594 lines
25 KiB
Plaintext
594 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3701053f-41f9-464d-a44f-cbda38c1caf7",
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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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"ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ СТРУКТУР ДАННЫХ\n",
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"Телефонный справочник - 10000 записей\n",
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"\n",
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" Результаты будут сохранены в: C:\\Users\\weron\\experiment_results.csv\n",
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" Сгенерировано 10000 записей\n",
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"\n",
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"2. Тестирование режима: случайный\n",
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"\n",
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" LINKED_LIST:\n",
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" Вставка 10000 записей\n",
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" Время: 9.2970 сек (среднее)\n",
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" Поиск 110 записей\n",
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" Время: 0.0946 сек (среднее)\n",
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" Удаление 50 записей\n",
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" Время: 0.0000 сек (среднее)\n",
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"\n",
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" HASH_TABLE:\n",
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" Вставка 10000 записей\n",
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" Время: 0.4810 сек (среднее)\n",
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" Поиск 110 записей\n",
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" Время: 0.0039 сек (среднее)\n",
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" Удаление 50 записей\n",
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" Время: 0.0030 сек (среднее)\n",
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"\n",
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" BST:\n",
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" Вставка 10000 записей\n",
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" Время: 0.0490 сек (среднее)\n",
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" Поиск 110 записей\n",
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" Время: 0.0004 сек (среднее)\n",
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" Удаление 50 записей\n",
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" Время: 0.1141 сек (среднее)\n",
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"\n",
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"2. Тестирование режима: отсортированный\n",
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"\n",
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" LINKED_LIST:\n",
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" Вставка 10000 записей\n",
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" Время: 9.2504 сек (среднее)\n",
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" Поиск 110 записей\n",
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" Время: 0.1115 сек (среднее)\n",
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" Удаление 50 записей\n",
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" Время: 0.0000 сек (среднее)\n",
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"\n",
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" HASH_TABLE:\n",
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" Вставка 10000 записей\n",
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" Время: 0.4928 сек (среднее)\n",
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" Поиск 110 записей\n",
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" Время: 0.0061 сек (среднее)\n",
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" Удаление 50 записей\n",
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" Время: 0.0029 сек (среднее)\n",
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"\n",
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" BST:\n",
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" Вставка 10000 записей\n",
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" Время: 22.1688 сек (среднее)\n",
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" Поиск 110 записей\n",
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" Время: 0.1297 сек (среднее)\n",
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" Удаление 50 записей\n",
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" Время: 0.1115 сек (среднее)\n",
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"\n",
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"3. Сохранение результатов\n",
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" Результаты сохранены в: C:\\Users\\weron\\experiment_results.csv\n",
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"СВОДНАЯ ТАБЛИЦА РЕЗУЛЬТАТОВ\n",
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"Структура Режим Операция Среднее время (сек) \n",
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"linked_list случайный вставка 9.296975 \n",
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"linked_list случайный поиск 0.094569 \n",
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"linked_list случайный удаление 0.000022 \n",
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"hash_table случайный вставка 0.481027 \n",
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"hash_table случайный поиск 0.003911 \n",
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"hash_table случайный удаление 0.003046 \n",
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"bst случайный вставка 0.049011 \n",
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"bst случайный поиск 0.000368 \n",
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"bst случайный удаление 0.114051 \n",
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"linked_list отсортированный вставка 9.250436 \n",
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"linked_list отсортированный поиск 0.111506 \n",
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"linked_list отсортированный удаление 0.000018 \n",
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"hash_table отсортированный вставка 0.492765 \n",
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"hash_table отсортированный поиск 0.006051 \n",
