303 lines
9.1 KiB
Python
303 lines
9.1 KiB
Python
import random
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import time
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import csv
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import sys
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import pandas as pd
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import matplotlib.pyplot as plt
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sys.setrecursionlimit(20000)
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def ll_insert(head, name, phone):
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current = head
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while current is not None:
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if current['name'] == name:
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current['phone'] = phone
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return head
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current = current['next']
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new_node = {'name': name, 'phone': phone, 'next': None}
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if head is None:
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return new_node
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current = head
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while current['next'] is not None:
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current = current['next']
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current['next'] = new_node
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return head
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def ll_find(head, name):
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current = head
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while current is not None:
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if current['name'] == name:
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return current['phone']
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current = current['next']
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return None
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def ll_delete(head, name):
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if head is None:
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return None
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if head['name'] == name:
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return head['next']
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prev = head
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current = head['next']
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while current is not None:
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if current['name'] == name:
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prev['next'] = current['next']
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return head
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prev = current
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current = current['next']
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return head
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def ll_list_all(head):
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records = []
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current = head
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while current is not None:
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records.append((current['name'], current['phone']))
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current = current['next']
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records.sort(key=lambda x: x[0])
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return records
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HASH_SIZE = 997
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def hash_func(name, size):
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return hash(name) % size
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def ht_create():
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return [None] * HASH_SIZE
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def ht_insert(table, name, phone):
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idx = hash_func(name, len(table))
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table[idx] = ll_insert(table[idx], name, phone)
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return table
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def ht_find(table, name):
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idx = hash_func(name, len(table))
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return ll_find(table[idx], name)
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def ht_delete(table, name):
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idx = hash_func(name, len(table))
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table[idx] = ll_delete(table[idx], name)
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return table
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def ht_list_all(table):
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all_records = []
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for head in table:
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current = head
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while current is not None:
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all_records.append((current['name'], current['phone']))
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current = current['next']
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all_records.sort(key=lambda x: x[0])
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return all_records
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def bst_create_node(name, phone):
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return {'name': name, 'phone': phone, 'left': None, 'right': None}
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def bst_insert(root, name, phone):
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if root is None:
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return bst_create_node(name, phone)
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if name == root['name']:
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root['phone'] = phone
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elif name < root['name']:
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root['left'] = bst_insert(root['left'], name, phone)
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else:
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root['right'] = bst_insert(root['right'], name, phone)
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return root
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def bst_find(root, name):
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if root is None:
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return None
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if name == root['name']:
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return root['phone']
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elif name < root['name']:
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return bst_find(root['left'], name)
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else:
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return bst_find(root['right'], name)
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def bst_find_min(node):
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while node['left'] is not None:
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node = node['left']
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return node
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def bst_delete(root, name):
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if root is None:
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return None
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if name < root['name']:
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root['left'] = bst_delete(root['left'], name)
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elif name > root['name']:
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root['right'] = bst_delete(root['right'], name)
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else:
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if root['left'] is None:
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return root['right']
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if root['right'] is None:
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return root['left']
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min_node = bst_find_min(root['right'])
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root['name'] = min_node['name']
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root['phone'] = min_node['phone']
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root['right'] = bst_delete(root['right'], min_node['name'])
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return root
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def bst_list_all(root):
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result = []
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def inorder(node):
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if node is None:
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return
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inorder(node['left'])
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result.append((node['name'], node['phone']))
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inorder(node['right'])
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inorder(root)
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return result
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def generate_records(num_records, seed=42):
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random.seed(seed)
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records = []
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for i in range(1, num_records + 1):
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name = f"User_{i:05d}"
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phone = f"{random.randint(100,999)}-{random.randint(1000,9999)}"
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records.append((name, phone))
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return records
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def prepare_datasets(base_records):
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shuffled = base_records.copy()
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random.shuffle(shuffled)
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sorted_records = sorted(base_records, key=lambda x: x[0])
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return shuffled, sorted_records
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def run_experiment_for_structure(struct_funcs, records, mode_name, repeats=5):
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results = []
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for rep in range(repeats):
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ds = struct_funcs['create']()
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start = time.perf_counter()
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for name, phone in records:
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ds = struct_funcs['insert'](ds, name, phone)
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insert_time = time.perf_counter() - start
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existing_names = [rec[0] for rec in records]
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sample_existing = random.sample(existing_names, 100)
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nonexistent = [f"None_{i}" for i in range(10)]
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search_names = sample_existing + nonexistent
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random.shuffle(search_names)
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start = time.perf_counter()
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for name in search_names:
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_ = struct_funcs['find'](ds, name)
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find_time = time.perf_counter() - start
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to_delete = random.sample(existing_names, 50)
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start = time.perf_counter()
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for name in to_delete:
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ds = struct_funcs['delete'](ds, name)
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delete_time = time.perf_counter() - start
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results.append({
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'structure': struct_funcs['name'],
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'mode': mode_name,
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'repetition': rep + 1,
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'insert_time': insert_time,
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'find_time': find_time,
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'delete_time': delete_time
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})
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return results
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def main_experiment():
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N = 10000
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REPEATS = 5
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print("Генерация тестовых данных...")
