939 lines
171 KiB
Plaintext
939 lines
171 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6dc3ad27",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Отчёт: Поиск выхода из лабиринта (объектно-ориентированная реализация с паттернами)\n",
|
||
"\n",
|
||
"- Описание задачи и выбранных паттернов (с диаграммой классов из Mermaid).\n",
|
||
"- Результаты экспериментов (таблицы, графики).\n",
|
||
"- Анализ эффективности алгоритмов и применимости паттернов.\n",
|
||
"- Выводы: как ООП и паттерны помогли сделать код гибким и расширяемым. Что было бы сложно изменить без них."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "59b2f0d3",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1. Описание задачи и выбранных паттернов\n",
|
||
"\n",
|
||
"Необходимо создать программу с применением паттернов ООП для решения задачи поиска из лабиринта. Для этого, подготовим схему из Mermaid, отражающую связи между объектами:\n",
|
||
"\n",
|
||
"```mermaid\n",
|
||
"classDiagram\n",
|
||
" class Maze {\n",
|
||
" -Cell[] cells\n",
|
||
" -int width, height\n",
|
||
" -Cell start\n",
|
||
" -Cell exit\n",
|
||
" +getCell(x,y): Cell\n",
|
||
" +getNeighbors(cell): List~Cell~\n",
|
||
" }\n",
|
||
" \n",
|
||
" class Cell {\n",
|
||
" -int x, y\n",
|
||
" -bool isWall\n",
|
||
" -bool isStart\n",
|
||
" -bool isExit\n",
|
||
" +isPassable(): bool\n",
|
||
" }\n",
|
||
" \n",
|
||
" class MazeBuilder {\n",
|
||
" <<interface>>\n",
|
||
" +buildFromFile(filename): Maze\n",
|
||
" }\n",
|
||
" \n",
|
||
" class TextFileMazeBuilder {\n",
|
||
" +buildFromFile(filename): Maze\n",
|
||
" }\n",
|
||
" \n",
|
||
" class PathFindingStrategy {\n",
|
||
" <<interface>>\n",
|
||
" +findPath(maze, start, exit): List~Cell~\n",
|
||
" }\n",
|
||
" \n",
|
||
" class BFS\n",
|
||
" class DFS\n",
|
||
" class AStar\n",
|
||
" \n",
|
||
" class SearchStats {\n",
|
||
" +timeMs: float\n",
|
||
" +visitedCells: int\n",
|
||
" +pathLength: int\n",
|
||
" }\n",
|
||
" \n",
|
||
" class MazeSolver {\n",
|
||
" -Maze maze\n",
|
||
" -PathFindingStrategy strategy\n",
|
||
" +setStrategy(strategy)\n",
|
||
" +solve(): SearchStats\n",
|
||
" }\n",
|
||
" \n",
|
||
" class Observer {\n",
|
||
" <<interface>>\n",
|
||
" +update(event)\n",
|
||
" }\n",
|
||
" \n",
|
||
" class ConsoleView {\n",
|
||
" +update(event)\n",
|
||
" +render(maze, player, path)\n",
|
||
" }\n",
|
||
" \n",
|
||
" MazeBuilder <|.. TextFileMazeBuilder\n",
|
||
" MazeBuilder --> Maze : creates\n",
|
||
" PathFindingStrategy <|.. BFS\n",
|
||
" PathFindingStrategy <|.. DFS\n",
|
||
" PathFindingStrategy <|.. AStarStrategy\n",
|
||
" MazeSolver --> PathFindingStrategy : uses\n",
|
||
" MazeSolver --> Maze : uses\n",
|
||
" Observer <|.. ConsoleView\n",
|
||
" MazeSolver --> Observer : notifies\n",
|
||
"```"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e268fdf0",
|
||
"metadata": {},
|
||
"source": [
|
||
"Builder — позволяет отделить создание сложного объекта от его представления. \n",
|
||
"Strategy — позволяет инкапсулировать разные алгоритмы поиска пути так, чтобы их можно было подставлять динамически. \n",
|
||
"Observer — позволяет обеспечить реакцию отображения на изменения состояния без жёсткой привязки. \n",
|
||
"\n",
|
||
"Код можно найти в репозитории: http://31.128.43.79:3000/musinaa/2026-rff_mp"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6ffa70d6",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 2. Практическая часть\n",
|
||
"# 2.0 Подготовим окружение"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "d457dda4",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:49.060242Z",
|
||
"start_time": "2026-09-03T22:09:49.026865Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"import sys\n",
|
||
"import os\n",
|
||
"sys.path.insert(0, os.path.abspath( '../'))\n",
|
||
"\n",
|
||
"from task2.mazeBuilder import TextFileMazeBuilder\n",
|
||
"from task2.tester import Tester"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 1
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "888f0e3c",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2.1 Данные для анализа\n",
|
||
"\n",
|
||
