stepinim #232

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stepinim wants to merge 5 commits from stepinim/2026-rff_mp:stepinim into develop
20 changed files with 1670 additions and 54 deletions

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# Игнорируемые файлы по умолчанию
/shelf/
/workspace.xml
# HTTP-клиент на основе редактора
/httpRequests/

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<?xml version="1.0" encoding="UTF-8"?>
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<orderEntry type="jdk" jdkName="Python 3.14" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

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<component name="InspectionProjectProfileManager">
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<?xml version="1.0" encoding="UTF-8"?>
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# 2026-MP
Практика по курсам "Методы программирования" и "Программная инженерия" РФФ ННГУ
[Презентация по курсу (обновляемая)](https://docs.google.com/presentation/d/1wmYjy5QDoYECEHi7NAAINPulU9pLsaIi-aLaUppspps/edit?usp=sharing)
Для работы необходим python 3.11 и выше. Библиотеки: numpy, pandas, matplotlib, tensorflow, Pillow. Редактор любой. Из неплохих: IDLE (родной, идёт вместе с установщиком), Visual Studio Code, notepad++, PyCharm, vim (для любителей сначала страдать, потом наслаждаться).
Работа с блокнотами онлайн, с возможностью подключения удалённых мощностей гугла (GPU, TPU): https://colab.research.google.com/
Мой контакт: nsmorozov@rf.unn.ru
Внутри папки группы создать папку имени себя (фамилия и имя). В своей папке можете делать все что угодно, в чужие не залезать, в корневую тоже. Я буду ориентироваться на файлы, где в названии будет номер лабораторной.
**Название пулл-реквеста должно начинаться с квадратных скобок, в которых перечислены номера сдаваемых лабораторных работ. Не больше одного активного реквеста, если надо довнести -- надо обновить текущий.**
### Крайний срок приема работ 25.05.2026 до 14:00
## Задание 1 -- репозиторий
0. Создай пользователя (логин — фамилия+инициалы слитно транслитом, как в терминал-классе).
1. Зайди в этот репозиторий на Gitea, нажми кнопку **Форкнуть**, чтобы создать копию в своем аккаунте.
2. **Клонирование:** Скопируй ссылку на свой форк и выполни:
```bash
git clone <ссылкаааш_форк>
cd <название_репозитория>
```
3. **Создай ветку** (название — фамилия+инициалы слитно транслитом, буква в букву как логин):
```bash
git checkout -b IvanovII
```
4. **Создай папку** с таким же названием (`IvanovII`) и внутри неё — текстовый файл, названный номером вашей группы (например, `101.md`).
5. **Сохрани изменения:**
```bash
git add -A
git commit -m "[0] initial commit"
```
6. Отправь ветку **в свой форк** на Gitea:
```bash
git push origin IvanovII
```
7. **Создай запрос на слияние (Pull Request):** На Gitea перейди в свой форк, выбери ветку `IvanovII`, нажмите **Запрос на слияние**. Убедитесь, что:
- Базовый репозиторий: **учебный** (преподавателя)
- Базовая ветка: **develop**
- Сравниваемая ветка: **свой форк / IvanovII**
8. Отправь PR.

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Структура,Режим,Повтор,Операция,Время (сек)
LinkedList,shuffled,1,insert,0.5023735999711789
LinkedList,shuffled,1,search,0.022223800013307482
LinkedList,shuffled,1,delete,0.010106799949426204
LinkedList,shuffled,2,insert,0.5151404999778606
LinkedList,shuffled,2,search,0.023844500014092773
LinkedList,shuffled,2,delete,0.010028599994257092
LinkedList,shuffled,3,insert,0.5328615000471473
LinkedList,shuffled,3,search,0.020557800016831607
LinkedList,shuffled,3,delete,0.012162799946963787
LinkedList,sorted,1,insert,0.4577932999818586
LinkedList,sorted,1,search,0.017212599981576204
LinkedList,sorted,1,delete,0.012185800005681813
LinkedList,sorted,2,insert,0.43183969997335225
LinkedList,sorted,2,search,0.01829650002764538
LinkedList,sorted,2,delete,0.012130599992815405
LinkedList,sorted,3,insert,0.436789300001692
LinkedList,sorted,3,search,0.017460400005802512
LinkedList,sorted,3,delete,0.012465099978726357
HashTable,shuffled,1,insert,0.0032562999986112118
HashTable,shuffled,1,search,9.469996439293027e-05
HashTable,shuffled,1,delete,5.15999854542315e-05
HashTable,shuffled,2,insert,0.0031429000082425773
HashTable,shuffled,2,search,9.000004502013326e-05
HashTable,shuffled,2,delete,4.360004095360637e-05
HashTable,shuffled,3,insert,0.003212600015103817
HashTable,shuffled,3,search,0.00010830000974237919
HashTable,shuffled,3,delete,4.650000482797623e-05
HashTable,sorted,1,insert,0.0030796999926678836
HashTable,sorted,1,search,8.420000085607171e-05
HashTable,sorted,1,delete,4.730001091957092e-05
HashTable,sorted,2,insert,0.0030180999892763793
HashTable,sorted,2,search,9.079999290406704e-05