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base_records = generate_records(N)
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shuffled_records, sorted_records = prepare_datasets(base_records)
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print(f"Создано {N} записей. Случайный порядок и отсортированный готовы.")
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structures = {
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'LinkedList': {
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'name': 'LinkedList',
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'create': lambda: None,
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'insert': ll_insert,
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'find': ll_find,
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'delete': ll_delete
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},
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'HashTable': {
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'name': 'HashTable',
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'create': ht_create,
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'insert': ht_insert,
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'find': ht_find,
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'delete': ht_delete
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},
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'BST': {
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'name': 'BST',
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'create': lambda: None,
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'insert': bst_insert,
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'find': bst_find,
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'delete': bst_delete
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}
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}
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all_results = []
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for struct_name, funcs in structures.items():
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print(f"Тестирование {struct_name} на случайном порядке...")
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all_results.extend(run_experiment_for_structure(funcs, shuffled_records, 'random', REPEATS))
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print(f"Тестирование {struct_name} на отсортированном порядке...")
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all_results.extend(run_experiment_for_structure(funcs, sorted_records, 'sorted', REPEATS))
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csv_file = "experiment_results.csv"
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with open(csv_file, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(['Structure', 'Mode', 'Repeat', 'Insert (sec)', 'Search (sec)', 'Delete (sec)'])
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for rec in all_results:
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writer.writerow([
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rec['structure'],
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rec['mode'],
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rec['repetition'],
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f"{rec['insert_time']:.6f}",
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f"{rec['find_time']:.6f}",
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f"{rec['delete_time']:.6f}"
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])
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print(f"Результаты сохранены в {csv_file}")
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plot_results(csv_file)
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def plot_results(csv_path):
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df = pd.read_csv(csv_path)
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mean_times = df.groupby(['Structure', 'Mode'])[['Insert (sec)', 'Search (sec)', 'Delete (sec)']].mean().reset_index()
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structures = mean_times['Structure'].unique()
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modes = mean_times['Mode'].unique()
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fig, axes = plt.subplots(1, 3, figsize=(15, 5))
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operations = ['Insert (sec)', 'Search (sec)', 'Delete (sec)']
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titles = ['Вставка', 'Поиск', 'Удаление']
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for ax, op, title in zip(axes, operations, titles):
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x = range(len(structures))
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width = 0.35
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random_vals = []
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sorted_vals = []
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for s in structures:
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rand_row = mean_times[(mean_times['Structure'] == s) & (mean_times['Mode'] == 'random')]
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sort_row = mean_times[(mean_times['Structure'] == s) & (mean_times['Mode'] == 'sorted')]
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random_vals.append(rand_row[op].values[0] if not rand_row.empty else 0)
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sorted_vals.append(sort_row[op].values[0] if not sort_row.empty else 0)
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ax.bar([i - width/2 for i in x], random_vals, width, label='Случайный порядок')
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ax.bar([i + width/2 for i in x], sorted_vals, width, label='Отсортированный порядок')
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ax.set_xticks(x)
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ax.set_xticklabels(structures)
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ax.set_ylabel('Время (секунды)')
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ax.set_title(title)
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ax.legend()
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plt.tight_layout()
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plt.savefig('performance_comparison.png', dpi=150)
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plt.show()
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print("График сохранён как performance_comparison.png")
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if __name__ == "__main__":
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main_experiment() |