利用有向网的邻接表存储结构实现Dijkstra算法的最短路径代码

好的，以下是利用邻接表存储结构实现Dijkstra算法的最短路径的代码：

import sys

class Graph:
    def __init__(self, vertices):
        self.V = vertices
        self.graph = [[] for i in range(vertices)]

    def add_edge(self, u, v, w):
        self.graph[u].append((v, w))

    def dijkstra(self, src):
        dist = [sys.maxsize] * self.V
        dist[src] = 0
        visited = [False] * self.V

        for i in range(self.V):
            u = self.min_distance(dist, visited)
            visited[u] = True

            for v, w in self.graph[u]:
                if not visited[v] and dist[u] != sys.maxsize and dist[u] + w < dist[v]:
                    dist[v] = dist[u] + w

        self.print_solution(dist)

    def min_distance(self, dist, visited):
        min_value = sys.maxsize
        min_index = -1

        for v in range(self.V):
            if not visited[v] and dist[v] < min_value:
                min_value = dist[v]
                min_index = v

        return min_index

    def print_solution(self, dist):
        print("Vertex Distance from Source")
        for node in range(self.V):
            print(node, " : ", dist[node])


g = Graph(9)
g.add_edge(0, 1, 4)
g.add_edge(0, 7, 8)
g.add_edge(1, 2, 8)
g.add_edge(1, 7, 11)
g.add_edge(2, 3, 7)
g.add_edge(2, 8, 2)
g.add_edge(2, 5, 4)
g.add_edge(3, 4, 9)
g.add_edge(3, 5, 14)
g.add_edge(4, 5, 10)
g.add_edge(5, 6, 2)
g.add_edge(6, 7, 1)
g.add_edge(6, 8, 6)
g.add_edge(7, 8, 7)

g.dijkstra(0)

这段代码实现了一个有向图的邻接表存储结构，并使用Dijkstra算法求出了从源点到其他各点的最短路径。其中，`Graph`类表示一个有向图，`add_edge`方法用于添加边，`dijkstra`方法是Dijkstra算法的实现，`min_distance`方法用于寻找未访问节点中距离源点最近的节点，`print_solution`方法用于输出结果。在这个例子中，我们使用了一个9个节点的图进行了测试。