HashTable,sorted,2,delete,5.299999611452222e-05
HashTable,sorted,3,insert,0.0029779999749734998
HashTable,sorted,3,search,8.510000770911574e-05
HashTable,sorted,3,delete,6.589997792616487e-05
BST,shuffled,1,insert,0.011618499993346632
BST,shuffled,1,search,0.00031289999606087804
BST,shuffled,1,delete,0.0002456999500282109
BST,shuffled,2,insert,0.021565500006545335
BST,shuffled,2,search,0.00032350001856684685
BST,shuffled,2,delete,0.0002101999707520008
BST,shuffled,3,insert,0.011865400010719895
BST,shuffled,3,search,0.0003497999859973788
BST,shuffled,3,delete,0.0002114999806508422
BST,sorted,1,insert,1.961912199971266
BST,sorted,1,search,0.025325599999632686
BST,sorted,1,delete,0.03309909999370575
BST,sorted,2,insert,1.8450072000268847
BST,sorted,2,search,0.025074300006963313
BST,sorted,2,delete,0.03284020000137389
BST,sorted,3,insert,1.8502263000118546
BST,sorted,3,search,0.028948499995749444
BST,sorted,3,delete,0.040639499959070235
1 Структура Режим Повтор Операция Время (сек)
2 LinkedList shuffled 1 insert 0.5023735999711789
3 LinkedList shuffled 1 search 0.022223800013307482
4 LinkedList shuffled 1 delete 0.010106799949426204
5 LinkedList shuffled 2 insert 0.5151404999778606
6 LinkedList shuffled 2 search 0.023844500014092773
7 LinkedList shuffled 2 delete 0.010028599994257092
8 LinkedList shuffled 3 insert 0.5328615000471473
9 LinkedList shuffled 3 search 0.020557800016831607
10 LinkedList shuffled 3 delete 0.012162799946963787
11 LinkedList sorted 1 insert 0.4577932999818586
12 LinkedList sorted 1 search 0.017212599981576204
13 LinkedList sorted 1 delete 0.012185800005681813
14 LinkedList sorted 2 insert 0.43183969997335225
15 LinkedList sorted 2 search 0.01829650002764538
16 LinkedList sorted 2 delete 0.012130599992815405
17 LinkedList sorted 3 insert 0.436789300001692
18 LinkedList sorted 3 search 0.017460400005802512
19 LinkedList sorted 3 delete 0.012465099978726357
20 HashTable shuffled 1 insert 0.0032562999986112118
21 HashTable shuffled 1 search 9.469996439293027e-05
22 HashTable shuffled 1 delete 5.15999854542315e-05
23 HashTable shuffled 2 insert 0.0031429000082425773
24 HashTable shuffled 2 search 9.000004502013326e-05
25 HashTable shuffled 2 delete 4.360004095360637e-05
26 HashTable shuffled 3 insert 0.003212600015103817
27 HashTable shuffled 3 search 0.00010830000974237919
28 HashTable shuffled 3 delete 4.650000482797623e-05
29 HashTable sorted 1 insert 0.0030796999926678836
30 HashTable sorted 1 search 8.420000085607171e-05
31 HashTable sorted 1 delete 4.730001091957092e-05
32 HashTable sorted 2 insert 0.0030180999892763793
33 HashTable sorted 2 search 9.079999290406704e-05
34 HashTable sorted 2 delete 5.299999611452222e-05
35 HashTable sorted 3 insert 0.0029779999749734998
36 HashTable sorted 3 search 8.510000770911574e-05
37 HashTable sorted 3 delete 6.589997792616487e-05
38 BST shuffled 1 insert 0.011618499993346632
39 BST shuffled 1 search 0.00031289999606087804
40 BST shuffled 1 delete 0.0002456999500282109
41 BST shuffled 2 insert 0.021565500006545335
42 BST shuffled 2 search 0.00032350001856684685
43 BST shuffled 2 delete 0.0002101999707520008
44 BST shuffled 3 insert 0.011865400010719895
45 BST shuffled 3 search 0.0003497999859973788
46 BST shuffled 3 delete 0.0002114999806508422
47 BST sorted 1 insert 1.961912199971266
48 BST sorted 1 search 0.025325599999632686
49 BST sorted 1 delete 0.03309909999370575
50 BST sorted 2 insert 1.8450072000268847
51 BST sorted 2 search 0.025074300006963313
52 BST sorted 2 delete 0.03284020000137389
53 BST sorted 3 insert 1.8502263000118546
54 BST sorted 3 search 0.028948499995749444
55 BST sorted 3 delete 0.040639499959070235

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В ходе экспериментов было показано, что производительность структуры данных сильно зависит
от её внутреннего устройства и характера входных данных.
BST работает быстро на случайных данных, но при отсортированном порядке деградирует почти до
связного списка, из-за чего время вставки и удаления резко увеличивается. Хеш-таблица
практически не зависит от порядка входных данных, так как доступ к элементам происходит через
хеш-функцию, поэтому она показала лучшие результаты при поиске и вставке. Связный список
оказался самым медленным при поиске, так как требует последовательного обхода элементов.
Удаление также работает по-разному: в связном списке и BST сначала требуется поиск элемента,
а в хеш-таблице удаление обычно выполняется быстрее за счёт обращения к нужному бакету.
На практике хеш-таблицы лучше подходят для частого поиска и вставки данных, BST — когда
важно хранить элементы в отсортированном виде, а связные списки полезны в более простых
задачах, где структура данных часто изменяется и не требуется быстрый поиск.

