[0] initial commit

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Veronika Minina 2026-05-16 18:08:27 +03:00
parent 061eb851af
commit c342aee89c

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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "3701053f-41f9-464d-a44f-cbda38c1caf7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ СТРУКТУР ДАННЫХ\n",
"Телефонный справочник - 10000 записей\n",
"\n",
" Результаты будут сохранены в: C:\\Users\\weron\\experiment_results.csv\n",
" Сгенерировано 10000 записей\n",
"\n",
"2. Тестирование режима: случайный\n",
"\n",
" LINKED_LIST:\n",
" Вставка 10000 записей\n",
" Время: 9.2970 сек (среднее)\n",
" Поиск 110 записей\n",
" Время: 0.0946 сек (среднее)\n",
" Удаление 50 записей\n",
" Время: 0.0000 сек (среднее)\n",
"\n",
" HASH_TABLE:\n",
" Вставка 10000 записей\n",
" Время: 0.4810 сек (среднее)\n",
" Поиск 110 записей\n",
" Время: 0.0039 сек (среднее)\n",
" Удаление 50 записей\n",
" Время: 0.0030 сек (среднее)\n",
"\n",
" BST:\n",
" Вставка 10000 записей\n",
" Время: 0.0490 сек (среднее)\n",
" Поиск 110 записей\n",
" Время: 0.0004 сек (среднее)\n",
" Удаление 50 записей\n",
" Время: 0.1141 сек (среднее)\n",
"\n",
"2. Тестирование режима: отсортированный\n",
"\n",
" LINKED_LIST:\n",
" Вставка 10000 записей\n",
" Время: 9.2504 сек (среднее)\n",
" Поиск 110 записей\n",
" Время: 0.1115 сек (среднее)\n",
" Удаление 50 записей\n",
" Время: 0.0000 сек (среднее)\n",
"\n",
" HASH_TABLE:\n",
" Вставка 10000 записей\n",
" Время: 0.4928 сек (среднее)\n",
" Поиск 110 записей\n",
" Время: 0.0061 сек (среднее)\n",
" Удаление 50 записей\n",
" Время: 0.0029 сек (среднее)\n",
"\n",
" BST:\n",
" Вставка 10000 записей\n",
" Время: 22.1688 сек (среднее)\n",
" Поиск 110 записей\n",
" Время: 0.1297 сек (среднее)\n",
" Удаление 50 записей\n",
" Время: 0.1115 сек (среднее)\n",
"\n",
"3. Сохранение результатов\n",
" Результаты сохранены в: C:\\Users\\weron\\experiment_results.csv\n",
"СВОДНАЯ ТАБЛИЦА РЕЗУЛЬТАТОВ\n",
"Структура Режим Операция Среднее время (сек) \n",
"linked_list случайный вставка 9.296975 \n",
"linked_list случайный поиск 0.094569 \n",
"linked_list случайный удаление 0.000022 \n",
"hash_table случайный вставка 0.481027 \n",
"hash_table случайный поиск 0.003911 \n",
"hash_table случайный удаление 0.003046 \n",
"bst случайный вставка 0.049011 \n",
"bst случайный поиск 0.000368 \n",
"bst случайный удаление 0.114051 \n",
"linked_list отсортированный вставка 9.250436 \n",
"linked_list отсортированный поиск 0.111506 \n",
"linked_list отсортированный удаление 0.000018 \n",
"hash_table отсортированный вставка 0.492765 \n",
"hash_table отсортированный поиск 0.006051 \n",
"hash_table отсортированный удаление 0.002869 \n",
"bst отсортированный вставка 22.168779 \n",
"bst отсортированный поиск 0.129713 \n",
"bst отсортированный удаление 0.111534 \n",
"\n",
"4. Построение графиков\n",
" Графики сохранены в: C:\\Users\\weron\\performance_graphs.png\n",
"ЭКСПЕРИМЕНТ ЗАВЕРШЕН УСПЕШНО!\n",
"\n",
" СОЗДАННЫЕ ФАЙЛЫ:\n",
" Данные: C:\\Users\\weron\\experiment_results.csv\n",
" Графики: C:\\Users\\weron\\performance_graphs.png\n"
]
}
],
"source": [
"import time\n",
"import random\n",
"import csv\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import sys\n",
"sys.setrecursionlimit(20000) \n",
"#Linked List Phone Book:\n",
"\n",
"def ll_insert(head, name, phone):\n",
" new_node = {'name': name, 'phone' : phone, 'next': None}\n",
" if head is None:\n",
" return new_node\n",
" if head['name'] == name:\n",
" head['phone'] = phone\n",
" return head\n",
" current = head \n",
" while current['next'] is not None:\n",
" if current['next']['name'] == name:\n",
" current['next']['phone'] = phone\n",
" return head\n",
" current = current['next']\n",
" current['next'] = new_node\n",
" return head \n",
"def ll_find(head, name):\n",
" current = head\n",
" while current != None:\n",
" if current['name']==name:\n",
" return current['phone']\n",
" current = current['next']\n",
" return None\n",
"def ll_delete(head, name):\n",
" if head is None:\n",
" return None\n",
" if head['name'] == name:\n",
