[Error] parameter 'is_visited' has just a forward declaration

"Error: parameter 'is_visited' has just a forward declaration" 这个错误通常发生在C++或其他支持预处理器的编程语言中。当你声明了一个函数或类的参数 `is_visited`，但是没有提供完整的定义，只是简单地提前宣告它的存在（即 forward declaration），而在实际的函数体或方法中尝试使用这个参数的时候，编译器找不到其确切的类型信息，因此会报错。

例如：

void visit(Node* is_visited); // 只有声明，没有定义

void someFunction() {
    visit(&myNode); // 这里会因为缺少is_visited的完整定义而报错
}

为了修复这个问题，你需要在引用 `is_visited` 参数之前给出完整的定义，比如在一个包含 Node 类的头文件或者适当的地方：

class Node;

// 完整定义
void visit(Node* is_visited);

class Node { /* ... */ }

void visit(Node* is_visited) {
    // 函数体...
}

相关问题

import Astar import heapq start_cor = (19, 0) waypoints = [(5, 15), (5, 1), (9, 3), (11, 17), (7, 19), (15, 19), (13, 1), (15, 5)] end_cor = (1, 20) def distance(_from, _to): x1, y1 = _from x2, y2 = _to distancepath = Astar.find_path(x1, y1, x2, y2) return distancepath n = len(waypoints) adj_matrix = [[0] * n for _ in range(n)] for i in range(n): for j in range(i + 1, n): dist = distance(waypoints[i], waypoints[j]) adj_matrix[i][j] = dist adj_matrix[j][i] = dist start = 0 end = n - 1 distances = [[float('inf')] * (n + 1) for _ in range(n)] visited = set() heap = [(0, 0, start)] while heap: (dist, num_visited, current) = heapq.heappop(heap) if current == end and num_visited == 8: break if (current, num_visited) in visited: continue visited.add((current, num_visited)) for neighbor, weight in enumerate(adj_matrix[current]): if weight > 0: new_num_visited = num_visited if neighbor in range(start + 1, end) and (current not in range(start + 1, end)) and num_visited < 8: new_num_visited += 1 new_distance = dist + weight if new_distance < distances[neighbor][new_num_visited]: distances[neighbor][new_num_visited] = new_distance heapq.heappush(heap, (new_distance, new_num_visited, neighbor)) min_dist = float('inf') min_num_visited = 8 for i in range(8): if distances[end][i] < min_dist: min_dist = distances[end][i] min_num_visited = i path = [end] current = end num_visited = min_num_visited for i in range(len(waypoints), 0, -1): if current in range(i): 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 path.reverse() 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]}")

这段代码是用来解决一个路径规划问题，目标是找到从起点到终点经过8个给定点的最短路径。其中使用了A*算法来计算两点之间的距离，并利用堆来实现最小优先队列。具体流程是先计算出8个给定点之间的距离，再从起点开始，每次取出离当前点最近的点，如果该点是终点并且已经经过了8个给定点，则找到了最短路径。如果该点不是终点，则将该点的所有邻居加入队列中，并更新到达邻居的距离和经过的给定点数。最后根据经过的给定点数从终点开始倒推出最短路径。

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]}")