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import sys
sys.setrecursionlimit(30000)
csv_path = '/stepinim/docs/data/lab1_results.csv'
#Связный список
def ll_insert(head, name, phone):
new_node = {'name': name, 'phone': phone, 'next': None}
if head is None:
return new_node
curr = head
prev = None
while curr is not None:
if curr['name'] == name:
curr['phone'] = phone
return head
prev = curr
curr = curr['next']
prev['next'] = new_node
return head
def ll_find(head, name):
curr = head
while curr:
if curr['name'] == name:
return curr['phone']
curr = curr['next']
return None
def ll_delete(head, name):
if head is None:
return None
if head['name'] == name:
return head['next']
curr = head
while curr['next']:
if curr['next']['name'] == name:
curr['next'] = curr['next']['next']
return head
curr = curr['next']
return head
def ll_list_all(head):
result = []
curr = head
while curr:
result.append((curr['name'], curr['phone']))
curr = curr['next']
result.sort(key=lambda x: x[0])
return result
#Хэш-таблица
HASH_SIZE = 1009
def _hash_name(name):
return hash(name) % HASH_SIZE
def ht_insert(buckets, name, phone):
idx = _hash_name(name)
buckets[idx] = ll_insert(buckets[idx], name, phone)
def ht_find(buckets, name):
idx = _hash_name(name)
return ll_find(buckets[idx], name)
def ht_delete(buckets, name):
idx = _hash_name(name)
buckets[idx] = ll_delete(buckets[idx], name)
def ht_list_all(buckets):
all_entries = []
for bucket in buckets:
if bucket is not None:
curr = bucket
while curr:
all_entries.append((curr['name'], curr['phone']))
curr = curr['next']
all_entries.sort(key=lambda x: x[0])
return all_entries
#Двоичное дерево поиска
def bst_insert(root, name, phone):
if root is None:
return {'name': name, 'phone': phone, 'left': None, 'right': None}
if name < root['name']:
root['left'] = bst_insert(root['left'], name, phone)
elif name > root['name']:
root['right'] = bst_insert(root['right'], name, phone)
else:
root['phone'] = phone
return root
def bst_find(root, name):
curr = root
while curr:
if name == curr['name']:
return curr['phone']
elif name < curr['name']:
curr = curr['left']
else:
curr = curr['right']
return None
def bst_delete(root, name):
if root is None:
return None
if name < root['name']:
root['left'] = bst_delete(root['left'], name)
elif name > root['name']:
root['right'] = bst_delete(root['right'], name)
else:
if root['left'] is None:
return root['right']
if root['right'] is None:
return root['left']
min_node = root['right']
while min_node['left']:
min_node = min_node['left']
root['name'] = min_node['name']
root['phone'] = min_node['phone']
root['right'] = bst_delete(root['right'], min_node['name'])
return root
def bst_list_all(root):
result = []
def inorder(node):
if node:
inorder(node['left'])
result.append((node['name'], node['phone']))
inorder(node['right'])
inorder(root)
return result
# ============================================================
# TECT
# ============================================================
import os
import random
import time
import csv
import pandas as pd
import matplotlib.pyplot as plt
# ============================================================
# ПОДГОТОВКА ПАПОК
# ============================================================
DATA_DIR = os.path.join("docs", "data")
os.makedirs(DATA_DIR, exist_ok=True)
csv_path = os.path.join(DATA_DIR, "lab1_results.csv")
graph_path = os.path.join(DATA_DIR, "lab1_graph.png")
# ============================================================
# ТЕСТОВЫЕ ДАННЫЕ
# ============================================================
random.seed(42)
N = 3000
base_records = [
(f"User_{i:05d}", f"123-{i:05d}")
for i in range(N)
]
records_shuffled = base_records.copy()
random.shuffle(records_shuffled)
records_sorted = sorted(base_records, key=lambda x: x[0])
# Поиск
search_existing = [
name for name, _ in random.sample(base_records, 100)
]
search_nonexist = [
f"None_{i}"
for i in range(10)
]
# Удаление
delete_names = [
name for name, _ in random.sample(base_records, 50)
]
# ============================================================
# СОЗДАНИЕ СТРУКТУР
# ============================================================
def build_structure(records, struct_type):
if struct_type == "ll":
structure = None
for name, phone in records:
structure = ll_insert(structure, name, phone)
return structure
elif struct_type == "ht":
structure = [None] * HASH_SIZE
for name, phone in records:
ht_insert(structure, name, phone)
return structure
elif struct_type == "bst":
structure = None
for name, phone in records:
structure = bst_insert(structure, name, phone)
return structure
# ============================================================
# INSERT
# ============================================================
def measure_insert(records, struct_type):
start = time.perf_counter()
build_structure(records, struct_type)
end = time.perf_counter()
return end - start
# ============================================================
# SEARCH
# ============================================================
def measure_search(records, struct_type):
structure = build_structure(records, struct_type)
start = time.perf_counter()
if struct_type == "ll":
for name in search_existing + search_nonexist:
ll_find(structure, name)
elif struct_type == "ht":
for name in search_existing + search_nonexist:
ht_find(structure, name)
elif struct_type == "bst":
for name in search_existing + search_nonexist:
bst_find(structure, name)
end = time.perf_counter()
return end - start
# ============================================================
# DELETE
# ============================================================
def measure_delete(records, struct_type):
structure = build_structure(records, struct_type)
start = time.perf_counter()