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"hash_table отсортированный удаление 0.002869 \n",
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"bst отсортированный вставка 22.168779 \n",
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"bst отсортированный поиск 0.129713 \n",
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"bst отсортированный удаление 0.111534 \n",
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"\n",
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"4. Построение графиков\n",
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" Графики сохранены в: C:\\Users\\weron\\performance_graphs.png\n",
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"ЭКСПЕРИМЕНТ ЗАВЕРШЕН УСПЕШНО!\n",
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"\n",
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" СОЗДАННЫЕ ФАЙЛЫ:\n",
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" Данные: C:\\Users\\weron\\experiment_results.csv\n",
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" Графики: C:\\Users\\weron\\performance_graphs.png\n"
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]
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}
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],
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"source": [
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"import time\n",
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"import random\n",
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"import csv\n",
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"import os\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import sys\n",
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"sys.setrecursionlimit(20000) \n",
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"#Linked List Phone Book:\n",
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"\n",
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"def ll_insert(head, name, phone):\n",
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" new_node = {'name': name, 'phone' : phone, 'next': None}\n",
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" if head is None:\n",
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" return new_node\n",
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" if head['name'] == name:\n",
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" head['phone'] = phone\n",
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" return head\n",
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" current = head \n",
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" while current['next'] is not None:\n",
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" if current['next']['name'] == name:\n",
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" current['next']['phone'] = phone\n",
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" return head\n",
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" current = current['next']\n",
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" current['next'] = new_node\n",
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" return head \n",
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"def ll_find(head, name):\n",
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" current = head\n",
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" while current != None:\n",
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" if current['name']==name:\n",
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" return current['phone']\n",
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" current = current['next']\n",
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" return None\n",
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"def ll_delete(head, name):\n",
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" if head is None:\n",
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" return None\n",
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" if head['name'] == name:\n",
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" return head['next']\n",
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" current = head \n",
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" while current['next'] is not None:\n",
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" if current['next']['name'] == name:\n",
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" current['next']==current['next']['next']\n",
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" return head\n",
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" current=current['next']\n",
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" return head\n",
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"def ll_list_all(head): \n",
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" records= []\n",
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" current = head \n",
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" while current is not None:\n",
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" records.append({'name': current['name'], 'phone': current['phone']})\n",
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" current = current['next']\n",
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" records.sort(key=lambda x: x['name'])\n",
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" return records \n",
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"def ll_print_all(head):\n",
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" records = ll_list_all(head)\n",
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" for record in records:\n",
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" print(f\"{record['name']}: {record['phone']}\")\n",
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"\n",
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"#Hash Function:\n",
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"\n",
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"def hash_function(name, table_size):\n",
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" return sum(ord(c) for c in name) % table_size\n",
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"\n",
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"\n",
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"def ht_create(size=1000):\n",
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" return [None] * size\n",
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"\n",
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"\n",
