arutunyan_dmitry_lab_7 is ready

Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/137

.gitignore

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+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+.idea

abanin_daniil_lab_7/README.md

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+# Лабораторная работа №7
+
+### Рекуррентная нейронная сеть и задача генерации текста
+
+## ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, keras, tensorflow
+* запустить проект (стартовая точка lab7)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, keras, tensorflow
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+* На основе выбранных художественных текстов происходит обучение рекуррентной нейронной сети для решения задачи генерации. 
+* Необходимо подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.
+
+### Тест
+* Чтение текста из файлов .txt (eng_text.txt, rus_text.txt)
+* Вызов функция get_model_data, из которой мы получаем входные, выходные данные (X, y), размер словаря и токенайзер. Используем Tokenizer с настройкой char_level=True, что позволяет упразднить использование Embedding слоя далее
+* Создание объекта Sequential (последовательная рекуррентная нейронная сеть) и добавление двух слоёв LSTM. LSTM (Long Short-Term Memory) представляет собой разновидность рекуррентной нейронной сети, которая эффективно работает с последовательными данными. Использование нескольких слоёв даёт большую гибкость. Dropout — это метод регуляризации для нейронных сетей и моделей глубокого обучения, решение проблемы переобучения. Слой Dense с функцией активации softmax используется для предсказания следующего слова
+* Компилирование модели с использованием sparse_categorical_crossentropy
+* Обучение модели на 100 эпохах (оптимальный вариант)
+* Генерация текста
+
+Сгенерированные тексты
+
+* ENG: I must be getting somewhere near the centre of the earth. how funny it'll seem to come out among the people that walk with their heads downward! the antipathies, i think—' (for, you see, alice had learnt several things of this
+
+* RUS: господин осматривал свою комнату, внесены были его пожитки: прежде всего чемодан из белой кожи, несколько поистасканный, показывавший, что был не в первый раз в дороге. чемодан внесли кучер селифан отправился на конюшню вози
+
+![Rus](result_rus.png)
+
+![Eng](result_eng.png)
+
+По итогу, программа способна сгенерировать осмысленный текст в каждом из случаев

abanin_daniil_lab_7/eng_text.txt

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+Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled 'ORANGE MARMALADE', but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody, so managed to put it into one of the cupboards as she fell past it.
+
+'Well!' thought Alice to herself, 'after such a fall as this, I shall think nothing of tumbling down stairs! How brave they'll all think me at home! Why, I wouldn't say anything about it, even if I fell off the top of the house!' (Which was very likely true.)
+
+Down, down, down. Would the fall NEVER come to an end! 'I wonder how many miles I've fallen by this time?' she said aloud. 'I must be getting somewhere near the centre of the earth. Let me see: that would be four thousand miles down, I think—' (for, you see, Alice had learnt several things of this sort in her lessons in the schoolroom, and though this was not a VERY good opportunity for showing off her knowledge, as there was no one to listen to her, still it was good practice to say it over) '—yes, that's about the right distance—but then I wonder what Latitude or Longitude I've got to?' (Alice had no idea what Latitude was, or Longitude either, but thought they were nice grand words to say.)
+
+Presently she began again. 'I wonder if I shall fall right THROUGH the earth! How funny it'll seem to come out among the people that walk with their heads downward! The Antipathies, I think—' (she was rather glad there WAS no one listening, this time, as it didn't sound at all the right word) '—but I shall have to ask them what the name of the country is, you know. Please, Ma'am, is this New Zealand or Australia?' (and she tried to curtsey as she spoke—fancy CURTSEYING as you're falling through the air!

abanin_daniil_lab_7/lab7.py

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+from keras import Sequential
+from keras.layers import LSTM, Dense, Dropout
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+import numpy as np
+
+with open('rus_text.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+
+def create_sequences(text, seq_len):
+    sequences = []
+    next_chars = []
+    for i in range(0, len(text) - seq_len):
+        sequences.append(text[i:i + seq_len])
+        next_chars.append(text[i + seq_len])
+    return sequences, next_chars
+
+
+def get_model_data(seq_length):
+    tokenizer = Tokenizer(char_level=True)
+    tokenizer.fit_on_texts([text])
+
+    token_text = tokenizer.texts_to_sequences([text])[0]
+
+    sequences, next_chars = create_sequences(token_text, seq_length)
+
+    vocab_size = len(tokenizer.word_index) + 1
+    x = pad_sequences(sequences, maxlen=seq_length)
+    y = np.array(next_chars)
+
+    return x, y, vocab_size, tokenizer
+
+
+def model_build(model, vocab_size):
+    model.add(LSTM(256, input_shape=(seq_length, 1), return_sequences=True))
+    model.add(LSTM(128, input_shape=(seq_length, 1)))
+    model.add(Dropout(0.2, input_shape=(60,)))
+    model.add(Dense(vocab_size, activation='softmax'))
+
+    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+# Функция для генерации текста
+def generate_text(seed_text, gen_length, tokenizer, model):
+    generated_text = seed_text
+
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_char = tokenizer.index_word[predicted_index]
+        generated_text += predicted_char
+        seed_text += predicted_char
+        seed_text = seed_text[1:]
+
+    return generated_text
+
+
+seq_length = 10
+seed_text = "господин осматривал свою"
+
+# Создание экземпляра Tokenizer и обучение на тексте
+
+X, y, vocab_size, tokenizer = get_model_data(seq_length)
+
+model = Sequential()
+
+model_build(model, vocab_size)
+
+model.fit(X, y, epochs=100, verbose=1)
+
+generated_text = generate_text(seed_text, 200, tokenizer, model)
+print(generated_text)

abanin_daniil_lab_7/rus_text.txt

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+В ворота гостиницы губернского города NN въехала довольно красивая рессорная небольшая бричка, в какой ездят холостяки: отставные подполковники, штабс-капитаны, помещики, имеющие около сотни душ крестьян, — словом, все те, которых называют господами средней руки. В бричке сидел господин, не красавец, но и не дурной наружности, ни слишком толст, ни слишком тонок; нельзя сказать, чтобы стар, однако ж и не так чтобы слишком молод. Въезд его не произвел в городе совершенно никакого шума и не был сопровожден ничем особенным; только два русские мужика, стоявшие у дверей кабака против гостиницы, сделали кое-какие замечания, относившиеся, впрочем, более к экипажу, чем к сидевшему в нем. «Вишь ты, — сказал один другому, — вон какое колесо! что ты думаешь, доедет то колесо, если б случилось, в Москву или не доедет?» — «Доедет», — отвечал другой. «А в Казань-то, я думаю, не доедет?» — «В Казань не доедет», — отвечал другой. Этим разговор и кончился. Да еще, когда бричка подъехала к гостинице, встретился молодой человек в белых канифасовых панталонах, весьма узких и коротких, во фраке с покушеньями на моду, из-под которого видна была манишка, застегнутая тульскою булавкою с бронзовым пистолетом. Молодой человек оборотился назад, посмотрел экипаж, придержал рукою картуз, чуть не слетевший от ветра, и пошел своей дорогой.
+Когда экипаж въехал на двор, господин был встречен трактирным слугою, или половым, как их называют в русских трактирах, живым и вертлявым до такой степени, что даже нельзя было рассмотреть, какое у него было лицо. Он выбежал проворно, с салфеткой в руке, весь длинный и в длинном демикотонном сюртуке со спинкою чуть не на самом затылке, встряхнул волосами и повел проворно господина вверх по всей деревянной галдарее показывать ниспосланный ему Богом покой. Покой был известного рода, ибо гостиница была тоже известного рода, то есть именно такая, как бывают гостиницы в губернских городах, где за два рубля в сутки проезжающие получают покойную комнату с тараканами, выглядывающими, как чернослив, из всех углов, и дверью в соседнее помещение, всегда заставленную комодом, где устроивается сосед, молчаливый и спокойный человек, но чрезвычайно любопытный, интересующийся знать о всех подробностях проезжающего. Наружный фасад гостиницы отвечал ее внутренности: она была очень длинна, в два этажа; нижний не был выщекатурен и оставался в темно-красных кирпичиках, еще более потемневших от лихих погодных перемен и грязноватых уже самих по себе; верхний был выкрашен вечною желтою краскою; внизу были лавочки с хомутами, веревками и баранками. В угольной из этих лавочек, или, лучше, в окне, помещался сбитенщик с самоваром из красной меди и лицом так же красным, как самовар, так что издали можно бы подумать, что на окне стояло два самовара, если б один самовар не был с черною как смоль бородою.
+Пока приезжий господин осматривал свою комнату, внесены были его пожитки: прежде всего чемодан из белой кожи, несколько поистасканный, показывавший, что был не в первый раз в дороге. Чемодан внесли кучер Селифан, низенький человек в тулупчике, и лакей Петрушка, малый лет тридцати, в просторном подержанном сюртуке, как видно с барского плеча, малый немного суровый на взгляд, с очень крупными губами и носом. Вслед за чемоданом внесен был небольшой ларчик красного дерева с штучными выкладками из карельской березы, сапожные колодки и завернутая в синюю бумагу жареная курица. Когда все это было внесено, кучер Селифан отправился на конюшню возиться около лошадей, а лакей Петрушка стал устраиваться в маленькой передней, очень темной конурке, куда уже успел притащить свою шинель и вместе с нею какой-то свой собственный запах, который был сообщен и принесенному вслед за тем мешку с разным лакейским туалетом. В этой конурке он приладил к стене узенькую трехногую кровать, накрыв ее небольшим подобием тюфяка, убитым и плоским, как блин, и, может быть, так же замаслившимся, как блин, который удалось ему вытребовать у хозяина гостиницы.