if struct_type == "ll":
for name in delete_names:
structure = ll_delete(structure, name)
elif struct_type == "ht":
for name in delete_names:
ht_delete(structure, name)
elif struct_type == "bst":
for name in delete_names:
structure = bst_delete(structure, name)
end = time.perf_counter()
return end - start
# ============================================================
# ЗАМЕРЫ
# ============================================================
all_data = []
experiments = [
("LinkedList", "ll"),
("HashTable", "ht"),
("BST", "bst")
]
modes = [
("shuffled", records_shuffled),
("sorted", records_sorted)
]
for struct_name, struct_type in experiments:
for mode_name, records in modes:
for rep in range(1, 4):
insert_time = measure_insert(records, struct_type)
search_time = measure_search(records, struct_type)
delete_time = measure_delete(records, struct_type)
all_data.append([
struct_name,
mode_name,
rep,
"insert",
insert_time
])
all_data.append([
struct_name,
mode_name,
rep,
"search",
search_time
])
all_data.append([
struct_name,
mode_name,
rep,
"delete",
delete_time
])
# ============================================================
# CSV
# ============================================================
with open(csv_path, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow([
"Структура",
"Режим",
"Повтор",
"Операция",
"Время (сек)"
])
writer.writerows(all_data)
print(f"CSV сохранён: {csv_path}")
# ============================================================
# ГРАФИК
# ============================================================
df = pd.read_csv(csv_path)
df_avg = (
df.groupby(
["Структура", "Режим", "Операция"]
)["Время (сек)"]
.mean()
.reset_index()
)
fig, ax = plt.subplots(figsize=(12, 6))
ops = ["insert", "search", "delete"]
x = range(len(ops))
width = 0.12
configs = [
("LinkedList", "shuffled"),
("LinkedList", "sorted"),
("HashTable", "shuffled"),
("HashTable", "sorted"),
("BST", "shuffled"),
("BST", "sorted")
]
for i, (struct, mode) in enumerate(configs):
subset = df_avg[
(df_avg["Структура"] == struct)
&
(df_avg["Режим"] == mode)
]
times = [
subset[
subset["Операция"] == op
]["Время (сек)"].values[0]
for op in ops
]
ax.bar(
[p + i * width for p in x],
times,
width,
label=f"{struct} ({mode})"
)
ax.set_xticks([p + 2.5 * width for p in x])
ax.set_xticklabels(ops)
ax.set_ylabel("Среднее время (сек)")
ax.set_title("Сравнение структур данных")
ax.legend(
bbox_to_anchor=(1.05, 1),
loc="upper left"
)
plt.tight_layout()
plt.savefig(graph_path)
print(f"График сохранён: {graph_path}")
plt.show()

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## # # # #### ## # # # # # # ### # # ##### # # # # # # # # #
# ## ## # ## # # #### # # ## #### # ## # # # # # # # # #
# # ### # # # ## # # # # ## ## # # # # ## # # # # ## ## #### # ## # # #
# # ### # # ## # ## #### #### ## # #### # ### ## # # # # ## ## #
##### # # # # # ## # ## # ### # # # # # # # # # # ### ## # ## ## #### # # #
# # # #### ## ## ## # # # # # # # # # ## # # ## # ### # # ## # # # ##
### # ### ####### ### # # # # # ## ## # ## # ## # ## ###
# # ### ######## ### ## # # # # # # ## ## # ## ### # # # ### ### ###
# # ## ## # # ### ### ## # # ## # # # # # ## ## ### # # #### #### ###
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# # ###### # # # # # # # # ## ## # # # ## # # # # # # ## # # ## # # ##
# # # # # ## # # # # # # # # ## # ## # #### # ### #### ## # # ## ##
## # # ## # ### # #### ## # ### ## # # # # ### # ## # # # # #
# # ### # ### # ## # ## ## # # # ### ##### ## ### # ## ### # # # ## ###### #
## # # # ## # # #### # # ## # ## # # # # # # # # # # # # #
## # # # ## #### # ### ### ## # ## ##### # # # # # # # # # # ##### # # ##
# ## ## # # # # ### ## # # ### ### ## # # # # ## # # ## ### # # # ###
# ## ## ## ### # # #### # ## ## ## ### ### # # ## ##
## # # ##### #### # # ## ## ### # # # # # # # # # # ### # #### #
# # # # # # #### # # ## # # # ## # # # # # # # ### # # ##
## ### ## ### # ## ## ### # ## # # # # # # # # # # #### ## ##
## # ### ## ## # # ### # # #### # # # ### # # # # # # #### # #
# # # ## ### # # # # # # ## ## # # ## ## # ## # # # # ### ## # #
# ### # ### ## # # # # # # ## # ###### # ## ## ## # ## ## ## # ## # #
# # # # # ## # # # # # # ## # # # ###### # # ### # #
# # ## # # ## # # ## ### # ## ## ## # # # # # # # # # ## # # #
## #### ## ### #### # # ### # # # # # ## # ## # # ##### ## ## # # ## #
## # # # ## # ## # # ## # #### # # ### # #### # ## # ##### # # # #
# # # # # ## #### # # ## # # # # ## # ## ## # # # ## # # ## ## ### # #
# # # ## # ## # # ## ## # ## # # ## # ## # # # ##### #
## #### ## ##### # ### # ### # # ## # ## # # # # # # # ## # ### # ## #
# # # # # ## # ## # ## ## # # ### # ####### # # ## # ### ## ## ## ## ## #
### ## # ## ## # ## # ### ### # ### # # # ###### ### ### # # # ## ### # #
## # ## ## # ## ### # # # # ## # ### # ### ## # ## # # # # #
# # ## ## ## # # # # ## # # ## # ## # # # ## # # # # # # # # ## # # #
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# # ## # # # # ## # # # # # ## # #### ## # ## # ### # ## #
# # ## # # # ### # # # # ## ### ## # ## # # # # # ## # # # ### ## # #
# ## # # # ## # # # # # ## # # # # ## # # # # ###### ## ### # #
# ## ## # ### # # ## ####### ## # ####### # # # # ## # ### # ## # # #
## ### ## ### # ## # # ## ### # ### ### # # # ## # # ## # ### # #
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## # ### ## # ### ## ## # ## # ## ## ### ## ### ### ## ## # ## #