" return head['next']\n",
" current = head \n",
" while current['next'] is not None:\n",
" if current['next']['name'] == name:\n",
" current['next']==current['next']['next']\n",
" return head\n",
" current=current['next']\n",
" return head\n",
"def ll_list_all(head): \n",
" records= []\n",
" current = head \n",
" while current is not None:\n",
" records.append({'name': current['name'], 'phone': current['phone']})\n",
" current = current['next']\n",
" records.sort(key=lambda x: x['name'])\n",
" return records \n",
"def ll_print_all(head):\n",
" records = ll_list_all(head)\n",
" for record in records:\n",
" print(f\"{record['name']}: {record['phone']}\")\n",
"\n",
"#Hash Function:\n",
"\n",
"def hash_function(name, table_size):\n",
" return sum(ord(c) for c in name) % table_size\n",
"\n",
"\n",
"def ht_create(size=1000):\n",
" return [None] * size\n",
"\n",
"\n",
"def ht_insert(buckets, name, phone):\n",
" size = len(buckets)\n",
" index = hash_function(name, size)\n",
" buckets[index] = ll_insert(buckets[index], name, phone)\n",
"\n",
"\n",
"def ht_find(buckets, name):\n",
" size = len(buckets)\n",
" index = hash_function(name, size)\n",
" return ll_find(buckets[index], name)\n",
"\n",
"\n",
"def ht_delete(buckets, name):\n",
" size = len(buckets)\n",
" index = hash_function(name, size)\n",
" buckets[index] = ll_delete(buckets[index], name)\n",
"\n",
"\n",
"def ht_list_all(buckets):\n",
" records = []\n",
" for bucket in buckets:\n",
" current = bucket\n",
" while current is not None:\n",
" records.append((current['name'], current['phone']))\n",
" current = current['next']\n",
" records.sort(key=lambda x: x[0])\n",
" return records\n",
"\n",
"#Tree function:\n",
"\n",
"def bst_insert(root, name, phone):\n",
" \n",
" if root is None:\n",
" return {'name': name, 'phone': phone, 'left': None, 'right': None}\n",
" \n",
" if name < root['name']:\n",
" root['left'] = bst_insert(root['left'], name, phone)\n",
" elif name > root['name']:\n",
" root['right'] = bst_insert(root['right'], name, phone)\n",
" else:\n",
" root['phone'] = phone\n",
" \n",
" return root\n",
"\n",
"\n",
"def bst_find(root, name):\n",
" \n",
" current = root\n",
" while current is not None:\n",
" if name == current['name']:\n",
" return current['phone']\n",
" elif name < current['name']:\n",
" current = current['left']\n",
" else:\n",
" current = current['right']\n",
" return None\n",
"\n",
"\n",
"def bst_find_min(node):\n",
" \n",
" current = node\n",
" while current['left'] is not None:\n",
" current = current['left']\n",
" return current\n",
"\n",
"\n",
"def bst_delete(root, name):\n",
" \n",
" if root is None:\n",
" return None\n",
" \n",
" if name < root['name']:\n",
" root['left'] = bst_delete(root['left'], name)\n",
" elif name > root['name']:\n",
" root['right'] = bst_delete(root['right'], name)\n",
" else:\n",
" if root['left'] is None:\n",
" return root['right']\n",
" elif root['right'] is None:\n",
" return root['left']\n",
" \n",
" min_node = bst_find_min(root['right'])\n",
" root['name'] = min_node['name']\n",
" root['phone'] = min_node['phone']\n",
" root['right'] = bst_delete(root['right'], min_node['name'])\n",
" \n",
" return root\n",
"\n",
"\n",
"def bst_list_all(root):\n",
" \n",
" records = []\n",
" \n",
" def inorder_traversal(node):\n",
" if node is not None:\n",
" inorder_traversal(node['left'])\n",
" records.append((node['name'], node['phone']))\n",
" inorder_traversal(node['right'])\n",
" \n",
" inorder_traversal(root)\n",
" return records\n",
"\n",
"#Experemental part \n",
"#1. Test data generation \n",
"\n",
"def generate_records(count=10000):\n",
" \n",
" records = []\n",
" for i in range(count):\n",
" name = f\"User_{i:05d}\"\n",
" phone = f\"+7-{random.randint(100,999)}-{random.randint(100,999)}-{random.randint(1000,9999)}\"\n",
" records.append((name, phone))\n",
" \n",
" shuffled = records.copy()\n",
" random.shuffle(shuffled)\n",
" sorted_records = sorted(records, key=lambda x: x[0])\n",
" \n",
" return shuffled, sorted_records\n",
"\n",
"#2. Timing\n",
"\n",
"def measure_insertion(structure_name, records):\n",
" \n",
