Here's a Python implementation of Dijkstra's algorithm that uses a priority queue to improve the efficiency of finding the minimum distance vertex. This implementation is more efficient than the one which follows modern Python practices.
In this improved code:
We use a priority queue implemented with Python's heapq module to efficiently extract the vertex with the minimum distance.
The add_edge method is used to add edges to the graph. Each edge is represented as a tuple (v, w), where v is the adjacent vertex, and w is the edge weight.
The dijkstra method computes the shortest path from the source vertex to all other vertices using Dijkstra's algorithm.
The print_solution method is used to print the shortest distances from the source vertex to all other vertices.
This code is more efficient and follows better Python coding practices for Dijkstra's algorithm.
Here's a Python implementation of Dijkstra's algorithm that uses a priority queue to improve the efficiency of finding the minimum distance vertex. This implementation is more efficient than the one which follows modern Python practices.
In this improved code:
We use a priority queue implemented with Python's
heapqmodule to efficiently extract the vertex with the minimum distance.The
add_edgemethod is used to add edges to the graph. Each edge is represented as a tuple (v, w), where v is the adjacent vertex, and w is the edge weight.The
dijkstramethod computes the shortest path from the source vertex to all other vertices using Dijkstra's algorithm.The
print_solutionmethod is used to print the shortest distances from the source vertex to all other vertices.This code is more efficient and follows better Python coding practices for Dijkstra's algorithm.