# # #### ## # # ## ## # #### # ### # ## ## # # # # # ## # #
# # # # #### # # # ### ## # ## ## ## # # ## # ##### # # # # # ## # #### # # #
# # ### # # ## #### # # # # ## ## # ## ## ### # ## ## ## # ## #
## # ## # # # # # ## ## # ## ## ## # ## # #### ## # ## #
# # # ## # ## # # # # ### # # ## ## # # # # # ### # ### ## # # ## #
# # # ### # # ## ## # # ## # ### # # ### # ## # ### # ## ## ## # #
### # ## # # #### ### ## # ## # ## # ## #### # # ###### # # #### # ## #
# # # # # # ### # # # # ## #### ### # # # # # ## # # # #### # #### # #
## ## # ## # # # # # ## # ### #### # ## ### # ##### ### ## ### # # #
# # # ####### ## ### # ####### # # # ####### # # # ### # # ## # # # #
## # # ## ## ## # ### ## ## ### ## ## # # ## # ## ## # # # # #
### # # #### ### # # ## # ## # # # #### # # # # # # # # ## # ## #
# ##### # ## ### ## #### # # # ## #### # # # # ## # # ## # #
# # # # # ### # ## #### # # # ### # # # # ## # # # # # # #
# # ## ## #### # # ### ## ## ### ### ## ## # # # # # ## # ## ### #
## # # ## # # ## # ## # ### # ### # # # ## # ### #### # ## #
# ## # # # # ## ## # # # # ## # ### ## ###### ### # ## ### # # # # # #
### # # # # #### # # # # ### ##### # ## # ## # # ## # # # ## #
# # # # ##### # # #### # # # ## # ## # # ## ### # # ## # #### #
### ## # # # ## # # ## # # ## # # # ### ## ## # ### # # ## # # # # #
#### # # # # ## # # # ## ### # # # ### # ## # # ## # # # ## # #
# # # ## # ## # # ## ### # ## ## # ## # ## ## # # # ## # # ### ## ## ## #
# # # ## # # # ## # ## ## # ## # ## ## #### #### # ## # # #
## # # #### ### ## # #### ### ### # ## # # ## ## # ## # ### #
# ## ##### # # ## ### ### # # # # ### #### ## ## ## ## ### ## # #
# # ## # ## ## # # ### # # # # ## # ### ## ## ## ## ## # # # #
# #### # # #### # ## ### ###### # # # ## # # # # # # # # # #### ## # # # #
# #### # ### ##### # ## ## # # ## ## # ## ## # ## ### # # # # # # #
# # ## # # # ## # # ## ## # # # # ## # # # ## ### # ## # #
### # ## # # # ## # # # #### ##### # # ## # ## # # # # ### # #
# # # # ### # ## # # # # ## # # # ### # ## # # # # # # # # # # # #
## # # # # ## ## # #### ## # # ## # # # # # ### # # # #### # # ## ## # # #
## # # ## # #### # # # # ## # ### ## ## #### # ## ## # ## ## #### ## ### #### #
# ### ### ### # ### ### ## # # # ###### # # ### ## # # # # # # # #
## ### ## # ## # ## # ## ### # # # #### ### ### # #
# # ## # # # ## ## # # # # ### ### # ### ### # # # # ### ## # #
## # ## # # # # ###### ## ### # ### # # # ## # ## ## # ### # # # ## # ## #
# ## # ## # # # ## ## # ## ## ## # ## # ## # # # # ## # # # #
# ### # ## ## # #### # ##### # ## ## ## ### # # # # ### #
## # ## # # # # ##### # ## # # # ## # ### # # # ## # ## # # # ## E#
####################################################################################################

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##################################################
#S ## ### # # # #### # ## #
# # ## # # # # ##### ### # ### # #
# ## #### # ### ### ## # # #### ### # # #
#### # # # # # ## # ####
# ## # #### ## # # ## # # # #
# ### # # # # ### # ##
# # # # ## # # ## # ## # # #
## # # # #### # # # # # ### ##
## # # ## ## ### # # ## #
## # # # # # # # # # # # # #
# # ## # ## ## ## ## ## # ###
# ### # # # ## ## # # # # ## # ##
## # # # # ## # # # # #
# # # # # ### # ### # # ##
# # # # # # # ##### # ### ##
# # # # ## ## # # # # ### #### #
## # # # ## # # ## # ### ## ### # #
## # ## # # # # #
# ##### ## ## # # # ## # ## # # #
# # # # ### ##### ### # # ## #
## # # # ## # # ## # # # # ## #
#### # # ## # # # ## ## # ## ## #
# # ### ### ## # ## #### # #
# # ### # ## ##### # # # # ## # #
## #### # # # # # #
# ## # # # # ## ## # # ## # #
# # ## # ### # ### ## # ## #
# # # # # ## ## ## # #
# # ## ### ## ## # # # # # ## # #
## # # #### # #### # ## ## # ## #
# # # ## # # # # # # # # #
# ### ### # # # # # # # #
# ## # # # ####### # # # # # # ### #
## # # # # # # # # # ## # ## #
# # # # ## ## # # ## ### # # # # # # #
# ## # ### # # # # # # # #
# # # # # # # ## # ### # #
# # ### # # # ### # ## # # #
# ### # # # # ## # # ## # #
# ## # ### # ## ## ### # # # #
# ## # # ## ## # # # ## # #
# # ## # # # # # # # # ## #
## # # # # # # # # # # # #
# # # # ### ## ### # ## # # # #
## ##### # # # ## # ## ### # #
# ## # ## ##### # # # ## #
# # # # # # ### # # # # # #
### # # # # # # ## ## ### ## #E#
##################################################

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###############
#S # #
# #
## # #
# # # #
## ## # # # #
# # # #
# # #
# # # ## #
## # # #
## #
## # # # #
# # # ##
# # #E#
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maze,strategy,time_ms,visited,path_length
small,BFS,0.1971,28,15
small,DFS,0.062,16,15
small,A*,0.1713,28,15
small,Dijkstra,0.148,28,15
medium,BFS,5.3354,1377,95
medium,DFS,0.7772,282,151
medium,A*,3.8703,500,95
medium,Dijkstra,8.3548,1363,95
large,BFS,16.9817,4391,195
large,DFS,3.414,614,285
large,A*,5.7519,559,195
large,Dijkstra,31.018,4380,195
empty,BFS,2.3012,400,39
empty,DFS,1.4237,400,191
empty,A*,3.6105,400,39
empty,Dijkstra,2.9606,400,39
no_exit,BFS,0.5791,136,0
no_exit,DFS,0.5479,136,0
no_exit,A*,0.9933,136,0
no_exit,Dijkstra,0.8121,136,0
1 maze strategy time_ms visited path_length
2 small BFS 0.1971 28 15
3 small DFS 0.062 16 15
4 small A* 0.1713 28 15
5 small Dijkstra 0.148 28 15
6 medium BFS 5.3354 1377 95
7 medium DFS 0.7772 282 151
8 medium A* 3.8703 500 95
9 medium Dijkstra 8.3548 1363 95
10 large BFS 16.9817 4391 195
11 large DFS 3.414 614 285
12 large A* 5.7519 559 195
13 large Dijkstra 31.018 4380 195
14 empty BFS 2.3012 400 39
15 empty DFS 1.4237 400 191
16 empty A* 3.6105 400 39
17 empty Dijkstra 2.9606 400 39
18 no_exit BFS 0.5791 136 0
19 no_exit DFS 0.5479 136 0
20 no_exit A* 0.9933 136 0
21 no_exit Dijkstra 0.8121 136 0

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##########
#S #
# ###### #
# # # #
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# # ## # #
# # # #
# ###### #
# E#
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ОПИСАНИЕ ЗАДАЧИ И ВЫБРАННЫХ ПАТТЕРНОВ
Цель работы — разработать систему поиска пути в лабиринте с использованием
оопп и паттернов проектирования.