abanin_danill_lab_6/README.md

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+## Лабораторная работа №6
+
+### MLPClassifier
+
+## Cтудент группы ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (lab6)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* По данным "Eligibility Prediction for Loan" решает задачу классификации, в которой необходимо выявить риски выдачи кредита. В качестве исходных данных используются признаки: 
+Credit_History - соответствие кредитной истории стандартам банка, ApplicantIncome - доход заявителя, LoanAmount - сумма кредитаб, Self_Employed - самозанятость (Да/Нет), Education - наличие образования, Married - заявитель женат/замужем (Да/Нет).
+
+### Примеры работы:
+
+#### Результаты:
+* Было проведено несколько прогонов на разном количестве итераций (200, 400, 600, 800, 1000)
+
+![Result](score_1.png)
+![Result](score_2.png)
+
+Средняя точность находится в диапазоне 50-60%, что является недостаточным значением. Увеличение итераций не дало значительного улучшения результата, 
+максиальный прирост составляет 10%
+
+
+![Result](result_mean.jpg)

abanin_danill_lab_6/lab6.py

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+from matplotlib import pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+import pandas as pd
+import numpy as np
+
+
+def test_iter(iters_num, x_train, x_test, y_train, y_test):
+
+    print("Количество итераций: ", iters_num)
+    scores = []
+
+    for i in range(10):
+        neuro = MLPClassifier(max_iter=iters_num)
+        neuro.fit(x_train, y_train.values.ravel())
+        score = neuro.score(x_test, y_test)
+        print(f'Оценка №{i + 1} - {score}')
+        scores.append(score)
+
+    mean_value = np.mean(scores)
+
+    print(f"Средняя оценка - {mean_value}")
+
+    return mean_value
+
+
+def start():
+    data = pd.read_csv('loan.csv')
+    x = data[['ApplicantIncome', 'LoanAmount', 'Credit_History', 'Self_Employed', 'Education', 'Married']]
+    y = data[['Loan_Status']]
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
+
+    iters = [200, 400, 600, 800, 1000]
+    iters_means = []
+
+    for i in range(len(iters)):
+        mean_value = test_iter(iters[i], x_train, x_test, y_train, y_test)
+        iters_means.append(mean_value)
+
+    plt.figure(1, figsize=(16, 9))
+    plt.plot(iters, iters_means, c='r')
+    plt.show()
+
+
+start()

abanin_danill_lab_6/loan.csv

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+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP001002,Male,0.0,0,1,0.0,5849,0.0,360.0,1.0,0,Y,0.0
+LP001003,Male,1.0,1,1,0.0,4583,1508.0,128.0,360.0,1,Rural,0.0
+LP001005,Male,1.0,0,1,1.0,3000,0.0,66.0,360.0,1,Urban,1.0
+LP001006,Male,1.0,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
+LP001008,Male,0.0,0,1,0.0,6000,0.0,141.0,360.0,1,Urban,1.0
+LP001011,Male,1.0,2,1,1.0,5417,4196.0,267.0,360.0,1,Urban,1.0
+LP001013,Male,1.0,0,0,0.0,2333,1516.0,95.0,360.0,1,Urban,1.0
+LP001014,Male,1.0,3+,1,0.0,3036,2504.0,158.0,360.0,0,Semiurban,0.0
+LP001018,Male,1.0,2,1,0.0,4006,1526.0,168.0,360.0,1,Urban,1.0
+LP001020,Male,1.0,1,1,0.0,12841,10968.0,349.0,360.0,1,Semiurban,0.0
+LP001024,Male,1.0,2,1,0.0,3200,700.0,70.0,360.0,1,Urban,1.0
+LP001027,Male,1.0,2,1,0.0,2500,1840.0,109.0,360.0,1,Urban,1.0
+LP001028,Male,1.0,2,1,0.0,3073,8106.0,200.0,360.0,1,Urban,1.0
+LP001029,Male,0.0,0,1,0.0,1853,2840.0,114.0,360.0,1,Rural,0.0
+LP001030,Male,1.0,2,1,0.0,1299,1086.0,17.0,120.0,1,Urban,1.0
+LP001032,Male,0.0,0,1,0.0,4950,0.0,125.0,360.0,1,Urban,1.0
+LP001034,Male,0.0,1,0,0.0,3596,0.0,100.0,240.0,0,Urban,1.0
+LP001036,Female,0.0,0,1,0.0,3510,0.0,76.0,360.0,0,Urban,0.0
+LP001038,Male,1.0,0,0,0.0,4887,0.0,133.0,360.0,1,Rural,0.0
+LP001041,Male,1.0,0,1,0.0,2600,3500.0,115.0,,1,Urban,1.0
+LP001043,Male,1.0,0,0,0.0,7660,0.0,104.0,360.0,0,Urban,0.0
+LP001046,Male,1.0,1,1,0.0,5955,5625.0,315.0,360.0,1,Urban,1.0
+LP001047,Male,1.0,0,0,0.0,2600,1911.0,116.0,360.0,0,Semiurban,0.0
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almukhammetov_bulat_lab_3/README.md

@@ -0,0 +1,64 @@
+Вариант 2
+
+Задание:
+Предсказание категории возраста дома (housingMedianAge) на основе других признаков, таких как широта, долгота, общее количество комнат и т.д.
+
+Данные: 
+Данный набор данных использовался во второй главе недавней книги Аурелиена Жерона "Практическое машинное обучение с помощью Scikit-Learn и TensorFlow". Он служит отличным введением в реализацию алгоритмов машинного обучения, потому что требует минимальной предварительной обработки данных, содержит легко понимаемый список переменных и находится в оптимальном размере, который не слишком мал и не слишком большой.
+
+Данные содержат информацию о домах в определенном районе Калифорнии и некоторую сводную статистику на основе данных переписи 1990 года. Следует отметить, что данные не прошли предварительную очистку, и для них требуются некоторые этапы предварительной обработки. Столбцы включают в себя следующие переменные, их названия весьма наглядно описывают их суть:
+
+долгота longitude
+
+широта latitude
+
+средний возраст жилья median_house_value
+
+общее количество комнат total_rooms
+
+общее количество спален total_bedrooms
+
+население population
+
+домохозяйства households
+
+медианный доход median_income
+
+Запуск:
+Запустите файл lab3.py
+
+Описание программы:
+
+1. Загружает набор данных из файла 'housing.csv', который содержит информацию о домах в Калифорнии, включая их координаты, возраст, количество комнат, население, доход и другие характеристики.
+
+2. Удаляет строки с нулевыми значениями из набора данных для чистоты анализа.
+
+3. Выбирает набор признаков (features) из данных, которые будут использоваться для обучения моделей регрессии и классификации.
+
+4. Определяет задачу регрессии, где целевой переменной (target) является 'housing_median_age', и задачу классификации, где целевой переменной является 'housing_median_age'.
+
+5. Разделяет данные на обучающий и тестовый наборы для обеих задач с использованием функции train_test_split. Тестовый набор составляет 1% от исходных данных.
+
+6. Создает и обучает дерево решений для регрессии и классификации с использованием моделей DecisionTreeRegressor и DecisionTreeClassifier.
+
+7. Предсказывает значения целевой переменной на тестовых наборах для обеих задач.
+
+8. Оценивает качество моделей с помощью среднеквадратичной ошибки (MSE) для регрессии и точности (accuracy) для классификации.
+
+9. Выводит среднеквадратичную ошибку для регрессии и точность для классификации, а также важности признаков для обеих задач.
+
+Результаты:
+
+![Alt text](1.png)
+
+Выводы: 
+
+Для задачи регрессии, где целью было предсказать возраст жилья (housing_median_age), модель дерева решений показала среднюю ошибку (MSE) равную 117.65. Это означает, что модель регрессии вполне приемлемо предсказывает возраст жилья на основе выбранных признаков.
+
+Для задачи классификации, где целью было предсказать стоимость жилья (housing_median_age), модель дерева решений показала низкую точность, всего 8.29%. Это свидетельствует о том, что модель классификации не справляется с предсказанием стоимости жилья на основе выбранных признаков. Низкая точность указывает на необходимость улучшения модели или выбора других методов для решения задачи классификации.
+
+Анализ важности признаков для задачи регрессии показал, что наибольший вклад в предсказание возраста жилья вносят признаки 'longitude', 'latitude' и 'total_rooms'. Эти признаки оказывают наибольшее влияние на результаты модели.
+
+Для задачи классификации наибольший вклад в предсказание стоимости жилья вносят признаки 'median_income', 'longitude' и 'latitude'. Эти признаки имеют наибольшее значение при определении классов стоимости жилья.
+
+В целом, результаты указывают на успешное решение задачи регрессии с использованием модели дерева решений. Однако задача классификации требует дополнительных улучшений.

