845 lines
211 KiB
Plaintext
845 lines
211 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "c533959c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import LinkedList as ll\n",
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"import HashTable as ht\n",
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"import BinaryTree as bt\n",
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"import time \n",
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"import random as rand\n",
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"import csv\n",
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"import numpy as np\n",
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"import sys"
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]
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},
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{
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"cell_type": "markdown",
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"id": "15cd6183",
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"metadata": {},
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"source": [
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"## Данные для обработки"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "88611f78",
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"metadata": {},
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"outputs": [],
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"source": [
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"N = 10000\n",
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"sys.setrecursionlimit(10000) \n",
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"records_sorted = [(f\"User_{i:05d}\", f\"+7999{i:07d}\") for i in range(N)] \n",
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"records_shuffled = records_sorted.copy()\n",
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"rand.shuffle(records_shuffled)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9fd1b8cd",
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"metadata": {},
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"source": [
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"## Исследование для LinkedList"
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]
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},
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{
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"cell_type": "markdown",
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"id": "083d49d0",
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"metadata": {},
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"source": [
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"### Добавление всех элементов произвольного кортежа\n",
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"- **data_ll_sh** - Структура произвольных данных (только последний замер)\n",
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"- **time_ll_insert_sh** - Замер времени работы 10000 элементов (5 замеров) \n",
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"- **heads_ll_sh** - Массив голов для массив для произвольного массива"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "11634fa4",
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"metadata": {},
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"outputs": [],
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"source": [
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"time_ll_insert_sh = [] \n",
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"heads_ll_sh = []\n",
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"for n in range(5):\n",
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" head = None\n",
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" data_ll_sh = []\n",
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" start = time.perf_counter()\n",
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" for i in range(N):\n",
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" head = ll.ll_insert(head, records_shuffled[i][0], records_shuffled[i][1])\n",
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" data_ll_sh.append(head)\n",
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" end = time.perf_counter()\n",
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" heads_ll_sh.append(head)\n",
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" time_ll_insert_sh.append(end - start)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0a5f161e",
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"metadata": {},
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"source": [
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"### Добавление всех элементов сортированного кортежа\n",
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"- **data_ll_so** - Структура отсортированных данных (только последний замер)\n",
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"- **time_ll_insert_so** - Замер времени работы 10000 элементов (5 замеров) \n",
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"- **heads_ll_so** - Массив голов для массив для сортированного массива"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "9eab4641",
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"metadata": {},
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"outputs": [],
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"source": [
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"time_ll_insert_so = [] \n",
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"heads_ll_so = []\n",
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"for n in range(5):\n",
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" head = None\n",
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" data_ll_so = []\n",
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" start = time.perf_counter()\n",
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" for i in range(N):\n",
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" head = ll.ll_insert(head, records_sorted[i][0], records_sorted[i][1])\n",
