forked from UNN/2026-rff_mp
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
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#!/usr/bin/env python
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# coding: utf-8
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# In[ ]:
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import heapq
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from typing import List, Dict, Optional, Tuple
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from strategiesPathfinding_strategy import PathFindingStrategy
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from modelsMaze import Maze
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from modelsCell import Cell
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class AStarStrategy(PathFindingStrategy):
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"""Алгоритм A* с манхэттенской эвристикой."""
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@property
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def name(self) -> str:
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return "A*"
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def _heuristic(self, a: Cell, b: Cell) -> int:
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"""Манхэттенское расстояние."""
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return abs(a.x - b.x) + abs(a.y - b.y)
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def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
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if start == exit_cell:
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return [start]
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# Приоритетная очередь: (f_score, counter, cell)
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open_set = [(0, 0, start)]
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counter = 1
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came_from: Dict[Cell, Optional[Cell]] = {}
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g_score: Dict[Cell, float] = {start: 0}
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f_score: Dict[Cell, float] = {start: self._heuristic(start, exit_cell)}
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visited_count = 0
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while open_set:
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current_f, _, current = heapq.heappop(open_set)
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visited_count += 1
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if current == exit_cell:
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self._last_visited_count = visited_count
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return self._reconstruct_path(came_from, start, current)
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for neighbor in maze.get_neighbors(current):
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tentative_g_score = g_score.get(current, float('inf')) + 1
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if tentative_g_score < g_score.get(neighbor, float('inf')):
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came_from[neighbor] = current
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g_score[neighbor] = tentative_g_score
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f_score[neighbor] = tentative_g_score + self._heuristic(neighbor, exit_cell)
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heapq.heappush(open_set, (f_score[neighbor], counter, neighbor))
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counter += 1
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self._last_visited_count = visited_count
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return []
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@property
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def last_visited_count(self) -> int:
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return getattr(self, '_last_visited_count', 0)
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