Need Help with Custom Heuristic in Dijkstra's Algorithm in Python: Unexpected Path Cost
I'm prototyping a solution and I'm testing a new approach and I'm implementing Dijkstra's algorithm for a graph-based routing application using Python's NetworkX library, but I'm encountering an unexpected issue with the custom heuristic I've created. I'm trying to lower the path cost based on certain criteria, but the results are not aligning with my expectations. Here's a simplified version of my graph and how I'm using the algorithm: ```python import networkx as nx # Create a directed graph G = nx.DiGraph() G.add_weighted_edges_from([ (1, 2, 1), (1, 3, 4), (2, 3, 1), (2, 4, 2), (3, 4, 1) ]) # Custom heuristic function based on some criteria def custom_heuristic(a, b): return 0 if a == 4 else 1 # Set heuristic to 0 for node 4 # Using Dijkstra's algorithm with the custom heuristic path = nx.dijkstra_path(G, source=1, target=4, heuristic=custom_heuristic) print("Path found:", path) ``` The output I'm getting is `Path found: [1, 2, 4]`, which is correct. However, when I check the total cost using `nx.path_weight(G, path, 'weight')`, I get a total cost of 3. I expected the cost to be lower because my heuristic function is supposed to guide the algorithm more efficiently towards the target node 4. I’ve checked NetworkX's documentation, but it seems my heuristic function is not being utilized as expected in Dijkstra’s context. I suspect that I might be misunderstanding how to integrate the heuristic with the weights or the algorithm itself. Has anyone successfully implemented a custom heuristic with Dijkstra's in Python? What might I be missing here? Any help would be greatly appreciated! I'm working on a CLI tool that needs to handle this. I'd really appreciate any guidance on this. This is my first time working with Python latest. What am I doing wrong? This is for a CLI tool running on Ubuntu 20.04.