forked from UNN/2026-rff_mp
255 lines
7.8 KiB
Python
255 lines
7.8 KiB
Python
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import sys
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sys.setrecursionlimit(20000)
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csv_path = 'C:/Users/xalva/2026-rff_mp/stepinim/docs/data/results.csv'
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#Связный список
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def ll_insert(head, name, phone):
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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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curr = head
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prev = None
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while curr is not None:
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if curr['name'] == name:
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curr['phone'] = phone
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return head
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prev = curr
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curr = curr['next']
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prev['next'] = new_node
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return head
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def ll_find(head, name):
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curr = head
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while curr:
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if curr['name'] == name:
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return curr['phone']
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curr = curr['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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curr = head
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while curr['next']:
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if curr['next']['name'] == name:
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curr['next'] = curr['next']['next']
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return head
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curr = curr['next']
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return head
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def ll_list_all(head):
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result = []
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curr = head
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while curr:
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result.append((curr['name'], curr['phone']))
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curr = curr['next']
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result.sort(key=lambda x: x[0])
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return result
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#Хэш-таблица
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HASH_SIZE = 1009
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def _hash_name(name):
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return hash(name) % HASH_SIZE
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def ht_insert(buckets, name, phone):
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idx = _hash_name(name)
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buckets[idx] = ll_insert(buckets[idx], name, phone)
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def ht_find(buckets, name):
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idx = _hash_name(name)
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return ll_find(buckets[idx], name)
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def ht_delete(buckets, name):
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idx = _hash_name(name)
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buckets[idx] = ll_delete(buckets[idx], name)
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def ht_list_all(buckets):
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all_entries = []
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for bucket in buckets:
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if bucket is not None:
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curr = bucket
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while curr:
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all_entries.append((curr['name'], curr['phone']))
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curr = curr['next']
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all_entries.sort(key=lambda x: x[0])
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return all_entries
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#Двоичное дерево поиска
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def bst_insert(root, name, phone):
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if root is None:
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return {'name': name, 'phone': phone, 'left': None, 'right': None}
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if name < root['name']:
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root['left'] = bst_insert(root['left'], name, phone)
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elif name > root['name']:
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root['right'] = bst_insert(root['right'], name, phone)
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else:
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root['phone'] = phone
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return root
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def bst_find(root, name):
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curr = root
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while curr:
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if name == curr['name']:
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return curr['phone']
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elif name < curr['name']:
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curr = curr['left']
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else:
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curr = curr['right']
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return None
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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 = root['right']
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while min_node['left']:
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min_node = min_node['left']
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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:
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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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#ТЕСТ
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import random
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random.seed(42)
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N = 10000
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base_records = [(f"User_{i:05d}", f"123-{i:05d}") for i in range(N)]
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records_shuffled = base_records.copy()
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random.shuffle(records_shuffled)
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records_sorted = sorted(base_records, key=lambda x: x[0])
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# 100 случайных существующих имён из всего набора
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random_sample = random.sample(base_records, 100)
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search_existing = [name for name, _ in random_sample]
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# 10 несуществующих
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search_nonexist = [f"None_{i}" for i in range(10)]
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# 50 случайных для удаления
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delete_sample = random.sample(base_records, 50)
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delete_names = [name for name, _ in delete_sample]
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import time
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import csv
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import statistics
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def measure_operations(records, struct_type):
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results = []
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for rep in range(5):
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if struct_type == 'll':
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head = None
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elif struct_type == 'ht':
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head = [None] * HASH_SIZE
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else:
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root = None
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start = time.perf_counter()
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if struct_type == 'll':
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for name, phone in records:
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head = ll_insert(head, name, phone)
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elif struct_type == 'ht':
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for name, phone in records:
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ht_insert(head, name, phone)
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else:
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for name, phone in records:
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root = bst_insert(root, name, phone)
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insert_time = time.perf_counter() - start
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results.append((rep + 1, 'insert', insert_time))
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start = time.perf_counter()
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if struct_type == 'll':
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for name in search_existing + search_nonexist:
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ll_find(head, name)
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elif struct_type == 'ht':
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for name in search_existing + search_nonexist:
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ht_find(head, name)
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else:
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for name in search_existing + search_nonexist:
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bst_find(root, name)
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search_time = time.perf_counter() - start
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results.append((rep + 1, 'search', search_time))
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start = time.perf_counter()
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if struct_type == 'll':
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for name in delete_names:
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head = ll_delete(head, name)
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elif struct_type == 'ht':
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for name in delete_names:
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ht_delete(head, name)
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else:
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for name in delete_names:
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root = bst_delete(root, name)
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delete_time = time.perf_counter() - start
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results.append((rep + 1, 'delete', delete_time))
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return results
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all_data = []
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for struct_name, mode_label, records in [
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("LinkedList", "shuffled", records_shuffled),
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("LinkedList", "sorted", records_sorted),
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("HashTable", "shuffled", records_shuffled),
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("HashTable", "sorted", records_sorted),
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("BST", "shuffled", records_shuffled),
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("BST", "sorted", records_sorted),
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]:
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for rep, op, t in measure_operations(records, struct_name.split('d')[0].lower()[:2] if 'Linked' in struct_name else ('ht' if 'Hash' in struct_name else 'bst')):
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all_data.append([struct_name, mode_label, op, f"{t:.6f}"])
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with open(csv_path, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(["Структура", "Режим", "Операция", "Время (сек)"])
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writer.writerows(all_data)
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print("CSV сохранён в docs/data/results.csv")
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import matplotlib.pyplot as plt
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import pandas as pd
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df = pd.read_csv(csv_path)
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df_avg = df.groupby(['Структура', 'Режим', 'Операция'])['Время (сек)'].mean().reset_index()
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fig, ax = plt.subplots(figsize=(10,6))
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ops = ['insert', 'search', 'delete']
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x = range(len(ops))
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width = 0.12
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for i, (struct, mode) in enumerate([('LinkedList','shuffled'),('LinkedList','sorted'),
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('HashTable','shuffled'),('HashTable','sorted'),
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('BST','shuffled'),('BST','sorted')]):
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subset = df_avg[(df_avg['Структура']==struct) & (df_avg['Режим']==mode)]
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times = [subset[subset['Операция']==op]['Время (сек)'].values[0] for op in ops]
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ax.bar([p + i*width for p in x], times, width, label=f"{struct} ({mode})")
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ax.set_xticks([p + 2.5*width for p in x])
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ax.set_xticklabels(ops)
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ax.set_ylabel('Среднее время (сек)')
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ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
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plt.tight_layout()
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plt.savefig('C:/Users/xalva/2026-rff_mp/stepinim/docs/data/grafik.png')
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plt.show()
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