forked from UNN/2026-rff_mp
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
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import heapq
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from typing import List, Dict
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from .path_finding_strategy import PathFindingStrategy
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class AStarStrategy(PathFindingStrategy):
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"""A* поиск с эвристикой (манхэттенское расстояние)"""
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def __init__(self):
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self._visited_count = 0
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@property
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def name(self) -> str:
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return "AStar"
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def _heuristic(self, cell, exit_cell) -> int:
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"""Манхэттенское расстояние между клетками"""
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return abs(cell.x - exit_cell.x) + abs(cell.y - exit_cell.y)
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def find_path(self, maze, start, exit_cell) -> List:
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"""Находит путь с помощью A*"""
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if not start or not exit_cell:
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return []
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# Приоритетная очередь (F-значение, ID для уникальности, клетка)
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open_set = []
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heapq.heappush(open_set, (0, id(start), start))
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# Откуда пришли в каждую клетку
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came_from = {}
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# Стоимость пути от старта до клетки (G-значение)
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g_score = {start: 0}
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# Оценочная стоимость (F-значение = G + H)
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f_score = {start: self._heuristic(start, exit_cell)}
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self._visited_count = 0
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while open_set:
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_, _, current = heapq.heappop(open_set)
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self._visited_count += 1
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# Нашли выход
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if current == exit_cell:
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return self._reconstruct_path(came_from, exit_cell)
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# Проверяем всех соседей
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for neighbor in maze.get_neighbors(current):
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tentative_g = g_score[current] + 1
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if neighbor not in g_score or tentative_g < g_score[neighbor]:
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came_from[neighbor] = current
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g_score[neighbor] = tentative_g
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f = tentative_g + self._heuristic(neighbor, exit_cell)
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f_score[neighbor] = f
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heapq.heappush(open_set, (f, id(neighbor), neighbor))
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# Путь не найден
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return []
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def _reconstruct_path(self, came_from: Dict, current) -> List:
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"""Восстанавливает путь от старта до exit"""
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path = [current]
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while current in came_from:
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current = came_from[current]
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path.append(current)
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return list(reversed(path))
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