用Python或者c++实现A*

在Python中实现A*算法，我们可以使用内置的数据结构，如列表和字典，配合优先级队列（例如heapq模块）。以下是基本的Python代码实现：

import heapq

# 定义一个表示节点的数据结构
class AStarNode:
    def __init__(self, pos, cost=0, parent=None):
        self.pos = pos
        self.cost = cost
        self.parent = parent
        self.g = cost
        self.h = heuristic_function(self.pos, target_pos)  # 假设heuristic_function是你自定义的估价函数
        self.f = self.g + self.h

def a_star_search(graph, start, target):
    open_list = [(start.f, start)]
    closed_list = set()

    while open_list:
        _, current = heapq.heappop(open_list)

        if current.pos == target:
            return reconstruct_path(current)

        if current in closed_list:
            continue

        closed_list.add(current)

        for neighbor in graph.neighbors(current.pos):  # 假设graph.neighbors获取邻接节点
            tentative_g = current.cost + graph.cost(current.pos, neighbor.pos)
            if neighbor not in open_list or tentative_g < neighbor.g:
                neighbor.g = tentative_g
                neighbor.parent = current
                neighbor.h = heuristic_function(neighbor.pos, target_pos)
                neighbor.f = neighbor.g + neighbor.h
                heapq.heappush(open_list, (neighbor.f, neighbor))

    return None  # 如果找不到路径，则返回None

# 辅助函数，重建路径
def reconstruct_path(end_node):
    path = [end_node]
    while end_node.parent:
        end_node = end_node.parent
        path.append(end_node)
    return list(reversed(path))

对于C++，你可以使用STL库来实现类似的功能：

#include <queue>
#include <unordered_set>

struct AStarNode {
    std::pair<int, int> position;
    int cost;
    AStarNode *parent;
    int g, h, f;

    AStarNode(const std::pair<int, int>& pos, int initial_cost = 0, AStarNode *parent_ = nullptr)
        : position(pos), cost(initial_cost), parent(parent_), g(initial_cost), h(h_value(position)), f(g+h)
    {}

    bool operator<(const AStarNode& other) const {
        return f > other.f;
    }
};

std::vector<std::pair<int, int>> a_star_search(std::function<std::vector<AStarNode>(const std::pair<int, int>)>& neighbors, const AStarNode& start, const AStarNode& target) {
    std::priority_queue<AStarNode, std::vector<AStarNode>, std::greater<AStarNode>> open_list;
    std::unordered_set<AStarNode*> closed_list;

    open_list.push(start);
    
    while (!open_list.empty()) {
        auto current = open_list.top(); open_list.pop();

        if (current.position == target.position) {
            return reconstruct_path(current);
        }

        if (closed_list.count(&current)) continue;

        closed_list.insert(&current);

        for (const auto& neighbor : neighbors(current.position)) {
            int tentative_g = current.cost + neighbor.cost;  // 假设cost函数返回两个节点之间的代价
            if (!closed_list.count(&neighbor) || tentative_g < neighbor.g) {
                neighbor.g = tentative_g;
                neighbor.parent = &current;
                neighbor.h = h_value(neighbor.position);  // 假设h_value是估价函数
                neighbor.f = neighbor.g + neighbor.h;
                open_list.push(neighbor);
            }
        }
    }

    return {};  // 如果找不到路径，返回空向量
}

// 辅助函数，重构路径
std::vector<std::pair<int, int>> reconstruct_path(const AStarNode& end_node) {
    std::vector<std::pair<int, int>> path;
    while (end_node.parent) {
        path.push_back(end_node.position);
        end_node = *end_node.parent;
    }
    return path;
}

别忘了根据你的需求调整`heuristic_function`、`neighbors`、`h_value`等部分。