forked from UNN/2026-rff_mp
278 lines
7.4 KiB
Python
278 lines
7.4 KiB
Python
def linked_list_add(head, name, phone):
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curr = head
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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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curr = curr['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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curr = head
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while curr['next'] is not None:
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curr = curr['next']
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curr['next'] = new_node
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return head
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def linked_list_find(head, name):
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curr = head
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while curr is not None:
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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 linked_list_remove(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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curr = head['next']
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while curr is not None:
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if curr['name'] == name:
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prev['next'] = curr['next']
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return head
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prev = curr
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curr = curr['next']
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return head
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def linked_list_collect_all(head):
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records = []
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curr = head
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while curr is not None:
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records.append((curr['name'], curr['phone']))
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curr = curr['next']
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records.sort(key=lambda pair: pair[0])
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return records
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#HASH
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def _hash_bucket_index(key, table_size):
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return hash(key) % table_size
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def hash_table_create(bucket_count=10):
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return [None] * bucket_count
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def hash_table_put(table, name, phone):
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idx = _hash_bucket_index(name, len(table))
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table[idx] = linked_list_add(table[idx], name, phone)
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return table
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def hash_table_get(table, name):
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idx = _hash_bucket_index(name, len(table))
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return linked_list_find(table[idx], name)
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def hash_table_remove(table, name):
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idx = _hash_bucket_index(name, len(table))
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table[idx] = linked_list_remove(table[idx], name)
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return table
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def hash_table_collect_all(table):
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all_records = []
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for head in table:
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curr = head
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while curr is not None:
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all_records.append((curr['name'], curr['phone']))
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curr = curr['next']
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all_records.sort(key=lambda pair: pair[0])
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return all_records
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#BST
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def _bst_new_node(name, phone):
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return {'name': name, 'phone': phone, 'left': None, 'right': None}
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def bst_add(root, name, phone):
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"""Insert or update. Returns (possibly new) root."""
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if root is None:
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return _bst_new_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_add(root['left'], name, phone)
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else:
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root['right'] = bst_add(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_minimum(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_remove(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_remove(root['left'], name)
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elif name > root['name']:
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root['right'] = bst_remove(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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successor = _bst_find_minimum(root['right'])
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root['name'] = successor['name']
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root['phone'] = successor['phone']
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root['right'] = bst_remove(root['right'], successor['name'])
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return root
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def bst_collect_inorder(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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#Benchmarking
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import random
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import time
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import csv
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import os
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import sys
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sys.setrecursionlimit(20000)
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def generate_test_data(n, seed=42):
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random.seed(seed)
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records = []
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for i in range(1, n+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_ordered_and_shuffled(records):
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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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def measure_operations(struct_ops, 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_ops['create']()
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start = time.perf_counter()
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for name, phone in records:
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ds = struct_ops['insert'](ds, name, phone)
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insert_time = time.perf_counter() - start
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existing_names = [name for name, _ in records]
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sample_existing = random.sample(existing_names, 100)
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nonexistent = [f"Missing_{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_ops['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_ops['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_ops['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 run_full_benchmark():
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N = 10000
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base_records = generate_test_data(N)
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shuffled, sorted_records = prepare_ordered_and_shuffled(base_records)
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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': linked_list_add,
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'find': linked_list_find,
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'delete': linked_list_remove,
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},
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'HashTable': {
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'name': 'HashTable',
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'create': lambda: hash_table_create(100),
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'insert': hash_table_put,
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'find': hash_table_get,
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'delete': hash_table_remove,
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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_add,
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'find': bst_find,
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'delete': bst_remove,
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}
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}
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all_results = []
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for name, ops in structures.items():
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print(f"Benchmarking {name} on random order...")
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all_results.extend(measure_operations(ops, shuffled, 'random', repeats=5))
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print(f"Benchmarking {name} on sorted order...")
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all_results.extend(measure_operations(ops, sorted_records, 'sorted', repeats=5))
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os.makedirs('docs/data', exist_ok=True)
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csv_path = 'docs/data/experiment_results.csv'
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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(['Structure', 'Mode', 'Repeat', 'Insert (sec)', 'Search (sec)', 'Delete (sec)'])
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for r in all_results:
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writer.writerow([
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r['structure'],
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r['mode'],
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r['repetition'],
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f"{r['insert_time']:.6f}",
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f"{r['find_time']:.6f}",
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f"{r['delete_time']:.6f}"
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])
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print(f"Experiment finished. Results saved to {csv_path}")
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if __name__ == '__main__':
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run_full_benchmark() |