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@ -1,477 +0,0 @@
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import time
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import random
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import csv
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import sys
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sys.setrecursionlimit(20000)
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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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if head['name'] == name:
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head['phone'] = phone
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return head
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current = head
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while current['next'] is not None:
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if current['next']['name'] == name:
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current['next']['phone'] = phone
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return head
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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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current = head
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while current['next'] is not None:
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if current['next']['name'] == name:
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current['next'] = current['next']['next']
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return head
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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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# Хэш функции
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def hash_function(name, table_size):
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return sum(ord(c) for c in name) % table_size
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def ht_create(size=1000):
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return [None] * size
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def ht_insert(buckets, name, phone):
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size = len(buckets)
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index = hash_function(name, size)
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buckets[index] = ll_insert(buckets[index], name, phone)
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def ht_find(buckets, name):
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size = len(buckets)
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index = hash_function(name, size)
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return ll_find(buckets[index], name)
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def ht_delete(buckets, name):
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size = len(buckets)
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index = hash_function(name, size)
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buckets[index] = ll_delete(buckets[index], name)
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def ht_list_all(buckets):
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records = []
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for bucket in buckets:
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current = bucket
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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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# Функции для дерева 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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current = root
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while current is not None:
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if name == current['name']:
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return current['phone']
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elif name < current['name']:
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current = current['left']
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else:
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current = current['right']
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return None
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def bst_find_min(node):
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current = node
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while current['left'] is not None:
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current = current['left']
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return current
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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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elif 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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records = []
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def inorder_traversal(node):
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if node is not None:
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inorder_traversal(node['left'])
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records.append((node['name'], node['phone']))
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inorder_traversal(node['right'])
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inorder_traversal(root)
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return records
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# Генерация тестовых данных
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def generate_records(count=10000):
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records = []
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for i in range(count):
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name = f"User_{i:05d}"
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phone = f"+7-{random.randint(100,999)}-{random.randint(100,999)}-{random.randint(1000,9999)}"
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records.append((name, phone))
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shuffled = records.copy()
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random.shuffle(shuffled)
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sorted_records = sorted(records, key=lambda x: x[0])
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return shuffled, sorted_records
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# Замер времени
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def measure_insertion(structure_name, records):
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times = []
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filled_structure = None
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for run in range(5):
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if structure_name == "linked_list":
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structure = None
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elif structure_name == "hash_table":
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structure = ht_create(1000)
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elif structure_name == "bst":
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structure = None
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start = time.perf_counter()
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for name, phone in records:
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if structure_name == "linked_list":
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structure = ll_insert(structure, name, phone)
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elif structure_name == "hash_table":
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ht_insert(structure, name, phone)
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elif structure_name == "bst":
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structure = bst_insert(structure, name, phone)
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end = time.perf_counter()
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times.append(end - start)
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if run == 4:
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filled_structure = structure
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return times, filled_structure
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def measure_search(structure_name, structure, search_names):
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times = []
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for run in range(5):
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start = time.perf_counter()
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for name in search_names:
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if structure_name == "linked_list":
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ll_find(structure, name)
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elif structure_name == "hash_table":
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ht_find(structure, name)
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elif structure_name == "bst":
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bst_find(structure, name)
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end = time.perf_counter()
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times.append(end - start)
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return times
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def measure_deletion(structure_name, original_structure, delete_names):
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times = []
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for run in range(5):
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if structure_name == "linked_list":
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all_records = ll_list_all(original_structure)
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test_structure = None
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for name, phone in all_records:
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test_structure = ll_insert(test_structure, name, phone)
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elif structure_name == "hash_table":
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all_records = ht_list_all(original_structure)
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test_structure = ht_create(1000)
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for name, phone in all_records:
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ht_insert(test_structure, name, phone)
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elif structure_name == "bst":
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all_records = bst_list_all(original_structure)
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test_structure = None
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for name, phone in all_records:
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test_structure = bst_insert(test_structure, name, phone)
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start = time.perf_counter()
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for name in delete_names:
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if structure_name == "linked_list":
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test_structure = ll_delete(test_structure, name)
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elif structure_name == "hash_table":
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ht_delete(test_structure, name)
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elif structure_name == "bst":
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test_structure = bst_delete(test_structure, name)
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end = time.perf_counter()
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times.append(end - start)
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return times
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# Основная часть
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def run_experiment():
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current_dir = os.path.dirname(__name__)
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docs_dir = os.path.dirname(current_dir)
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csv_file = os.path.join(docs_dir, "experiment_results.csv")
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print("=" * 70)
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print("ЭКСПЕРИМЕНТАЛЬНОЕ СРАВНЕНИЕ СТРУКТУР ДАННЫХ")
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print("Телефонный справочник - 10000 записей")
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print("=" * 70)
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print(f"\nРезультаты будут сохранены в: {csv_file}")
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print("\n1. Генерация тестовых данных...")