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" data_ll_so.append(head)\n",
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" end = time.perf_counter()\n",
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" heads_ll_so.append(head)\n",
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" time_ll_insert_so.append(end - start)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5862d31b",
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"metadata": {},
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"source": [
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"### Поиск элементов в произвольном массиве\n",
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"- **time_ll_find_sh** - Време поиска в произвольном массиве (для 5 замеров)\n",
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"- **find_ll_sh** - массив найденных данных в произвольном массиве (только последний замер)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "aac6cd23",
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"metadata": {},
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"outputs": [],
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"source": [
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"time_ll_find_sh = []\n",
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"for n in range(5):\n",
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" find_ll_sh = []\n",
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" start = time.perf_counter()\n",
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" for m in range(100): # замер для 100 случайных узлов \n",
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" i = rand.randint(0, N-1)\n",
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" str_find = records_shuffled[i][0]\n",
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" find_ll_sh.append(ll.ll_find(data_ll_sh[0], str_find))\n",
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" for m in range(10): # недоступные данные\n",
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" str_find = f\"Node_{m}\"\n",
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" find_ll_sh.append(ll.ll_find(data_ll_sh[0], str_find))\n",
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" end = time.perf_counter()\n",
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" time_ll_find_sh.append(end - start)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "651aac23",
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"metadata": {},
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"source": [
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"### Поиск элементов в отсортированном массиве\n",
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"- **time_ll_find_so** - Време поиска в отсортированном массиве (для 5 замеров)\n",
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"- **find_ll_so** - Массив найденных данных в отсортированном массиве (только последний замер)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "5e5ae537",
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"metadata": {},
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"outputs": [],
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"source": [
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"time_ll_find_so = []\n",
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"for n in range(5):\n",
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" find_ll_so = []\n",
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" start = time.perf_counter()\n",
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" for m in range(100): # замер для 100 случайных узлов \n",
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" i = rand.randint(0, N-1)\n",
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" str_find = records_sorted[i][0]\n",
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" find_ll_so.append(ll.ll_find(data_ll_sh[0], str_find))\n",
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" for m in range(10): # недоступные данные \n",
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" str_find = f\"Node_{m}\"\n",
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" find_ll_so.append(ll.ll_find(data_ll_sh[0], str_find))\n",
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" end = time.perf_counter()\n",
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" time_ll_find_so.append(end - start)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1f70be9",
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|
"metadata": {},
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"source": [
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"### Удаление элементов в произвольном массиве\n",
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"- **time_ll_delete_sh** - Време поиска в произвольном массиве (для 5 замеров)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "6cdf8a70",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.04597238700080197,\n",
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" 0.0345118730001559,\n",
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" 0.04677163999804179,\n",
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" 0.0356504750016029,\n",
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" 0.033157756999571575]"
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]
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},
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"execution_count": 29,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"time_ll_delete_sh = []\n",
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"for n in range(5):\n",
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" current_head = heads_ll_sh[n]\n",
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"\n",
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" start = time.perf_counter()\n",
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" for m in range(50): \n",
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" i = rand.randint(0, N-1)\n",
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" str_delete = records_shuffled[i][0]\n",
