task2

petryaninyas/task2/README.md

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+# Maze Solver Project
+
+ООП-проект для поиска выхода из лабиринта с паттернами:
+- Builder
+- Strategy
+- Observer
+- Command
+
+## Запуск
+```bash
+python main.py
+```
+
+## Эксперименты
+```bash
+python experiment.py
+```
+
+Результаты сохраняются в папку `experiment_results/`.
+
+## Требования
+```bash
+pip install -r requirements.txt
+```

petryaninyas/task2/builders/maze_builder.py

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+from abc import ABC, abstractmethod
+
+
+class MazeBuilder(ABC):
+    @abstractmethod
+    def buildFromFile(self, filename):
+        raise NotImplementedError

petryaninyas/task2/builders/text_file_maze_builder.py

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+from core.cell import Cell
+from core.maze import Maze
+from builders.maze_builder import MazeBuilder
+
+
+class TextFileMazeBuilder(MazeBuilder):
+    def buildFromFile(self, filename):
+        with open(filename, "r", encoding="utf-8") as f:
+            lines = [line.rstrip("\n") for line in f]
+
+        if not lines:
+            raise ValueError("Maze file is empty")
+
+        width = max(len(line) for line in lines)
+        height = len(lines)
+
+        cells = []
+        startCell = None
+        exitCell = None
+
+        for y, line in enumerate(lines):
+            row = []
+            for x in range(width):
+                ch = line[x] if x < len(line) else "#"
+
+                if ch == "#":
+                    cell = Cell(x, y, isWall=True)
+                elif ch == "S":
+                    if startCell is not None:
+                        raise ValueError("Multiple start cells found")
+                    cell = Cell(x, y, isWall=False, isStart=True)
+                    startCell = cell
+                elif ch == "E":
+                    if exitCell is not None:
+                        raise ValueError("Multiple exit cells found")
+                    cell = Cell(x, y, isWall=False, isExit=True)
+                    exitCell = cell
+                elif ch in (" ", "."):
+                    cell = Cell(x, y, isWall=False)
+                elif ch.isdigit():
+                    cell = Cell(x, y, isWall=False, weight=max(1, int(ch)))
+                else:
+                    raise ValueError(f"Unsupported symbol '{ch}' at ({x}, {y})")
+                row.append(cell)
+            cells.append(row)
+
+        if startCell is None:
+            raise ValueError("Start cell 'S' not found")
+        if exitCell is None:
+            raise ValueError("Exit cell 'E' not found")
+
+        return Maze(cells, width, height, startCell, exitCell)

petryaninyas/task2/commands/command.py

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+from abc import ABC, abstractmethod
+
+
+class Command(ABC):
+    @abstractmethod
+    def execute(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def undo(self):
+        raise NotImplementedError

petryaninyas/task2/commands/move_command.py

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+from commands.command import Command
+
+
+class MoveCommand(Command):
+    DIRECTION_TO_DELTA = {
+        "W": (0, -1),
+        "A": (-1, 0),
+        "S": (0, 1),
+        "D": (1, 0),
+    }
+
+    def __init__(self, player, maze, direction):
+        self.player = player
+        self.maze = maze
+        self.direction = direction.upper()
+        self.previousCell = None
+
+    def execute(self):
+        if self.direction not in self.DIRECTION_TO_DELTA:
+            return False
+
+        dx, dy = self.DIRECTION_TO_DELTA[self.direction]
+        current = self.player.currentCell
+        new_cell = self.maze.getCell(current.x + dx, current.y + dy)
+
+        if new_cell is None or not new_cell.isPassable():
+            return False
+
+        self.previousCell = current
+        self.player.setCell(new_cell)
+        return True
+
+    def undo(self):
+        if self.previousCell is None:
+            return False
+        self.player.setCell(self.previousCell)
+        return True

petryaninyas/task2/controller/game_controller.py

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+from commands.move_command import MoveCommand
+
+
+class GameController:
+    def __init__(self, maze, player, view):
+        self.maze = maze
+        self.player = player
+        self.view = view
+        self.history = []
+
+    def move(self, direction):
+        command = MoveCommand(self.player, self.maze, direction)
+        if command.execute():
+            self.history.append(command)
+            self.view.update({"type": "move", "direction": direction})
+            self.view.render(self.maze, player_position=self.player.currentCell)
+            return True
+        print("Cannot move there")
+        return False
+
+    def undo(self):
+        if not self.history:
+            print("Nothing to undo")
+            return False
+        command = self.history.pop()
+        if command.undo():
+            self.view.update({"type": "undo"})
+            self.view.render(self.maze, player_position=self.player.currentCell)
+            return True
+        return False