"В папке `mazeExamples` лежит несколько лабиринтов разных размеров: 5x5, 10x10, 50x50, 100x100. Для каждого лабиринта будем искать путь с помощью всех доступных алгоритмов поиска: BFS, DFS и A*. Измерения будем проводить 10 раз, а затем усреднённое значение записывать."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "22ac68eb",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:49.511112Z",
|
||
"start_time": "2026-09-03T22:09:49.074074Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"builder = TextFileMazeBuilder()\n",
|
||
"tester = Tester(builder, \"docs/data/task2/results.csv\")\n",
|
||
"tester.setTestingDirectory(\"task2/mazeExamples\")\n",
|
||
"tester.test()\n",
|
||
"tester.saveCSV()"
|
||
],
|
||
"outputs": [],
|
||
"execution_count": 2
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "27441b5f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 2.2 Данные по лабиринтам и графики"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "702c1844",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:49.771721Z",
|
||
"start_time": "2026-09-03T22:09:49.512480Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/task2/results.csv\")\n",
|
||
"for name in [\"5x5\", \"10x10\", \"50x50\", \"100x100\"]:\n",
|
||
" print(f\"\\n Лабиринт {name}\")\n",
|
||
" display(df[df[\"Лабиринт\"] == name].set_index(\"Алгоритм\"))"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт 5x5\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS 5x5 0.070421 8 7\n",
|
||
"DFS 5x5 0.019817 8 7\n",
|
||
"AStar 5x5 0.023188 8 7"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>5x5</td>\n",
|
||
" <td>0.070421</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>7</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>5x5</td>\n",
|
||
" <td>0.019817</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>7</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>5x5</td>\n",
|
||
" <td>0.023188</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>7</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт 10x10\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS 10x10 0.334054 53 23\n",
|
||
"DFS 10x10 0.079800 35 31\n",
|
||
"AStar 10x10 0.076250 23 23"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>10x10</td>\n",
|
||
" <td>0.334054</td>\n",
|
||
" <td>53</td>\n",
|
||
" <td>23</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>10x10</td>\n",
|
||
" <td>0.079800</td>\n",
|
||
" <td>35</td>\n",
|
||
" <td>31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>10x10</td>\n",
|
||
" <td>0.076250</td>\n",
|
||
" <td>23</td>\n",
|
||
" <td>23</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт 50x50\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS 50x50 3.734350 640 427\n",
|
||
"DFS 50x50 2.704262 995 435\n",
|
||
"AStar 50x50 1.655029 496 427"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>50x50</td>\n",
|
||
" <td>3.734350</td>\n",
|
||
" <td>640</td>\n",
|
||
" <td>427</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>50x50</td>\n",
|
||
" <td>2.704262</td>\n",
|
||
" <td>995</td>\n",
|
||
" <td>435</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>50x50</td>\n",
|
||
" <td>1.655029</td>\n",
|
||
" <td>496</td>\n",
|
||
" <td>427</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт 100x100\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS 100x100 12.758183 2495 1171\n",
|
||
"DFS 100x100 8.950550 3219 1243\n",
|
||
"AStar 100x100 4.468517 1286 1171"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>100x100</td>\n",
|
||
" <td>12.758183</td>\n",
|
||
" <td>2495</td>\n",
|
||
" <td>1171</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>100x100</td>\n",
|
||
" <td>8.950550</td>\n",
|
||
" <td>3219</td>\n",
|
||
" <td>1243</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>100x100</td>\n",
|
||
" <td>4.468517</td>\n",
|
||
" <td>1286</td>\n",
|
||
" <td>1171</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"execution_count": 3
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c7a1b48e",
|
||
"metadata": {},
|
||
"source": [
|
||
"Как видно из таблиц, A* рекордно низкое врея выполнения, а так же хорошая длинна пути, часто совпадающая с медленными алгоритмами вроде BFS. Примечательно, что у A* всегда посещает меньше клеток, чем остальные алгоритмы, сильнее всего это заметно на большом лабиринте в 100 клеток, где разрыв от BFS: примерно в 2 раза меньше посещённых клеток, с DFS и вовсе почти в 3 раза.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "d5078321",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:49.859382Z",