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" current_head = ll.ll_delete(current_head, str_delete)\n",
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" end = time.perf_counter()\n",
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"\n",
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" time_ll_delete_sh.append(end - start)\n",
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"time_ll_delete_sh"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8d6156e9",
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|
"metadata": {},
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"source": [
|
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"### Удаление элементов в сортированном массиве\n",
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"- **time_ll_delete_so** - Време поиска в произвольном массиве (для 5 замеров)"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": 30,
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|
"id": "575e375c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.03482511599941063,\n",
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" 0.02823204100059229,\n",
|
|
" 0.029369250001764158,\n",
|
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" 0.01980331899903831,\n",
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" 0.030789607000770047]"
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]
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|
},
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|
"execution_count": 30,
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|
"metadata": {},
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"output_type": "execute_result"
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|
}
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|
],
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"source": [
|
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"time_ll_delete_so = []\n",
|
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"for n in range(5):\n",
|
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" current_head = heads_ll_so[n]\n",
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"\n",
|
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" start = time.perf_counter()\n",
|
|
" for m in range(50): \n",
|
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" i = rand.randint(0, N-1)\n",
|
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" str_delete = records_sorted[i][0]\n",
|
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" current_head = ll.ll_delete(current_head, str_delete)\n",
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" end = time.perf_counter()\n",
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"\n",
|
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" time_ll_delete_so.append(end - start)\n",
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"time_ll_delete_so"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a95a40b",
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"metadata": {},
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"source": [
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"## Исследование BinaryTree"
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|
]
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|
},
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|
{
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|
"cell_type": "markdown",
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|
"id": "54c92d21",
|
|
"metadata": {},
|
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"source": [
|
|
"### Добавление всех элементов произвольного кортежа\n",
|
|
"- **data_bt_sh** - Структура произвольных данных (только последний замер)\n",
|
|
"- **time_bt_insert_sh** - Замер времени работы 10000 элементов (5 замеров) \n",
|
|
"- **heads_bt_sh** - Массив голов для массив для произвольного массива"
|
|
]
|
|
},
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|
{
|
|
"cell_type": "code",
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|
"execution_count": 31,
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|
"id": "939aa900",
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"metadata": {},
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|
"outputs": [],
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"source": [
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"time_bt_insert_sh = [] \n",
|
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"heads_bt_sh = []\n",
|
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"for n in range(5):\n",
|
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" head = None\n",
|
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" data_bt_sh = []\n",
|
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" start = time.perf_counter()\n",
|
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" for i in range(N):\n",
|
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" head = bt.bst_insert(head, records_shuffled[i][0], records_shuffled[i][1])\n",
|
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" data_bt_sh.append(head)\n",
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" end = time.perf_counter()\n",
|
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" heads_bt_sh.append(head)\n",
|
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" time_bt_insert_sh.append(end - start)\n"
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|
]
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|
},
|
|
{
|
|
"cell_type": "markdown",
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|
"id": "e91b5893",
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|
"metadata": {},
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"source": [
|
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"### Добавление всех элементов сортированного кортежа\n",
|
|
"- **data_bt_so** - Структура сортированных данных (только последний замер)\n",
|
|
"- **time_bt_insert_so** - Замер времени работы 10000 элементов (5 замеров) \n",
|
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"- **heads_bt_so** - Массив голов для массив для сортированного массива"
|
|
]