petryaninyas/task2/core/cell.py

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+from dataclasses import dataclass
+
+
+@dataclass
+class Cell:
+    x: int
+    y: int
+    isWall: bool = False
+    isStart: bool = False
+    isExit: bool = False
+    weight: int = 1
+
+    def isPassable(self):
+        return not self.isWall
+
+    def __repr__(self):
+        parts = [f"Cell({self.x}, {self.y}"]
+        if self.isWall:
+            parts.append("WALL")
+        if self.isStart:
+            parts.append("START")
+        if self.isExit:
+            parts.append("EXIT")
+        if self.weight != 1:
+            parts.append(f"w={self.weight}")
+        return ", ".join(parts) + ")"

petryaninyas/task2/core/maze.py

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+class Maze:
+    def __init__(self, cells, width, height, startCell=None, exitCell=None):
+        self.cells = cells
+        self.width = width
+        self.height = height
+        self.startCell = startCell
+        self.exitCell = exitCell
+
+    def getCell(self, x, y):
+        if 0 <= x < self.width and 0 <= y < self.height:
+            return self.cells[y][x]
+        return None
+
+    def getNeighbors(self, cell):
+        neighbors = []
+        for dx, dy in ((0, -1), (0, 1), (-1, 0), (1, 0)):
+            nx, ny = cell.x + dx, cell.y + dy
+            neighbor = self.getCell(nx, ny)
+            if neighbor is not None and neighbor.isPassable():
+                neighbors.append(neighbor)
+        return neighbors
+
+    def render_lines(self, player_position=None, path=None):
+        path_set = {(c.x, c.y) for c in path} if path else set()
+        player_pos = None if player_position is None else (player_position.x, player_position.y)
+        lines = []
+        for y in range(self.height):
+            row = []
+            for x in range(self.width):
+                cell = self.cells[y][x]
+                if player_pos == (x, y):
+                    row.append("P")
+                elif cell.isStart:
+                    row.append("S")
+                elif cell.isExit:
+                    row.append("E")
+                elif cell.isWall:
+                    row.append("#")
+                elif (x, y) in path_set:
+                    row.append("*")
+                elif cell.weight > 1:
+                    row.append(str(cell.weight))
+                else:
+                    row.append(" ")
+            lines.append("".join(row))
+        return lines
+
+    def render(self, player_position=None, path=None):
+        return "\n".join(self.render_lines(player_position=player_position, path=path))

petryaninyas/task2/core/player.py

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+class Player:
+    def __init__(self, currentCell):
+        self.currentCell = currentCell
+
+    def setCell(self, cell):
+        self.currentCell = cell

petryaninyas/task2/core/search_stats.py

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+from dataclasses import dataclass, field
+
+
+@dataclass
+class SearchStats:
+    timeMs: float
+    visitedCells: int
+    pathLength: int
+    path: list = field(default_factory=list)
+    found: bool = False
+    algorithm: str = ""