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shuffled_records, sorted_records = generate_records(10000)
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print(f" Сгенерировано 10000 записей")
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existing_names = [shuffled_records[i][0] for i in random.sample(range(10000), 100)]
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nonexisting_names = [f"NotExist_{i}" for i in range(10)]
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search_names = existing_names + nonexisting_names
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delete_names = [shuffled_records[i][0] for i in random.sample(range(10000), 50)]
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results = [["Структура", "Режим", "Операция",
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"Замер1(с)", "Замер2(с)", "Замер3(с)", "Замер4(с)", "Замер5(с)",
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"Среднее(с)"]]
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for mode_name, records in [("случайный", shuffled_records),
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("отсортированный", sorted_records)]:
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print(f"\n2. Тестирование режима: {mode_name}")
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print("-" * 50)
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for struct_name in ["linked_list", "hash_table", "bst"]:
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print(f"\n {struct_name.upper()}:")
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print(" Вставка 10000 записей...")
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insert_times, filled_struct = measure_insertion(struct_name, records)
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avg_insert = sum(insert_times) / 5
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print(f" Время: {avg_insert:.4f} сек (среднее)")
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print(" Поиск 110 записей...")
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search_times = measure_search(struct_name, filled_struct, search_names)
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avg_search = sum(search_times) / 5
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print(f" Время: {avg_search:.4f} сек (среднее)")
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print(" Удаление 50 записей...")
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delete_times = measure_deletion(struct_name, filled_struct, delete_names)
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avg_delete = sum(delete_times) / 5
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print(f" Время: {avg_delete:.4f} сек (среднее)")
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results.append([struct_name, mode_name, "вставка"] + insert_times + [avg_insert])
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results.append([struct_name, mode_name, "поиск"] + search_times + [avg_search])
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results.append([struct_name, mode_name, "удаление"] + delete_times + [avg_delete])
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print("\n3. Сохранение результатов...")
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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.writerows(results)
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print(f" Результаты сохранены в: {csv_file}")
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print("\n" + "=" * 70)
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print("СВОДНАЯ ТАБЛИЦА РЕЗУЛЬТАТОВ")
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print("=" * 70)
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print(f"{'Структура':<15} {'Режим':<12} {'Операция':<10} {'Среднее время (сек)':<20}")
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print("-" * 70)
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for row in results[1:]:
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struct, mode, op, t1, t2, t3, t4, t5, avg = row
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print(f"{struct:<15} {mode:<12} {op:<10} {avg:<20.6f}")
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return results, docs_dir
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# Графики
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def create_graphs(results, docs_dir):
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print("\n4. Построение графиков...")
