implementing Implementing Dijkstra's Algorithm in Python - Priority Queue Not Returning Expected Paths
I'm deploying to production and I'm experimenting with I recently switched to I've been struggling with this for a few days now and could really use some help....... I'm currently implementing Dijkstra's algorithm in Python to find the shortest path in a weighted graph, but I'm working with an unexpected scenario with the priority queue not returning the correct paths. I’m using Python 3.10 along with the `heapq` library to manage the priority queue. My graph is represented as an adjacency list. Here’s a snippet of my implementation: ```python import heapq from collections import defaultdict class Graph: def __init__(self): self.graph = defaultdict(list) def add_edge(self, u, v, weight): self.graph[u].append((v, weight)) def dijkstra(self, start): priority_queue = [] heapq.heappush(priority_queue, (0, start)) distances = {node: float('infinity') for node in self.graph} distances[start] = 0 while priority_queue: current_distance, current_node = heapq.heappop(priority_queue) if current_distance > distances[current_node]: continue for neighbor, weight in self.graph[current_node]: distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(priority_queue, (distance, neighbor)) return distances ``` I expect the `distances` dictionary to hold the shortest distances from the start node to every other node in the graph. However, when I run the algorithm on the following graph: ```python g = Graph() g.add_edge('A', 'B', 1) g.add_edge('A', 'C', 4) g.add_edge('B', 'C', 2) g.add_edge('B', 'D', 5) g.add_edge('C', 'D', 1) ``` And call `g.dijkstra('A')`, the output for distances is: ``` {'A': 0, 'B': 1, 'C': 3, 'D': 6} ``` This output looks correct at first glance, but I'm finding that if I add an edge with a weight of zero (for example, `g.add_edge('A', 'C', 0)`), the resultant distances become inconsistent. The `distances['C']` should be 0 instead of 3, but it’s not updating correctly when I run the algorithm again after adding the new edge. I’ve confirmed that the edge is correctly added to the graph, but it seems the priority queue is not processing it as expected. I've tried adding debug statements within the loop to track the values of `current_distance`, `current_node`, and `distance`, but I still need to pinpoint where the logic is failing. What could be causing this behavior? Is there a fundamental mistake in how I'm handling the priority queue or updating the distances? My development environment is Linux. I'm working on a REST API that needs to handle this. Thanks for your help in advance! My team is using Python for this desktop app.