forked from UNN/2026-rff_mp
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
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from abc import ABC, abstractmethod
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from collections import deque
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from heapq import heappush, heappop
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from typing import List, Dict, Optional
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from models import Cell, Maze
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class PathFindingStrategy(ABC):
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@abstractmethod
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def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
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pass
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class BFSStrategy(PathFindingStrategy):
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def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
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queue = deque([start])
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visited = {start}
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parent: Dict[Cell, Optional[Cell]] = {start: None}
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while queue:
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current = queue.popleft()
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if current == exit_cell:
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return self._reconstruct_path(parent, current)
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for neighbor in maze.get_neighbors(current):
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if neighbor not in visited:
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visited.add(neighbor)
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parent[neighbor] = current
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queue.append(neighbor)
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return []
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def _reconstruct_path(self, parent: Dict[Cell, Optional[Cell]], current: Cell) -> List[Cell]:
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path = []
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while current is not None:
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path.append(current)
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current = parent.get(current)
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return list(reversed(path))
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class DFSStrategy(PathFindingStrategy):
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def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
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stack = [(start, [start])]
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visited = {start}
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while stack:
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current, path = stack.pop()
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if current == exit_cell:
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return path
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for neighbor in maze.get_neighbors(current):
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if neighbor not in visited:
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visited.add(neighbor)
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stack.append((neighbor, path + [neighbor]))
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return []
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class AStarStrategy(PathFindingStrategy):
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def _heuristic(self, cell: Cell, exit_cell: Cell) -> int:
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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: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
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counter = 0
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open_set = [(self._heuristic(start, exit_cell), counter, start)]
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g_score: Dict[Cell, float] = {start: 0}
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parent: Dict[Cell, Optional[Cell]] = {start: None}
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while open_set:
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_, _, current = heappop(open_set)
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if current == exit_cell:
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return self._reconstruct_path(parent, current)
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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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parent[neighbor] = current
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g_score[neighbor] = tentative_g
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counter += 1
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f = tentative_g + self._heuristic(neighbor, exit_cell)
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heappush(open_set, (f, counter, neighbor))
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return []
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def _reconstruct_path(self, parent: Dict[Cell, Optional[Cell]], current: Cell) -> List[Cell]:
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path = []
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while current is not None:
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path.append(current)
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current = parent.get(current)
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return list(reversed(path))
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