path = [end] ordered_waypoints = [] current = end num_visited = min_num_visited for i in range(len(waypoints), 0, -1): if current in range(i): ordered_waypoints.insert(0, waypoints[i - 1]) num_visited -= 1 for neighbor, weight in enumerate(adj_matrix[current]): if weight > 0 and (neighbor, num_visited) in visited and distances[neighbor][num_visited] + weight == \ distances[current][num_visited]: path.append(neighbor) current = neighbor break ordered_waypoints.insert(0, start_cor) ordered_waypoints.append(end_cor) path.reverse() print("The optimal path from start to end through the 8 waypoints is:") for wp in ordered_waypoints: print(wp) print(f"The optimal path from start to end through the 8 waypoints is: {path}") print(f"The total distance is: {distances[end][min_num_visited]}")
时间: 2024-03-30 13:36:29 浏览: 15
这段代码看起来像是路径规划相关的代码,它使用了某种图搜索算法(可能是Dijkstra或A*)来找到从起点到终点穿过8个指定中间点的最优路径。其中,`adj_matrix`是表示节点之间距离的邻接矩阵,`distances`是起点到各个节点的最短距离矩阵,`visited`是已经访问过的节点和访问次数的元组的集合。最后,它输出了最优路径和总距离。
相关问题
path = [end] ordered_waypoints = [] current = end num_visited = min_num_visited for i in range(len(waypoints), 0, -1): if current in range(i): ordered_waypoints.insert(0, waypoints[i - 1]) num_visited -= 1 for neighbor, weight in enumerate(adj_matrix[current]): if weight > 0 and (neighbor, num_visited) in visited and distances[neighbor][num_visited] + weight == \ distances[current][num_visited]: path.append(neighbor) current = neighbor break ordered_waypoints.insert(0, start_cor) ordered_waypoints.append(end_cor) path.reverse() print("The optimal path from start to end through the 8 waypoints is:") for wp in ordered_waypoints: print(wp) print(f"The optimal path from start to end through the 8 waypoints is: {path}") print(f"The total distance is: {distances[end][min_num_visited]}")每行什么意思帮我加上注释
```
path = [end] # 初始化路径,将终点加入路径
ordered_waypoints = [] # 初始化有序中间点列表
current = end # 初始化当前节点为终点
num_visited = min_num_visited # 初始化已访问中间点数量为最小值
# 从后向前遍历中间点
for i in range(len(waypoints), 0, -1):
# 如果当前节点在当前遍历中的范围内
if current in range(i):
# 将当前中间点加入有序中间点列表
ordered_waypoints.insert(0, waypoints[i - 1])
# 已访问中间点数量减1
num_visited -= 1
# 针对当前节点的所有邻居
for neighbor, weight in enumerate(adj_matrix[current]):
# 如果邻居节点可达且已经访问过并且到达邻居节点的距离加上邻居节点到当前节点的距离等于到达当前节点的距离
if weight > 0 and (neighbor, num_visited) in visited and distances[neighbor][num_visited] + weight == \
distances[current][num_visited]:
# 将邻居节点加入路径
path.append(neighbor)
# 更新当前节点为邻居节点
current = neighbor
# 退出循环
break
# 将起点和终点加入有序中间点列表
ordered_waypoints.insert(0, start_cor)
ordered_waypoints.append(end_cor)
# 反转路径,使其从起点到终点
path.reverse()
# 输出结果
print("The optimal path from start to end through the 8 waypoints is:")
for wp in ordered_waypoints:
print(wp)
print(f"The optimal path from start to end through the 8 waypoints is: {path}")
print(f"The total distance is: {distances[end][min_num_visited]}")
```
for(int i = 0; i < CACHE_NUM; i++){ raw_head[i] = new raw_list; raw_tail[i] = new raw_list; raw_head[i]->next = raw_tail[i]; raw_head[i]->pre = nullptr; raw_tail[i]->pre = raw_head[i]; raw_tail[i]->next = nullptr; result_head[i] = new result_list; result_tail[i] = new result_list; result_head[i]->next = result_tail[i]; result_head[i]->pre = nullptr; result_tail[i]->pre = result_head[i]; result_tail[i]->next = nullptr; recv_data_cnt[i] = 0; raw_num[i] = 0; processed_num[i] = 0; processing_num[i] = 0; result_num[i] = 0; ordered_num[i] = 0; current_pos[i] = raw_head[i]; ordered_edge[i] = result_head[i]; }
这是一个初始化函数,用于对多个链表进行初始化操作。具体来说,这个函数会创建 `CACHE_NUM` 个双向链表,并对这些链表进行初始化操作,包括:
1. 创建头节点和尾节点,并分别将它们的指针存储在 `raw_head[i]`、`raw_tail[i]`、`result_head[i]` 和 `result_tail[i]` 四个数组中,其中 `i` 的范围是从0到 `CACHE_NUM - 1`。
2. 将头节点的下一个节点指向尾节点,将尾节点的前一个节点指向头节点,从而形成一个空链表。
3. 初始化一些计数器和指针,用于追踪链表的状态和操作。具体来说,这些计数器和指针包括 `recv_data_cnt[i]`、`raw_num[i]`、`processed_num[i]`、`processing_num[i]`、`result_num[i]`、`ordered_num[i]`、`current_pos[i]` 和 `ordered_edge[i]`,其中 `i` 的范围是从0到 `CACHE_NUM - 1`。
4. 将 `current_pos[i]` 指向 `raw_head[i]`,将 `ordered_edge[i]` 指向 `result_head[i]`。
这样,我们就可以向这些链表中添加数据或者从这些链表中删除数据了。
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