laba 2

ZelentsovAV/task2/builders.py

@@ -0,0 +1,56 @@
+from abc import ABC, abstractmethod
+from models import Cell, Maze
+
+
+class MazeBuilder(ABC):
+    
+    @abstractmethod
+    def build_from_file(self, filename: str) -> Maze:
+        pass
+
+
+class TextFileMazeBuilder(MazeBuilder):
+    
+    WALL_CHAR = '#'
+    START_CHAR = 'S'
+    EXIT_CHAR = 'E'
+    PASS_CHAR = ' '
+    
+    def build_from_file(self, filename: str) -> Maze:
+        with open(filename, 'r', encoding='utf-8') as f:
+            lines = [line.rstrip('\n') for line in f.readlines()]
+        
+        if not lines:
+            raise ValueError("Файл с лабиринтом пуст")
+        
+        height = len(lines)
+        width = max(len(line) for line in lines)
+        
+        maze = Maze(width, height)
+        
+        for y, line in enumerate(lines):
+            for x, ch in enumerate(line):
+                if x >= width:
+                    continue
+                
+                cell = Cell(x, y)
+                
+                if ch == self.WALL_CHAR:
+                    cell.is_wall = True
+                elif ch == self.START_CHAR:
+                    cell.is_start = True
+                elif ch == self.EXIT_CHAR:
+                    cell.is_exit = True
+                elif ch == self.PASS_CHAR:
+                    pass
+                else:
+                    cell.is_wall = True
+                
+                maze.set_cell(x, y, cell)
+        
+        if maze.start is None:
+            raise ValueError("В лабиринте нет стартовой клетки (S)")
+        if maze.exit is None:
+            raise ValueError("В лабиринте нет выхода (E)")
+        
+        return maze

ZelentsovAV/task2/commands.py

@@ -0,0 +1,62 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+from models import Cell, Maze
+
+
+class Player:
+    
+    def __init__(self, start_cell: Cell):
+        self.current_cell = start_cell
+    
+    def move_to(self, new_cell: Cell) -> None:
+        self.current_cell = new_cell
+
+
+class Command(ABC):
+    
+    @abstractmethod
+    def execute(self) -> bool:
+        pass
+    
+    @abstractmethod
+    def undo(self) -> None:
+        pass
+
+
+class MoveCommand(Command):
+    
+    def __init__(self, player: Player, maze: Maze, direction: str):
+        self.player = player
+        self.maze = maze
+        self.direction = direction
+        self.previous_cell: Optional[Cell] = None
+        self.new_cell: Optional[Cell] = None
+    
+    def _get_target_cell(self) -> Optional[Cell]:
+        x, y = self.player.current_cell.x, self.player.current_cell.y
+        
+        if self.direction == 'w':
+            y -= 1
+        elif self.direction == 's':
+            y += 1
+        elif self.direction == 'a':
+            x -= 1
+        elif self.direction == 'd':
+            x += 1
+        else:
+            return None
+        
+        return self.maze.get_cell(x, y)
+    
+    def execute(self) -> bool:
+        self.previous_cell = self.player.current_cell
+        self.new_cell = self._get_target_cell()
+        
+        if self.new_cell and self.new_cell.is_passable():
+            self.player.move_to(self.new_cell)
+            return True
+        return False
+    
+    def undo(self) -> None:
+        if self.previous_cell:
+            self.player.move_to(self.previous_cell)

ZelentsovAV/task2/experiment_results.csv

@@ -0,0 +1,13 @@
+maze_file,maze_size,strategy,time_mean,time_min,time_max,visited_mean,path_length_mean,path_found
+small.txt,10×10,BFS,0.13488009572029114,0.10789930820465088,0.22369995713233948,15.0,15.0,True
+small.txt,10×10,DFS,0.06621982902288437,0.05200039595365524,0.11539924889802933,21.0,21.0,True
+small.txt,10×10,A*,0.1621600240468979,0.11659972369670868,0.21409988403320312,15.0,15.0,True
+medium.txt,20×11,BFS,0.8280398324131966,0.6230995059013367,1.116500236093998,26.0,26.0,True
+medium.txt,20×11,DFS,0.9217998012900352,0.771399587392807,1.2620994821190834,90.0,90.0,True
+medium.txt,20×11,A*,1.2338800355792046,1.066099852323532,1.5382999554276466,26.0,26.0,True
+large.txt,30×15,BFS,1.9566401839256287,1.3727005571126938,2.646399661898613,40.0,40.0,True
+large.txt,30×15,DFS,1.7152601853013039,1.3266997411847115,2.037300728261471,196.0,196.0,True
+large.txt,30×15,A*,1.906839944422245,1.2140991166234016,2.70990002900362,40.0,40.0,True
+empty.txt,30×1,BFS,0.09321998804807663,0.07409974932670593,0.12030079960823059,30.0,30.0,True
+empty.txt,30×1,DFS,0.24830028414726257,0.21299999207258224,0.2831006422638893,30.0,30.0,True
+empty.txt,30×1,A*,0.17731990665197372,0.09519979357719421,0.30350033193826675,30.0,30.0,True

