T标号排序的Dijkstra算法代码，输出最短路径和成本

以下是使用T标号排序的Dijkstra算法代码，该代码输出最短路径和成本：

import heapq

def dijkstra(graph, start):
    # 初始化距离字典和T标号数组
    distance = {node: float('inf') for node in graph}
    t_label = [(0, start)]  # (距离, 节点)
    distance[start] = 0
    path = {}  # 记录最短路径
    
    while t_label:
        # 选择当前距离最小的节点
        current_distance, current_node = heapq.heappop(t_label)
        
        if current_distance > distance[current_node]:
            continue
        
        for neighbor, weight in graph[current_node].items():
            # 计算新的距离
            new_distance = current_distance + weight
            
            if new_distance < distance[neighbor]:
                # 更新距离和路径
                distance[neighbor] = new_distance
                path[neighbor] = current_node
                
                heapq.heappush(t_label, (new_distance, neighbor))
    
    return distance, path


def shortest_path(graph, start, end):
    distances, paths = dijkstra(graph, start)
    
    if end not in distances:
        return None, None
    
    # 构建最短路径
    shortest_path = []
    node = end
    
    while node != start:
        shortest_path.append(node)
        node = paths[node]
    
    shortest_path.append(start)
    
    return list(reversed(shortest_path)), distances[end]


# 测试示例
graph = {
    'A': {'B': 5, 'C': 2},
    'B': {'A': 5, 'C': 1, 'D': 3},
    'C': {'A': 2, 'B': 1, 'D': 2},
    'D': {'B': 3, 'C': 2}
}

start_node = 'A'
end_node = 'D'
path, cost = shortest_path(graph, start_node, end_node)
print("最短路径:", path)
print("最短路径成本:", cost)

该代码在使用T标号排序的Dijkstra算法的基础上，添加了最短路径的计算。通过调用`shortest_path`函数，可以获取从起始节点到目标节点的最短路径和对应的成本。最短路径以列表形式返回，成本作为额外的返回值。以上代码仅为示例，你可以根据自己的需求进行修改和扩展。