366 lines
130 KiB
Plaintext
366 lines
130 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a1dff6b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"import csv\n",
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"import matplotlib.pyplot as plt\n",
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"from statistics import mean\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "66bfd079",
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"metadata": {},
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"outputs": [],
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"source": [
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"from Builder.Builder import TextFileMazeBuilder\n",
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"from Core.Benchmark import RunBenchmark\n",
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"from MazeSolver.Solver import MazeSolver\n",
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"from Observer_Command.ConsoleView import ConsoleView\n",
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"from Strategies.BFS import BFS\n",
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"from Strategies.DFS import DFS\n",
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"from Strategies.AStar import AStar"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "50c7010d",
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"metadata": {},
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"outputs": [],
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"source": [
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"builder = TextFileMazeBuilder()\n",
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"\n",
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"mazes = {\n",
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" \"small\": builder.build_from_file(\"Mazes/small.txt\"),\n",
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" \"medium\": builder.build_from_file(\"Mazes/medium.txt\"),\n",
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" \"large\": builder.build_from_file(\"Mazes/large.txt\"),\n",
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" \"empty\": builder.build_from_file(\"Mazes/empty.txt\"),\n",
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" \"no_exit\": builder.build_from_file(\"Mazes/no_exit.txt\"),\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "7326fe7d",
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"metadata": {},
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"outputs": [],
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"source": [
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"strategies = {\n",
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" \"BFS\": BFS(),\n",
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" \"DFS\": DFS(),\n",
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" \"A*\": AStar()\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "8d47b4cf",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Лабиринт: small\n",
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"Testing BFS\n",
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"BFS: time=0.21313328579708468 ms | visited=58.0 | path_len=15.0\n",
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"Testing DFS\n",
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"DFS: time=0.0768381428965118 ms | visited=31.0 | path_len=19.0\n",
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"Testing A*\n",
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"A*: time=0.2827478572596322 ms | visited=57.0 | path_len=15.0\n",
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"Лабиринт: medium\n",
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"Testing BFS\n",
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"BFS: time=5.220513428477196 ms | visited=1263.0 | path_len=173.0\n",
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"Testing DFS\n",
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"DFS: time=4.18784342861857 ms | visited=1229.0 | path_len=173.0\n",
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"Testing A*\n",
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"A*: time=4.219951571420617 ms | visited=806.0 | path_len=173.0\n",
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"Лабиринт: large\n",
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"Testing BFS\n",
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"BFS: time=11.833781999874711 ms | visited=3918.0 | path_len=269.0\n",
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"Testing DFS\n",
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"DFS: time=5.629428999977141 ms | visited=1905.0 | path_len=269.0\n",
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"Testing A*\n",
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"A*: time=10.02670385716036 ms | visited=2040.0 | path_len=269.0\n",
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"Лабиринт: empty\n",
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"Testing BFS\n",
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"BFS: time=0.19871557131929357 ms | visited=64.0 | path_len=15.0\n",
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"Testing DFS\n",
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"DFS: time=0.13947814282541263 ms | visited=64.0 | path_len=29.0\n",
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"Testing A*\n",
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"A*: time=0.28600042846197277 ms | visited=63.0 | path_len=15.0\n",
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"Лабиринт: no_exit\n",
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"Testing BFS\n",
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"BFS: time=0.18080571427552578 ms | visited=58.0 | path_len=0.0\n",
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"Testing DFS\n",
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"DFS: time=0.19448514272621 ms | visited=58.0 | path_len=0.0\n",
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"Testing A*\n"
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]
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},
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{
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"ename": "AttributeError",
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"evalue": "'NoneType' object has no attribute 'x'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[5], line 19\u001b[0m\n\u001b[1;32m 16\u001b[0m solver \u001b[38;5;241m=\u001b[39m MazeSolver(maze, strategy)\n\u001b[1;32m 18\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m---> 19\u001b[0m stats, path \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 22\u001b[0m elapsed_ms \u001b[38;5;241m=\u001b[39m (end_time \u001b[38;5;241m-\u001b[39m start_time) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m\n",
