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Удалить SmirnovaVYu/docs/data/strategies.py

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smirnovavyu 2026-09-04 15:13:15 +00:00
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SmirnovaVYu/docs/data/strategies.py
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from abc import ABC, abstractmethod
from collections import deque
from heapq import heappush, heappop
from typing import List, Dict, Optional
from models import Cell, Maze
class PathFindingStrategy(ABC):
@abstractmethod
def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
pass
class BFSStrategy(PathFindingStrategy):
def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
queue = deque([start])
visited = {start}
parent: Dict[Cell, Optional[Cell]] = {start: None}
while queue:
current = queue.popleft()
if current == exit_cell:
return self._reconstruct_path(parent, current)
for neighbor in maze.get_neighbors(current):
if neighbor not in visited:
visited.add(neighbor)
parent[neighbor] = current
queue.append(neighbor)
return []
def _reconstruct_path(self, parent: Dict[Cell, Optional[Cell]], current: Cell) -> List[Cell]:
path = []
while current is not None:
path.append(current)
current = parent.get(current)
return list(reversed(path))
class DFSStrategy(PathFindingStrategy):
def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
stack = [(start, [start])]
visited = {start}
while stack:
current, path = stack.pop()
if current == exit_cell:
return path
for neighbor in maze.get_neighbors(current):
if neighbor not in visited:
visited.add(neighbor)
stack.append((neighbor, path + [neighbor]))
return []
class AStarStrategy(PathFindingStrategy):
def _heuristic(self, cell: Cell, exit_cell: Cell) -> int:
return abs(cell.x - exit_cell.x) + abs(cell.y - exit_cell.y)
def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
counter = 0
open_set = [(self._heuristic(start, exit_cell), counter, start)]
g_score: Dict[Cell, float] = {start: 0}
parent: Dict[Cell, Optional[Cell]] = {start: None}
while open_set:
_, _, current = heappop(open_set)
if current == exit_cell:
return self._reconstruct_path(parent, current)
for neighbor in maze.get_neighbors(current):
tentative_g = g_score[current] + 1
if neighbor not in g_score or tentative_g < g_score[neighbor]:
parent[neighbor] = current
g_score[neighbor] = tentative_g
counter += 1
f = tentative_g + self._heuristic(neighbor, exit_cell)
heappush(open_set, (f, counter, neighbor))
return []
def _reconstruct_path(self, parent: Dict[Cell, Optional[Cell]], current: Cell) -> List[Cell]:
path = []
while current is not None:
path.append(current)
current = parent.get(current)
return list(reversed(path))