В работе были использованы следующие паттерны:
Strategy — для реализации алгоритмов поиска пути (BFS, DFS, A*, Dijkstra).
Позволяет менять алгоритм без изменения кода основного класса MazeSolver.
Builder — для создания лабиринта из текстового файла.
Отделяет логику загрузки данных от основной системы.
'''mermaid
classDiagram
class Cell {
+x
+y
+is_wall
+is_start
+is_exit
+weight
+isPassable()
}
class Maze {
+width
+height
+start
+exit
+getCell()
+getNeighbors()
+getWeightedNeighbors()
}
class MazeBuilder {
+buildFromFile()
}
class TextFileMazeBuilder
MazeBuilder <|-- TextFileMazeBuilder
class PathFindingStrategy {
+findPath()
}
class BFSStrategy
class DFSStrategy
class AStarStrategy
class DijkstraStrategy
PathFindingStrategy <|-- BFSStrategy
PathFindingStrategy <|-- DFSStrategy
PathFindingStrategy <|-- AStarStrategy
PathFindingStrategy <|-- DijkstraStrategy
class MazeSolver {
+setStrategy()
+solve()
}
MazeSolver --> PathFindingStrategy
Maze --> Cell
'''
ЛИСТИНГИ КЛЮЧЕВЫХ КЛАССОВ
В проекте реализованы основные классы:
Cell — хранение информации о клетке лабиринта
Maze — структура лабиринта и работа с соседями
MazeSolver — запуск поиска пути
PathFindingStrategy — интерфейс алгоритмов
BFSStrategy, DFSStrategy, AStarStrategy, DijkstraStrategy — реализации алгоритмов
TextFileMazeBuilder — загрузка лабиринта из файла
SearchStats — хранение статистики
РЕЗУЛЬТАТЫ ЭКСПЕРИМЕНТОВ
Алгоритмы тестировались на разных лабиринтах: small, medium, large, empty, no_exit.
Сравнивались:
время выполнения
количество посещённых клеток
длина найденного пути
Результаты в общем виде:
BFS — гарантирует кратчайший путь, но посещает много клеток
DFS — быстрый, но не гарантирует оптимальный путь
A* — самый быстрый в большинстве случаев за счёт эвристики
Dijkstra — стабильный, но медленнее A* на больших лабиринтах
АНАЛИЗ ЭФФЕКТИВНОСТИ И ПАТТЕРНОВ
Результаты показали, что A* является наиболее эффективным алгоритмом на больших данных,
так как использует эвристику и уменьшает количество проверяемых клеток.
BFS всегда находит оптимальный путь, но работает медленнее из-за полного обхода пространства.
DFS быстрее по времени, но не гарантирует лучший результат.
Dijkstra корректно работает с весами, но в данной задаче часто уступает A*.
Паттерн Strategy позволил легко переключать алгоритмы без изменения основной логики программы.
Паттерн Builder упростил создание лабиринтов и отделил загрузку данных от логики поиска.
ВЫВОДЫ
В ходе работы была создана гибкая система поиска пути в лабиринте с использованием ООП
и паттернов проектирования. Благодаря Strategy алгоритмы стали независимыми и легко
заменяемыми. Благодаря Builder упростилась работа с созданием и загрузкой лабиринтов.
В целом, архитектура получилась расширяемой: можно легко добавить новый алгоритм или тип
лабиринта без переписывания существующего кода.
Таким образом, наиболее сбалансированным алгоритмом для поиска пути в лабиринте является A*,
так как он обеспечивает:
высокую скорость работы,
оптимальность результата,
минимальное количество исследуемых состояний.
Алгоритмы BFS и Dijkstra гарантируют оптимальность, но проигрывают по производительности,
а DFS является самым быстрым, но не гарантирует качество решения.