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"File \u001b[0;32m~/2026-rff_mp/pomelovsd/ExitMaze/MazeSolver/Solver.py:16\u001b[0m, in \u001b[0;36mMazeSolver.solve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msolve\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 14\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m---> 16\u001b[0m path, visited \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfindPath\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaze\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaze\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaze\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m SearchStats((end\u001b[38;5;241m-\u001b[39mstart)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1000\u001b[39m, visited, \u001b[38;5;28mlen\u001b[39m(path)), path\n",
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"File \u001b[0;32m~/2026-rff_mp/pomelovsd/ExitMaze/Strategies/AStar.py:33\u001b[0m, in \u001b[0;36mAStar.findPath\u001b[0;34m(self, maze, start, exit)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m g \u001b[38;5;129;01mor\u001b[39;00m tentative \u001b[38;5;241m<\u001b[39m g[n]:\n\u001b[1;32m 32\u001b[0m g[n] \u001b[38;5;241m=\u001b[39m tentative\n\u001b[0;32m---> 33\u001b[0m priority \u001b[38;5;241m=\u001b[39m tentative \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheuristic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m heapq\u001b[38;5;241m.\u001b[39mheappush(heap, (priority, counter, n))\n\u001b[1;32m 35\u001b[0m counter \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
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"File \u001b[0;32m~/2026-rff_mp/pomelovsd/ExitMaze/Strategies/AStar.py:8\u001b[0m, in \u001b[0;36mAStar.heuristic\u001b[0;34m(self, a, b)\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mheuristic\u001b[39m(\u001b[38;5;28mself\u001b[39m, a, b):\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mabs\u001b[39m(a\u001b[38;5;241m.\u001b[39mx \u001b[38;5;241m-\u001b[39m \u001b[43mb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mabs\u001b[39m(a\u001b[38;5;241m.\u001b[39my \u001b[38;5;241m-\u001b[39m b\u001b[38;5;241m.\u001b[39my)\n",
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"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'x'"
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]
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}
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],
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"source": [
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"results = []\n",
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"\n",
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"N = 7\n",
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"\n",
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"for maze_name, maze in mazes.items():\n",
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" print(f\"Лабиринт: {maze_name}\")\n",
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"\n",
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" for strategy_name, strategy in strategies.items():\n",
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" total_time = 0\n",
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" total_visited = 0\n",
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" total_path_len = 0\n",
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"\n",
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" print(f\"Testing {strategy_name}\")\n",
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"\n",
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" for _ in range(N):\n",
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" solver = MazeSolver(maze, strategy)\n",
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"\n",
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" start_time = time.perf_counter()\n",
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" stats, path = solver.solve()\n",
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" end_time = time.perf_counter()\n",
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"\n",
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" elapsed_ms = (end_time - start_time) * 1000\n",
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"\n",
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" total_time += elapsed_ms\n",
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" total_visited += stats.visited_cells \n",
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" total_path_len += len(path) \n",
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"\n",
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" avg_time = total_time / N\n",
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" avg_visited = total_visited / N\n",
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" avg_path_len = total_path_len / N\n",
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"\n",
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" results.append({\n",
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" \"maze\": maze_name,\n",
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" \"strategy\": strategy_name,\n",
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" \"time_ms\": round(avg_time, 3),\n",
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" \"visited_cells\": int(avg_visited), \n",
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" \"path_length\": int(avg_path_len), \n",
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" })\n",
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"\n",
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" print(\n",
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" f\"{strategy_name}: \"\n",
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" f\"time={avg_time} ms | \"\n",
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" f\"visited={avg_visited} | \"\n",
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" f\"path_len={avg_path_len}\"\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "347cb7be",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"CSV saved: results.csv\n"
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]
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}
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],
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"source": [
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"csv_file = \"results.csv\"\n",
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"\n",
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"with open(csv_file, \"w\", newline=\"\", encoding=\"utf-8\") as file:\n",
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"\n",
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" writer = csv.writer(file)\n",