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data = {}
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for row in results[1:]:
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struct = row[0]
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mode = row[1]
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op = row[2]
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avg = row[8]
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|
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if struct not in data:
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|
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data[struct] = {}
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|
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if mode not in data[struct]:
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|
|
data[struct][mode] = {}
|
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|
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data[struct][mode][op] = avg
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|
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struct_labels = {
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'linked_list': 'LinkedList',
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'hash_table': 'HashTable',
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'bst': 'BST'
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}
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|
|
colors = {
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'linked_list': '#3498db',
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'hash_table': '#2ecc71',
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|
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'bst': '#e74c3c'
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|
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}
|
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|
|
|
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|
|
fig, axes = plt.subplots(1, 3, figsize=(15, 6))
|
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|
|
fig.suptitle('Сравнение производительности структур данных', fontsize=16, fontweight='bold')
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|
|
|
|
|
|
operations = ['вставка', 'поиск', 'удаление']
|
|
|
|
|
operation_titles = ['Вставка\n(10000 записей)', 'Поиск\n(110 запросов)', 'Удаление\n(50 записей)']
|
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|
|
|
modes = ['случайный', 'отсортированный']
|
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|
|
|
mode_labels = ['Случайный', 'Отсортированный']
|
|
|
|
|
|
|
|
|
|
for idx, (op, op_title) in enumerate(zip(operations, operation_titles)):
|
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|
|
ax = axes[idx]
|
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|
|
|
|
|
|
|
|
# Позиции для групп столбцов
|
|
|
|
|
x = np.arange(len(modes)) # [0, 1]
|
|
|
|
|
width = 0.25 # ширина одного столбца
|
|
|
|
|
multiplier = 0
|
|
|
|
|
|
|
|
|
|
for struct in ['linked_list', 'hash_table', 'bst']:
|
|
|
|
|
values = [data[struct][mode][op] for mode in modes]
|
|
|
|
|
offset = width * multiplier
|
|
|
|
|
bars = ax.bar(x + offset, values, width,
|
|
|
|
|
label=struct_labels[struct],
|
|
|
|
|
color=colors[struct],
|
|
|
|
|
edgecolor='black', linewidth=0.5)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for bar, val in zip(bars, values):
|
|
|
|
|
if val < 0.01:
|
|
|
|
|
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + val*0.05,
|
|
|
|
|
f'{val:.5f}', ha='center', va='bottom', fontsize=8, rotation=0)
|
|
|
|
|
else:
|
|
|
|
|
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + val*0.02,
|
|
|
|
|
f'{val:.4f}', ha='center', va='bottom', fontsize=8, rotation=0)
|
|
|
|
|
|
|
|
|
|
multiplier += 1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ax.set_title(op_title, fontsize=12, fontweight='bold')
|
|
|
|
|
ax.set_ylabel('Время (секунды)', fontsize=10)
|
|
|
|
|
ax.set_xlabel('Режим данных', fontsize=10)
|
|
|
|
|
ax.set_xticks(x + width)
|
|
|
|
|
ax.set_xticklabels(mode_labels)
|
|
|
|
|
ax.legend(loc='upper left', fontsize=8)
|
|
|
|
|
ax.grid(True, alpha=0.3, axis='y')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
all_values = [data[s][m][op] for s in ['linked_list', 'hash_table', 'bst'] for m in modes]
|
|
|
|
|
if max(all_values) / min(all_values) > 100:
|
|
|
|
|
ax.set_yscale('log')
|
|
|
|
|
ax.set_ylabel('Время (секунды) - логарифмическая шкала', fontsize=9)
|
|
|
|
|
|
|
|
|
|
plt.tight_layout()
|
|
|
|
|
graph_path = os.path.join(docs_dir, "performance_graphs.png")
|
|
|
|
|
plt.savefig(graph_path, dpi=150, bbox_inches='tight')
|
|
|
|
|
plt.close()
|
|
|
|
|
print(f" Графики сохранены в: {graph_path}")
|
|
|
|
|
|
|
|
|
|
return graph_path
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
|
|
|
|
|
|
results, docs_dir = run_experiment()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
graph_file = create_graphs(results, docs_dir)
|
|
|
|
|
|
|
|
|
|
print("\n" + "=" * 70)
|
|
|
|
|
print("ЭКСПЕРИМЕНТ ЗАВЕРШЕН УСПЕШНО!")
|
|
|
|
|
print("=" * 70)
|
|
|
|
|
print("\nСОЗДАННЫЕ ФАЙЛЫ:")
|
|
|
|
|
print(f" Данные: {os.path.join(docs_dir, 'experiment_results.csv')}")
|
|
|
|
|
print(f" Графики: {graph_file}")
|
|
|
|
|
|
|
|
|
|
except Exception as e:
|
|
|
|
|
print(f"\nОшибка при построении графиков: {e}")
|
|
|
|
|
print(" Убедитесь, что установлен matplotlib: pip install matplotlib")
|
|
|
|
|
print("\n" + "=" * 70)
|
|
|
|
|
print("ЭКСПЕРИМЕНТ ЗАВЕРШЕН (без графиков)")
|
|
|
|
|
print("=" * 70)
|
|
|
|
|
print(f"\nCSV файл сохранен: {os.path.join(docs_dir, 'experiment_results.csv')}")
|