ZelentsovAV/task2/experiments.py

@@ -0,0 +1,94 @@
+import csv
+import time
+from typing import List, Dict
+from models import Maze
+from builders import TextFileMazeBuilder
+from strategies import BFSStrategy, DFSStrategy, AStarStrategy
+from solver import MazeSolver
+
+
+def run_experiment(maze: Maze, strategy_name: str, strategy, repeats: int = 5) -> Dict:
+    times = []
+    visited_counts = []
+    path_lengths = []
+    path_found = True
+    
+    for _ in range(repeats):
+        solver = MazeSolver(maze, strategy)
+        path, stats = solver.solve()
+        
+        times.append(stats.time_ms)
+        visited_counts.append(stats.visited_cells)
+        path_lengths.append(stats.path_length)
+        path_found = stats.path_found
+    
+    return {
+        'strategy': strategy_name,
+        'time_mean': sum(times) / len(times),
+        'time_min': min(times),
+        'time_max': max(times),
+        'visited_mean': sum(visited_counts) / len(visited_counts),
+        'path_length_mean': sum(path_lengths) / len(path_lengths) if path_found else 0,
+        'path_found': path_found
+    }
+
+
+def run_all_experiments(maze_files: List[str], repeats: int = 5) -> List[Dict]:
+    builder = TextFileMazeBuilder()
+    strategies = [
+        ('BFS', BFSStrategy()),
+        ('DFS', DFSStrategy()),
+        ('A*', AStarStrategy())
+    ]
+    
+    results = []
+    
+    for maze_file in maze_files:
+        try:
+            maze = builder.build_from_file(maze_file)
+        except (ValueError, FileNotFoundError) as e:
+            print(f" Ошибка: {e}")
+            continue
+        
+        print(f"    Размер: {maze.width}×{maze.height}")
+        print(f"    Старт: ({maze.start.x}, {maze.start.y})")
+        print(f"    Выход: ({maze.exit.x}, {maze.exit.y})")
+        
+        for strategy_name, strategy in strategies:
+            print(f"    Тестирование: {strategy_name}")
+            result = run_experiment(maze, strategy_name, strategy, repeats)
+            result['maze_file'] = maze_file.split('/')[-1]
+            result['maze_size'] = f"{maze.width}×{maze.height}"
+            results.append(result)
+            
+            status = "ok" if result['path_found'] else "ne ok"
+            print(f"        {status} Время: {result['time_mean']:.2f} мс, "
+                  f"Посещено: {result['visited_mean']:.0f}, "
+                  f"Путь: {result['path_length_mean']:.0f}")
+    
+    return results
+
+
+def save_results_to_csv(results: List[Dict], filename: str = "experiment_results.csv") -> None:
+    with open(filename, 'w', newline='', encoding='utf-8') as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            'maze_file', 'maze_size', 'strategy',
+            'time_mean', 'time_min', 'time_max',
+            'visited_mean', 'path_length_mean', 'path_found'
+        ])
+        writer.writeheader()
+        writer.writerows(results)
+
+
+
+def print_results_table(results: List[Dict]) -> None:
+    print("\n" + "=" * 80)
+    print("РЕЗУЛЬТАТЫ ЭКСПЕРИМЕНТОВ")
+    print("=" * 80)
+    
+    for res in results:
+        print(f"\nЛабиринт: {res['maze_file']}")
+        print(f"  Стратегия: {res['strategy']}")
+        print(f"    Время (ср):   {res['time_mean']:.2f} мс")
+        print(f"    Посещено:     {res['visited_mean']:.0f} клеток")
+        print(f"    Длина пути:   {res['path_length_mean']:.0f}")

