forked from UNN/2026-rff_mp
244 lines
7.7 KiB
Python
244 lines
7.7 KiB
Python
import sys
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import time
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import csv
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from collections import deque
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from heapq import heappush, heappop
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from abc import ABC, abstractmethod
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from typing import List, Optional, Tuple, Dict, Any
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import os
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class Cell:
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def __init__(self, x: int, y: int, is_wall: bool = False):
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self.x = x
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self.y = y
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self.is_wall = is_wall
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self.is_start = False
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self.is_exit = False
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def is_passable(self) -> bool:
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return not self.is_wall
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def __repr__(self):
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return f"Cell({self.x},{self.y})"
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class Maze:
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def __init__(self, width: int, height: int):
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self.width = width
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self.height = height
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self.cells: List[List[Cell]] = []
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self.start: Optional[Cell] = None
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self.exit: Optional[Cell] = None
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def set_cell(self, x: int, y: int, cell: Cell):
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if not self.cells:
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self.cells = [[None] * self.width for _ in range(self.height)]
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self.cells[y][x] = cell
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def get_cell(self, x: int, y: int) -> Optional[Cell]:
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if 0 <= x < self.width and 0 <= y < self.height:
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return self.cells[y][x]
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return None
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def get_neighbors(self, cell: Cell) -> List[Cell]:
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neighbors = []
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for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
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nx, ny = cell.x + dx, cell.y + dy
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neighbor = self.get_cell(nx, ny)
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if neighbor and neighbor.is_passable():
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neighbors.append(neighbor)
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return neighbors
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class MazeBuilder(ABC):
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@abstractmethod
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def build_from_file(self, filename: str) -> Maze:
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pass
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class TextFileMazeBuilder(MazeBuilder):
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def build_from_file(self, filename: str) -> Maze:
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with open(filename, 'r', encoding='utf-8') as f:
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lines = [line.rstrip('\n') for line in f.readlines()]
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if not lines:
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raise ValueError("Файл пуст")
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height = len(lines)
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width = max(len(line) for line in lines)
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maze = Maze(width, height)
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start_cell = None
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exit_cell = None
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for y, line in enumerate(lines):
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for x, ch in enumerate(line):
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is_wall = (ch == '#')
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cell = Cell(x, y, is_wall)
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if ch == 'S':
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cell.is_start = True
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start_cell = cell
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elif ch == 'E':
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cell.is_exit = True
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exit_cell = cell
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maze.set_cell(x, y, cell)
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if start_cell is None or exit_cell is None:
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for y in range(height):
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for x in range(width):
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cell = maze.get_cell(x, y)
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if cell and cell.is_start:
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start_cell = cell
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if cell and cell.is_exit:
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exit_cell = cell
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if start_cell is None:
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raise ValueError("Нет стартовой клетки (S)")
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if exit_cell is None:
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raise ValueError("Нет выходной клетки (E)")
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maze.start = start_cell
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maze.exit = exit_cell
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return 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) -> 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) -> List[Cell]:
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if start == exit:
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self.last_visited = 1
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return [start]
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queue = deque()
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queue.append(start)
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parent = {start: None}
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visited = {start}
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visited_count = 1
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while queue:
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current = queue.popleft()
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if current == exit:
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break
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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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visited_count += 1
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parent[neighbor] = current
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queue.append(neighbor)
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self.last_visited = visited_count
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if exit not in parent:
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return []
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path = []
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cur = exit
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while cur is not None:
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path.append(cur)
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cur = parent[cur]
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path.reverse()
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return path
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class DFSStrategy(PathFindingStrategy):
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def find_path(self, maze: Maze, start: Cell, exit: Cell) -> List[Cell]:
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stack = [(start, [start])]
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visited = {start}
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visited_count = 1
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while stack:
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current, path = stack.pop()
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if current == exit:
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self.last_visited = visited_count
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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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visited_count += 1
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stack.append((neighbor, path + [neighbor]))
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self.last_visited = visited_count
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return []
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class AStarStrategy(PathFindingStrategy):
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def heuristic(self, a: Cell, b: Cell) -> int:
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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) -> List[Cell]:
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open_set = []
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counter = 0
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heappush(open_set, (0, counter, start))
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came_from = {}
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g_score = {start: 0}
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f_score = {start: self.heuristic(start, exit)}
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visited_count = 0
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while open_set:
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_, _, current = heappop(open_set)
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visited_count += 1
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if current == exit:
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path = []
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while current in came_from:
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path.append(current)
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current = came_from[current]
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path.append(start)
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path.reverse()
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self.last_visited = visited_count
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return path
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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)
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f_score[neighbor] = f
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counter += 1
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heappush(open_set, (f, counter, neighbor))
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self.last_visited = visited_count
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return []
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class SearchStats:
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def __init__(self, time_ms: float, visited_cells: int, path_length: int):
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self.time_ms = time_ms
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self.visited_cells = visited_cells
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self.path_length = path_length
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def __repr__(self):
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return f"Stats(time={self.time_ms:.2f}ms, visited={self.visited_cells}, path_len={self.path_length})"
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class MazeSolver:
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def __init__(self, maze: Maze, strategy: PathFindingStrategy):
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self.maze = maze
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self.strategy = strategy
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self.observers = []
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def set_strategy(self, strategy: PathFindingStrategy):
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self.strategy = strategy
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def attach(self, observer):
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self.observers.append(observer)
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def detach(self, observer):
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self.observers.remove(observer)
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def notify(self, event: str, data: Any = None):
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for obs in self.observers:
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obs.update(event, data)
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def solve(self) -> Tuple[List[Cell], SearchStats]:
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start_time = time.perf_counter()
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path = self.strategy.find_path(self.maze, self.maze.start, self.maze.exit)
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end_time = time.perf_counter()
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elapsed_ms = (end_time - start_time) * 1000.0
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visited_cells = getattr(self.strategy, 'last_visited', len(path) if path else 0)
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stats = SearchStats(elapsed_ms, visited_cells, len(path))
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self.notify("solved", {"path": path, "stats": stats})
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return path, stats
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class Observer(ABC):
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class ConsoleView(Observer):
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class MoveCommand(ABC):
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class Player:
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class MoveCommandImpl(MoveCommand): |