petryaninyas/task2/experiment.py

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+from pathlib import Path
+from statistics import mean
+import csv
+import random
+
+import matplotlib.pyplot as plt
+
+from core.cell import Cell
+from core.maze import Maze
+from solver.maze_solver import MazeSolver
+from strategies.astar_strategy import AStarStrategy
+from strategies.bfs_strategy import BFSStrategy
+from strategies.dfs_strategy import DFSStrategy
+from strategies.dijkstra_strategy import DijkstraStrategy
+
+
+BASE_DIR = Path(__file__).resolve().parent
+OUT_DIR = BASE_DIR / "experiment_results"
+
+
+def build_maze_from_symbols(lines):
+    height = len(lines)
+    width = max(len(line) for line in lines)
+    cells = []
+    start = None
+    exit_cell = None
+    for y, line in enumerate(lines):
+        row = []
+        for x in range(width):
+            ch = line[x] if x < len(line) else "#"
+            if ch == "#":
+                cell = Cell(x, y, isWall=True)
+            elif ch == "S":
+                cell = Cell(x, y, isWall=False, isStart=True)
+                start = cell
+            elif ch == "E":
+                cell = Cell(x, y, isWall=False, isExit=True)
+                exit_cell = cell
+            elif ch == " " or ch == ".":
+                cell = Cell(x, y, isWall=False)
+            elif ch.isdigit():
+                cell = Cell(x, y, isWall=False, weight=int(ch))
+            else:
+                raise ValueError(f"Unknown symbol '{ch}' at {x},{y}")
+            row.append(cell)
+        cells.append(row)
+    return Maze(cells, width, height, start, exit_cell)
+
+
+def generate_empty_maze(width, height):
+    lines = [" " * width for _ in range(height)]
+    lines = [list(row) for row in lines]
+    lines[1][1] = "S"
+    lines[height - 2][width - 2] = "E"
+    return build_maze_from_symbols(["".join(row) for row in lines])
+
+
+def generate_simple_maze(width, height):
+    grid = [["#" for _ in range(width)] for _ in range(height)]
+    for x in range(1, width - 1):
+        grid[1][x] = " "
+    for y in range(1, height - 1):
+        grid[y][width - 2] = " "
+    grid[1][1] = "S"
+    grid[height - 2][width - 2] = "E"
+    return build_maze_from_symbols(["".join(row) for row in grid])
+
+
+def generate_branching_maze(width, height, seed=42, wall_density=0.30):
+    rng = random.Random(seed)
+    grid = [["#" for _ in range(width)] for _ in range(height)]
+    x, y = 1, 1
+    grid[y][x] = "S"
+    while (x, y) != (width - 2, height - 2):
+        candidates = []
+        for dx, dy in [(1, 0), (0, 1)]:
+            nx, ny = x + dx, y + dy
+            if 1 <= nx < width - 1 and 1 <= ny < height - 1:
+                candidates.append((nx, ny))
+        if not candidates:
+            break
+        x, y = rng.choice(candidates)
+        grid[y][x] = " "
+    grid[height - 2][width - 2] = "E"
+
+    # carve extra corridors and dead ends
+    for yy in range(1, height - 1):
+        for xx in range(1, width - 1):
+            if grid[yy][xx] == "#" and rng.random() > wall_density:
+                grid[yy][xx] = " "
+    grid[1][1] = "S"
+    grid[height - 2][width - 2] = "E"
+    return build_maze_from_symbols(["".join(row) for row in grid])
+
+
+def generate_no_path_maze(width, height):
+    grid = [[" " for _ in range(width)] for _ in range(height)]
+    for x in range(width):
+        grid[height // 2][x] = "#"
+    grid[1][1] = "S"
+    grid[height - 2][width - 2] = "E"
+    return build_maze_from_symbols(["".join(row) for row in grid])
+
+
+def generate_weighted_maze(width, height, seed=123):
+    rng = random.Random(seed)
+    grid = [[" " for _ in range(width)] for _ in range(height)]
+    for y in range(height):
+        for x in range(width):
+            r = rng.random()
+            if r < 0.12:
+                grid[y][x] = "#"
+            elif r < 0.25:
+                grid[y][x] = "3"