almukhammetov_bulat_lab_3/lab3(old).py

@@ -0,0 +1,48 @@
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.tree import DecisionTreeClassifier
+
+# Загрузка данных
+data = pd.read_csv('titanic.csv', index_col='PassengerId')
+
+
+# Функция для преобразования пола в числовое значение
+def Sex_to_bool(sex):
+    if sex == "male":
+        return 0
+    return 1
+
+
+# Преобразование пола в числовое значение
+data['Sex'] = data['Sex'].apply(Sex_to_bool)
+
+# Отбор строк с непустыми значениями
+# Отбор строк с непустыми значениями
+data = data.loc[~data['Name'].isna()
+                & ~data['Age'].isna()
+                & ~data['Sex'].isna()
+                & ~data['Survived'].isna()]
+
+
+# Отбор нужных столбцов
+features = data[['Name', 'Sex', 'Age']]
+
+# Применение Label Encoding к столбцу 'Name'
+label_encoder = LabelEncoder()
+features['Name'] = label_encoder.fit_transform(features['Name'])
+
+# Определение целевой переменной
+y = data['Survived']
+
+# Создание и обучение дерева решений
+clf = DecisionTreeClassifier(random_state=241)
+clf.fit(features, y)
+
+# Получение важностей признаков
+importance = clf.feature_importances_
+
+# Печать важности каждого признака
+print("Важность 'Name':", importance[0])
+print("Важность 'Sex':", importance[1])
+print("Важность 'Age':", importance[2])
+

almukhammetov_bulat_lab_3/lab3.py

@@ -0,0 +1,77 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, accuracy_score
+
+# Загрузка данных
+data = pd.read_csv('housing.csv')
+data = data.dropna()
+
+# Отбор нужных столбцов
+features = data[
+    ['longitude', 'latitude', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income']]
+
+# Задача регрессии
+target_regression = data['housing_median_age']
+
+# Разделение данных на обучающий и тестовый наборы для регрессии
+X_train_regression, X_test_regression, y_train_regression, y_test_regression = train_test_split(features,
+                                                                                                target_regression,
+                                                                                                test_size=0.01,
+                                                                                                random_state=241)
+
+# Создание и обучение дерева решений для регрессии
+clf_regression = DecisionTreeRegressor(random_state=241)
+clf_regression.fit(X_train_regression, y_train_regression)
+
+# Предсказание на тестовом наборе для регрессии
+y_pred_regression = clf_regression.predict(X_test_regression)
+
+# Оценка качества модели для регрессии (MSE)
+mse_regression = mean_squared_error(y_test_regression, y_pred_regression)
+print("Средняя ошибка для регрессии:", mse_regression)
+
+# Задача классификации
+target_classification = data['median_house_value']
+
+# Разделение данных на обучающий и тестовый наборы для классификации
+X_train_classification, X_test_classification, y_train_classification, y_test_classification = train_test_split(
+    features, target_classification, test_size=0.01, random_state=241)
+
+# Создание и обучение дерева классификации
+clf_classification = DecisionTreeClassifier(random_state=241)
+clf_classification.fit(X_train_classification, y_train_classification)
+
+# Предсказание на тестовом наборе для классификации
+y_pred_classification = clf_classification.predict(X_test_classification)
+
+# Оценка качества модели для классификации (точность)
+accuracy_classification = accuracy_score(y_test_classification, y_pred_classification)
+print("Точность для классификации: {:.2f}%".format(accuracy_classification * 100))
+
+# Важности признаков для регрессии
+importance_regression = clf_regression.feature_importances_
+
+print("Важность для регрессии")
+# Печать важности каждого признака для регрессии
+print("Важность 'longitude':", importance_regression[0])  # За западную долготу дома
+print("Важность 'latitude':", importance_regression[1])  # За северную широту дома
+print("Важность 'total_rooms':", importance_regression[2])  # За общее количество комнат в блоке
+print("Важность 'total_bedrooms':", importance_regression[3])  # За общее количество спален в блоке
+print("Важность 'population':", importance_regression[4])  # За общее количество проживающих в блоке
+print("Важность 'households':", importance_regression[5])  # За общее количество домохозяйств в блоке
+print("Важность 'median_income':", importance_regression[6])  # За медианный доход домохозяйств в блоке
+
+# Важности признаков для классификации
+importance_classification = clf_classification.feature_importances_
+
+print()
+print("Важность для классификации")
+# Печать важности каждого признака для классификации
+print("Важность 'longitude':", importance_classification[0])  # За западную долготу дома
+print("Важность 'latitude':", importance_classification[1])  # За северную широту дома
+print("Важность 'total_rooms':", importance_classification[2])  # За общее количество комнат в блоке
+print("Важность 'total_bedrooms':", importance_classification[3])  # За общее количество спален в блоке
+print("Важность 'population':", importance_classification[4])  # За общее количество проживающих в блоке
+print("Важность 'households':", importance_classification[5])  # За общее количество домохозяйств в блоке
+print("Важность 'median_income':", importance_classification[6])  # За медианный доход домохозяйств в блоке

