forked from UNN/2026-rff_mp
285 lines
10 KiB
Python
285 lines
10 KiB
Python
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import csv
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import time
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import os
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import random
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from collections import deque
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import heapq
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import matplotlib.pyplot as plt
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import pandas as pd
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class Cell:
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def __init__(self, x, y):
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self.x = x
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self.y = y
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self.is_wall = False
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self.is_start = False
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self.is_exit = False
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def isPassable(self):
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return not self.is_wall
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class Maze:
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def __init__(self, width, height):
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self.width = width
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self.height = height
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self.cells = []
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self.start = None
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self.exit = None
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def getCell(self, x, y):
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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 getNeighbors(self, cell):
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neighbors = []
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for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
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neighbor = self.getCell(cell.x + dx, cell.y + dy)
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if neighbor and neighbor.isPassable():
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neighbors.append(neighbor)
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return neighbors
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class MazeBuilder:
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def buildFromFile(self, filename):
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raise NotImplementedError
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class TextFileMazeBuilder(MazeBuilder):
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def buildFromFile(self, filename):
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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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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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maze.cells = [[Cell(x, y) for x in range(width)] for y in range(height)]
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for y, line in enumerate(lines):
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for x, char in enumerate(line):
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cell = maze.cells[y][x]
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if char == '#':
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cell.is_wall = True
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elif char == 'S':
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cell.is_start = True
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maze.start = cell
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elif char == 'E':
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cell.is_exit = True
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maze.exit = cell
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if maze.start is None or maze.exit is None:
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raise ValueError("В файле должны быть символы S и E")
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return maze
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class PathFindingStrategy:
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def findPath(self, maze, start, exit):
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raise NotImplementedError
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class BFSStrategy(PathFindingStrategy):
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def findPath(self, maze, start, exit):
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queue = deque([start])
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came_from = {start: None}
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visited = set([start])
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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.getNeighbors(current):
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if neighbor not in visited:
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visited.add(neighbor)
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queue.append(neighbor)
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came_from[neighbor] = current
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path = self._reconstruct_path(came_from, exit)
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return path, len(visited)
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def _reconstruct_path(self, came_from, exit):
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path = []
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current = exit
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while current is not None:
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path.append(current)
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current = came_from.get(current)
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path.reverse()
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return path if path and path[0] == came_from.get(exit) or path[0] == exit else []
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class DFSStrategy(PathFindingStrategy):
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def findPath(self, maze, start, exit):
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stack = [start]
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came_from = {start: None}
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visited = set([start])
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while stack:
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current = stack.pop()
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if current == exit:
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break
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for neighbor in maze.getNeighbors(current):
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if neighbor not in visited:
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visited.add(neighbor)
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stack.append(neighbor)
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came_from[neighbor] = current
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path = self._reconstruct_path(came_from, exit)
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return path, len(visited)
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def _reconstruct_path(self, came_from, exit):
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path = []
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current = exit
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while current is not None:
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path.append(current)
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current = came_from.get(current)
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path.reverse()
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return path
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class AStarStrategy(PathFindingStrategy):
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def heuristic(self, a, b):
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return abs(a.x - b.x) + abs(a.y - b.y)
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def findPath(self, maze, start, exit):
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open_set = []
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counter = 0
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heapq.heappush(open_set, (0, counter, start))
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came_from = {start: None}
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g_score = {start: 0}
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visited = set()
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while open_set:
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_, _, current = heapq.heappop(open_set)
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if current in visited:
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continue
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visited.add(current)
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if current == exit:
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break
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for neighbor in maze.getNeighbors(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_score = tentative_g + self.heuristic(neighbor, exit)
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counter += 1
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heapq.heappush(open_set, (f_score, counter, neighbor))
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path = self._reconstruct_path(came_from, exit)
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return path, len(visited)
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def _reconstruct_path(self, came_from, exit):
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path = []
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current = exit