ZelentsovAV/task2/main.py

@@ -0,0 +1,146 @@
+import os
+from builders import TextFileMazeBuilder
+from strategies import BFSStrategy, DFSStrategy, AStarStrategy
+from solver import MazeSolver
+from observers import ConsoleView
+from commands import Player
+from experiments import run_all_experiments, save_results_to_csv, print_results_table
+
+
+def create_test_mazes():
+    os.makedirs("mazes", exist_ok=True)
+    
+    small = """##########
+#S       #
+# ### ## #
+#   #    #
+### # ####
+#   #    #
+# ### #  #
+#     #  #
+#     # E#
+##########"""
+    
+    medium = """####################
+#S                 #
+# # # # # # # # # #
+#                 #
+# # # # # # # # # #
+#                 #
+# # # # # # # # # #
+#                 #
+# # # # # # # # # #
+#                 E#
+####################"""
+    
+    large = """##############################
+#S                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           E#
+##############################"""
+    
+    empty = "S" + " " * 28 + "E"
+    
+    no_exit = """#######
+#S    #
+# ### #
+#   # #
+#######"""
+    
+    with open("mazes/small.txt", "w") as f:
+        f.write(small)
+    with open("mazes/medium.txt", "w") as f:
+        f.write(medium)
+    with open("mazes/large.txt", "w") as f:
+        f.write(large)
+    with open("mazes/empty.txt", "w") as f:
+        f.write(empty)
+    with open("mazes/no_exit.txt", "w") as f:
+        f.write(no_exit)
+    
+
+
+def demo_maze_solver():
+    print("\n" + "=" * 60)
+    print("ДЕМОНСТРАЦИЯ РАБОТЫ MAZE SOLVER")
+    print("=" * 60)
+    
+    builder = TextFileMazeBuilder()
+    view = ConsoleView()
+    
+    maze = builder.build_from_file("mazes/small.txt")
+    view.update("maze_loaded", {"maze": maze})
+    
+    strategies = [
+        ("BFS", BFSStrategy(), "BFS"),
+        ("DFS", DFSStrategy(), "DFSs"),
+        ("A*", AStarStrategy(), "A*")
+    ]
+    
+    for name, strategy, description in strategies:
+        solver = MazeSolver(maze, strategy)
+        view.update("search_start", {"algorithm": description})
+        
+        path, stats = solver.solve()
+        
+        if stats.path_found:
+            view.update("path_found", {"maze": maze, "path": path, "stats": stats})
+        else:
+            view.update("no_path", {"stats": stats})
+
+
+def demo_player_controls():
+    print("\n" + "=" * 60)
+    print("Command + Observer")
+    print("=" * 60)
+    
+    builder = TextFileMazeBuilder()
+    view = ConsoleView()
+    maze = builder.build_from_file("mazes/small.txt")
+    
+    player = Player(maze.start)
+    
+    view.update("maze_loaded", {"maze": maze})
+    view.render(maze, player_position=player.current_cell)
+
+
+def run_experiments():
+    print("\n" + "=" * 60)
+    print("ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ АЛГОРИТМОВ")
+    print("=" * 60)
+    
+    maze_files = [
+        "mazes/small.txt",
+        "mazes/medium.txt",
+        "mazes/large.txt",
+        "mazes/empty.txt",
+        "mazes/no_exit.txt"
+    ]
+    
+    results = run_all_experiments(maze_files, repeats=5)
+    save_results_to_csv(results)
+    print_results_table(results)
+
+
+def main():
+    print("Объектно-ориентированная реализация с паттернами")
+    print("Паттерны: Builder, Strategy, Observer, Command")
+    
+    create_test_mazes()
+    demo_maze_solver()
+    demo_player_controls()
+    run_experiments()
+
+
+if __name__ == "__main__":
+    main()

ZelentsovAV/task2/mazes/empty.txt

@@ -0,0 +1 @@
+S                            E

ZelentsovAV/task2/mazes/large.txt

@@ -0,0 +1,15 @@
+##############################
+#S                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           #
+# # # # # # # # # # # # # # #
+#                           E#
+##############################

ZelentsovAV/task2/mazes/medium.txt

@@ -0,0 +1,11 @@
+####################
+#S                 #
+# # # # # # # # # #
+#                 #
+# # # # # # # # # #
+#                 #
+# # # # # # # # # #
+#                 #
+# # # # # # # # # #
+#                 E#
+####################

ZelentsovAV/task2/mazes/no_exit.txt

@@ -0,0 +1,5 @@
+#######
+#S    #
+# ### #
+#   # #
+#######

ZelentsovAV/task2/mazes/small.txt

@@ -0,0 +1,10 @@
+##########
+#S       #
+# ### ## #
+#   #    #
+### # ####
+#   #    #
+# ### #  #
+#     #  #
+#     # E#
+##########

