Theta* 算法python代码

Theta*（也称为A* with relaxation）是一种改进版的A*搜索算法，它允许在估算路径成本时使用更宽松的估价函数，这在某些情况下可以加速搜索过程。以下是使用Python实现基本的A*算法的一个简单示例，Theta*通常是通过类似的方式进行扩展：

import heapq

class Node:
    def __init__(self, pos, g=0, h=0, f=0):
        self.pos = pos
        self.g = g  # 实际代价
        self.h = h  # 估计代价
        self.f = f  # 总代价 f = g + h

    def __lt__(self, other):
        return self.f < other.f

def theta_star(start, end, heuristic, closed_set=None, relaxed_heuristic=None):
    if relaxed_heuristic is None:
        relaxed_heuristic = heuristic

    open_set = [Node(start)]
    closed_set = [] if closed_set is None else closed_set
    came_from = {}

    while open_set:
        current = heapq.heappop(open_set)

        if current.pos == end:
            path = []
            while current in came_from:
                path.append(current)
                current = came_from[current]
            path.reverse()
            return path

        for neighbor in get_neighbors(current.pos):
            tentative_g_score = current.g + distance(current.pos, neighbor)
            if neighbor not in open_set or tentative_g_score < neighbor.g:
                neighbor.g = tentative_g_score
                neighbor.h = relaxed_heuristic(neighbor)  # 使用relaxed_heuristic
                neighbor.f = neighbor.g + neighbor.h
                if neighbor not in open_set:
                    heapq.heappush(open_set, neighbor)
                came_from[neighbor] = current

    return None  # No path found

# 假设get_neighbors()返回给定位置的所有邻居节点
# distance()计算两点之间的实际距离

在这个代码中，`heuristic`是你自定义的距离启发式函数，而`relaxed_heuristic`是用于优化搜索的松弛估价函数。注意实际应用中你需要根据具体需求来实现这两个函数。