|
||
"start_time": "2026-09-03T22:09:49.774267Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"tester.setTestingDirectory(\"task2/mazeExamplesSpeical\")\n",
|
||
"tester.writefile = \"../docs/data/task2/results2.csv\"\n",
|
||
"tester.test()\n",
|
||
"tester.saveCSV()\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/task2/results2.csv\")\n",
|
||
"for name in [\"maze_25x25_wo_exit\", \"maze_25x25_empty\"]:\n",
|
||
" print(f\"\\n Лабиринт {name}\")\n",
|
||
" display(df[df[\"Лабиринт\"] == name].set_index(\"Алгоритм\"))"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт maze_25x25_wo_exit\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS maze_25x25_wo_exit 0.0 0 -1\n",
|
||
"DFS maze_25x25_wo_exit 0.0 0 -1\n",
|
||
"AStar maze_25x25_wo_exit 0.0 0 -1"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>maze_25x25_wo_exit</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>maze_25x25_wo_exit</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>maze_25x25_wo_exit</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>-1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт maze_25x25_empty\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS maze_25x25_empty 4.057983 625 49\n",
|
||
"DFS maze_25x25_empty 1.152621 625 337\n",
|
||
"AStar maze_25x25_empty 0.199208 49 49"
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>maze_25x25_empty</td>\n",
|
||
" <td>4.057983</td>\n",
|
||
" <td>625</td>\n",
|
||
" <td>49</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>maze_25x25_empty</td>\n",
|
||
" <td>1.152621</td>\n",
|
||
" <td>625</td>\n",
|
||
" <td>337</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>maze_25x25_empty</td>\n",
|
||
" <td>0.199208</td>\n",
|
||
" <td>49</td>\n",
|
||
" <td>49</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"execution_count": 4
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "41192153",
|
||
"metadata": {},
|
||
"source": [
|
||
"Из выходных данных видно, что A* всегда быстро находит путь в пустом лабиринте, в реальных системах это очень большой плюс. BFS и BFS, из-за своей особенности пытается найти кратчайший путь и ему приходится оббегать весь лабиринт в поисках оптимального пути. \n",
|
||
"\n",
|
||
"Для того, чтобы оценить различия в алгоритмах на обычных лабиринтах нагляднее, построем серию графиков."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "43409471",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:50.094662Z",
|
||
"start_time": "2026-09-03T22:09:49.872357Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/task2/results.csv\")\n",
|
||
"\n",
|
||
"size_order = [\"5x5\", \"10x10\", \"50x50\", \"100x100\"]\n",
|
||
"algo_order = [\"BFS\", \"DFS\", \"AStar\"]\n",
|
||
"colors = {\"BFS\": \"blue\", \"DFS\": \"green\", \"AStar\": \"orange\"}\n",
|
||
"\n",
|
||
"x = range(len(size_order))\n",
|
||
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
|
||
"\n",
|
||
"params = [\"Время (мс)\", \"Посещённые клетки\", \"Длинна пути\"]\n",
|
||
"titles = [\"Время выполнения (мс)\", \"Посещённые клетки\", \"Длина пути\"]\n",
|
||
"\n",
|
||
"for i, (param, title) in enumerate(zip(params, titles)):\n",
|
||
" ax = axes[i]\n",
|
||
" for algo in algo_order:\n",
|
||
" values = []\n",
|
||
" for s in size_order:\n",
|
||
" val = df[(df[\"Алгоритм\"] == algo) & (df[\"Лабиринт\"] == s)][param].values\n",
|
||
" if len(val) > 0:\n",
|
||
" values.append(val[0])\n",
|
||
" else:\n",
|
||
" values.append(None)\n",
|
||
" ax.plot(x, values, marker='o', label=algo, color=colors[algo], linewidth=2, markersize=6)\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(size_order)\n",
|
||
" ax.set_title(title)\n",
|
||
" ax.set_xlabel(\"Размер лабиринта\")\n",
|
||
" if i == 0:\n",
|
||
" ax.set_ylabel(\"Значение\")\n",
|
||
" ax.grid(True, linestyle='--', alpha=0.7)\n",
|
||
"\n",
|
||
"# Общая легенда сверху\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1500x400 with 3 Axes>"
|
||
],
|
||