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|
},
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|
{
|
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"cell_type": "code",
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"execution_count": 32,
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"id": "d17b8108",
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"metadata": {},
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"outputs": [],
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"source": [
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"time_bt_insert_so = [] \n",
|
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"heads_bt_so = []\n",
|
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"for n in range(5):\n",
|
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" head = None\n",
|
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" data_bt_so = []\n",
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" start = time.perf_counter()\n",
|
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" #head = bt.bst_insert(head, records_sorted[i][0],records_sorted[i][1])\n",
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" head = bt.bst_insert_sort(records_sorted, 0, len(records_sorted) - 1)\n",
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" data_bt_so.append(head)\n",
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" end = time.perf_counter()\n",
|
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" heads_bt_so.append(head)\n",
|
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" time_bt_insert_so.append(end - start)\n"
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]
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},
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|
{
|
|
"cell_type": "markdown",
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|
"id": "1e8a3f9e",
|
|
"metadata": {},
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|
"source": [
|
|
"### Поиск элементов в произвольном массиве\n",
|
|
"- **time_bt_find_sh** - Време поиска в произвольном массиве (для 5 замеров)\n",
|
|
"- **find_bt_sh** - массив найденных данных в произвольном массиве (только последний замер)"
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]
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|
},
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|
{
|
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"cell_type": "code",
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|
"execution_count": 33,
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|
"id": "4352b11d",
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|
"metadata": {},
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|
"outputs": [],
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|
"source": [
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"time_bt_find_sh = []\n",
|
|
"for n in range(5):\n",
|
|
" find_bt_sh = []\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(100): # замер для 100 случайных узлов \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_find = records_shuffled[i][0]\n",
|
|
" find_bt_sh.append(bt.bst_find(data_bt_sh[0], str_find))\n",
|
|
" for m in range(10): # недоступные данные\n",
|
|
" str_find = f\"Node_{m}\"\n",
|
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" find_bt_sh.append(bt.bst_find(data_bt_sh[0], str_find))\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_bt_find_sh.append(end - start)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8db5208b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Поиск элементов в отсортированном массиве\n",
|
|
"- **time_bt_find_so** - Време поиска в сортированном массиве (для 5 замеров)\n",
|
|
"- **find_bt_so** - массив найденных данных в сортированном массиве (только последний замер)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
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|
"id": "7941e689",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_bt_find_so = []\n",
|
|
"for n in range(5):\n",
|
|
" find_bt_so = []\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(100): # замер для 100 случайных узлов \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_find = records_sorted[i][0]\n",
|
|
" find_bt_so.append(bt.bst_find(data_bt_so[0], str_find))\n",
|
|
" for m in range(10): # недоступные данные\n",
|
|
" str_find = f\"Node_{m}\"\n",
|
|
" find_bt_so.append(bt.bst_find(data_bt_so[0], str_find))\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_bt_find_so.append(end - start)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ffbe3dfe",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Удаление элементов в произвольном массиве\n",
|
|
"- **time_bt_delete_sh** - Време поиска в произвольном массиве (для 5 замеров)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "4043a9dc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_bt_delete_sh = []\n",
|
|
"for n in range(5):\n",
|
|
" current_head = heads_bt_sh[n]\n",
|
|
"\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(50): \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_delete = records_shuffled[i][0]\n",
|
|
" current_head = bt.bst_delete(current_head, str_delete)\n",
|
|
" end = time.perf_counter()\n",
|
|
"\n",
|
|
" time_bt_delete_sh.append(end - start)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7db94391",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Удаление элементов в сортированном массиве\n",
|
|
"- **time_bt_delete_so** - Време поиска в произвольном массиве (для 5 замеров)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "7ab6136c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_bt_delete_so = []\n",
|
|
"for n in range(5):\n",
|
|
" current_head = heads_bt_so[n]\n",
|
|
"\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(50): \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_delete = records_sorted[i][0]\n",
|
|
" current_head = bt.bst_delete(current_head, str_delete)\n",
|
|
" end = time.perf_counter()\n",
|
|
"\n",
|
|