+            elif r < 0.40:
+                grid[y][x] = "2"
+            else:
+                grid[y][x] = "1"
+    # ensure path-ish
+    for x in range(width):
+        grid[1][x] = "1"
+    for y in range(1, height):
+        grid[y][width - 2] = "1"
+    grid[1][1] = "S"
+    grid[height - 2][width - 2] = "E"
+    return build_maze_from_symbols(["".join(row) for row in grid])
+
+
+def bench_one_maze(maze_name, maze, strategies, repeats=5):
+    summary_rows = []
+    raw_rows = []
+    for strategy_name, strategy_factory in strategies:
+        times, visiteds, lengths = [], [], []
+        for run in range(1, repeats + 1):
+            solver = MazeSolver(maze)
+            solver.setStrategy(strategy_factory())
+            stats = solver.solve()
+            raw_rows.append([maze_name, strategy_name, run, f"{stats.timeMs:.6f}", stats.visitedCells, stats.pathLength])
+            times.append(stats.timeMs)
+            visiteds.append(stats.visitedCells)
+            lengths.append(stats.pathLength)
+        summary_rows.append([maze_name, strategy_name, f"{mean(times):.6f}", f"{mean(visiteds):.2f}", f"{mean(lengths):.2f}", repeats])
+    return summary_rows, raw_rows
+
+
+def save_csv(path, rows):
+    with open(path, "w", newline="", encoding="utf-8") as f:
+        csv.writer(f).writerows(rows)
+
+
+def plot_summary(summary_rows):
+    by_maze = {}
+    for row in summary_rows[1:]:
+        maze_name, strategy, avg_time, avg_visited, avg_len, runs = row
+        by_maze.setdefault(maze_name, []).append((strategy, float(avg_time), float(avg_visited), float(avg_len)))
+
+    for maze_name, items in by_maze.items():
+        items.sort(key=lambda t: t[0])
+        strategies = [i[0] for i in items]
+        x = list(range(len(strategies)))
+
+        plt.figure(figsize=(8, 4))
+        plt.bar(x, [i[1] for i in items])
+        plt.xticks(x, strategies)
+        plt.ylabel("ms")
+        plt.title(f"{maze_name} — avg time")
+        plt.tight_layout()
+        plt.savefig(OUT_DIR / f"{maze_name}_time.png", dpi=150)
+        plt.close()
+
+        plt.figure(figsize=(8, 4))
+        plt.bar(x, [i[2] for i in items])
+        plt.xticks(x, strategies)
+        plt.ylabel("cells")
+        plt.title(f"{maze_name} — visited cells")
+        plt.tight_layout()
+        plt.savefig(OUT_DIR / f"{maze_name}_visited.png", dpi=150)
+        plt.close()
+
+        plt.figure(figsize=(8, 4))
+        plt.bar(x, [i[3] for i in items])
+        plt.xticks(x, strategies)
+        plt.ylabel("cells")
+        plt.title(f"{maze_name} — path length")
+        plt.tight_layout()
+        plt.savefig(OUT_DIR / f"{maze_name}_length.png", dpi=150)
+        plt.close()
+
+
+def main():
+    OUT_DIR.mkdir(exist_ok=True)
+
+    strategies = [
+        ("BFS", BFSStrategy),
+        ("DFS", DFSStrategy),
+        ("A*", AStarStrategy),
+        ("Dijkstra", DijkstraStrategy),
+    ]
+
+    mazes = [
+        ("small_10x10", generate_simple_maze(10, 10)),
+        ("medium_50x50", generate_branching_maze(50, 50)),
+        ("large_100x100", generate_branching_maze(100, 100, seed=99, wall_density=0.35)),
+        ("empty_30x30", generate_empty_maze(30, 30)),
+        ("no_path_30x30", generate_no_path_maze(30, 30)),
+        ("weighted_30x30", generate_weighted_maze(30, 30)),
+    ]
+
+    summary = [["maze", "strategy", "avg_time_ms", "avg_visited_cells", "avg_path_length", "runs"]]
+    raw = [["maze", "strategy", "run", "time_ms", "visited_cells", "path_length"]]
+
+    for maze_name, maze in mazes:
+        s_rows, r_rows = bench_one_maze(maze_name, maze, strategies, repeats=5)
+        summary.extend(s_rows)
+        raw.extend(r_rows)
+
+    save_csv(OUT_DIR / "summary.csv", summary)
+    save_csv(OUT_DIR / "raw.csv", raw)
+    plot_summary(summary)
+
+    print("Saved to", OUT_DIR.resolve())
+
+
+if __name__ == "__main__":
+    main()