arutunyan_dmitry_lab_7/README.md

@@ -0,0 +1,224 @@
+
+## Лабораторная работа 7. Вариант 4.
+### Задание 
+- Выбрать художественный текст и обучить на нем рекуррентную нейронную сеть 
+для решения задачи генерации. 
+- Подобрать архитектуру и параметры так, 
+чтобы приблизиться к максимально осмысленному результату. 
+- Подобрать компромиссную архитектуру, справляющуюся 
+достаточно хорошо русским и английским текстами.
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+```
+python main.py
+```
+Результат выполнения программы будет выведен в консоль.
+> **Warning**
+>
+> Данное решение использует конфигурацию, созданную на основе комплектующих машины, на которых она запущена. Запуск программы на машинах с отличной конфигурацией может привести к ошибкам.
+
+### Используемые технологии
+- Библиотека `numpy`, используемая для обработки массивов данных и вычислений
+- Библиотека `sys`, используемая для потокового вывода данных в консоль.
+- Библиотека `nltk` - (Natural Language Toolkit) библиотека для обработки естественного языка, используемая для предобработки текста:
+    - `RegexpTokenizer` - инструмент для токенизации текста на основе регулярных выражений.
+    - `stopwords` - коллекция стоп-слов корпуса языка.
+- Библиотека `tensorflow` - открытая библиотека глубокого обучения, используемая для создания и обучения моделеи машинного обучения, основанной на рекурентной нейронной сети.
+- Библиотека `keras` - высокоуровневая библиотека глубокого обучения:
+    - `Sequential` - класс, который представляет собой линейную стековую модель нейронной сети.
+    - `Dense`, используемый для создания слоя, в котором каждый нейрон соединен со всеми нейронами в предыдущем слое.
+    - `Dropout` - метод регуляризации, который применяется в нейронных сетях для борьбы с переобучением. Он заключается во временном исключении случайно выбранных нейронов во время обучения модели.
+    - `LSTM` -  (Long Short-Term Memory) тип рекуррентной нейронной сети, который используется для обработки последовательностей данных. Он отличается от обычных рекуррентных нейронных сетей (RNN) своей способностью эффективно улавливать долгосрочные зависимости в последовательностях.
+
+### Описание работы
+#### Предобработка текстовых данных
+
+Нам нужно преобразовать наш вводимый текст в числа, а затем обучить модель последовательностям этих чисел. 
+
+Для начала загрузим текстовые данные. У нас это будет небольшое современное художественное произведение примерно на 180 строк тектса:
+```python
+file = open("P:\\ULSTU\\ИИС\\Лабораторные\\Lab7\\texts\\text-ru.txt", encoding='utf-8').read()
+```
+Теперь переведём текст в нижний регистр, и создадим токены из слов с помощью `NLTK`. 
+```python
+input = input.lower()
+tokenizer = RegexpTokenizer(r'\w+')
+tokens = tokenizer.tokenize(input)
+```
+Отфильтруем список токенов и оставим только те токены, которых нет в списке стоп-слов или общих слов русского корпуса, дающих мало информации о рассматриваемом предложении, с помощью `NLTK`:
+```python
+filtered = filter(lambda token: token not in stopwords.words('russian'), tokens)
+```
+Теперь преобразуем символы нашего текста в числа: 
+- Отсортируем список из набора всех символов, которые появляются во входном тексте.
+- Получим числа, представляющие символы с помощью `enumerate`.
+- Создадим словарь, в котором хранятся символы и числа, которые их представляют.
+```python
+chars = sorted(list(set(processed_inputs)))
+char_to_num = dict((c, i) for i, c in enumerate(chars))
+input_len = len(processed_inputs)
+vocab_len = len(chars)
+```
+Также сохраним общее кол-во символов и размер словаря для создания набора данных.
+
+#### Создание набора данных
+
+Для начала необходимо задать длину последовательности (одно полное отображение входных символов в целые числа). Укажем её размер равный 100. 
+
+Теперь необходимо пройти весь список входов и преобразовать символы в числа, для создания групп последовательностей входных и выходных данных для обучения:
+```python
+seq_length = 100
+x_data = []
+y_data = []
+for i in range(0, input_len - seq_length, 1):
+    in_seq = processed_inputs[i:i + seq_length]
+    out_seq = processed_inputs[i + seq_length]
+    x_data.append([char_to_num[char] for char in in_seq])
+    y_data.append(char_to_num[out_seq])
+n_patterns = len(x_data)
+print("Кол-во паттернов:", n_patterns)
+```
+Также выведем общее кол-во обучающих последовательностей (паттернов).
+
+Преобразуем входные последовательности в обработанный массив `numpy`, с преобразованием значений массива `numpy` в числа с плавающей запятой, чтобы функция активации сигмоида, которую использует рекурентная нейронная сеть, могла интерпретировать их и выводить вероятности от 0 до 1.
+```python
+X = np.reshape(x_data, (n_patterns, seq_length, 1))
+X = X / float(vocab_len)
+y = np_utils.to_categorical(y_data)
+```
+
+#### Разработка архитектуры модели
+
+Создадим модель `LSTM` типа `Sequential` и добавим слои:
+```python
+model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
+model.add(Dropout(0.2))
+model.add(LSTM(256, return_sequences=True))
+model.add(Dropout(0.2))
+model.add(LSTM(256))
+model.add(Dropout(0.2))
+model.add(Dense(y.shape[1], activation='softmax'))
+```
+- 1й слой - слой в 256 нейронов по размерности входных данных с обратными зависимостями.
+- 2й слой - слой в 256 нейронов с обратными зависимостями
+- 3й слой - слой в 256 нейронов
+
+Между каждыми слоями используется функция `Dropout` для случайного исключения нейронов с вероятностью 0.2 в целях борьбы с переобучением.
+
+После этого компилируем модель и обучаем. Лучшие модели с наименьшими ошибками определения связей символов будут сохраняться в файл. Добавим функцию для вывода сгенерированного текста:
+
+```python
+start = np.random.randint(0, len(x_data) - 1)
+pattern = x_data[start]
+print("Случайная выборка:")
+print("\"", ''.join([num_to_char[value] for value in pattern]), "\"")
+
+for i in range(1000):
+    x = np.reshape(pattern, (1, len(pattern), 1))
+    x = x / float(vocab_len)
+    prediction = model.predict(x, verbose=0)
+    index = np.argmax(prediction)
+    result = num_to_char[index]
+    sys.stdout.write(result)
+    pattern.append(index)
+    pattern = pattern[1:len(pattern)]
+```
+В качестве стартового набора для генерации используем случайную выборку слов текста. 
+
+#### Оптимизация модели
+
+Рекурентные нейронные сети основаны на матричных вычислениях, которых в данной модели огромное количество. Процессор обрабатывает такие данные достаточно медленно (к примеру на данной машине время выполнение одной эпохи обучения ресурсами процессора было около 50 минут). Однако, рекурентные сети способны учиться и на графических картах. 
+
+На данной машине установлена GPU NVidia GTX 980Ti с графической памятью DDR6 на 4Гб. Чтобы использовать её для вычислений, необходимо установить ПО от NVidia - `CUDA` и драйвер `cudnn`. После этого необходимо установить `tensorflow` с поддержкой GPU, задать ему конфигурации машины и настроить распределённую архитектуру вычислений:
+```python
+strategy = tf.distribute.MultiWorkerMirroredStrategy()
+with strategy.scope():
+    parallel_model = model
+parallel_model.fit(X, y, epochs=200, batch_size=256, callbacks=desired_callbacks)
+```
+Данная стратегия распределяет вычисления на ЦП и ГП в зависимости от их загруженности. С ней время одной эпохи обучения скратилось до 50 секунд.
+
+Нагрузка на ЦП и ГП во время обучения: 
+![](1.png "")
+
+#### Генерация текста
+Информация о входных данных: 
+```
+Общее кол-во символов: 31258
+Размер словаря: 34
+Кол-во паттернов: 31158
+```
+Сгенерируем русский текст на 5 эпохах обучения:
+```
+Epoch 1/5
+122/122 [==============================] - ETA: 0s - loss: 3.1638
+Epoch 1: loss improved from inf to 3.16378, saving model to model_weights_saved.hdf5
+...
+Epoch 5/5
+122/122 [==============================] - ETA: 0s - loss: 3.0314
+Epoch 5: loss improved from 3.08348 to 3.03140, saving model to model_weights_saved.hdf5
+Случайная выборка:
+" ти могли привлекать чужой взгляд сводить ума молить помиловании тёмные полосы тени высоких деревьев  "
+ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо  ооо
+```
+На 5ти эпохах обучения получается абсолютно бессвязный текст, слова логически не сгенерированы. Сгенерируем русский текст на 10 эпохах обучения:
+```
+Epoch 1/10
+122/122 [==============================] - ETA: 0s - loss: 3.1625
+Epoch 1: loss improved from inf to 3.16249, saving model to model_weights_saved.hdf5
+...
+Epoch 10/10
+122/122 [==============================] - ETA: 0s - loss: 2.7291
+Epoch 10: loss improved from 2.78984 to 2.72912, saving model to model_weights_saved.hdf5
+Случайная выборка:
+" чески осматривая огромный двор откуда выскочить таким страхом оглядываются охранники стоящие высоких "
+ пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо пооооо поооо
+```
+На 10 эпохах обучения результат генерации текста получился таким же неудовлетворительным, что и на 5. Сгенерируем русский текст на 50 эпохах обучения:
+```
+Epoch 1/50
+122/122 [==============================] - ETA: 0s - loss: 3.1663
+Epoch 1: loss improved from inf to 3.16626, saving model to model_weights_saved.hdf5
+...
+Epoch 50/50
+122/122 [==============================] - ETA: 0s - loss: 0.7051
+Epoch 50: loss improved from 0.72650 to 0.70508, saving model to model_weights_saved.hdf5
+Случайная выборка:
+" небольшом экране здешние пейзажи конца жизни будут вынуждать молодого парня отчетливо ощущать очаров "
+ание восторг всё сильнее становится рринесеть семе выподить делает принуатию весас корорый держа свой взгляд видляд жверя кишь счрали одного всемательно поститался фртографию выстрогои джухёк одлом бысоких деревьев обнаружить гедрм доого нову поптитал ррдинается сроровняе кемечелия срику тэхён поношался кажртванног выродить деухёк  льфа видот отвечает тэхён выглядит сраки делал сочни пассавляет камену невероятно красивый вслух подобного поведения отнышие своим ведлм доооге начинает прднимая нугань мальчика сочно поднуся альфе каждый анетасалтся обреть ссрашивает голову слышит приближающиеся шаги полностью ззменной ботсване моральную физическую боль машина останавливается постояения своем волос пугеляни соснойно проазаться мевольно прогодит леметт рену тозну тэхёна оокучается пририсенную скоро боле гзга смотрит вооруженный взгляд жиего произносит мюди обращают внимание альфа рутаньство сронцно просиает сочно подбородок вынуждая чонгука просно подобного поведения отнышие водитель сразу 
+```
+На 50 эпохах обучения результат генерации текста заметно лучше, чем на 10: присутствуют слова и даже связанные по смыслу словосочетания. В словах замечается большое кол-во ошибок и связанных полных предложений пока всё ещё нет. Сгенерируем русский текст на 100 эпохах обучения:
+```
+Epoch 1/100
+122/122 [==============================] - ETA: 0s - loss: 3.1675
+Epoch 1: loss improved from inf to 3.16754, saving model to model_weights_saved.hdf5
+...
+Epoch 100/100
+122/122 [==============================] - ETA: 0s - loss: 0.2224
+Epoch 100: loss did not improve from 0.22002
+Случайная выборка:
+" ь правильнее ещё никто делал чон рено нему настолько близко находится живёт одной комнате смеет боят "
+ься зверя которого спас воспитал тигр лишь шутку укусить причиняя сильной боли ези кажется настал день придётся попрощаться своей короткой жизнью точно думал умрёт клыков тигра подобные ужасы голову лезли диего замечая чуя кусок мяса ускоряет свой шаг издает негромкий рык сильнее напугать мальчика который прежнему надеется разумные мысли чонгука стоящего позади останавливаться собирается суждено умереть сегодня пусть ези смело доает язык внесний подирают грооко пидел дома простудой жуткой болью костях мог летать сеул экзамены занимался круглосуточно очередным беспокойством произносит понимая это альфе понравится джухёк кидает злобный взгляд угрожая суженными зрачками очерчивая скулы омега поджимает губы всё равно отстраняется брата крепче обнимает слегка хмурит брови зная очередном отказе тэхён пошёл рано африканскую школу поэтому корейскую закончил семнадцать лет самым младшим классе находясь первом курсе журналистики омега остаётся самым мелким парнишкой среди своих одногруппников
+```
+На 100 эпохах обучения мы получили пракически полносвязный текст со связями не только внутри предложений, но и между ними. При прочтении данного текста можно понять некую историю. Ошибок в словах практически нет. При этом, степень похожести оригинального и сгенерированного текста не более 60%, а значит, модель не переобучилась и сама генерирует более-менее связный текст. На этом эксперименты генерации русского текста завершены.
+
+Сгенерируем английский текст на 100 эпохах обучения:
+```
+Epoch 1/100
+104/104 [==============================] - ETA: 0s - loss: 2.9685
+Epoch 1: loss improved from inf to 2.96853, saving model to model_weights_saved.hdf5
+...
+Epoch 100/100
+104/104 [==============================] - ETA: 0s - loss: 0.1609
+Epoch 100: loss improved from 0.16124 to 0.16091, saving model to model_weights_saved.hdf5
+Случайная выборка:
+"brother never return seoul juhyuk chuckles turns around noticing taxi driver two bright suitcases  "
+belonging taehyung elder kim gives younger one suspicious look approaches driver black jeep takes suitcases quietly thanking taehyung nods taxi driver seat smiles thanking omega turns back home causing tears gather corners honey colored eyes relaxes whole body takes warm air lungs looking around house front definitely changed four years taehyung become beautiful although like changes clearly visible outside apparently everything become different inside senior brother definitely done good job improving everything concerns hearth also neighboring houses taehyung considerable pocket money definitely came handy nothing secretly transferred juhyuk sense come back alpha asks seriously comes closer dreaming biting lip dreaming forbid grunting cheerfully takes suitcase brother leaves house man narrows eyes looks carefully taehyung retreating back thinking soon following omega first enter house holding breath pursing lips hurts slightly juhyuk hurry glances younger reason worries reaction caref
+```
+На 100 эпохах обучения английский текст сгенерировался также удовлетворительно, как и русский.
+
+### Вывод
+Модель рекурентной нейронной сети, созданная в рамках данной лабороторной, на 100 эпохах обучения показала хорошие результаты в генерации русского и английского текста. Потери связей символов в обоих случаях были меньше 1. На 50-60 % текст получился оригинальный, а значит модель не переобучилась.
+
+Таким образом, спроектированная архитектура рекурентной нейронной сети подходит для генерации текстов.