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"def ht_insert(buckets, name, phone):\n",
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" size = len(buckets)\n",
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" index = hash_function(name, size)\n",
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" buckets[index] = ll_insert(buckets[index], name, phone)\n",
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"\n",
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"\n",
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"def ht_find(buckets, name):\n",
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" size = len(buckets)\n",
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" index = hash_function(name, size)\n",
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" return ll_find(buckets[index], name)\n",
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"\n",
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"\n",
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"def ht_delete(buckets, name):\n",
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" size = len(buckets)\n",
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" index = hash_function(name, size)\n",
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" buckets[index] = ll_delete(buckets[index], name)\n",
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"\n",
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"\n",
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"def ht_list_all(buckets):\n",
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" records = []\n",
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" for bucket in buckets:\n",
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" current = bucket\n",
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" while current is not None:\n",
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" records.append((current['name'], current['phone']))\n",
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" current = current['next']\n",
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" records.sort(key=lambda x: x[0])\n",
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" return records\n",
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"\n",
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"#Tree function:\n",
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"\n",
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"def bst_insert(root, name, phone):\n",
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" \n",
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" if root is None:\n",
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" return {'name': name, 'phone': phone, 'left': None, 'right': None}\n",
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" \n",
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" if name < root['name']:\n",
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" root['left'] = bst_insert(root['left'], name, phone)\n",
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" elif name > root['name']:\n",
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" root['right'] = bst_insert(root['right'], name, phone)\n",
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" else:\n",
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" root['phone'] = phone\n",
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" \n",
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" return root\n",
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"\n",
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"\n",
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"def bst_find(root, name):\n",
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" \n",
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" current = root\n",
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" while current is not None:\n",
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" if name == current['name']:\n",
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" return current['phone']\n",
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" elif name < current['name']:\n",
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" current = current['left']\n",
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" else:\n",
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" current = current['right']\n",
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" return None\n",
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"\n",
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"\n",
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"def bst_find_min(node):\n",
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" \n",
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" current = node\n",
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" while current['left'] is not None:\n",
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" current = current['left']\n",
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" return current\n",
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"\n",
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"\n",
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"def bst_delete(root, name):\n",
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" \n",
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" if root is None:\n",
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" return None\n",
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" \n",
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" if name < root['name']:\n",
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" root['left'] = bst_delete(root['left'], name)\n",
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" elif name > root['name']:\n",
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" root['right'] = bst_delete(root['right'], name)\n",
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" else:\n",
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" if root['left'] is None:\n",
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" return root['right']\n",
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" elif root['right'] is None:\n",
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" return root['left']\n",
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" \n",
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" min_node = bst_find_min(root['right'])\n",
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" root['name'] = min_node['name']\n",