ZelentsovAV/task2/models.py

@@ -0,0 +1,79 @@
+from typing import List, Optional
+
+
+class Cell:
+    
+    def __init__(self, x: int, y: int):
+        self.x = x
+        self.y = y
+        self.is_wall = False
+        self.is_start = False
+        self.is_exit = False
+    
+    def is_passable(self) -> bool:
+        return not self.is_wall
+    
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, Cell):
+            return False
+        return self.x == other.x and self.y == other.y
+    
+    def __hash__(self):
+        return hash((self.x, self.y))
+    
+    def __repr__(self):
+        return f"Cell({self.x}, {self.y})"
+
+
+class Maze:
+    
+    def __init__(self, width: int, height: int):
+        self.width = width
+        self.height = height
+        self._cells: List[List[Optional[Cell]]] = [[None for _ in range(width)] for _ in range(height)]
+        self.start: Optional[Cell] = None
+        self.exit: Optional[Cell] = None
+    
+    def set_cell(self, x: int, y: int, cell: Cell) -> None:
+        if 0 <= x < self.width and 0 <= y < self.height:
+            self._cells[y][x] = cell
+            if cell.is_start:
+                self.start = cell
+            if cell.is_exit:
+                self.exit = cell
+    
+    def get_cell(self, x: int, y: int) -> Optional[Cell]:
+        if 0 <= x < self.width and 0 <= y < self.height:
+            return self._cells[y][x]
+        return None
+    
+    def get_neighbors(self, cell: Cell) -> List[Cell]:
+        neighbors = []
+        directions = [(0, -1), (0, 1), (-1, 0), (1, 0)]
+        
+        for dx, dy in directions:
+            nx, ny = cell.x + dx, cell.y + dy
+            neighbor = self.get_cell(nx, ny)
+            if neighbor and neighbor.is_passable():
+                neighbors.append(neighbor)
+        
+        return neighbors
+    
+    def __str__(self) -> str:
+        result = []
+        for y in range(self.height):
+            row = []
+            for x in range(self.width):
+                cell = self.get_cell(x, y)
+                if cell is None:
+                    row.append('?')
+                elif cell.is_start:
+                    row.append('S')
+                elif cell.is_exit:
+                    row.append('E')
+                elif cell.is_wall:
+                    row.append('#')
+                else:
+                    row.append(' ')
+            result.append(''.join(row))
+        return '\n'.join(result)

ZelentsovAV/task2/observers.py

@@ -0,0 +1,66 @@
+from abc import ABC, abstractmethod
+from typing import List, Optional
+from models import Cell, Maze
+
+
+class Observer(ABC):
+    
+    @abstractmethod
+    def update(self, event: str, data: dict) -> None:
+        pass
+
+
+class ConsoleView(Observer):
+    
+    def render(self, maze: Maze, player_position: Optional[Cell] = None, path: Optional[List[Cell]] = None) -> None:
+        path_set = set(path) if path else set()
+        
+        print("\n+" + "-" * maze.width + "+")
+        
+        for y in range(maze.height):
+            row = []
+            for x in range(maze.width):
+                cell = maze.get_cell(x, y)
+                if cell is None:
+                    row.append('?')
+                elif player_position and cell == player_position:
+                    row.append('@')
+                elif cell.is_start:
+                    row.append('S')
+                elif cell.is_exit:
+                    row.append('E')
+                elif cell in path_set:
+                    row.append('*')
+                elif cell.is_wall:
+                    row.append('#')
+                else:
+                    row.append(' ')
+            print("|" + ''.join(row) + "|")
+        
+        print("+" + "-" * maze.width + "+")
+    
+    def update(self, event: str, data: dict) -> None:
+        if event == "maze_loaded":
+            maze = data.get('maze')
+            print("\n Лабиринт загружен:")
+            self.render(maze)
+        
+        elif event == "search_start":
+            algorithm = data.get('algorithm', 'Unknown')
+            print(f"\n Начинаем поиск алгоритмом: {algorithm}")
+        
+        elif event == "path_found":
+            maze = data.get('maze')
+            path = data.get('path')
+            stats = data.get('stats')
+            self.render(maze, path=path)
+        
+        elif event == "no_path":
+            stats = data.get('stats')
+            print(f"\n {stats}")
+        
+        elif event == "player_moved":
+            maze = data.get('maze')
+            player = data.get('player')
+            if player:
+                self.render(maze, player_position=player.current_cell)