arutunyan_dmitry_lab_7/main.py

@@ -0,0 +1,97 @@
+import sys
+import numpy as np
+from nltk.tokenize import RegexpTokenizer
+from nltk.corpus import stopwords
+import tensorflow as tf
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, LSTM
+from keras.utils import np_utils
+from keras.callbacks import ModelCheckpoint
+
+file = open("P:\\ULSTU\\ИИС\\Лабораторные\\Lab7\\texts\\text-en.txt", encoding='utf-8').read()
+
+
+def tokenize_words(input):
+    # переводим весть текст в строчные буквы
+    input = input.lower()
+
+    # инициализируем токенизатор
+    tokenizer = RegexpTokenizer(r'\w+')
+    tokens = tokenizer.tokenize(input)
+
+    # выбираем и выбрасываем все стоп слова, находящиеся в списке стоп слов русского языка
+    filtered = filter(lambda token: token not in stopwords.words('english'), tokens)
+    return " ".join(filtered)
+
+
+if __name__ == '__main__':
+    # предобрабатываем текст, создаём токены
+    processed_inputs = tokenize_words(file)
+
+    chars = sorted(list(set(processed_inputs)))
+    char_to_num = dict((c, i) for i, c in enumerate(chars))
+
+    input_len = len(processed_inputs)
+    vocab_len = len(chars)
+    print("Общее кол-во символов:", input_len)
+    print("Размер словаря:", vocab_len)
+
+    seq_length = 100
+    x_data = []
+    y_data = []
+    for i in range(0, input_len - seq_length, 1):
+        in_seq = processed_inputs[i:i + seq_length]
+        out_seq = processed_inputs[i + seq_length]
+        x_data.append([char_to_num[char] for char in in_seq])
+        y_data.append(char_to_num[out_seq])
+
+    n_patterns = len(x_data)
+    print("Кол-во паттернов:", n_patterns)
+
+    X = np.reshape(x_data, (n_patterns, seq_length, 1))
+    X = X / float(vocab_len)
+    y = np_utils.to_categorical(y_data)
+
+    model = Sequential()
+    model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
+    model.add(Dropout(0.2))
+    model.add(LSTM(256, return_sequences=True))
+    model.add(Dropout(0.2))
+    model.add(LSTM(256))
+    model.add(Dropout(0.2))
+    model.add(Dense(y.shape[1], activation='softmax'))
+
+    model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+    filepath = "model_weights_saved.hdf5"
+    checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
+    desired_callbacks = [checkpoint]
+
+    # Создание распределенной стратегии
+    strategy = tf.distribute.MultiWorkerMirroredStrategy()
+    # Распределение модели на устройства
+    with strategy.scope():
+        parallel_model = model
+    # Обучение модели на GPU и CPU
+    parallel_model.fit(X, y, epochs=100, batch_size=256, callbacks=desired_callbacks)
+
+    model.load_weights(filepath)
+    model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+    num_to_char = dict((i, c) for i, c in enumerate(chars))
+
+    start = np.random.randint(0, len(x_data) - 1)
+    pattern = x_data[start]
+    print("Случайная выборка:")
+    print("\"", ''.join([num_to_char[value] for value in pattern]), "\"")
+
+    for i in range(1000):
+        x = np.reshape(pattern, (1, len(pattern), 1))
+        x = x / float(vocab_len)
+        prediction = model.predict(x, verbose=0)
+        index = np.argmax(prediction)
+        result = num_to_char[index]
+        sys.stdout.write(result)
+        pattern.append(index)
+        pattern = pattern[1:len(pattern)]
+

