forked from UNN/2026-rff_mp
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
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import heapq
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from typing import Optional
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from source.models.base import Cell, Maze
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from source.strategy.algorithms import PathFindingStrategy
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def _manhattan(a: Cell, b: Cell) -> int:
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"""Манхэттенское расстояние между двумя клетками."""
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return abs(a.x - b.x) + abs(a.y - b.y)
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class AStarStrategy(PathFindingStrategy):
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"""Алгоритм A* с манхэттенской эвристикой."""
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def find_path(self, maze: Maze, start: Optional[Cell] = None, exit: Optional[Cell] = None) -> list[Cell]:
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if start is None:
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start = self._find_start(maze)
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if exit is None:
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exit = self._find_exit(maze)
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g_score: dict[Cell, int] = {start: 0}
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came_from: dict[Cell, Optional[Cell]] = {start: None}
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counter = 0
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open_heap: list[tuple[int, int, Cell]] = [
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(_manhattan(start, exit), counter, start)
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]
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while open_heap:
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_, _, current = heapq.heappop(open_heap)
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if current is exit:
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return self._reconstruct_path(came_from, exit)
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for neighbor in maze.get_neighbors(current.x, current.y):
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tentative_g = g_score[current] + 1
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if tentative_g < g_score.get(neighbor, float("inf")):
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g_score[neighbor] = tentative_g
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came_from[neighbor] = current
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f = tentative_g + _manhattan(neighbor, exit)
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counter += 1
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heapq.heappush(open_heap, (f, counter, neighbor))
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return []
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