petryaninyas/task2/experiment_results/raw.csv

@@ -0,0 +1,121 @@
+maze,strategy,run,time_ms,visited_cells,path_length
+small_10x10,BFS,1,0.086300,15,15
+small_10x10,BFS,2,0.061100,15,15
+small_10x10,BFS,3,0.059300,15,15
+small_10x10,BFS,4,0.058400,15,15
+small_10x10,BFS,5,0.058500,15,15
+small_10x10,DFS,1,0.073400,15,15
+small_10x10,DFS,2,0.063500,15,15
+small_10x10,DFS,3,0.062500,15,15
+small_10x10,DFS,4,0.062700,15,15
+small_10x10,DFS,5,0.070900,15,15
+small_10x10,A*,1,0.110100,15,15
+small_10x10,A*,2,0.089200,15,15
+small_10x10,A*,3,0.087800,15,15
+small_10x10,A*,4,0.087600,15,15
+small_10x10,A*,5,0.087000,15,15
+small_10x10,Dijkstra,1,0.290000,15,15
+small_10x10,Dijkstra,2,0.083300,15,15
+small_10x10,Dijkstra,3,0.091500,15,15
+small_10x10,Dijkstra,4,0.081000,15,15
+small_10x10,Dijkstra,5,0.080400,15,15
+medium_50x50,BFS,1,6.799200,1579,95
+medium_50x50,BFS,2,6.960100,1579,95
+medium_50x50,BFS,3,6.337000,1579,95
+medium_50x50,BFS,4,7.431700,1579,95
+medium_50x50,BFS,5,6.517900,1579,95
+medium_50x50,DFS,1,6.463000,1277,647
+medium_50x50,DFS,2,6.815500,1277,647
+medium_50x50,DFS,3,5.816100,1277,647
+medium_50x50,DFS,4,6.492400,1277,647
+medium_50x50,DFS,5,6.532500,1277,647
+medium_50x50,A*,1,6.940500,927,95
+medium_50x50,A*,2,7.275400,927,95
+medium_50x50,A*,3,7.062500,927,95
+medium_50x50,A*,4,7.727600,927,95
+medium_50x50,A*,5,7.321000,927,95
+medium_50x50,Dijkstra,1,11.483200,1579,95
+medium_50x50,Dijkstra,2,11.194200,1579,95
+medium_50x50,Dijkstra,3,11.255200,1579,95
+medium_50x50,Dijkstra,4,10.512500,1579,95
+medium_50x50,Dijkstra,5,10.696400,1579,95
+large_100x100,BFS,1,25.623500,5566,195
+large_100x100,BFS,2,24.348800,5566,195
+large_100x100,BFS,3,25.452600,5566,195
+large_100x100,BFS,4,30.516900,5566,195
+large_100x100,BFS,5,33.694700,5566,195
+large_100x100,DFS,1,19.415200,3543,1531
+large_100x100,DFS,2,19.919000,3543,1531
+large_100x100,DFS,3,19.104600,3543,1531
+large_100x100,DFS,4,20.000600,3543,1531
+large_100x100,DFS,5,17.840200,3543,1531
+large_100x100,A*,1,7.509300,853,195
+large_100x100,A*,2,7.221200,853,195
+large_100x100,A*,3,6.486700,853,195
+large_100x100,A*,4,6.357600,853,195
+large_100x100,A*,5,6.723800,853,195
+large_100x100,Dijkstra,1,40.782300,5571,195
+large_100x100,Dijkstra,2,41.155000,5571,195
+large_100x100,Dijkstra,3,39.456200,5571,195
+large_100x100,Dijkstra,4,41.388700,5571,195
+large_100x100,Dijkstra,5,40.962500,5571,195
+empty_30x30,BFS,1,4.143200,896,55
+empty_30x30,BFS,2,3.987000,896,55
+empty_30x30,BFS,3,3.777100,896,55
+empty_30x30,BFS,4,3.682300,896,55
+empty_30x30,BFS,5,3.737900,896,55
+empty_30x30,DFS,1,4.024200,842,815
+empty_30x30,DFS,2,4.333900,842,815
+empty_30x30,DFS,3,5.411000,842,815
+empty_30x30,DFS,4,4.677200,842,815
+empty_30x30,DFS,5,5.177400,842,815
+empty_30x30,A*,1,6.603700,784,55
+empty_30x30,A*,2,6.200600,784,55
+empty_30x30,A*,3,6.798400,784,55
+empty_30x30,A*,4,7.178500,784,55
+empty_30x30,A*,5,6.660800,784,55
+empty_30x30,Dijkstra,1,6.396000,896,55
+empty_30x30,Dijkstra,2,6.275200,896,55
+empty_30x30,Dijkstra,3,6.845700,896,55
+empty_30x30,Dijkstra,4,6.531200,896,55
+empty_30x30,Dijkstra,5,6.783400,896,55
+no_path_30x30,BFS,1,2.000100,450,0
+no_path_30x30,BFS,2,1.797900,450,0
+no_path_30x30,BFS,3,1.796200,450,0
+no_path_30x30,BFS,4,1.774100,450,0
+no_path_30x30,BFS,5,1.775200,450,0
+no_path_30x30,DFS,1,2.090400,450,0
+no_path_30x30,DFS,2,2.222600,450,0
+no_path_30x30,DFS,3,2.454300,450,0
+no_path_30x30,DFS,4,2.476200,450,0
+no_path_30x30,DFS,5,2.073700,450,0
+no_path_30x30,A*,1,3.651700,450,0
+no_path_30x30,A*,2,3.495200,450,0
+no_path_30x30,A*,3,3.754200,450,0
+no_path_30x30,A*,4,3.286800,450,0
+no_path_30x30,A*,5,3.335200,450,0
+no_path_30x30,Dijkstra,1,3.050900,450,0
+no_path_30x30,Dijkstra,2,3.109900,450,0
+no_path_30x30,Dijkstra,3,3.292500,450,0
+no_path_30x30,Dijkstra,4,3.418600,450,0
+no_path_30x30,Dijkstra,5,3.212100,450,0
+weighted_30x30,BFS,1,3.418900,788,55
+weighted_30x30,BFS,2,3.368200,788,55
+weighted_30x30,BFS,3,3.516400,788,55
+weighted_30x30,BFS,4,3.224300,788,55
+weighted_30x30,BFS,5,3.131100,788,55
+weighted_30x30,DFS,1,3.291200,693,479
+weighted_30x30,DFS,2,3.362300,693,479
+weighted_30x30,DFS,3,3.523200,693,479
+weighted_30x30,DFS,4,3.521400,693,479
+weighted_30x30,DFS,5,3.332300,693,479
+weighted_30x30,A*,1,1.181000,126,55
+weighted_30x30,A*,2,1.080200,126,55
+weighted_30x30,A*,3,1.368400,126,55
+weighted_30x30,A*,4,1.109800,126,55
+weighted_30x30,A*,5,1.079300,126,55
+weighted_30x30,Dijkstra,1,6.112700,781,55
+weighted_30x30,Dijkstra,2,5.464800,781,55
+weighted_30x30,Dijkstra,3,5.794500,781,55
+weighted_30x30,Dijkstra,4,6.171700,781,55
+weighted_30x30,Dijkstra,5,6.640500,781,55