789
stepinim/lab2_oop/poisk.py Normal file
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import time
from collections import deque
import heapq
import csv
import os
import random
import matplotlib.pyplot as plt
# ============================================================
# ЭТАП 1. МОДЕЛЬ ЛАБИРИНТА
# ============================================================
class Cell:
def __init__(self, x, y, is_wall=False, is_start=False, is_exit=False):
self.x = x
self.y = y
self.is_wall = is_wall
self.is_start = is_start
self.is_exit = is_exit
self.weight = 1
def isPassable(self):
return not self.is_wall
def __repr__(self):
return f"Cell({self.x},{self.y})"
def __hash__(self):
return hash((self.x, self.y))
def __eq__(self, other):
return isinstance(other, Cell) and self.x == other.x and self.y == other.y
class Maze:
def __init__(self, width, height):
self.width = width
self.height = height
self.cells = []
self.start = None
self.exit = None
def getCell(self, x, y):
if 0 <= x < self.width and 0 <= y < self.height:
return self.cells[y][x]
return None
def getNeighbors(self, cell):
neighbors = []
for dx, dy in [(0, -1), (0, 1), (-1, 0), (1, 0)]:
nx = cell.x + dx
ny = cell.y + dy
neighbor = self.getCell(nx, ny)
if neighbor and neighbor.isPassable():
neighbors.append(neighbor)
return neighbors
def getWeightedNeighbors(self, cell):
return [(n, n.weight) for n in self.getNeighbors(cell)]
# ============================================================
# ЭТАП 2. BUILDER
# ============================================================
class MazeBuilder:
def buildFromFile(self, filename):
raise NotImplementedError
class TextFileMazeBuilder(MazeBuilder):
def buildFromFile(self, filename):
with open(filename, 'r', encoding='utf-8') as f:
lines = [line.rstrip('\n') for line in f]
height = len(lines)
width = max(len(line) for line in lines)
maze = Maze(width, height)
for y, line in enumerate(lines):
row = []
for x, char in enumerate(line):
if char == '#':
cell = Cell(x, y, is_wall=True)
elif char == 'S':
cell = Cell(x, y, is_start=True)
maze.start = cell
elif char == 'E':
cell = Cell(x, y, is_exit=True)
maze.exit = cell
else:
cell = Cell(x, y)
row.append(cell)
while len(row) < width:
row.append(Cell(len(row), y, is_wall=True))
maze.cells.append(row)
if maze.start is None or maze.exit is None:
raise ValueError("В лабиринте нет S или E")
return maze
# ============================================================
# ВОССТАНОВЛЕНИЕ ПУТИ
# ============================================================
def reconstruct_path(parents, end_cell):
path = []
current = end_cell
while current is not None:
path.append(current)
current = parents[current]
path.reverse()
return path
# ============================================================
# ЭТАП 3. STRATEGY
# ============================================================
class PathFindingStrategy:
@property
def name(self):
return "Unknown"
def findPath(self, maze, start, exit):
raise NotImplementedError
# ============================================================
# BFS
# ============================================================
class BFSStrategy(PathFindingStrategy):
@property
def name(self):
return "BFS"
def findPath(self, maze, start, exit):
queue = deque([start])
visited = {start}
parents = {
start: None
}
visited_count = 1
while queue:
current = queue.popleft()
if current == exit:
path = reconstruct_path(parents, exit)
return path, visited_count
for neighbor in maze.getNeighbors(current):
if neighbor not in visited:
visited.add(neighbor)
parents[neighbor] = current
visited_count += 1
queue.append(neighbor)
return [], visited_count
# ============================================================
# DFS
# ============================================================
class DFSStrategy(PathFindingStrategy):
@property
def name(self):
return "DFS"
def findPath(self, maze, start, exit):
stack = [start]
visited = {start}
parents = {
start: None
}
visited_count = 1
while stack:
current = stack.pop()
if current == exit:
path = reconstruct_path(parents, exit)
return path, visited_count
for neighbor in maze.getNeighbors(current):
if neighbor not in visited:
visited.add(neighbor)
parents[neighbor] = current
visited_count += 1
stack.append(neighbor)
return [], visited_count
# ============================================================
# A*
# ============================================================
class AStarStrategy(PathFindingStrategy):
@property
def name(self):
return "A*"
def heuristic(self, a, b):
return abs(a.x - b.x) + abs(a.y - b.y)
def findPath(self, maze, start, exit):
counter = 0
open_set = []
heapq.heappush(open_set, (0, counter, start))
parents = {
start: None
}
g_score = {
start: 0
}
visited = set()
visited_count = 0
while open_set:
_, _, current = heapq.heappop(open_set)
if current in visited:
continue
visited.add(current)
visited_count += 1
if current == exit:
path = reconstruct_path(parents, exit)
return path, visited_count
for neighbor in maze.getNeighbors(current):
tentative_g = g_score[current] + 1
if neighbor not in g_score or tentative_g < g_score[neighbor]:
g_score[neighbor] = tentative_g
parents[neighbor] = current
f_score = tentative_g + self.heuristic(neighbor, exit)
counter += 1
heapq.heappush(
open_set,
(f_score, counter, neighbor)
)
return [], visited_count
# ============================================================
# DIJKSTRA
# ============================================================
class DijkstraStrategy(PathFindingStrategy):
@property
def name(self):
return "Dijkstra"
def findPath(self, maze, start, exit):
counter = 0
queue = []
heapq.heappush(queue, (0, counter, start))
distances = {
start: 0
}
parents = {
start: None
}
visited = set()
visited_count = 0
while queue:
dist, _, current = heapq.heappop(queue)
if current in visited:
continue
visited.add(current)
visited_count += 1
if current == exit:
path = reconstruct_path(parents, exit)
return path, visited_count
for neighbor, weight in maze.getWeightedNeighbors(current):
new_dist = dist + weight
if neighbor not in distances or new_dist < distances[neighbor]:
distances[neighbor] = new_dist
parents[neighbor] = current
counter += 1
heapq.heappush(
queue,
(new_dist, counter, neighbor)
)
return [], visited_count
# ============================================================
# ЭТАП 4. STATS + SOLVER
# ============================================================
class SearchStats:
def __init__(
self,
strategy_name,
time_ms,
visited_cells,
path_length,
path_found
):
self.strategy_name = strategy_name
self.time_ms = time_ms
self.visited_cells = visited_cells
self.path_length = path_length
self.path_found = path_found
class MazeSolver:
def __init__(self, maze, strategy=None):
self.maze = maze
self.strategy = strategy
def setStrategy(self, strategy):
self.strategy = strategy
def solve(self):
if self.strategy is None:
raise ValueError("Стратегия не выбрана")
start_time = time.perf_counter()
path, visited = self.strategy.findPath(
self.maze,
self.maze.start,
self.maze.exit
)
end_time = time.perf_counter()
elapsed_ms = (end_time - start_time) * 1000
return SearchStats(
self.strategy.name,
elapsed_ms,
visited,
len(path),
len(path) > 0
), path
# ============================================================
# ВИЗУАЛИЗАЦИЯ
# ============================================================
def render(maze, path=None):
path_set = set(path) if path else set()
for y in range(maze.height):
line = ""
for x in range(maze.width):
cell = maze.getCell(x, y)
if cell == maze.start:
line += "S"
elif cell == maze.exit:
line += "E"
elif cell in path_set:
line += "."
