迷宫路径算法多条代码

迷宫路径算法是一种常见的图搜索问题解决方案，通常用于解决从起点到终点在迷宫中找到一条无死路的路径。这里我会简述几种常见的迷宫路径算法及其示例代码：

1. **深度优先搜索 (DFS)**

   def dfs(maze, start, end):
       stack = [start]
       visited = set()
       while stack:
           node = stack.pop()
           if node == end:
               return True
           for neighbor in get_neighbors(maze, node):
               if neighbor not in visited:
                   visited.add(neighbor)
                   stack.append(neighbor)
       return False

   def get_neighbors(maze, node):
       # 根据实际情况获取邻居节点，比如二维数组
       row, col = node
       neighbors = [(row + dx, col + dy) for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)] if 0 <= row + dx < maze.shape[0] and 0 <= col + dy < maze.shape[1]]
       return neighbors

2. **广度优先搜索 (BFS)**

   from collections import deque

   def bfs(maze, start, end):
       queue = deque([start])
       visited = set()
       while queue:
           node = queue.popleft()
           if node == end:
               return True
           visited.add(node)
           for neighbor in get_neighbors(maze, node):
               if neighbor not in visited:
                   queue.append(neighbor)
       return False

3. **A*搜索算法（带启发式函数）**

   def a_star_search(maze, start, end, heuristic_func):
       open_set = [(0, start)]
       closed_set = set()
       came_from = {}
       g_score = {start: 0}
       f_score = {start: heuristic_func(start, end)}

       while open_set:
           current = min(open_set, key=lambda x: f_score[x[1]])
           open_set.remove(current)
           closed_set.add(current[1])

           if current[1] == end:
               path = []
               while current[1] != start:
                   path.append(current[1])
                   current = came_from[current[1]]
               path.append(start)
               path.reverse()
               return path

           for neighbor in get_neighbors(maze, current[1]):
               tentative_g_score = g_score[current[1]] + 1
               if neighbor not in closed_set or tentative_g_score < g_score.get(neighbor, float('inf')):
                   came_from[neighbor] = current[1]
                   g_score[neighbor] = tentative_g_score
                   f_score[neighbor] = g_score[neighbor] + heuristic_func(neighbor, end)
                   if neighbor not in open_set:
                       open_set.append((f_score[neighbor], neighbor))

以上代码都是基于某种数据结构（如列表、栈或队列）实现的。每个算法都有其特点，选择哪种取决于实际情况，比如是否需要保证最短路径，以及对内存消耗的要求。