" time_bt_delete_so.append(end - start)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0bf5b406",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Исследование HashTable"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "75586bbc",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Добавление всех элементов произвольного кортежа\n",
|
|
"- **data_ht_sh** - Структура произвольных данных (только последний замер)\n",
|
|
"- **time_ht_insert_sh** - Замер времени работы 10000 элементов (5 замеров) \n",
|
|
"- **heads_ht_sh** - Массив голов для массив для произвольного массива"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "cb1788d1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_ht_insert_sh = [] \n",
|
|
"for n in range(5):\n",
|
|
" buckets = ht.create_ht(size = N * 4)\n",
|
|
" data_ht_sh = []\n",
|
|
" start = time.perf_counter()\n",
|
|
" for i in range(N):\n",
|
|
" buckets = ht.ht_insert(buckets, records_shuffled[i][0], records_shuffled[i][1])\n",
|
|
" data_ht_sh.append(buckets)\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_ht_insert_sh.append(end - start)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6bb2aa16",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Добавление всех элементов сортированного кортежа\n",
|
|
"- **data_ht_so** - Структура сортированных данных (только последний замер)\n",
|
|
"- **time_ht_insert_so** - Замер времени работы 10000 элементов (5 замеров) \n",
|
|
"- **heads_ht_so** - Массив голов для массив для сортированного массива"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "4cb524e4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_ht_insert_so = [] \n",
|
|
"for n in range(5):\n",
|
|
" buckets = ht.create_ht(size = N * 4)\n",
|
|
" data_ht_so = []\n",
|
|
" start = time.perf_counter()\n",
|
|
" for i in range(N):\n",
|
|
" buckets = ht.ht_insert(buckets, records_sorted[i][0], records_sorted[i][1])\n",
|
|
" data_ht_so.append(buckets)\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_ht_insert_so.append(end - start)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9d79016f",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Поиск элементов в произвольном массиве\n",
|
|
"- **time_ht_find_sh** - Време поиска в произвольном массиве (для 5 замеров)\n",
|
|
"- **find_ht_sh** - массив найденных данных в произвольном массиве (только последний замер)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "45cec102",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_ht_find_sh = []\n",
|
|
"for n in range(5):\n",
|
|
" find_ht_sh = []\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(100): # замер для 100 случайных узлов \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_find = records_shuffled[i][0]\n",
|
|
" find_ht_sh.append(ht.ht_find(data_ht_sh[0], str_find))\n",
|
|
" for m in range(10): # недоступные данные\n",
|
|
" str_find = f\"Node_{m}\"\n",
|
|
" find_ht_sh.append(ht.ht_find(data_ht_sh[0], str_find))\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_ht_find_sh.append(end - start)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8f11dbad",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Поиск элементов в отсортированном массиве\n",
|
|
"- **time_ht_find_so** - Време поиска в сортированном массиве (для 5 замеров)\n",
|
|
"- **find_ht_so** - массив найденных данных в сортированном массиве (только последний замер)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "19e7a19a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_ht_find_so = []\n",
|
|
"for n in range(5):\n",
|
|
" find_ht_so = []\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(100): # замер для 100 случайных узлов \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_find = records_shuffled[i][0]\n",
|
|
" find_ht_so.append(ht.ht_find(data_ht_so[0], str_find))\n",
|
|
" for m in range(10): # недоступные данные\n",
|
|
" str_find = f\"Node_{m}\"\n",
|
|
" find_ht_so.append(ht.ht_find(data_ht_so[0], str_find))\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_ht_find_so.append(end - start)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e39fa07c",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Удаление элементов в произвольном массиве\n",
|
|
"- **time_ht_delete_sh** - Време поиска в произвольном массиве (для 5 замеров)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "90a9613d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_ht_delete_sh = []\n",
|
|
"for n in range(5):\n",
|
|
" current_head = data_ht_sh[0]\n",
|
|
"\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(50): \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_delete = records_shuffled[i][0]\n",
|
|
" current_head = ht.ht_delete(current_head, str_delete)\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_ht_delete_sh.append(end - start)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "eb9fcf01",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Удаление элементов в сортированном массиве\n",
|
|
"- **time_ht_delete_so** - Време поиска в произвольном массиве (для 5 замеров)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "dfe01fe5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"time_ht_delete_so = []\n",
|
|
"for n in range(5):\n",
|
|
" current_head = data_ht_so[0]\n",
|
|
"\n",
|
|
" start = time.perf_counter()\n",
|
|
" for m in range(50): \n",
|
|
" i = rand.randint(0, N-1)\n",
|
|
" str_delete = records_shuffled[i][0]\n",
|
|
" current_head = ht.ht_delete(current_head, str_delete)\n",
|
|
" end = time.perf_counter()\n",
|
|
" time_ht_delete_so.append(end - start)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c0ce9bbd",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Создание CSV файла\n",
|
|
"Запишем все рание полученные данные в CSV файл"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "ce249772",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"results = [\n",
|
|
" [\"Структура\", \"Режим\", \"Операция\", \"Время (сек)\"],\n",
|
|
" [\"LinkedList\", \"случайный\", \"вставка\", sum(time_ll_insert_sh)/len(time_ll_insert_sh)],\n",
|
|
" [\"LinkedList\", \"сортированный\", \"вставка\", sum(time_ll_insert_so)/len(time_ll_insert_so)],\n",
|
|
" [\"LinkedList\", \"случайный\", \"поиск\", sum(time_ll_find_sh)/len(time_ll_find_sh)],\n",
|
|
