, weight: ").strip().split())
int(x) if src < 0 or src >= V or dest < 0 or dest >= V:
for x in input("Enter source, destination, weight: ").strip().split(" ") print(f"Invalid vertices: src and dest should be between 0 and {V - 1}")
) continue
graph[i] = {"src": src, "dst": dest, "weight": weight} graph.append(Edge(src, dest, weight))
source = int(input("\nEnter shortest path source:").strip()) source = int(input("\nEnter shortest path source vertex: ").strip())
shortest_distance = bellman_ford(graph, V, E, source) if source < 0 or source >= V:
print_distance(shortest_distance, 0) print(f"Invalid source: source should be between 0 and {V - 1}")
else:
shortest_distance, paths = bellman_ford(graph, V, source)
print_distance_and_paths(shortest_distance, paths, source)
except ValueError:
print("Please enter valid integer inputs.")
except Exception as e:
print(e)