"image/png": "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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"execution_count": 5
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c573b32b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3. Применение паттерна Observer\n",
|
||
"\n",
|
||
"Для того, чтобы продемонстрировать работу этого паттерна необходимо иметь возможность очищать вывод, но в Jupyter такой возможности нет. В терминале всё работает корректно."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "898e8536",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:50.116844Z",
|
||
"start_time": "2026-09-03T22:09:50.098009Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"from task2.consoleView import ConsoleView\n",
|
||
"from task2.mazeSolver import MazeSolver\n",
|
||
"from task2.strategyObjects.AStar import AStar\n",
|
||
"\n",
|
||
"maze = builder.buildFromFile(\"../task2/mazeExamples/25x25.txt\")\n",
|
||
"console = ConsoleView(maze)\n",
|
||
"solver = MazeSolver(AStar(), maze)\n",
|
||
"solver.attach(console)\n",
|
||
"console.render()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001B[H\u001B[2J\u001B[48;5;9m\u001B[38;5;7mS\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;12m\u001B[38;5;7mE\u001B[0m\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 6
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"id": "aa5a4c8c",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-09-03T22:09:50.143321Z",
|
||
"start_time": "2026-09-03T22:09:50.124383Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"# При нахождении решения рендер должен произойти автоматически\n",
|
||
"_ = solver.solve()"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001B[H\u001B[2J\u001B[48;5;9m\u001B[38;5;7mS\u001B[0m \u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;196m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;161m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;91m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\n",
|
||
"\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
"\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \n",
|
||
" \u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;126m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m \u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;56m\u001B[38;5;10m+\u001B[0m\u001B[48;5;7m\u001B[38;5;7m#\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;21m\u001B[38;5;10m+\u001B[0m\u001B[48;5;12m\u001B[38;5;7mE\u001B[0m\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "484b4ef6",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. Применимость паттернов\n",
|
||
"После выполнения работы можно оценить, насколько для этого помогли паттерны ООП. \n",
|
||
"Самый полезный паттерн - это конечно Strategy. Он позволил без лишней мороки провести замеры серийно, не используя каждый алгоритм вручную. \n",
|
||
" \n",
|
||
"Вторым по значимости я бы назвал Observer, он позволил сделать рендер лабиринта проще, однако сильнее всего оценить его получилось бы совместно с паттерном Commad и игроком с передвижениями. Его я не успел реализовать.\n",
|
||
" \n",
|
||
"Третий - Builder. В этой работе его преимущества оценить трудно из-за того, что использовался только один формат файла - plaintext. Однако, если понадобится искать пути в больших лабиринтах, то их придётся хрвнить в другом формате, например бинарном, и тогда преимщества паттерна Builder станут очевидны. \n",
|
||
" \n",
|
||
"Так же я создал класс-оркестратор MazeSolver, который помог перенести почти весь код тестов в одно место и удобно эти тесты использовать. Этот паттерн я так же считаю полезным. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d4026233",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5. Вывод\n",
|
||
"Применение объектно-ориентированного подхода и паттернов проектирования позволило разделить ответственность между независимыми модулями и обеспечить простоту внесения изменений. Если бы логика поиска пути, построения лабиринта и визуализации была сосредоточена в одном классе или реализована процедурно, любое расширение требовало бы переписывания значительной части кода."
|
||
]
|
||
}
|
||
],
|
||
"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": "3.14.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|