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while current is not None:
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path.append(current)
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current = came_from.get(current)
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path.reverse()
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return path
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class SearchStats:
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def __init__(self, time_ms, visited_cells, path_length):
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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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class MazeSolver:
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def __init__(self, maze=None, strategy=None):
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self.maze = maze
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self.strategy = strategy
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def setStrategy(self, strategy):
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self.strategy = strategy
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def solve(self):
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if not self.maze or not self.strategy:
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return None
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start_time = time.perf_counter()
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path, visited_count = self.strategy.findPath(self.maze, self.maze.start, self.maze.exit)
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end_time = time.perf_counter()
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time_ms = (end_time - start_time) * 1000
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path_length = len(path) if path and path[-1] == self.maze.exit else 0
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return SearchStats(round(time_ms, 4), visited_count, path_length)
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def create_maze_with_walls(size, wall_probability=0.3):
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maze = Maze(size, size)
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maze.cells = [[Cell(x, y) for x in range(size)] for y in range(size)]
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for y in range(size):
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for x in range(size):
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if random.random() < wall_probability:
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maze.cells[y][x].is_wall = True
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maze.start = maze.cells[0][0]
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maze.exit = maze.cells[size-1][size-1]
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maze.start.is_start = True
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maze.exit.is_exit = True
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maze.start.is_wall = False
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maze.exit.is_wall = False
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return maze
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def create_empty_maze(size):
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maze = Maze(size, size)
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maze.cells = [[Cell(x, y) for x in range(size)] for y in range(size)]
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maze.start = maze.cells[0][0]
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maze.exit = maze.cells[size-1][size-1]
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maze.start.is_start = True
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maze.exit.is_exit = True
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return maze
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def create_no_exit_maze(size, wall_probability=0.3):
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maze = create_maze_with_walls(size, wall_probability)
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maze.exit.is_wall = True
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return maze
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def run_experiment():
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maze_configs = {
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"10x10_simple": {"size": 10, "type": "normal", "wall_prob": 0.1},
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"50x50_with_deadends": {"size": 50, "type": "normal", "wall_prob": 0.3},
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"100x100_complex": {"size": 100, "type": "normal", "wall_prob": 0.35},
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"empty": {"size": 30, "type": "empty"},
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"no_exit": {"size": 30, "type": "no_exit", "wall_prob": 0.3},
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}
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strategies = {
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"BFS": BFSStrategy(),
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"DFS": DFSStrategy(),
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"AStar": AStarStrategy()
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}
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results = []
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for maze_name, config in maze_configs.items():
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size = config["size"]
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maze_type = config["type"]
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if maze_type == "empty":
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maze = create_empty_maze(size)
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elif maze_type == "no_exit":
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maze = create_no_exit_maze(size, config.get("wall_prob", 0.3))
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else:
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maze = create_maze_with_walls(size, config.get("wall_prob", 0.3))
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for strat_name, strategy in strategies.items():
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solver = MazeSolver(maze, strategy)
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times, visited_list, lengths = [], [], []
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for _ in range(7):
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stats = solver.solve()
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times.append(stats.time_ms)
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visited_list.append(stats.visited_cells)
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lengths.append(stats.path_length)
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avg_time = sum(times) / len(times)
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avg_visited = sum(visited_list) / len(visited_list)
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avg_length = sum(lengths) / len(lengths)
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results.append([
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maze_name, strat_name,
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round(avg_time, 4),
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int(avg_visited),
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int(avg_length)
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])
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os.makedirs("results", exist_ok=True)
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csv_path = "results/results.csv"
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with open(csv_path, "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["лабиринт", "стратегия", "время_мс", "посещено_клеток", "длина_пути"])
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writer.writerows(results)
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df = pd.read_csv(csv_path)
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plt.figure(figsize=(12, 6))
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for strat in df["стратегия"].unique():
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subset = df[df["стратегия"] == strat]
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plt.plot(subset["лабиринт"], subset["время_мс"], marker='o', label=strat)
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plt.title("Сравнение времени работы алгоритмов")
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plt.xlabel("Лабиринт")
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plt.ylabel("Время (мс)")
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plt.legend()
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plt.grid(True)
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.savefig("results/time_comparison.png")
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plt.close()
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plt.figure(figsize=(12, 6))
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for strat in df["стратегия"].unique():
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subset = df[df["стратегия"] == strat]
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plt.plot(subset["лабиринт"], subset["посещено_клеток"], marker='o', label=strat)
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plt.title("Количество посещённых клеток")
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plt.xlabel("Лабиринт")
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plt.ylabel("Посещено клеток")
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plt.legend()
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plt.grid(True)
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.savefig("results/visited_comparison.png")
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plt.close()
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if __name__ == "__main__":
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run_experiment()
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