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" root['phone'] = min_node['phone']\n",
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" root['right'] = bst_delete(root['right'], min_node['name'])\n",
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" \n",
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" return root\n",
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"\n",
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"\n",
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"def bst_list_all(root):\n",
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" \n",
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" records = []\n",
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" \n",
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" def inorder_traversal(node):\n",
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" if node is not None:\n",
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" inorder_traversal(node['left'])\n",
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" records.append((node['name'], node['phone']))\n",
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" inorder_traversal(node['right'])\n",
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" \n",
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" inorder_traversal(root)\n",
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" return records\n",
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"\n",
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"#Experemental part \n",
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"#1. Test data generation \n",
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"\n",
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"def generate_records(count=10000):\n",
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" \n",
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" records = []\n",
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" for i in range(count):\n",
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" name = f\"User_{i:05d}\"\n",
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" phone = f\"+7-{random.randint(100,999)}-{random.randint(100,999)}-{random.randint(1000,9999)}\"\n",
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" records.append((name, phone))\n",
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" \n",
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" shuffled = records.copy()\n",
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" random.shuffle(shuffled)\n",
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" sorted_records = sorted(records, key=lambda x: x[0])\n",
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" \n",
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" return shuffled, sorted_records\n",
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"\n",
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"#2. Timing\n",
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"\n",
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"def measure_insertion(structure_name, records):\n",
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" \n",
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" times = []\n",
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" filled_structure = None\n",
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" \n",
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" for run in range(5):\n",
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" if structure_name == \"linked_list\":\n",
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" structure = None\n",
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" elif structure_name == \"hash_table\":\n",
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" structure = ht_create(1000)\n",
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" elif structure_name == \"bst\":\n",
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" structure = None\n",
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" \n",
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" start = time.perf_counter()\n",
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" \n",
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" for name, phone in records:\n",
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" if structure_name == \"linked_list\":\n",
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" structure = ll_insert(structure, name, phone)\n",
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" elif structure_name == \"hash_table\":\n",
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" ht_insert(structure, name, phone)\n",
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" elif structure_name == \"bst\":\n",
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" structure = bst_insert(structure, name, phone)\n",
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" \n",
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" end = time.perf_counter()\n",
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" times.append(end - start)\n",
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" \n",
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" if run == 4:\n",
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" filled_structure = structure\n",
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" \n",
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" return times, filled_structure\n",
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"\n",
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"\n",
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"def measure_search(structure_name, structure, search_names):\n",
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" \n",
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" times = []\n",
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" \n",
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" for run in range(5):\n",
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" start = time.perf_counter()\n",
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" \n",
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" for name in search_names:\n",
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" if structure_name == \"linked_list\":\n",
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" ll_find(structure, name)\n",
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" elif structure_name == \"hash_table\":\n",
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" ht_find(structure, name)\n",