petryaninyas/task2/experiment_results/summary.csv

@@ -0,0 +1,25 @@
+maze,strategy,avg_time_ms,avg_visited_cells,avg_path_length,runs
+small_10x10,BFS,0.064720,15.00,15.00,5
+small_10x10,DFS,0.066600,15.00,15.00,5
+small_10x10,A*,0.092340,15.00,15.00,5
+small_10x10,Dijkstra,0.125240,15.00,15.00,5
+medium_50x50,BFS,6.809180,1579.00,95.00,5
+medium_50x50,DFS,6.423900,1277.00,647.00,5
+medium_50x50,A*,7.265400,927.00,95.00,5
+medium_50x50,Dijkstra,11.028300,1579.00,95.00,5
+large_100x100,BFS,27.927300,5566.00,195.00,5
+large_100x100,DFS,19.255920,3543.00,1531.00,5
+large_100x100,A*,6.859720,853.00,195.00,5
+large_100x100,Dijkstra,40.748940,5571.00,195.00,5
+empty_30x30,BFS,3.865500,896.00,55.00,5
+empty_30x30,DFS,4.724740,842.00,815.00,5
+empty_30x30,A*,6.688400,784.00,55.00,5
+empty_30x30,Dijkstra,6.566300,896.00,55.00,5
+no_path_30x30,BFS,1.828700,450.00,0.00,5
+no_path_30x30,DFS,2.263440,450.00,0.00,5
+no_path_30x30,A*,3.504620,450.00,0.00,5
+no_path_30x30,Dijkstra,3.216800,450.00,0.00,5
+weighted_30x30,BFS,3.331780,788.00,55.00,5
+weighted_30x30,DFS,3.406080,693.00,479.00,5
+weighted_30x30,A*,1.163740,126.00,55.00,5
+weighted_30x30,Dijkstra,6.036840,781.00,55.00,5

petryaninyas/task2/main.py

@@ -0,0 +1,59 @@
+from builders.text_file_maze_builder import TextFileMazeBuilder
+from core.player import Player
+from observer.console_view import ConsoleView
+from solver.maze_solver import MazeSolver
+from strategies.astar_strategy import AStarStrategy
+from strategies.bfs_strategy import BFSStrategy
+from strategies.dfs_strategy import DFSStrategy
+from controller.game_controller import GameController
+
+
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parent
+
+
+def run_demo():
+    builder = TextFileMazeBuilder()
+    maze = builder.buildFromFile(str(BASE_DIR / "mazes" / "maze_small.txt"))
+
+    view = ConsoleView()
+    view.update({"type": "maze_loaded", "message": "Maze loaded"})
+    view.render(maze)
+
+    solver = MazeSolver(maze)
+    solver.addObserver(view)
+
+    for strategy in (BFSStrategy(), DFSStrategy(), AStarStrategy()):
+        solver.setStrategy(strategy)
+        stats = solver.solve()
+
+        print()
+        print(f"=== {strategy.name} ===")
+        print(f"Time: {stats.timeMs:.3f} ms")
+        print(f"Visited cells: {stats.visitedCells}")
+        print(f"Path length: {stats.pathLength}")
+        print(f"Path found: {'yes' if stats.found else 'no'}")
+
+        view.render(maze, path=stats.path)
+
+    player = Player(maze.startCell)
+    controller = GameController(maze, player, view)
+
+    print("Manual mode: W/A/S/D move, Z undo, Q quit")
+    view.render(maze, player_position=player.currentCell)
+
+    while True:
+        cmd = input("Command: ").strip().upper()
+        if cmd == "Q":
+            break
+        if cmd == "Z":
+            controller.undo()
+        elif cmd in {"W", "A", "S", "D"}:
+            controller.move(cmd)
+        else:
+            print("Unknown command")
+
+
+if __name__ == "__main__":
+    run_demo()

petryaninyas/task2/mazes/maze_empty.txt

@@ -0,0 +1,9 @@
+S                          
+
+
+
+
+
+
+
+                         E 

petryaninyas/task2/mazes/maze_large.txt

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

petryaninyas/task2/mazes/maze_medium.txt

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

petryaninyas/task2/mazes/maze_no_path.txt

@@ -0,0 +1,9 @@
+##########
+#S       #
+# ###### #
+#      # #
+##########
+#      #E#
+# ###### #
+#        #
+##########

