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Improved Bellman-Ford Algorithm : Added Path Reconstruction With Better Output Representation
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@ -1,73 +1,103 @@
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from __future__ import annotations
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from typing import List, Tuple, Dict
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from collections import namedtuple
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Edge = namedtuple("Edge", ["src", "dst", "weight"])
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def print_distance(distance: list[float], src):
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def print_distance_and_paths(distance: List[float], paths: List[List[int]], src: int):
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print(f"Vertex\tShortest Distance from vertex {src}")
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"""
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for i, d in enumerate(distance):
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Prints the shortest distance and paths from the source vertex to each vertex.
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print(f"{i}\t\t{d}")
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"""
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print(f"Vertex\tShortest Distance from Vertex {src}\tPath")
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for vertex, (dist, path) in enumerate(zip(distance, paths)):
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path_str = " -> ".join(map(str, path)) if path else "No path"
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print(f"{vertex}\t\t{dist}\t\t\t\t{path_str}")
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def check_negative_cycle(
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def check_negative_cycle(graph: List[Edge], distance: List[float], predecessor: List[int]) -> bool:
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graph: list[dict[str, int]], distance: list[float], edge_count: int
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"""
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):
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Checks if there is a negative weight cycle reachable from the source vertex.
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for j in range(edge_count):
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If found, return True, indicating a negative cycle.
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u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
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"""
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if distance[u] != float("inf") and distance[u] + w < distance[v]:
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for edge in graph:
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if distance[edge.src] != float("inf") and distance[edge.src] + edge.weight < distance[edge.dst]:
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# Update predecessors to indicate a cycle for affected paths
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predecessor[edge.dst] = -1 # Use -1 as a marker for negative cycle detection
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return True
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return True
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return False
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return False
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def bellman_ford(
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def reconstruct_paths(predecessor: List[int], vertex_count: int, src: int) -> List[List[int]]:
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graph: list[dict[str, int]], vertex_count: int, edge_count: int, src: int
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) -> list[float]:
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"""
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"""
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Returns shortest paths from a vertex src to all
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Reconstructs the shortest paths from the source vertex to each vertex using the predecessor list.
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other vertices.
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"""
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>>> edges = [(2, 1, -10), (3, 2, 3), (0, 3, 5), (0, 1, 4)]
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paths = [[] for _ in range(vertex_count)]
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>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges]
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for vertex in range(vertex_count):
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>>> bellman_ford(g, 4, 4, 0)
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if predecessor[vertex] == -1:
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[0.0, -2.0, 8.0, 5.0]
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paths[vertex] = ["Negative cycle detected"]
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>>> g = [{"src": s, "dst": d, "weight": w} for s, d, w in edges + [(1, 3, 5)]]
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elif predecessor[vertex] is not None:
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>>> bellman_ford(g, 4, 5, 0)
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path = []
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Traceback (most recent call last):
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current = vertex
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...
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while current is not None:
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Exception: Negative cycle found
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path.insert(0, current)
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if current == src:
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break
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current = predecessor[current]
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paths[vertex] = path
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return paths
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def bellman_ford(graph: List[Edge], vertex_count: int, src: int) -> Tuple[List[float], List[List[int]]]:
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"""
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Returns the shortest paths from a vertex src to all other vertices, including path reconstruction.
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"""
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"""
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distance = [float("inf")] * vertex_count
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distance = [float("inf")] * vertex_count
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predecessor = [None] * vertex_count # Keeps track of the path predecessors
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distance[src] = 0.0
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distance[src] = 0.0
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# Step 1: Relax edges repeatedly
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for _ in range(vertex_count - 1):
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for _ in range(vertex_count - 1):
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for j in range(edge_count):
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for edge in graph:
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u, v, w = (graph[j][k] for k in ["src", "dst", "weight"])
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if distance[edge.src] != float("inf") and distance[edge.src] + edge.weight < distance[edge.dst]:
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distance[edge.dst] = distance[edge.src] + edge.weight
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predecessor[edge.dst] = edge.src
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if distance[u] != float("inf") and distance[u] + w < distance[v]:
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# Step 2: Check for negative weight cycles
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distance[v] = distance[u] + w
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if check_negative_cycle(graph, distance, predecessor):
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negative_cycle_exists = check_negative_cycle(graph, distance, edge_count)
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if negative_cycle_exists:
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raise Exception("Negative cycle found")
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raise Exception("Negative cycle found")
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return distance
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# Step 3: Reconstruct paths from predecessor list
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paths = reconstruct_paths(predecessor, vertex_count, src)
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return distance, paths
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if __name__ == "__main__":
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if __name__ == "__main__":
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# Example graph input for testing purposes
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import doctest
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import doctest
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doctest.testmod()
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doctest.testmod()
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try:
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V = int(input("Enter number of vertices: ").strip())
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V = int(input("Enter number of vertices: ").strip())
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E = int(input("Enter number of edges: ").strip())
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E = int(input("Enter number of edges: ").strip())
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graph: list[dict[str, int]] = [{} for _ in range(E)]
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graph: List[Edge] = []
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for i in range(E):
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for i in range(E):
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print("Edge ", i + 1)
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print(f"Edge {i + 1}")
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src, dest, weight = (
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src, dest, weight = map(int, input("Enter source, destination, weight: ").strip().split())
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int(x)
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if src < 0 or src >= V or dest < 0 or dest >= V:
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for x in input("Enter source, destination, weight: ").strip().split(" ")
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print(f"Invalid vertices: src and dest should be between 0 and {V - 1}")
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)
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continue
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graph[i] = {"src": src, "dst": dest, "weight": weight}
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graph.append(Edge(src, dest, weight))
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source = int(input("\nEnter shortest path source:").strip())
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source = int(input("\nEnter shortest path source vertex: ").strip())
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shortest_distance = bellman_ford(graph, V, E, source)
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if source < 0 or source >= V:
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print_distance(shortest_distance, 0)
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print(f"Invalid source: source should be between 0 and {V - 1}")
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else:
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shortest_distance, paths = bellman_ford(graph, V, source)
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print_distance_and_paths(shortest_distance, paths, source)
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except ValueError:
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print("Please enter valid integer inputs.")
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except Exception as e:
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print(e)
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