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"\n",
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" writer.writerow([\n",
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" \"maze\",\n",
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" \"strategy\",\n",
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" \"time_ms\",\n",
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" \"visited_cells\",\n",
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" \"path_length\"\n",
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" ])\n",
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"\n",
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" for row in results:\n",
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" writer.writerow([\n",
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" row[\"maze\"],\n",
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" row[\"strategy\"],\n",
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" row[\"time_ms\"],\n",
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" row[\"visited_cells\"],\n",
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" row[\"path_length\"]\n",
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" ])\n",
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"\n",
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"print(f\"\\nCSV saved: {csv_file}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4b6fb0b0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Результаты:\n",
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"small | BFS | 0.029 ms | 17 visited \n",
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"small | DFS | 0.018 ms | 14 visited \n",
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"small | A* | 0.033 ms | 16 visited \n",
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"medium | BFS | 0.018 ms | 10 visited \n",
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"medium | DFS | 0.019 ms | 10 visited \n",
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"medium | A* | 0.024 ms | 10 visited \n",
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"large | BFS | 0.292 ms | 198 visited \n",
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"large | DFS | 0.201 ms | 198 visited \n",
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"large | A* | 0.277 ms | 198 visited \n",
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"empty | BFS | 0.014 ms | 10 visited \n",
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"empty | DFS | 0.015 ms | 10 visited \n",
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"empty | A* | 0.021 ms | 9 visited \n",
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"no_exit | BFS | 0.007 ms | 1 visited \n",
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"no_exit | DFS | 0.006 ms | 1 visited \n",
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"no_exit | A* | 0.006 ms | 1 visited \n"
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]
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}
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],
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"source": [
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"\n",
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"print(\"Результаты:\")\n",
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"\n",
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"for row in results:\n",
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"\n",
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" print(\n",
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" f\"{row['maze']} | \"\n",
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" f\"{row['strategy']} | \"\n",
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" f\"{row['time_ms']} ms | \"\n",
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" f\"{row['visited_cells']} visited \"\n",
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" )\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "95a41c2e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1200x1400 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"maze_names = [\"small\", \"medium\", \"large\", \"empty\", \"no_exit\"]\n",
|
|
"strategies = [\"BFS\", \"DFS\", \"A*\"]\n",
|
|
"\n",
|
|
"colors = {\"BFS\": \"#EEDC82\", \"DFS\": \"#88D94C\", \"A*\": \"#FF43A4\"}\n",
|
|
"\n",
|
|
"# Подготовим данные для графиков\n",
|
|
"time_data = {maze: {s: None for s in strategies} for maze in maze_names}\n",
|
|
"visited_data = {maze: {s: None for s in strategies} for maze in maze_names}\n",
|
|
"path_data = {maze: {s: None for s in strategies} for maze in maze_names}\n",
|
|
"\n",
|
|
"for row in results:\n",
|
|
" maze = row[\"maze\"]\n",
|
|
" strat = row[\"strategy\"]\n",
|
|
" time_data[maze][strat] = row[\"time_ms\"]\n",
|
|
" visited_data[maze][strat] = row[\"visited_cells\"]\n",
|
|
" path_data[maze][strat] = row[\"path_length\"]\n",
|
|
"\n",
|
|
"fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 14))\n",
|
|
"\n",
|
|
"x = np.arange(len(maze_names))\n",
|
|
"width = 0.25 \n",
|
|
"multiplier = 0\n",
|
|
"\n",
|
|
"for strategy in strategies:\n",
|
|
" values = [time_data[maze][strategy] for maze in maze_names]\n",
|
|
" offset = width * multiplier\n",
|
|
" ax1.bar(x + offset, values, width, label=strategy, color=colors[strategy])\n",
|
|
" multiplier += 1\n",
|
|
"\n",
|
|
"ax1.set_xticks(x + width, maze_names)\n",
|
|
"ax1.set_ylabel(\"Время (мс)\")\n",
|
|
"ax1.set_title(\"Сравнение времени выполнения алгоритмов\")\n",
|
|
"ax1.legend()\n",
|
|
"ax1.grid(axis='y', alpha=0.5, linestyle='--')\n",
|
|
"\n",
|
|
"multiplier = 0\n",
|
|
"for strategy in strategies:\n",
|
|
" values = [visited_data[maze][strategy] for maze in maze_names]\n",
|
|
" offset = width * multiplier\n",
|
|
" ax2.bar(x + offset, values, width, label=strategy, color=colors[strategy])\n",
|
|
" multiplier += 1\n",
|
|
"\n",
|
|
"ax2.set_xticks(x + width, maze_names)\n",
|
|
"ax2.set_ylabel(\"Количество исследованных клеток\")\n",
|
|
"ax2.set_title(\"Сравнение количества исследованных клеток\")\n",
|
|
"ax2.legend()\n",
|
|
"ax2.grid(axis='y', alpha=0.5, linestyle='--')\n",
|
|
"\n",
|
|
"multiplier = 0\n",
|
|
"for strategy in strategies:\n",
|
|
" values = [path_data[maze][strategy] for maze in maze_names]\n",
|
|
" offset = width * multiplier\n",
|
|
" ax3.bar(x + offset, values, width, label=strategy, color=colors[strategy])\n",
|
|
" multiplier += 1\n",
|
|
"\n",
|
|
"ax3.set_xticks(x + width, maze_names)\n",
|
|
"ax3.set_ylabel(\"Длина пути\")\n",
|
|
"ax3.set_title(\"Сравнение длины найденного пути\")\n",
|
|
"ax3.legend()\n",
|
|
"ax3.grid(axis='y', alpha=0.5, linestyle='--')\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.savefig('analysis.png')\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|