arutunyan_dmitry_lab_7/texts/text-en.txt

@@ -0,0 +1,178 @@
+Today there is no peace in the yard again, because the predatory beast is no longer sleeping.
+Everyone in toxic territory bites their tongue, as if waiting for them all to be torn to shreds: cruelly and bloodthirsty. They turn around: frantically and fearfully. The blood runs cold in their veins, their hearts beat dully in their chests, and devils dance before their eyes.
+No one allows them to move from their place and take shelter even behind tall trees nearby. They may be found there too. They hear very quiet steps, and all this is just imagination, built on fear and fear, which pulsate with acute pain in their temples. They whisper words to the gods they believe in and worship, and since these people are still alive on this earth, they probably hear them. But it seems to them that if they move too sharply one more time, they will be found and nothing resembling a person will be left behind. A thought like this excites and drives you crazy more and more with every worthless minute, saturated with cowardice.
+The only owner of the territory where the frightened and timid are located, occasionally outlines their silhouettes with a cold gaze and admires how much fear lives in them just because of one living creature. They don't know anything about courage. He sometimes gets sick of such behavior, but he is not going to blame these people for anything, since he is the only one here who knows not only human language. He grew up in such conditions, was raised differently: unusual and wild for the rest of the inhabitants of the planet. Almost from birth, he studied those things because of which they might consider him crazy, a madman in this toxic territory. And poison flows through the earth only because of lifestyle, views, principles, traditions.
+Alpha looks at the armed guards of his house - they fought, saw many deaths, but still have fear in their chests - now he keeps his expressive eyes on the orange setting sun, which illuminates the whole of Botswana with its dim light, and later returns to his hands with two large sharp knives. He sharpens them loudly against each other, notices the bright shine and reflection of his face. Sees the one whom many people on this earth fear. He built a terrible reputation for himself.
+The man again turns away from his occupation, puts the bladed weapon down on the rocky ground and turns his head. He hears approaching steps and completely switches to this sound. A dark-skinned alpha of about twelve years old is shaking all over, and beads of sweat are rolling down his temples. The tray he holds in his childish hands makes noise along with his heart. He is afraid, but tries to hide it in front of the owner of the poisonous territory. And he sees everything, does not get angry, only feels how interest awakens in him. This boy, moving in his direction, is now terrified, afraid of a living creature, but continues to walk towards him, periodically examining the huge yard from where he can jump out. The guards standing at the tall iron lattice gates look back with the same fear.
+They are all children. They're all the same.
+The boy approaches and immediately lowers his intimidated gaze, chewing his lips and having no idea where he should start. He is new here: not even a week has passed since his arrival in this huge house.
+“Sir...” his childish voice can be heard very quietly, and the older one just arches his eyebrow. “Chon-Reno,” he recalls how his dad told him that the owner of the house prefers to be called by his last name. “Your...” he holds the tray a little forward and realizes that he has again forgotten the name of the alcohol that fills the small crystal glass. “Your...” he repeats and swallows saliva. - What you asked.
+“Rum,” Chon-Reno helps him. “This is rum, Ezi,” he takes a glass of drink and nods. The boy looks up at him in surprise, not expecting the man to remember his name. - Why are you so afraid? — narrows his bright gray eyes, bowing his head slightly.
+Yezi bats his thick black eyelashes.
+Does the senior alpha really not know why everyone in this territory is scared right now? Even God himself knows about this.
+The Lion God should also guess...
+“You released Diego,” he says as if trying to convey to Reno the full depth of his madness.
+“He’s not a monster,” the steely voice frightens.
+- He's a predator, sir...
+The corners of the elder’s dry lips twitch a little, and his indifferent gaze is averted to the side.
+His predator.
+Diego is nowhere to be seen. His boy is having fun, content with walking on the ground, the clean evening air and the big orange sun that gives its warm light. The beast catches his rays and growls. He is also a living creature, and just like people, he wants to be free and let go, but unlike pitiful people, he is not a coward and is not afraid of what God has created. Stronger, wiser and more dangerous. This powerful predator is finally heard: the sounds it makes attract Reno’s precious attention, and the dark-skinned boy is forced to flinch and turn around, roaming around the territory with huge, frightened eyes. Fear dances in his blood, and his heart beats like a pitiful bird driven into a corner. He shudders from conflicting sensations, which the man next to him notices.
+He-Renault looks blankly at Bello Jesi, the twelve-year-old alpha from a poor family who works in this large mansion. The man has known him for a couple of days, and he still has not gotten used to the usual way of life of the owner of this center of horror and madness.
+“Yessi,” he coldly calls and receives due attention to himself. “Go into the house, ask to put meat for Diego,” the younger one quickly nods and is about to leave. “And you will bring it to me yourself,” and these words already seemed much more terrible.
+“What?...” he asks again, not believing.
+- You heard me.
+But it seems that I misheard.
+A terrible feeling of cowardice and resentment is swarming in his chest, but Ezi can’t object or express his reluctance to go out into the yard a second time, and even with a raw piece of meat for a predator. He can only vaguely nod to the elder again and hastily go to the stairs leading to the doors of the mansion.
+And the owner of the house himself chuckles at the boy’s timid reaction. Without taking a sip, he puts the glass of rum on the ground and rises from the small chair. Kneading his neck with his palm, on which the face of a lion is drawn in detail, the alpha is precisely preparing for a brutal, bloody battle. Again. His gray eyes are bright and calm, but interest in what is to come will sparkle in his pupils after a couple of minutes. Running the tip of his tongue along his lower lip, Chon-Reno's gaze runs around the vast courtyard, where his beloved figure is not yet visible - it is heard so clearly and loudly that it is impossible to hide his admiration and sincere love. But these sounds do nothing to calm the guards at the gate. They are not used to them. They will never get used to it because they didn’t raise him like Jungkook did.
+When Yezi finally comes out of the house, holding a small tray with a piece of meat in his hands, Reno grins again and puts his hands behind his back. He waits for him, looks at him and hears from afar how he desperately breaks the little alpha’s chest. He is getting closer to him, often looks around and whispers something under his breath. Is it really prayers? The man quirks an eyebrow and turns his head, hearing another loud roar that he could only be proud of. His boy has grown up a lot and become strong, which is why he scares everyone so much. Maybe Chon-Reno's sounds make him smile contentedly, but for Yezi they make him want to hide behind one of the columns. Only Reno himself is waiting for him, and he cannot afford to fulfill what he wants, otherwise the truth will receive its well-deserved punishment. And that’s the last thing I want right now.
+Every resident of Botswana has already heard about harsh methods of retribution for disobedience.
+“Sir, here’s the meat,” Yezi whispers, standing opposite the man and feeling tears welling up in his expressive eyes. -Can I go into the house now?..
+A child's heart just begs for dad.
+- No, - frost and cold.
+From such an answer, the boy tightens his grip and bites his lip until it hurts, and then obediently turns his gaze to where Jungkook is looking.
+Now Diego's eyes appear in the semi-darkness.
+Fear beats like a second heart in your chest, and your throat suddenly dries up. Ezi wants to run away from here when he sees that a predatory animal is coming out from around the corner of the mansion, showing that he is also the owner here. He looks at the dark-skinned boy and the tall man standing next to him, showing a proud smile on his dry lips. His steps are slow, confident, because he knows his worth well and how majestic he now looks under the orange light of the sunset sky. He walks boldly and gracefully, piercing with a sharp gaze, sensing food somewhere near him. The look is wild, free, omnipotent. The stripes, smoothly located on his velvet fur, could always attract someone else's gaze, drive him crazy and beg for mercy. The dark stripes are shadows from tall trees, and between them are the yellow reflections of the African sun. The whole appearance of the beast is peaceful, but for some reason it sends a terrible shiver through the skin.
+So handsome and young. Reno is unable to take his interested gaze away from him, just like poor Ezi, who is frozen. This is the first time he sees the beast so well and clearly; before that, he had only heard about the powerful Diego, whose appearance forces everyone to believe in God. Even ordinary steps hypnotize everyone in this area.
+Jungkook looks down at Bello Yezi and chuckles.
+“Bring him his snack closer,” the boy swallows his saliva nervously and looks up at Chon-Reno, dumbfounded. “The only way to escape from a predator is to feed him something else,” Ezi reads instructively to him something that Ezi does not believe in, and the elder squats down in front of him, looking into his glassy, rounded eyes. “You don’t want to be eaten by him, do you?” - he is silent. “Then give him what he wants and he won’t hurt you.”
+“Sir, please,” he begs in a trembling voice. “He’s a tiger, I never approached them...” he tells him the obvious, hoping for pity. - Is it dangerous…
+— Take this meat to Diego, Ezi, firmly and undeniably.
+In an instant, the boy becomes more afraid of Chon-Reno Jungkook than of the huge young tiger slowly moving towards them.
+“I can’t…” he whispers quietly, looking only into the gray eyes opposite.
+Who could? Apparently, only the owner of this beast himself.
+- You're not up toI’m lying to say that,” Jungkook tilts his head slightly to his shoulder. “You haven’t even come close to him, which means you don’t know for sure whether you can or not,” Yezi purses his lips. “Forward,” he orders, nodding towards Diego.
+The Bengal tiger is getting closer to them, continues to move and keeps its yellow eyes only on a person it does not know. He sees it for the first time, which is why he analyzes it so predatorily, forcing Ezi to hold her breath and finally take a tense step forward. To Diego.
+He is also closely watched by Reno, who calmly gets back to his feet and straightens his back. The boy is holding up really well. Jungkook was already hoping that he would burst into tears and just storm out of here. He wouldn’t follow him: it’s not for him to judge a teenager for a completely significant and logical fear. Not everyone here dares to approach Diego and feed him. To be more correct, no one has ever done this before. Only Chon-Reno is so close to him: he lives in the same room with him and does not even dare to be afraid of this beast, which he himself saved, and raised himself. A tiger can bite him only as a joke, without causing severe pain. But it seems to Yezi that the day has come when he will have to say goodbye to his short life. I certainly didn’t think that I would die from the fangs of a tiger. Such horrors never entered his head.
