forked from UNN/2026-rff_mp
46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
import heapq
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from itertools import count
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from task2.strategyObjects.pathFindingStrategy import PathFindingStrategy
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from task2.strategyObjects.util import restorePath
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from task2.mazeObjects.maze import Maze
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from task2.mazeObjects.cell import Cell
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from task2.mazeObjects.path import Path
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class AStar(PathFindingStrategy):
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"""Алгоритм с эвристикой (etc. манхэттенское расстояние) – компромисс между скоростью и оптимальностью."""
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def heuristic(self, first: Cell, second: Cell) -> int:
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return abs(first.x - second.x) + abs(first.y - second.y)
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def findPath(self, maze: Maze, start: Cell, exit: Cell) -> Path:
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tie_breaker = count()
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start_heuristic = self.heuristic(start, exit)
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heap: list[tuple[int, int, int, Cell]] = [
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(start_heuristic, start_heuristic, next(tie_breaker), start)
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]
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g_score: dict[Cell, int] = {start: 0}
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parents: dict[Cell, Cell | None] = {start: None}
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visited: set[Cell] = set()
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while heap:
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_, _, _, current = heapq.heappop(heap)
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if current in visited:
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continue
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visited.add(current)
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if current == exit:
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return Path(restorePath(parents, exit), len(visited))
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for neighbor in maze.getNeighbors(current):
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tentative_score = g_score[current]
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if tentative_score < g_score.get(neighbor, 10**12):
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g_score[neighbor] = tentative_score
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parents[neighbor] = current
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heuristic = self.heuristic(neighbor, exit)
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priority = tentative_score + heuristic
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heapq.heappush(
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heap,
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(priority, heuristic, next(tie_breaker), neighbor),
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)
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return Path(None, len(visited)) |