ZelentsovAV/task2/solver.py

@@ -0,0 +1,49 @@
+import time
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+from models import Cell, Maze
+from strategies import PathFindingStrategy
+
+
+@dataclass
+class SearchStats:
+    time_ms: float
+    visited_cells: int
+    path_length: int
+    path_found: bool = True
+    
+    def __str__(self) -> str:
+        if not self.path_found:
+            return f"Путь не найден (время: {self.time_ms:.2f} мс)"
+        return (f"Время: {self.time_ms:.2f} мс, "
+                f"Посещено клеток: {self.visited_cells}, "
+                f"Длина пути: {self.path_length}")
+
+
+class MazeSolver:
+
+    def __init__(self, maze: Maze, strategy: Optional[PathFindingStrategy] = None):
+        self.maze = maze
+        self._strategy = strategy
+    
+    def set_strategy(self, strategy: PathFindingStrategy) -> None:
+        self._strategy = strategy
+    
+    def solve(self) -> Tuple[List[Cell], SearchStats]:
+        if self._strategy is None:
+            raise ValueError("Стратегия не установлена")
+        
+        start_time = time.perf_counter()
+        path = self._strategy.find_path(self.maze, self.maze.start, self.maze.exit)
+        end_time = time.perf_counter()
+        
+        time_ms = (end_time - start_time) * 1000
+        
+        stats = SearchStats(
+            time_ms=time_ms,
+            visited_cells=len(path) if path else 0,
+            path_length=len(path) if path else 0,
+            path_found=bool(path)
+        )
+        
+        return path, stats

ZelentsovAV/task2/strategies.py

@@ -0,0 +1,99 @@
+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))

ZelentsovAV/task2/visualize.py

@@ -0,0 +1,77 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from pathlib import Path
+
+
+def plot_results(csv_file='experiment_results.csv'):
+    
+    if not Path(csv_file).exists():
+        print(f"❌ {csv_file} не найден. Сначала запустите main.py")
+        return
+    
+    df = pd.read_csv(csv_file)
+
+    df = df[df['path_found'] == True]
+    
+    if df.empty:
+        print("Нет данных для графиков")
+        return
+    
+    mazes = [m.replace('.txt', '') for m in df['maze_file'].unique()]
+    strategies = df['strategy'].unique()
+    
+    fig, axes = plt.subplots(1, 3, figsize=(14, 5))
+    fig.suptitle('Сравнение алгоритмов поиска в лабиринте', fontsize=14, fontweight='bold')
+    
+    x = np.arange(len(mazes))
+    width = 0.25
+    colors = {'BFS': '#3498db', 'DFS': '#2ecc71', 'A*': '#e74c3c'}
+    
+    for i, strategy in enumerate(strategies):
+        times, visited, lengths = [], [], []
+        
+        for maze in df['maze_file'].unique():
+            data = df[(df['strategy'] == strategy) & (df['maze_file'] == maze)]
+            if not data.empty:
+                times.append(data['time_mean'].values[0])
+                visited.append(data['visited_mean'].values[0])
+                lengths.append(data['path_length_mean'].values[0])
+            else:
+                times.append(0)
+                visited.append(0)
+                lengths.append(0)
+        
+        axes[0].bar(x + i*width, times, width, label=strategy,
+                    color=colors.get(strategy, 'gray'), alpha=0.7)
+        axes[1].bar(x + i*width, visited, width, label=strategy,
+                    color=colors.get(strategy, 'gray'), alpha=0.7)
+        axes[2].bar(x + i*width, lengths, width, label=strategy,
+                    color=colors.get(strategy, 'gray'), alpha=0.7)
+    
+    axes[0].set_title(' Время выполнения (мс)')
+    axes[0].set_xticks(x + width)
+    axes[0].set_xticklabels(mazes, rotation=45, ha='right')
+    axes[0].legend()
+    axes[0].grid(True, alpha=0.3)
+    
+    axes[1].set_title(' Посещённые клетки')
+    axes[1].set_xticks(x + width)
+    axes[1].set_xticklabels(mazes, rotation=45, ha='right')
+    axes[1].legend()
+    axes[1].grid(True, alpha=0.3)
+    
+    axes[2].set_title(' Длина пути')
+    axes[2].set_xticks(x + width)
+    axes[2].set_xticklabels(mazes, rotation=45, ha='right')
+    axes[2].legend()
+    axes[2].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig('experiment_results.png', dpi=150, bbox_inches='tight')
+    plt.show()
+    
+
+
+if __name__ == "__main__":
+    plot_results()