petryaninyas/task2/mazes/maze_small.txt

@@ -0,0 +1,7 @@
+##########
+#S     #E#
+# ## # # ##
+#    #   #
+# #### # #
+#      # #
+##########

petryaninyas/task2/mazes/maze_weighted.txt

@@ -0,0 +1,10 @@
+1111111111111111111111111111
+1S11111111111111111111111111
+1111111111111111111111111111
+1111111111111111111111111111
+1111111111111222222222222111
+1111111111111222222222222111
+1111111111111333333333333111
+1111111111111333333333333111
+111111111111111111111111111E
+1111111111111111111111111111

petryaninyas/task2/observer/console_view.py

@@ -0,0 +1,26 @@
+import os
+from observer.observer import Observer
+
+
+class ConsoleView(Observer):
+    def update(self, event):
+        if isinstance(event, str):
+            print(f"[EVENT] {event}")
+        elif isinstance(event, dict):
+            event_type = event.get("type", "unknown")
+            if event_type == "search_finished":
+                stats = event.get("stats")
+                print(f"[EVENT] search finished: {stats}")
+            else:
+                print(f"[EVENT] {event_type}: {event}")
+        else:
+            print("[EVENT] unknown")
+
+    def clear(self):
+        os.system("cls" if os.name == "nt" else "clear")
+
+    def render(self, maze, player_position=None, path=None, clear_screen=False):
+        if clear_screen:
+            self.clear()
+        print(maze.render(player_position=player_position, path=path))
+        print()

petryaninyas/task2/observer/observer.py

@@ -0,0 +1,7 @@
+from abc import ABC, abstractmethod
+
+
+class Observer(ABC):
+    @abstractmethod
+    def update(self, event):
+        raise NotImplementedError

petryaninyas/task2/requirements.txt

@@ -0,0 +1 @@
+matplotlib

petryaninyas/task2/solver/maze_solver.py

@@ -0,0 +1,50 @@
+import time
+from core.search_stats import SearchStats
+
+
+class MazeSolver:
+    def __init__(self, maze, strategy=None):
+        self.maze = maze
+        self.strategy = strategy
+        self.observers = []
+
+    def setStrategy(self, strategy):
+        self.strategy = strategy
+
+    def addObserver(self, observer):
+        if observer not in self.observers:
+            self.observers.append(observer)
+
+    def removeObserver(self, observer):
+        if observer in self.observers:
+            self.observers.remove(observer)
+
+    def notify(self, event):
+        for observer in self.observers:
+            observer.update(event)
+
+    def solve(self):
+        if self.strategy is None:
+            raise ValueError("Strategy is not set")
+        self.notify({"type": "search_started", "strategy": self.strategy.name})
+
+        start_time = time.perf_counter()
+        path = self.strategy.findPath(self.maze, self.maze.startCell, self.maze.exitCell)
+        end_time = time.perf_counter()
+
+        stats = SearchStats(
+            timeMs=(end_time - start_time) * 1000.0,
+            visitedCells=getattr(self.strategy, "visitedCount", 0),
+            pathLength=len(path),
+            path=path,
+            found=bool(path),
+            algorithm=getattr(self.strategy, "name", "")
+        )
+
+        if stats.found:
+            self.notify({"type": "path_found", "strategy": stats.algorithm, "length": stats.pathLength})
+        else:
+            self.notify({"type": "path_not_found", "strategy": stats.algorithm})
+
+        self.notify({"type": "search_finished", "stats": stats})
+        return stats

petryaninyas/task2/strategies/astar_strategy.py

@@ -0,0 +1,45 @@
+import heapq
+from strategies.pathfinding_strategy import PathFindingStrategy
+
+
+class AStarStrategy(PathFindingStrategy):
+    name = "A*"
+
+    def heuristic(self, cell, exitCell):
+        return abs(cell.x - exitCell.x) + abs(cell.y - exitCell.y)
+
+    def findPath(self, maze, start, exitCell):
+        self.visitedCount = 0
+        if start is None or exitCell is None:
+            return []
+
+        open_set = []
+        heapq.heappush(open_set, (0, 0, start.x, start.y, start))
+        parent = {}
+        g_score = {(start.x, start.y): 0}
+        closed = set()
+
+        while open_set:
+            f_score, current_g, _, _, current = heapq.heappop(open_set)
+            pos = (current.x, current.y)
+
+            if pos in closed:
+                continue
+
+            closed.add(pos)
+            self.visitedCount += 1
+
+            if current.x == exitCell.x and current.y == exitCell.y:
+                return self._restore_path(parent, start, exitCell)
+
+            for neighbor in maze.getNeighbors(current):
+                npos = (neighbor.x, neighbor.y)
+                tentative_g = current_g + getattr(neighbor, "weight", 1)
+
+                if tentative_g < g_score.get(npos, float("inf")):
+                    g_score[npos] = tentative_g
+                    parent[npos] = current
+                    new_f = tentative_g + self.heuristic(neighbor, exitCell)
+                    heapq.heappush(open_set, (new_f, tentative_g, neighbor.x, neighbor.y, neighbor))
+
+        return []