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" elif structure_name == \"bst\":\n",
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" bst_find(structure, name)\n",
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" \n",
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" end = time.perf_counter()\n",
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" times.append(end - start)\n",
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" \n",
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" return times\n",
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"\n",
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"\n",
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"def measure_deletion(structure_name, original_structure, delete_names):\n",
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" \n",
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" times = []\n",
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" \n",
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" for run in range(5):\n",
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" if structure_name == \"linked_list\":\n",
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" all_records = ll_list_all(original_structure)\n",
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" test_structure = None\n",
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" for name, phone in all_records:\n",
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" test_structure = ll_insert(test_structure, name, phone)\n",
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" \n",
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" elif structure_name == \"hash_table\":\n",
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" all_records = ht_list_all(original_structure)\n",
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" test_structure = ht_create(1000)\n",
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" for name, phone in all_records:\n",
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" ht_insert(test_structure, name, phone)\n",
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" \n",
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" elif structure_name == \"bst\":\n",
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" all_records = bst_list_all(original_structure)\n",
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" test_structure = None\n",
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" for name, phone in all_records:\n",
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" test_structure = bst_insert(test_structure, name, phone)\n",
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" \n",
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" start = time.perf_counter()\n",
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" \n",
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" for name in delete_names:\n",
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" if structure_name == \"linked_list\":\n",
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" test_structure = ll_delete(test_structure, name)\n",
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" elif structure_name == \"hash_table\":\n",
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" ht_delete(test_structure, name)\n",
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" elif structure_name == \"bst\":\n",
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" test_structure = bst_delete(test_structure, name)\n",
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" \n",
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" end = time.perf_counter()\n",
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" times.append(end - start)\n",
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" \n",
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" return times\n",
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"#3. Launch and save results\n",
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"\n",
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"def run_experiment():\n",
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" \n",
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" current_dir = os.getcwd()\n",
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" docs_dir = current_dir\n",
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" csv_file = os.path.join(docs_dir, \"experiment_results.csv\")\n",
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" \n",
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" print(\"ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ СТРУКТУР ДАННЫХ\")\n",
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" print(\"Телефонный справочник - 10000 записей\")\n",
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" print(f\"\\n Результаты будут сохранены в: {csv_file}\")\n",
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" \n",
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" shuffled_records, sorted_records = generate_records(10000)\n",
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" print(f\" Сгенерировано 10000 записей\")\n",
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" \n",
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" existing_names = [shuffled_records[i][0] for i in random.sample(range(10000), 100)]\n",
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" nonexisting_names = [f\"NotExist_{i}\" for i in range(10)]\n",
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" search_names = existing_names + nonexisting_names\n",
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" delete_names = [shuffled_records[i][0] for i in random.sample(range(10000), 50)]\n",
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" \n",
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" results = [[\"Структура\", \"Режим\", \"Операция\", \n",
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" \"Замер1(с)\", \"Замер2(с)\", \"Замер3(с)\", \"Замер4(с)\", \"Замер5(с)\", \n",
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" \"Среднее(с)\"]]\n",
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" \n",
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" for mode_name, records in [(\"случайный\", shuffled_records), \n",
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" (\"отсортированный\", sorted_records)]:\n",
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" \n",
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" print(f\"\\n2. Тестирование режима: {mode_name}\")\n",