elif cell.is_wall:
line += "#"
else:
line += " "
print(line)
print()
# ============================================================
# ФАЙЛЫ И ПУТИ
# ============================================================
OUTPUT_DIR = os.path.join("docs", "data")
PREFIX = "_2lab"
os.makedirs(OUTPUT_DIR, exist_ok=True)
def get_path(filename):
name, ext = os.path.splitext(filename)
return os.path.join(
OUTPUT_DIR,
f"{name}{PREFIX}{ext}"
)
# ============================================================
# СОЗДАНИЕ ЛАБИРИНТА
# ============================================================
def create_test_maze(filename, lines):
with open(filename, 'w', encoding='utf-8') as f:
for line in lines:
f.write(line + '\n')
return filename
# ============================================================
# ГЕНЕРАЦИЯ
# ============================================================
def generate_maze(width, height, wall_density=0.3):
grid = [[' ' for _ in range(width)] for _ in range(height)]
for x in range(width):
grid[0][x] = '#'
grid[height - 1][x] = '#'
for y in range(height):
grid[y][0] = '#'
grid[y][width - 1] = '#'
x, y = 1, 1
path_cells = {(x, y)}
while x < width - 2 or y < height - 2:
if x < width - 2 and random.random() > 0.3:
x += 1
elif y < height - 2:
y += 1
else:
x += 1
path_cells.add((x, y))
for yy in range(1, height - 1):
for xx in range(1, width - 1):
if (xx, yy) not in path_cells:
if random.random() < wall_density:
grid[yy][xx] = '#'
grid[1][1] = 'S'
grid[height - 2][width - 2] = 'E'
return [''.join(row) for row in grid]
def generate_empty_maze(size):
lines = [" " * size for _ in range(size)]
lines[0] = "S" + " " * (size - 1)
lines[size - 1] = " " * (size - 1) + "E"
return lines
def generate_no_exit_maze(size):
lines = generate_maze(size, size, wall_density=0.2)
for y, line in enumerate(lines):
if 'E' in line:
x = line.index('E')
for dy, dx in [(-1, 0), (1, 0), (0, -1), (0, 1)]:
ny = y + dy
nx = x + dx
if 0 <= ny < size and 0 <= nx < size:
if lines[ny][nx] == ' ':
lines[ny] = (
lines[ny][:nx]
+ '#'
+ lines[ny][nx + 1:]
)
return lines
# ============================================================
# ЭКСПЕРИМЕНТЫ
# ============================================================
def run_experiments():
mazes = {
"small": [
"##########",
"#S #",
"# ###### #",
"# # # #",
"# # ## # #",
"# # ## # #",
"# # # #",
"# ###### #",
"# E#",
"##########"
],
"medium": generate_maze(50, 50, 0.35),
"large": generate_maze(100, 100, 0.4),
"empty": generate_empty_maze(20),
"no_exit": generate_no_exit_maze(15)
}
strategies = [
BFSStrategy(),
DFSStrategy(),
AStarStrategy(),
DijkstraStrategy()
]
results = []
print("=" * 60)
print("ЭКСПЕРИМЕНТЫ")
print("=" * 60)
for maze_name, lines in mazes.items():
filename = get_path(f"{maze_name}.txt")
create_test_maze(filename, lines)
maze = TextFileMazeBuilder().buildFromFile(filename)
print(f"\nЛабиринт: {maze_name}")
print("-" * 60)
for strategy in strategies:
times = []
visited_values = []
final_path_len = 0
for _ in range(5):
solver = MazeSolver(maze)
solver.setStrategy(strategy)
stats, path = solver.solve()
times.append(stats.time_ms)
visited_values.append(stats.visited_cells)
final_path_len = stats.path_length
avg_time = sum(times) / len(times)
avg_visited = sum(visited_values) / len(visited_values)
results.append({
"maze": maze_name,
"strategy": strategy.name,
"time_ms": round(avg_time, 4),
"visited": int(avg_visited),
"path_length": final_path_len
})
status = "найден" if final_path_len > 0 else "не найден"
print(
f"{strategy.name:<10} | "
f"{avg_time:>8.4f} мс | "
f"{int(avg_visited):>5} клеток | "
f"путь {status}"
)
csv_path = get_path("results.csv")
with open(csv_path, "w", newline="", encoding='utf-8') as f:
writer = csv.DictWriter(
f,
fieldnames=[
"maze",
"strategy",
"time_ms",
"visited",
"path_length"
]
)
writer.writeheader()
writer.writerows(results)
print(f"\nCSV сохранён: {csv_path}")
return results
# ============================================================
# ГРАФИК
# ============================================================
def build_charts(results):
mazes = list(dict.fromkeys(r["maze"] for r in results))
strategies = list(dict.fromkeys(r["strategy"] for r in results))
fig, ax = plt.subplots(figsize=(12, 6))
x = range(len(mazes))
width = 0.2
colors = {
'BFS': '#3498db',
'DFS': '#e74c3c',
'A*': '#2ecc71',
'Dijkstra': '#f39c12'
}
for i, strategy in enumerate(strategies):
times = [
r["time_ms"]
for r in results
if r["strategy"] == strategy
]
ax.bar(
[j + i * width for j in x],
times,
width,
label=strategy,
color=colors.get(strategy, 'gray')
)
ax.set_xlabel("Лабиринт")
ax.set_ylabel("Время (мс)")
ax.set_title("Сравнение алгоритмов")
ax.set_xticks([j + width * 1.5 for j in x])
ax.set_xticklabels(mazes)
ax.legend()
ax.grid(axis='y', alpha=0.3)
plt.tight_layout()
chart_path = get_path("chart_time.png")
plt.savefig(chart_path, dpi=150, bbox_inches='tight')
print(f"График сохранён: {chart_path}")
plt.show()
# ============================================================
# MAIN
# ============================================================
def main():
results = run_experiments()
build_charts(results)
if __name__ == "__main__":
main()