+Diego, noticing and smelling a piece of meat, quickens his pace and emits a low growl, which can only scare the boy more, who still hopes for the reasonable thoughts of Jungkook standing behind, but is not going to stop.
+If you are destined to die today, so be it.
+Yezi already boldly looks into the eyes of the tiger approaching him and stops a couple of meters from Chon-Reno, squatting down and keeping his gaze on the incredibly beautiful beast. The senior alpha is even a little surprised at this bold step, watching what is happening and grinning slightly. The younger one bites his lower lip and looks down at the scarlet piece of meat on the silver tray, which he places on the rocky ground, taking an uncertain small step back. He closes his eyes painfully and clenches his fingers tightly into a fist, already beginning to feel the non-existent pain brought to him in the near future by a hungry tiger. During these seconds, he manages to say goodbye to his parents, whispers asking them for forgiveness for all his mistakes, listening to the approaching steps. It seems his heart will stop before the beast approaches.
+But…
+The sun-kissed tiger - Diego - roars throughout the yard, majestically scattering the birds in the trees and on the roof of the mansion. Ezi opens his eyelids in fear and sees in front of him the evil face and huge mouth of an animal, which in the next second sinks its fangs into a piece of raw meat. He doesn't stop growling at the boy, as if he's trying to tell him something. But the teenager only looks dumbfounded into the yellow eyes and forgets to breathe, falling on his heel to the ground and opening his mouth. Diego just takes the meat and runs to the side, throws his snack away from the boy, lies down next to her and calmly begins to lick and eat his favorite delicacy, depriving Ezi of the thought of possible death. With pleasure and a growl, he tears off small pieces, immediately swallowing them and sometimes glancing at his owner.
+Jungkook smiles weakly, looking at the tiger and hiding both hands in the pockets of his black cargo pants. He slowly turns his gaze from him to the boy lying on the ground and looking with the same shock at the quiet Diego, who no longer pays any attention to him. The beast is busy with his real food, and not with the person who brought it to him. Probably, only Jungkook was convinced until the last seconds that Diego would not touch Yesi.
+He never doubts his tiger and his lions.
+He knows their every move in advance.
+The dark-skinned boy and many foreigners expect that every African speaks to animals as equals, understands their thoughts and language. But there are few like them in all of Botswana. Units. And among them, at the top stands Chon-Reno Jungkook - the Lion God. Only Bello Ezi now seems that the man, at twenty-eight years old, has also become the patron saint of tigers.
+The young alpha has difficulty getting up from the ground, looking away from Diego and turning to his owner, who was looking at him so sharply and piercingly. Now Jungkook is approaching them: calmly and slowly. But Yezi is no longer afraid, as if the tiger gave him incredible courage because of his proximity to him. Alpha believes in such tales heard from other people's lips. He also believes in that terrifying feeling that is stored in the chest of a man who has the face of a lion depicted in black ink on his neck. The true lion god. This God stands a meter away from the no longer frightened boy, but delighted with what he saw two minutes ago. Of course, he was amazed at how close the young Bengal tiger was to him. I would run to tell this story to my friends and classmates, but they would never believe it, so the important memory will settle deep in my heart.
+“Sir...” Yezi begins in shock, swallowing his saliva. “Diego didn’t touch me,” he looks at Jungkook, who chuckled under his breath. - Why? He could...
+A couple of seconds would be enough for the beast to tear him bloodthirsty to pieces.
+-You fed him what something else remained untouched,” a deep, heavy voice is heard. “The fangs and claws did not penetrate you only for this reason.” “He’s a predator, not a killer,” Yezi relaxes a little and nods briefly, remembering his words. “He needs food, not someone’s death,” Reno, having finished, looks at his striped boy.
+“He’s very handsome,” Ezi admits quietly, also looking in the direction of the beast.
+Jungkook looks down at him and grins.
+“Incredibly beautiful,” he confirms out loud.
+Yesi smiles and ultimately does not conceal the question:
+  -Can I go now?..
+Reno nods without a word.
+Yezi smiles broadly and takes the tray from the ground, after which he runs into the huge mansion, wanting to tell his father, who is in charge of the kitchen, about what happened.
+The bright rays of the setting African sun play fabulously on the striped coat. Jungkook is currently fascinated by the sight in front of him. Diego has grown a lot. He is only a year and a half old, but he is the size of an adult tiger. It’s hard to believe that not so long ago the animal was a small animal, and it constantly hovered next to Chon-Reno, did not really leave him: it climbed, playfully growled and bit its owner’s legs, wanting to attract proper attention to itself. Got used to Jungkook too quickly. After Jungkook took him to himself, wounded by adult animals, two weeks were enough for the tiger cub to trust completely and secretly climb onto the alpha’s chest at night. And when he couldn’t sleep, he scratched and bit his chin, forcing Jungkook to wake up. And he woke up, played, fed and returned back to bed, followed by little Diego with his majestic gait.
+To this day the tiger does the same.
+Jungkook, with a smile on his lips, slowly approaches the animal, which with wild appetite throws the remaining small piece of meat into its mouth. Diego himself jumps up and runs to his owner, who is squatting. The tiger immediately climbs on him, as if with a real human hug, and playfully begins to roar, causing Reno to smile wider, clapping and stroking the animal’s stomach with his large palm. He does not calm down, he already runs his tongue along the alpha’s neck - showing his true love for him. Jungkook lowers himself to the ground and runs both hands through the tiger’s soft fur, paying due attention to the place behind the ears, just as Diego loves. With such affection he becomes a kitten. He spins around, licks where he wants, and nuzzles Chon-Reno’s chin, who laughs at this behavior. No one will ever dare to scold him for this.
+“You’re getting heavier and heavier every day, Diego,” Jungkook chuckles and continues to stroke him. “You’re scaring everyone around with your appearance, big boy,” the tiger pulls back and walks around the man, already pressing against his back and running his tongue along the nape of his brown hair. “You’ll never scare me alone, don’t even count on my fear,” Reno turns his head to Diego. He growls at him quietly, as if responding to the words spoken. “You and I are two predators, and only God knows which of us is more dangerous.”
+And Chon-Reno already knows who is more dangerous on this earth.
+And so he lived: playing pranks in his dreams out of boredom, enduring torment and disappointment. Once again he promised himself to return home soon. Only there will he find salvation, peace in his heart, luck - a piece of happiness.
+This place will make him forget all the sorrows and all the pain, will make him feel as if he drank a bottle of wine.
+Another click and another divine frame saved on the flash drive of an expensive new camera. Honey eyes instantly catch on to the photograph taken, which is displayed on the small screen. The local landscapes will force the young guy to clearly feel charm, delight and admiration until the end of his life. By looking at these beauties, a person is able to get rid of any infection and pain. Here the blind will become sighted, the deaf will learn the world of sounds. The clear blue sky, the warm November sun, tall and short trees, wild animals running around the territory, everything—everything forced me to smile and relax.
+The young alpha, driving a black jeep, which was easy to drive on rough roads, turns his head to the omega sitting in the passenger seat, who is examining the photograph taken with a slight smile on his lips. Among the ashen silky hair, the pure rays of the immense sun are tangled. Sliding lower, they decide to touch open areas of light skin, as if they are deliberately highlighting noticeable marks on the body.
+From the first seconds, the driver was attracted by one striking feature of this guy. This quickly caught my eye. All the way, the man cannot find the strength to get curiosity out of his head: he either watches the road, or turns his gaze to the passenger, secretly looking at more than just a pretty face.
+I had never been able to see something like this before.
+This involuntarily frightens, frightens, forces you to think and construct your own theories in your thoughts, but an African hardly dares to ask. Perhaps the omega does not want to talk about it, hides something and feels pain when strangers pay attention.
+Alpha chews his lower lip and looks at the road, lightly pressing the pedal. Omega bats his eyelashes and slowly turns his head, looking at the driver. He thought that it seemed to him that the guy was examining him in detail for a fairly clear reason, but now he was finally convinced of this. He immediately panics, ashamed of his unscrupulous behavior. Meanwhile, the omega is simply embarrassed, covers himself with a light blush and pulls the deep neckline of his white T-shirt up, slightly covering the sharp collarbones that he had just carefully examined. This action makes both of them even more awkward.
+Kim Taehyung can't really hide anything. All he has to do is come to terms with this - he has been doing this for seven years.
+“Sorry,” the driver says quietly and purses his lips. - I shouldn't have.
+“You are not the first and not the last,” Taehyung purses his lips in a slight smile and looks kindly at the alpha. “I should get used to this kind of attention by now, don’t worry,” he chuckles and looks at the camera screen, adjusting the colors.
+“It’s not ugly,” he decides to give a compliment and is afraid of the guy’s reaction, hearing a quiet laugh. “True,” he nods several times, keeping his eyes on the road. - Rather, it is very unusual, mysterious...
+“There is no mystery in this,” Taehyung answers and smiles softly.
+Everything is elementary.
+But everyone keeps asking the question: “What is this?” No matter who you meet, they will definitely ask you, and he will calmly answer. He wasn't tired of it. Each time it amazes me more and more with its history.
+How did you survive?
+The dark-skinned alpha no longer touches on this topic, and Taehyung simply points the camera at the incredible landscapes. It still takes my breath away: everything around me looks too beautiful, too unreal. He takes several photographs at once for his archive and only after a while realizes that they will very soon arrive at the right place. This thought makes a flame ignite not only in honeyed eyes, but also in the very heart. The guy takes photographs of his favorite view from here too; there are definitely no city streets next to him. This is not Hong Kong, which quickly fell in love. This is not New York, where I rushed for a week. This is not even Seoul, where he began to build his new life. This is something truly beautiful, real, divine, untouched by the modern gaze. Here you can see the past.