" [\"LinkedList\", \"сортированный\", \"поиск\", sum(time_ll_find_so)/len(time_ll_find_so)],\n",
|
|
" [\"LinkedList\", \"случайный\", \"удаление\", sum(time_ll_delete_sh)/len(time_ll_delete_sh)],\n",
|
|
" [\"LinkedList\", \"сортированный\", \"удаление\", sum(time_ll_delete_so)/len(time_ll_delete_so)],\n",
|
|
" [\"HashTable\", \"случайный\", \"вставка\", sum(time_ht_insert_sh)/len(time_ht_insert_sh)],\n",
|
|
" [\"HashTable\", \"сортированный\", \"вставка\", sum(time_ht_insert_so)/len(time_ht_insert_so)],\n",
|
|
" [\"HashTable\", \"случайный\", \"поиск\", sum(time_ht_find_sh)/len(time_ht_find_sh)],\n",
|
|
" [\"HashTable\", \"сортированный\", \"поиск\", sum(time_ht_find_so)/len(time_ht_find_so)],\n",
|
|
" [\"HashTable\", \"случайный\", \"удаление\", sum(time_ht_delete_sh)/len(time_ht_delete_sh)],\n",
|
|
" [\"HashTable\", \"сортированный\", \"удаление\", sum(time_ht_delete_so)/len(time_ht_delete_so)],\n",
|
|
" [\"BinaryTree\", \"случайный\", \"вставка\", sum(time_bt_insert_sh)/len(time_bt_insert_sh)],\n",
|
|
" [\"BinaryTree\", \"сортированный\", \"вставка\", sum(time_bt_insert_so)/len(time_bt_insert_so)],\n",
|
|
" [\"BinaryTree\", \"случайный\", \"поиск\", sum(time_bt_find_sh)/len(time_bt_find_sh)],\n",
|
|
" [\"BinaryTree\", \"сортированный\", \"поиск\", sum(time_bt_find_so)/len(time_bt_find_so)],\n",
|
|
" [\"BinaryTree\", \"случайный\", \"удаление\", sum(time_bt_delete_sh)/len(time_bt_delete_sh)],\n",
|
|
" [\"BinaryTree\", \"сортированный\", \"удаление\", sum(time_bt_delete_so)/len(time_bt_delete_so)],\n",
|
|
"]\n",
|
|
"\n",
|
|
"with open(\"results.csv\", \"w\", newline=\"\") as f:\n",
|
|
" writer = csv.writer(f)\n",
|
|
" writer.writerows(results)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "69023ea2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Построение графиков и сравнение данных между собой\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"id": "d06c4bb0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
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"<Figure size 2000x1500 with 9 Axes>"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
|
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"source": [
|
|
"import matplotlib.pyplot as plt\n",
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"\n",
|
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"t_ll = [sum(time_ll_insert_sh)/len(time_ll_insert_sh), sum(time_ll_insert_so)/len(time_ll_insert_so), sum(time_ll_find_sh)/len(time_ll_find_sh), sum(time_ll_find_so)/len(time_ll_find_so), sum(time_ll_delete_sh)/len(time_ll_delete_sh), sum(time_ll_delete_so)/len(time_ll_delete_so)]\n",
|
|
"t_ht = [sum(time_ht_insert_sh)/len(time_ht_insert_sh), sum(time_ht_insert_so)/len(time_ht_insert_so), sum(time_ht_find_sh)/len(time_ht_find_sh), sum(time_ht_find_so)/len(time_ht_find_so), sum(time_ht_delete_sh)/len(time_ht_delete_sh), sum(time_ht_delete_so)/len(time_ht_delete_so)]\n",
|
|
"t_bt = [sum(time_bt_insert_sh)/len(time_bt_insert_sh), sum(time_bt_insert_so)/len(time_bt_insert_so), sum(time_bt_find_sh)/len(time_bt_find_sh), sum(time_bt_find_so)/len(time_bt_find_so), sum(time_bt_delete_sh)/len(time_bt_delete_sh), sum(time_bt_delete_so)/len(time_bt_delete_so)]\n",
|
|
"categoris = [\"рандомное добавление\", \"отсортированное добавление\", \"рандомный поиск\", \"отсортированный поиск\", \"рандомное удаление\", \"отсортированное удаление\"]\n",
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"\n",
|
|
"plt.figure(\"графики\", figsize = (20,15))\n",
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"\n",
|
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"plt.subplot(3,3,1)\n",
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"plt.bar(categoris[0], t_ll[0], color = \"orange\")\n",
|
|
"plt.bar(categoris[1], t_ll[1], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время добавление для LinkedList\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"plt.subplot(3,3,2)\n",
|
|
"plt.bar(categoris[2], t_ll[2], color = \"orange\")\n",
|
|
"plt.bar(categoris[3], t_ll[3], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время поиска элементов в LinkedList\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.subplot(3,3,3)\n",
|
|
"plt.bar(categoris[4], t_ll[4], color = \"orange\")\n",
|
|
"plt.bar(categoris[5], t_ll[5], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время удаления элементов в LinkedList\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"plt.subplot(3,3,4)\n",
|
|
"plt.bar(categoris[0], t_ht[0], color = \"orange\")\n",
|
|
"plt.bar(categoris[1], t_ht[1], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время добавление для HashTable\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"plt.subplot(3,3,5)\n",
|
|
"plt.bar(categoris[2], t_ht[2], color = \"orange\")\n",
|
|
"plt.bar(categoris[3], t_ht[3], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время поиска элементов в HashTable\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.subplot(3,3,6)\n",
|
|
"plt.bar(categoris[4], t_bt[4], color = \"orange\")\n",
|
|
"plt.bar(categoris[5], t_bt[5], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время удаления элементов в HashTable\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"plt.subplot(3,3,7)\n",
|
|
"plt.bar(categoris[0], t_bt[0], color = \"orange\")\n",
|
|
"plt.bar(categoris[1], t_bt[1], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время добавление для BinaryTree\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"plt.subplot(3,3,8)\n",
|
|
"plt.bar(categoris[2], t_bt[2], color = \"orange\")\n",
|
|
"plt.bar(categoris[3], t_bt[3], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время поиска элементов в BinaryTree\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.subplot(3,3,9)\n",
|
|
"plt.bar(categoris[4], t_bt[4], color = \"orange\")\n",
|
|
"plt.bar(categoris[5], t_bt[5], color = \"green\")\n",
|
|
"\n",
|
|
"plt.title(f\"Время удаления элементов в BinaryTree\")\n",
|
|
"\n",
|
|
"plt.ylabel(\"время работы(секунды)\")\n",
|
|
"\n",
|
|
"plt.savefig('analysis.png')\n",
|
|
"plt.tight_layout() \n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "77155f07",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|