petryaninyas/task2/strategies/bfs_strategy.py

@@ -0,0 +1,31 @@
+from collections import deque
+from strategies.pathfinding_strategy import PathFindingStrategy
+
+
+class BFSStrategy(PathFindingStrategy):
+    name = "BFS"
+
+    def findPath(self, maze, start, exitCell):
+        self.visitedCount = 0
+        if start is None or exitCell is None:
+            return []
+
+        queue = deque([start])
+        visited = {(start.x, start.y)}
+        parent = {}
+
+        while queue:
+            current = queue.popleft()
+            self.visitedCount += 1
+
+            if current.x == exitCell.x and current.y == exitCell.y:
+                return self._restore_path(parent, start, exitCell)
+
+            for neighbor in maze.getNeighbors(current):
+                pos = (neighbor.x, neighbor.y)
+                if pos not in visited:
+                    visited.add(pos)
+                    parent[pos] = current
+                    queue.append(neighbor)
+
+        return []

petryaninyas/task2/strategies/dfs_strategy.py

@@ -0,0 +1,35 @@
+from strategies.pathfinding_strategy import PathFindingStrategy
+
+
+class DFSStrategy(PathFindingStrategy):
+    name = "DFS"
+
+    def findPath(self, maze, start, exitCell):
+        self.visitedCount = 0
+        if start is None or exitCell is None:
+            return []
+
+        stack = [start]
+        visited = set()
+        parent = {}
+
+        while stack:
+            current = stack.pop()
+            pos = (current.x, current.y)
+            if pos in visited:
+                continue
+
+            visited.add(pos)
+            self.visitedCount += 1
+
+            if current.x == exitCell.x and current.y == exitCell.y:
+                return self._restore_path(parent, start, exitCell)
+
+            neighbors = maze.getNeighbors(current)
+            for neighbor in reversed(neighbors):
+                npos = (neighbor.x, neighbor.y)
+                if npos not in visited:
+                    parent[npos] = current
+                    stack.append(neighbor)
+
+        return []

petryaninyas/task2/strategies/dijkstra_strategy.py

@@ -0,0 +1,41 @@
+import heapq
+from strategies.pathfinding_strategy import PathFindingStrategy
+
+
+class DijkstraStrategy(PathFindingStrategy):
+    name = "Dijkstra"
+
+    def findPath(self, maze, start, exitCell):
+        self.visitedCount = 0
+        if start is None or exitCell is None:
+            return []
+
+        pq = [(0, start.x, start.y, start)]
+        dist = {(start.x, start.y): 0}
+        parent = {}
+        closed = set()
+
+        while pq:
+            current_cost, _, _, current = heapq.heappop(pq)
+            pos = (current.x, current.y)
+
+            if pos in closed:
+                continue
+
+            closed.add(pos)
+            self.visitedCount += 1
+
+            if current.x == exitCell.x and current.y == exitCell.y:
+                return self._restore_path(parent, start, exitCell)
+
+            for neighbor in maze.getNeighbors(current):
+                npos = (neighbor.x, neighbor.y)
+                step_cost = getattr(neighbor, "weight", 1)
+                new_cost = current_cost + step_cost
+
+                if new_cost < dist.get(npos, float("inf")):
+                    dist[npos] = new_cost
+                    parent[npos] = current
+                    heapq.heappush(pq, (new_cost, neighbor.x, neighbor.y, neighbor))
+
+        return []

petryaninyas/task2/strategies/pathfinding_strategy.py

@@ -0,0 +1,30 @@
+from abc import ABC, abstractmethod
+
+
+class PathFindingStrategy(ABC):
+    name = "Base"
+
+    def __init__(self):
+        self.visitedCount = 0
+
+    @abstractmethod
+    def findPath(self, maze, start, exitCell):
+        raise NotImplementedError
+
+    def _restore_path(self, parent, start, exitCell):
+        if exitCell is None or start is None:
+            return []
+
+        path = []
+        current = exitCell
+
+        while True:
+            path.append(current)
+            if current.x == start.x and current.y == start.y:
+                break
+            current = parent.get((current.x, current.y))
+            if current is None:
+                return []
+
+        path.reverse()
+        return path