306 lines
9.1 KiB
Python
306 lines
9.1 KiB
Python
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 sys
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sys.setrecursionlimit(30000)
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def ll_insert(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': head}
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return new_node
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def ll_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 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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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 ll_list_all(head):
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entries = []
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curr = head
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while curr is not None:
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entries.append((curr['name'], curr['phone']))
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curr = curr['next']
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entries.sort(key=lambda x: x[0])
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return entries
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def _hash(name, bucket_count):
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h = 0
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for ch in name:
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h = (h * 31 + ord(ch)) % bucket_count
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return h
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def ht_create(bucket_count=2000):
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return [None] * bucket_count
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def ht_insert(buckets, name, phone):
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idx = _hash(name, len(buckets))
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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, len(buckets))
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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, len(buckets))
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buckets[idx] = ll_delete(buckets[idx], name)
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def ht_list_all(buckets):
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entries = []
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for head in buckets:
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curr = head
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while curr is not None:
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entries.append((curr['name'], curr['phone']))
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curr = curr['next']
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entries.sort(key=lambda x: x[0])
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return entries
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def bst_insert(root, name, phone):
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new_node = {'name': name, 'phone': phone, 'left': None, 'right': None}
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if root is None:
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return new_node
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parent = None
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curr = root
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while curr is not None:
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parent = curr
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if name < curr['name']:
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curr = curr['left']
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elif name > curr['name']:
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curr = curr['right']
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else:
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curr['phone'] = phone
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return root
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if name < parent['name']:
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parent['left'] = new_node
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else:
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parent['right'] = new_node
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return root
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def bst_find(root, name):
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while root is not 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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root = root['left']
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else:
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root = root['right']
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return None
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def _bst_min_node(node):
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while node and 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_min_node(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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def inorder(node, res):
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if node is None:
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return
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inorder(node['left'], res)
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res.append((node['name'], node['phone']))
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inorder(node['right'], res)
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result = []
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inorder(root, result)
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return result
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def generate_test_data(n=10000):
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records = [(f"User_{i:05d}", f"+7-999-{i:05d}") for i in range(n)]
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records_sorted = records[:]
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records_shuffled = records[:]
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random.shuffle(records_shuffled)
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return records_sorted, records_shuffled
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def measure_insert(struct_name, records):
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start = time.perf_counter()
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if struct_name == "LinkedList":
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head = None
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for name, phone in records:
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head = ll_insert(head, name, phone)
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obj = head
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elif struct_name == "HashTable":
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buckets = ht_create(bucket_count=2000)
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for name, phone in records:
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ht_insert(buckets, name, phone)
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obj = buckets
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elif struct_name == "BST":
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root = None
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for name, phone in records:
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root = bst_insert(root, name, phone)
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obj = root
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else:
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raise ValueError(f"Unknown structure: {struct_name}")
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elapsed = time.perf_counter() - start
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return elapsed, obj
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def measure_find(obj, struct_name, existing_names, nonexisting_names):
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start = time.perf_counter()
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for name in existing_names:
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if struct_name == "LinkedList":
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ll_find(obj, name)
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elif struct_name == "HashTable":
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ht_find(obj, name)
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else:
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bst_find(obj, name)
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for name in nonexisting_names:
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if struct_name == "LinkedList":
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ll_find(obj, name)
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elif struct_name == "HashTable":
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ht_find(obj, name)
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else:
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bst_find(obj, name)
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return time.perf_counter() - start
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def measure_delete(obj, struct_name, names_to_delete):
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start = time.perf_counter()
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if struct_name == "LinkedList":
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for name in names_to_delete:
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obj = ll_delete(obj, name)
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elif struct_name == "HashTable":
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for name in names_to_delete:
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ht_delete(obj, name)
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else:
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for name in names_to_delete:
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obj = bst_delete(obj, name)
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elapsed = time.perf_counter() - start
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return elapsed, obj
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def run_experiment(n=10000, repeats=5):
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records_sorted, records_shuffled = generate_test_data(n)
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existing_names = [name for name, _ in records_sorted[:100]]
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nonexisting_names = [f"None_{i}" for i in range(10)]
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all_names = [name for name, _ in records_sorted]
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structures = ["LinkedList", "HashTable", "BST"]
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modes = [("shuffled", records_shuffled), ("sorted", records_sorted)]
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results = []
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for struct_name in structures:
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for mode_name, records in modes:
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for rep in range(repeats):
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insert_time, obj = measure_insert(struct_name, records)
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results.append([struct_name, mode_name, "insert", rep+1, insert_time])
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find_time = measure_find(obj, struct_name, existing_names, nonexisting_names)
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results.append([struct_name, mode_name, "find", rep+1, find_time])
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random.seed(rep)
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to_delete = random.sample(all_names, 50)
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delete_time, obj = measure_delete(obj, struct_name, to_delete)
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results.append([struct_name, mode_name, "delete", rep+1, delete_time])
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return results
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def save_results_to_csv(results, filename="docs/data/results.csv"):
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os.makedirs(os.path.dirname(filename), exist_ok=True)
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with open(filename, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(["Structure", "Mode", "Operation", "Repeat", "Time_sec"])
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writer.writerows(results)
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print(f"Результаты сохранены в {filename}")
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def aggregate_results(results):
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from collections import defaultdict
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agg = defaultdict(list)
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for row in results:
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struct, mode, op, rep, t = row
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agg[(struct, mode, op)].append(t)
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means = {k: sum(v)/len(v) for k, v in agg.items()}
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return means
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def plot_results(means, output_dir="docs"):
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try:
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import matplotlib.pyplot as plt
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import numpy as np
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except ImportError:
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print("Matplotlib не установлен. Графики не построены.")
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return
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operations = ["insert", "find", "delete"]
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structures = ["LinkedList", "HashTable", "BST"]
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modes = ["shuffled", "sorted"]
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fig, axes = plt.subplots(1, 3, figsize=(15, 5))
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for idx, op in enumerate(operations):
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ax = axes[idx]
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x = np.arange(len(structures))
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width = 0.35
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shuffled_means = [means.get((struct, "shuffled", op), 0) for struct in structures]
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sorted_means = [means.get((struct, "sorted", op), 0) for struct in structures]
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ax.bar(x - width/2, shuffled_means, width, label='случайный порядок', color='skyblue')
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ax.bar(x + width/2, sorted_means, width, label='отсортированный порядок', color='salmon')
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ax.set_xticks(x)
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ax.set_xticklabels(structures, rotation=15)
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ax.set_ylabel('Время (сек)')
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ax.set_title(f'{op.upper()}')
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ax.legend()
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ax.grid(axis='y', linestyle='--', alpha=0.7)
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, "comparison.png"), dpi=150)
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plt.show()
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if __name__ == "__main__":
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results = run_experiment(n=10000, repeats=5)
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save_results_to_csv(results)
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means = aggregate_results(results)
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print("\nСреднее время по операциям (сек):")
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for (struct, mode, op), t in sorted(means.items()):
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print(f"{struct:12} {mode:8} {op:6} : {t:.6f}")
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plot_results(means) |