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" \n",
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" for struct_name in [\"linked_list\", \"hash_table\", \"bst\"]:\n",
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" print(f\"\\n {struct_name.upper()}:\")\n",
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" \n",
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" print(\" Вставка 10000 записей\")\n",
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" insert_times, filled_struct = measure_insertion(struct_name, records)\n",
|
||
" avg_insert = sum(insert_times) / 5\n",
|
||
" print(f\" Время: {avg_insert:.4f} сек (среднее)\")\n",
|
||
" \n",
|
||
" print(\" Поиск 110 записей\")\n",
|
||
" search_times = measure_search(struct_name, filled_struct, search_names)\n",
|
||
" avg_search = sum(search_times) / 5\n",
|
||
" print(f\" Время: {avg_search:.4f} сек (среднее)\")\n",
|
||
" \n",
|
||
" print(\" Удаление 50 записей\")\n",
|
||
" delete_times = measure_deletion(struct_name, filled_struct, delete_names)\n",
|
||
" avg_delete = sum(delete_times) / 5\n",
|
||
" print(f\" Время: {avg_delete:.4f} сек (среднее)\")\n",
|
||
" \n",
|
||
" results.append([struct_name, mode_name, \"вставка\"] + insert_times + [avg_insert])\n",
|
||
" results.append([struct_name, mode_name, \"поиск\"] + search_times + [avg_search])\n",
|
||
" results.append([struct_name, mode_name, \"удаление\"] + delete_times + [avg_delete])\n",
|
||
" \n",
|
||
" print(\"\\n3. Сохранение результатов\")\n",
|
||
" with open(csv_file, \"w\", newline=\"\", encoding=\"utf-8\") as f:\n",
|
||
" writer = csv.writer(f)\n",
|
||
" writer.writerows(results)\n",
|
||
" print(f\" Результаты сохранены в: {csv_file}\")\n",
|
||
" \n",
|
||
" print(\"СВОДНАЯ ТАБЛИЦА РЕЗУЛЬТАТОВ\")\n",
|
||
" print(f\"{'Структура':<15} {'Режим':<12} {'Операция':<10} {'Среднее время (сек)':<20}\")\n",
|
||
" \n",
|
||
" for row in results[1:]:\n",
|
||
" struct, mode, op, t1, t2, t3, t4, t5, avg = row\n",
|
||
" print(f\"{struct:<15} {mode:<12} {op:<10} {avg:<20.6f}\")\n",
|
||
" \n",
|
||
" return results, docs_dir\n",
|
||
"\n",
|
||
"#4. Graphics\n",
|
||
"\n",
|
||
"def create_graphs(results, docs_dir):\n",
|
||
" \n",
|
||
" print(\"\\n4. Построение графиков\")\n",
|
||
" \n",
|
||
" data = {}\n",
|
||
" for row in results[1:]:\n",
|
||
" struct = row[0]\n",
|
||
" mode = row[1]\n",
|
||
" op = row[2]\n",
|
||
" avg = row[8]\n",
|
||
" \n",
|
||
" if struct not in data:\n",
|
||
" data[struct] = {}\n",
|
||
" if mode not in data[struct]:\n",
|
||
" data[struct][mode] = {}\n",
|
||
" data[struct][mode][op] = avg\n",
|
||
" \n",
|
||
" \n",
|
||
" struct_labels = {\n",
|
||
" 'linked_list': 'LinkedList',\n",
|
||
" 'hash_table': 'HashTable',\n",
|
||
" 'bst': 'BST'\n",
|
||
" }\n",
|
||
" \n",
|
||
" \n",
|
||
" colors = {\n",
|
||
" 'linked_list': '#8b00ff', \n",
|
||
" 'hash_table': '#81d8d0', \n",
|
||
" 'bst': '#000000' \n",
|
||
" }\n",
|
||
" \n",
|
||
" \n",
|
||
" fig, axes = plt.subplots(1, 3, figsize=(15, 6))\n",
|
||
" fig.suptitle('Сравнение производительности структур данных', fontsize=16, fontweight='bold')\n",
|
||
" \n",
|
||
" operations = ['вставка', 'поиск', 'удаление']\n",
|
||
" operation_titles = ['Вставка\\n(10000 записей)', 'Поиск\\n(110 запросов)', 'Удаление\\n(50 записей)']\n",
|
||
" modes = ['случайный', 'отсортированный']\n",
|
||
" mode_labels = ['Случайный', 'Отсортированный']\n",
|
||
" \n",
|
||
" for idx, (op, op_title) in enumerate(zip(operations, operation_titles)):\n",
|
||
" ax = axes[idx]\n",
|
||
" \n",
|
||
" # Позиции для групп столбцов\n",
|
||
" x = np.arange(len(modes)) # [0, 1]\n",
|
||
" width = 0.3 # ширина одного столбца\n",
|
||
" multiplier = 0\n",
|
||
" \n",
|
||
" for struct in ['linked_list', 'hash_table', 'bst']:\n",
|
||
" values = [data[struct][mode][op] for mode in modes]\n",
|
||
" offset = width * multiplier\n",
|
||
" bars = ax.bar(x + offset, values, width, \n",
|
||
" label=struct_labels[struct], \n",
|
||
" color=colors[struct],\n",
|
||
" edgecolor='black', linewidth=0.5)\n",
|
||
" \n",
|
||
" \n",
|
||
" for bar, val in zip(bars, values):\n",
|
||
" if val < 0.01:\n",
|
||
" ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + val*0.05, \n",
|
||
" f'{val:.5f}', ha='center', va='bottom', fontsize=8, rotation=0)\n",
|
||
" else:\n",
|
||
" ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + val*0.02, \n",
|
||
" f'{val:.4f}', ha='center', va='bottom', fontsize=8, rotation=0)\n",
|
||
" \n",
|
||
" multiplier += 1\n",
|
||
" \n",
|
||
" \n",
|
||
" ax.set_title(op_title, fontsize=12, fontweight='bold')\n",
|
||
" ax.set_ylabel('Время (секунды)', fontsize=10)\n",
|
||
" ax.set_xlabel('Режим данных', fontsize=10)\n",
|
||
" ax.set_xticks(x + width)\n",
|
||
" ax.set_xticklabels(mode_labels)\n",
|
||
" ax.legend(loc='upper left', fontsize=8)\n",
|
||
" ax.grid(True, alpha=0.3, axis='y')\n",
|
||
" \n",
|
||
" \n",
|
||
" all_values = [data[s][m][op] for s in ['linked_list', 'hash_table', 'bst'] for m in modes]\n",
|
||
" if max(all_values) / min(all_values) > 100:\n",
|
||
" ax.set_yscale('log')\n",
|
||
" ax.set_ylabel('Время (секунды) - логарифмическая шкала', fontsize=9)\n",
|
||
" \n",
|
||
" plt.tight_layout()\n",
|
||
" graph_path = os.path.join(docs_dir, \"performance_graphs.png\")\n",
|
||
" plt.savefig(graph_path, dpi=150, bbox_inches='tight')\n",
|
||
" plt.close()\n",
|
||
" print(f\" Графики сохранены в: {graph_path}\")\n",
|
||
" \n",
|
||
" return graph_path\n",
|
||
"\n",
|
||
"#5. Main program\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" \n",
|
||
" results, docs_dir = run_experiment()\n",
|
||
" \n",
|
||
" \n",
|
||
" try:\n",
|
||
" graph_file = create_graphs(results, docs_dir)\n",
|
||
" \n",
|
||
" print(\"ЭКСПЕРИМЕНТ ЗАВЕРШЕН УСПЕШНО!\")\n",
|
||
" print(\"\\n СОЗДАННЫЕ ФАЙЛЫ:\")\n",
|
||
" print(f\" Данные: {os.path.join(docs_dir, 'experiment_results.csv')}\")\n",
|
||
" print(f\" Графики: {graph_file}\")\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" print(f\"\\n Ошибка при построении графиков: {e}\")\n",
|
||
" print(\" Убедитесь, что установлен matplotlib: pip install matplotlib\")\n",
|
||
" print(\"ЭКСПЕРИМЕНТ ЗАВЕРШЕН (без графиков)\")\n",
|
||
" print(f\"\\n CSV файл сохранен: {os.path.join(docs_dir, 'experiment_results.csv')}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e02735f2-61dc-484b-b74c-1456f7399863",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python [conda env:base] *",
|
||
"language": "python",
|
||
"name": "conda-base-py"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|