mirror of
https://github.com/maoakeEnterprise/amazing.git
synced 2026-04-28 16:04:35 +02:00
fix(astar): function f() miscalculate the best path
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+46
-55
@@ -9,44 +9,30 @@ class MazeSolver(ABC):
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self.end = (end[1] - 1, end[0] - 1)
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@abstractmethod
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def solve(self, maze: Maze, height: int = None,
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width: int = None) -> str: ...
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def solve(
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self, maze: Maze, height: int = None, width: int = None
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) -> str: ...
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class AStar(MazeSolver):
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def __init__(self, start: tuple[int, int], end: tuple[int, int]) -> None:
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super().__init__(start, end)
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self.path = []
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def f(self, n):
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def g(n: tuple[int, int]) -> int:
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res = 0
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if n[0] < self.start[0]:
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res += self.start[0] - n[0]
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else:
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res += n[0] - self.start[0]
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if n[1] < self.start[1]:
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res += self.start[1] - n[1]
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else:
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res += n[1] - self.start[1]
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return res
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def g() -> int:
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return len(self.path) + 1
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def h(n: tuple[int, int]) -> int:
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res = 0
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if n[0] < self.end[0]:
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res += self.end[0] - n[0]
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else:
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res += n[0] - self.end[0]
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if n[1] < self.end[1]:
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res += self.end[1] - n[1]
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else:
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res += n[1] - self.end[1]
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return res
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return (
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max(n[0], self.end[0])
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- min(n[0], self.end[0])
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+ max(n[1], self.end[1])
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- min(n[1], self.end[1])
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)
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try:
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return g(n) + h(n)
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except Exception:
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return 1000
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return g() + h(n)
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def best_path(
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self,
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@@ -113,47 +99,46 @@ class AStar(MazeSolver):
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return actual
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def get_path(self, maze: np.ndarray) -> str | None:
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path = [(self.start, self.best_path(maze, self.start, None))]
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self.path = [(self.start, self.best_path(maze, self.start, None))]
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visited = [self.start]
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while len(path) > 0 and path[-1][0] != self.end:
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if len(path[-1][1]) == 0:
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path.pop(-1)
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if len(path) == 0:
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while len(self.path) > 0 and self.path[-1][0] != self.end:
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if len(self.path[-1][1]) == 0:
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self.path.pop(-1)
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if len(self.path) == 0:
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break
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k = next(iter(path[-1][1]))
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path[-1][1].pop(k)
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k = next(iter(self.path[-1][1]))
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self.path[-1][1].pop(k)
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continue
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while len(path[-1][1]) > 0:
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while len(self.path[-1][1]) > 0:
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next_pos = self.get_next_pos(
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list(path[-1][1].keys())[0], path[-1][0]
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list(self.path[-1][1].keys())[0], self.path[-1][0]
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)
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if next_pos in visited:
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k = next(iter(path[-1][1]))
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path[-1][1].pop(k)
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k = next(iter(self.path[-1][1]))
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self.path[-1][1].pop(k)
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else:
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break
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if len(path[-1][1]) == 0:
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path.pop(-1)
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if len(self.path[-1][1]) == 0:
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self.path.pop(-1)
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continue
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pre = self.get_opposit(list(path[-1][1].keys())[0])
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path.append(
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pre = self.get_opposit(list(self.path[-1][1].keys())[0])
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self.path.append(
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(
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next_pos,
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self.best_path(maze, next_pos, pre),
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)
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)
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visited += [next_pos]
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if len(path) == 0:
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if len(self.path) == 0:
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return None
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path[-1] = (self.end, {})
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self.path[-1] = (self.end, {})
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return "".join(
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str(list(c[1].keys())[0]) for c in path if len(c[1]) > 0
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str(list(c[1].keys())[0]) for c in self.path if len(c[1]) > 0
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)
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def solve(self, maze: Maze, height: int = None,
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width: int = None) -> str:
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def solve(self, maze: Maze, height: int = None, width: int = None) -> str:
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res = self.get_path(maze.get_maze())
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if res is None:
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raise Exception("Path not found")
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@@ -164,8 +149,7 @@ class DepthFirstSearchSolver(MazeSolver):
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def __init__(self, start, end):
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super().__init__(start, end)
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def solve(self, maze: Maze, height: int = None,
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width: int = None) -> str:
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def solve(self, maze: Maze, height: int = None, width: int = None) -> str:
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path_str = ""
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visited = np.zeros((height, width), dtype=bool)
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path = list()
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@@ -179,8 +163,9 @@ class DepthFirstSearchSolver(MazeSolver):
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rand_p = self.random_path(visited, coord, maze_s, h_w)
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if not rand_p:
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path, move = self.back_on_step(path, visited, maze_s, h_w,
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move)
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path, move = self.back_on_step(
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path, visited, maze_s, h_w, move
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)
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if not path:
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break
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coord = path[-1]
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@@ -195,8 +180,9 @@ class DepthFirstSearchSolver(MazeSolver):
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return path_str
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@staticmethod
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def random_path(visited: np.ndarray, coord: tuple,
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maze: np.ndarray, h_w: tuple) -> list:
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def random_path(
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visited: np.ndarray, coord: tuple, maze: np.ndarray, h_w: tuple
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) -> list:
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random_p = []
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h, w = h_w
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y, x = coord
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@@ -219,8 +205,13 @@ class DepthFirstSearchSolver(MazeSolver):
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return np.random.choice(rand_path)
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@staticmethod
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def back_on_step(path: list, visited: np.ndarray,
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maze: np.ndarray, h_w: tuple, move: list) -> list:
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def back_on_step(
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path: list,
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visited: np.ndarray,
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maze: np.ndarray,
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h_w: tuple,
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move: list,
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) -> list:
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while path:
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last = path[-1]
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if DepthFirstSearchSolver.random_path(visited, last, maze, h_w):
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