2026-rff_mp/romanovpv/task 2/docs/data/strategies.py

from abc import ABC, abstractmethod
from collections import deque
import heapq

class PathFindingStrategy(ABC):
    @abstractmethod
    def findPath(self, maze, start, exit_cell):
        pass
    def restorePath(
            self,
            parent,
            start,
            exit_cell
    ):
        path = []
        current = exit_cell
        while current != start:
            path.append(current)
            current = parent[current]
        path.append(start)
        path.reverse()
        return path
class BFSStrategy(PathFindingStrategy):
    def findPath(
            self,
            maze,
            start,
            exit_cell
    ):
        queue = deque([start])
        visited = {start}
        parent = {}
        while queue:
            current = queue.popleft()
            if current == exit_cell:
                return self.restorePath(
                    parent,
                    start,
                    exit_cell
                )
            for neighbor in maze.getNeighbors(current):
                if neighbor not in visited:
                    visited.add(
                        neighbor
                    )
                    parent[
                        neighbor
                    ] = current
                    queue.append(
                        neighbor
                    )
        return []

class DFSStrategy(PathFindingStrategy):
    def findPath(
            self,
            maze,
            start,
            exit_cell
    ):
        stack = [start]
        visited = {start}
        parent = {}
        while stack:
            current = stack.pop()
            if current == exit_cell:
                return self.restorePath(
                    parent,
                    start,
                    exit_cell
                )
            for neighbor in maze.getNeighbors(current):
                if neighbor not in visited:
                    visited.add(
                        neighbor
                    )
                    parent[
                        neighbor
                    ] = current

                    stack.append(
                        neighbor
                    )
        return []
    
class AStarStrategy(PathFindingStrategy):
    def heuristic(
            self,
            cell,
            exit_cell
    ):
        return (
                abs(
                    cell.x
                    - exit_cell.x
                )
                +
                abs(
                    cell.y
                    - exit_cell.y
                )
        )
    def findPath(
            self,
            maze,
            start,
            exit_cell
    ):
        pq = []
        heapq.heappush(
            pq,
            (
                0,
                id(start),
                start
            )
        )
        parent = {}
        g_score = {
            start: 0
        }
        visited = set()
        while pq:
            _, _, current = (
                heapq.heappop(
                    pq
                )
            )
            if current in visited:
                continue
            visited.add(
                current
            )
            if current == exit_cell:
                return self.restorePath(
                    parent,
                    start,
                    exit_cell
                )
            for neighbor in maze.getNeighbors(current):
                new_cost = (
                        g_score[current]
                        + 1
                )
                if (
                        neighbor
                        not in g_score
                        or
                        new_cost <
                        g_score[neighbor]
                ):
                    g_score[
                        neighbor
                    ] = new_cost
                    parent[
                        neighbor
                    ] = current
                    priority = (
                            new_cost
+
                            self.heuristic(
                                neighbor,
                                exit_cell
                            )
                    )
                    heapq.heappush(
                        pq,
                        (
                            priority,
                            id(neighbor),
                            neighbor
                        )
                    )
        return []