" times = []\n",
" filled_structure = None\n",
" \n",
" for run in range(5):\n",
" if structure_name == \"linked_list\":\n",
" structure = None\n",
" elif structure_name == \"hash_table\":\n",
" structure = ht_create(1000)\n",
" elif structure_name == \"bst\":\n",
" structure = None\n",
" \n",
" start = time.perf_counter()\n",
" \n",
" for name, phone in records:\n",
" if structure_name == \"linked_list\":\n",
" structure = ll_insert(structure, name, phone)\n",
" elif structure_name == \"hash_table\":\n",
" ht_insert(structure, name, phone)\n",
" elif structure_name == \"bst\":\n",
" structure = bst_insert(structure, name, phone)\n",
" \n",
" end = time.perf_counter()\n",
" times.append(end - start)\n",
" \n",
" if run == 4:\n",
" filled_structure = structure\n",
" \n",
" return times, filled_structure\n",
"\n",
"\n",
"def measure_search(structure_name, structure, search_names):\n",
" \n",
" times = []\n",
" \n",
" for run in range(5):\n",
" start = time.perf_counter()\n",
" \n",
" for name in search_names:\n",
" if structure_name == \"linked_list\":\n",
" ll_find(structure, name)\n",
" elif structure_name == \"hash_table\":\n",
" ht_find(structure, name)\n",
" elif structure_name == \"bst\":\n",
" bst_find(structure, name)\n",
" \n",
" end = time.perf_counter()\n",
" times.append(end - start)\n",
" \n",
" return times\n",
"\n",
"\n",
"def measure_deletion(structure_name, original_structure, delete_names):\n",
" \n",
" times = []\n",
" \n",
" for run in range(5):\n",
" if structure_name == \"linked_list\":\n",
" all_records = ll_list_all(original_structure)\n",
" test_structure = None\n",
" for name, phone in all_records:\n",
" test_structure = ll_insert(test_structure, name, phone)\n",
" \n",
" elif structure_name == \"hash_table\":\n",
" all_records = ht_list_all(original_structure)\n",
" test_structure = ht_create(1000)\n",
" for name, phone in all_records:\n",
" ht_insert(test_structure, name, phone)\n",
" \n",
" elif structure_name == \"bst\":\n",
" all_records = bst_list_all(original_structure)\n",
" test_structure = None\n",
" for name, phone in all_records:\n",
" test_structure = bst_insert(test_structure, name, phone)\n",
" \n",
" start = time.perf_counter()\n",
" \n",
" for name in delete_names:\n",
" if structure_name == \"linked_list\":\n",
" test_structure = ll_delete(test_structure, name)\n",
" elif structure_name == \"hash_table\":\n",
" ht_delete(test_structure, name)\n",
" elif structure_name == \"bst\":\n",
" test_structure = bst_delete(test_structure, name)\n",
" \n",
" end = time.perf_counter()\n",
" times.append(end - start)\n",
" \n",
" return times\n",
"#3. Launch and save results\n",
"\n",
"def run_experiment():\n",
" \n",
" current_dir = os.getcwd()\n",
" docs_dir = current_dir\n",
" csv_file = os.path.join(docs_dir, \"experiment_results.csv\")\n",
" \n",
" print(\"ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ СТРУКТУР ДАННЫХ\")\n",
" print(\"Телефонный справочник - 10000 записей\")\n",
" print(f\"\\n Результаты будут сохранены в: {csv_file}\")\n",
" \n",
" shuffled_records, sorted_records = generate_records(10000)\n",
" print(f\" Сгенерировано 10000 записей\")\n",
" \n",
" existing_names = [shuffled_records[i][0] for i in random.sample(range(10000), 100)]\n",
" nonexisting_names = [f\"NotExist_{i}\" for i in range(10)]\n",
" search_names = existing_names + nonexisting_names\n",
" delete_names = [shuffled_records[i][0] for i in random.sample(range(10000), 50)]\n",
" \n",
" results = [[\"Структура\", \"Режим\", \"Операция\", \n",
" \"Замер1(с)\", \"Замер2(с)\", \"Замер3(с)\", \"Замер4(с)\", \"Замер5(с)\", \n",
" \"Среднее(с)\"]]\n",
" \n",
" for mode_name, records in [(\"случайный\", shuffled_records), \n",
" (\"отсортированный\", sorted_records)]:\n",
" \n",
" print(f\"\\n2. Тестирование режима: {mode_name}\")\n",
" \n",
" for struct_name in [\"linked_list\", \"hash_table\", \"bst\"]:\n",
" print(f\"\\n {struct_name.upper()}:\")\n",
" \n",
" print(\" Вставка 10000 записей\")\n",
" 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",
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