交通图中两地之间的最短路径状态图搜索策略实验代码

以下是一个基于Dijkstra算法的最短路径状态图搜索策略实验代码示例，可以用于求解交通图中两地之间的最短路径问题：

import heapq

def dijkstra(graph, start, end):
    # 初始化距离字典和前置节点字典
    dist = {node: float('inf') for node in graph}
    dist[start] = 0
    previous = {node: None for node in graph}

    # 将起点加入堆中，开始搜索
    heap = [(0, start)]
    while heap:
        # 取出距离最小的节点
        current_dist, current_node = heapq.heappop(heap)
        # 如果当前节点已经访问过，则跳过
        if current_dist > dist[current_node]:
            continue
        # 遍历当前节点的邻居
        for neighbor, weight in graph[current_node].items():
            # 计算新的距离
            new_dist = current_dist + weight
            # 如果新的距离比原来的距离更短，则更新
            if new_dist < dist[neighbor]:
                dist[neighbor] = new_dist
                previous[neighbor] = current_node
                heapq.heappush(heap, (new_dist, neighbor))

    # 回溯路径
    path = []
    node = end
    while node is not None:
        path.append(node)
        node = previous[node]
    path.reverse()

    return path, dist[end]

# 测试代码
graph = {
    'A': {'B': 5, 'C': 1},
    'B': {'A': 5, 'C': 2, 'D': 1},
    'C': {'A': 1, 'B': 2, 'D': 4, 'E': 8},
    'D': {'B': 1, 'C': 4, 'E': 3, 'F': 6},
    'E': {'C': 8, 'D': 3},
    'F': {'D': 6}
}
start = 'A'
end = 'F'
path, dist = dijkstra(graph, start, end)
print("最短路径：", path)
print("最短距离：", dist)

该代码使用了堆优化的Dijkstra算法来求解最短路径。其中，`graph`变量表示图的邻接表，`start`和`end`变量分别表示起点和终点。该算法会返回一条最短路径以及对应的距离。