+The driver immediately notices such a reaction and raises the corner of his lips, looking at the omega. On his face. He no longer crosses the line and doesn’t look down where he shouldn’t. He analyzes only the eyes, looking painstakingly into the distance, because of which Taehyung cannot contain the desire to smile even wider. He hastily turns off his camera and is simply content with the view. The further they drive, the more they see wild animals resting and walking in the vast grassy area. Taehyung looks out the open window, noticing a curious zebra not too far away, batting his thick eyelashes and looking at the jeep passing by. When they find themselves further away from this beast, the omega still turns around and laughs, seeing that the zebra is still looking at him.
+How I missed you...
+From love for this place in the chest, the flowers open up, as if after a long sleep.
+— Is this your first time in Botswana? - asks the driver.
+Taehyung, breaking into a happy smile, turns to him.
+- Why did you think that?
+— You are fascinated by the local view.
+Here anyone will be delighted with what they see.
+Birds fly above them and sing loudly. There are only animals around: parents and their cubs. On one of the tall trees you can find a cheetah hiding from the scorching African sun. While driving along the rough road, Taehyung managed to see a family of elephants calmly drinking water. The omega captured them in a photograph after leaving the jeep for a couple of minutes.
+“This is my home,” Taehyung answers proudly, lifting his chin a little and taking in the view in front of him through the windshield. “I was born here,” a wide, snow-white smile sparkles on his lips. - I came to my homeland.
+Born in a magical and terribly beautiful world. The first steps were taken on this land, in Botswana the omega began to babble incomprehensible words, it was here that he learned a lot, and to this day he remembers every little thing that was hammered into his head not only by his parents, but also by other residents of the state in South Africa. Taehyung kept all the details in his head and heart, took them with him to another country and returned back without forgetting anything. And now he only understands how much he missed his native continent, all his acquaintances, classmates and friends with whom he grew up.
+But most of all, Taehyung missed his blood. According to his older brother, who is now, apparently, standing on the street and waiting for him to return home after four years.
+Taehyung counted the hours until the long-awaited trip. And here it is, before his eyes all the beauty that he dreams of on sweet nights.
+Alpha is really surprised by this fact. I didn’t at all expect to hear that this fair-haired guy was from South Africa, since he didn’t look like one, and thought he was a tourist. Alpha is an ordinary taxi driver, and Taehyung was his first client today. Early in the morning, with a wide smile and a camera in his hands, he jumped into a black jeep, dictating the address and immediately offering money.
+“I’ll be honest, I couldn’t even think that you were local with alpha eyebrows and grins.
+“Looking at an Asian man, no one would think he was Botswanan,” Taehyung shrugs, never stopping to smile.
+“I didn’t mean your race,” the driver immediately corrects him in a kind and gentle tone.
+Omega arches an eyebrow and looks at the dark-skinned alpha in confusion, gradually plunging into his confused thoughts. The words spoken in his direction had a strange effect on him: they forced him to think and involuntarily swallow his saliva.
+Most of all, he was afraid that in another country he would cease to be who he truly is. With all his might he kept within himself the origins, traditions and customs of African lands, what he was taught from birth. Parents made kind, peace-loving, honest people out of their sons, depriving both of them of greed and malice. Regardless of the actual nation, they tried to cultivate the soul of the African in them.
+Taehyung feels like he still is. Four years of living in South Korea did not take away his heart and soul.
+Kim Taehyung was a Botswana and will be one even in another world.
+Only after a while the omega’s gaze falls on his own expensive white shoes and denim shorts of the same color just above the knees. On her right wrist sparkles a diamond bracelet, given by her grandfather, which he could not refuse, although he is still ashamed to wear it. He is afraid to find out what the price of this jewelry is. He received it two months ago as a gift for his eighteenth birthday. He refused, begged him to remove such a bracelet from his eyes, assuring him that he could manage without it and live happily, but dad’s father was too generous towards his youngest grandson, with whom he lived under the same roof for four years. Taehyung received and, most likely, will receive things, jewelry, and large sums on his card in Botswana from his grandfather. He doesn’t want this at all on African soil, unless he intends to withdraw this money for those in need.
+And it was enough for him that he just finally got out of Seoul. I was able to return to my native land. He wants to smell like Africa again, and Africa wants to smell like him.
+Absorbed in his thoughts and worries, Taehyung does not have time to notice how they arrived at the right place. The place where he was born and where he grew up until he was fourteen years old. The jeep drives at low speed into a small settlement with quite a few neat, simple houses. It's calm and homely here. There is a chance to be saved here. Here Taehyung, surrounded by his past, will be able to breathe deeply. He will even try to forget the horror that happened to him seven years ago.
+Forgives Botswana all moral and physical pain.
+The car stops somewhere in the middle of an empty street, and it is at that moment that Taehyung’s heart begins to beat twice as fast. The driver quickly turns off the engine and gets out, heading to the trunk to help the omega with his suitcases, and Taehyung remains sitting in the seat. It’s scary to go out, to some extent even ashamed. He has not set foot on this land for such a long time that he now considers himself unworthy to walk these African streets again, as if he had betrayed them and exchanged them for Seoul ones.
+My brother didn't do that. Taehyung - yes, and it makes me sick. But at that time, others made decisions for him.
+His lips stretch into a gentle and weak smile, and his hand reaches for the door handle, which he dares to open after a couple of seconds. He fixes his light eyes on the already visible figure. The guy, who is eight years older than Taehyung, stands with his arms crossed over his chest, smiling at the corner of his lips and looking at the face of his younger brother. Omega closes the door behind him and adjusts the camera strap that hangs around his neck. They look at each other and just smile widely, not believing their own hearts, which tell them that they are together again in this territory. Taehyung still feels like he's in a dream. He wanted this so much. Hundreds of times the guy asked to return to his native land and was refused just as many times, so soon the omega began to doubt the possibility of returning home.
+“Juhyuk...” he says on an exhale.
+Taehyung throws the camera on his back and runs up to his brother first, bumping into him. The alpha laughs hoarsely and puts all his strength into the hug, at the same time kissing the ashen top of the head with all his love for this person. Omega is very tiny next to him, just as he was in childhood. Hasn't changed at all. He always stands on his tiptoes when he tries to hug Juhyuk, who is touched and strokes his back with his palm. Taehyung smiles brightly as the alpha easily lifts him off the ground and holds him tightly in his arms, leaving short kisses on his shoulder. Omega pulls back slightly and looks at him, radiating one warmth and sincere happiness that the elder Kim had been missing for six months.
+Six months apart. For six months they were in torment without each other.
+“Taehyung,” the omega finally hears his voice. Juhyuk lowers the guy to the ground and kisses his forehead. “Angel,” the younger one looks up at him and wrinkles his nose, smiling.
+A man can be so gentle and affectionate with him.
+“I was really looking forward to this trip.”
+“It seems to me that I will hardly find the strength to let you go again.”
+Omega laughs slyly and takes a step to the side to see standing small, but beautiful and neat house.
+“Don’t look,” Taehyung answers, looking at his brother. — What if I never return to Seoul, Juhyuk? — he chuckles and turns around, noticing a taxi driver with two bright suitcases belonging to Taehyung.
+The elder Kim gives the younger one a suspicious look, after which he approaches the driver of the black jeep and takes the suitcases from him, quietly thanking him. Taehyung nods to the taxi driver from his seat and smiles, thanking him. Omega turns back to his home, causing tears to gather in the corners of his honey-colored eyes. He relaxes his whole body and takes in warm air into his lungs, looking around the house in front of him. He definitely changed in the four years that Taehyung wasn’t here. He has become more beautiful, although he was like that before, but now changes are clearly visible in him on the outside, apparently, everything has become different on the inside. Senior Brother has definitely done a good job of improving everything here. And this concerns not only their hearth, but also neighboring houses.
+Taehyung's considerable pocket money definitely came in handy. It was not for nothing that he secretly transferred them to Juhyuk.
+- In what sense will you not come back? - the alpha asks seriously when he comes closer.
+“I’m just dreaming...” biting my lip.
+- Are you dreaming?
+- Do you forbid it? - Grunting cheerfully, he takes the suitcase from his brother and leaves for the house.
+The man narrows his eyes and looks carefully at Taehyung’s retreating back, thinking and soon following him. Omega is the first to enter the house, holding his breath and pursing his lips until it hurts slightly. Juhyuk is in no hurry, glances at the younger and for some reason worries about his reaction. He carefully steps on the new wooden floor and very slowly looks around, batting his black eyelashes with interest.
+Four years…
+Taehyung has really lost hope that he will return to this house again. One side of him asked to come back, the other was afraid. Previously there were four of them, now there are only two of them.
+Juhyuk closes the doors behind him, never taking his eyes off his brother for a second. Concentrates completely on him. It’s important for him to know that Taehyung still enjoys being here, regardless of some changes. Nothing really has changed: it has become more beautiful and cleaner. Juhyuk tried very hard to put his house in order and completely immersed himself in this matter. I was distracted by physical labor, I didn’t want to let pain and sadness get to me after I was left completely alone in South Africa. After my grandfather took his younger brother to live with him in Seoul.
+Dad's father expected that the fourteen-year-old would be much better off in Korea than in Botswana. There, the young omega would try to think about other things, devote time to various creative activities, since in Seoul there is such an opportunity, in Botswana there is no. Taehyung did just that, or rather, he simply listened to his grandfather and went to the school, which was crowded with students, unlike African schools. There were not so many children in school in my homeland, not even half. Therefore, at first Taehyung was very constrained around his peers: it took him quite a long time to get used to such big changes, and sometimes he was afraid to be in such large companies. They didn't look like Botswanans. Completely different people: they have a different lifestyle, different views and strange behavior.
+But at some point Taehyung realized that he just had a different heart.
+It was a very quiet six months. At night, under the blanket, he cried, trembled all over and moved his wet peach lips, asking for his brother. Grandfather tried to take Juhyuk with him, but the alpha categorically refused such an offer. At that time he was twenty-two years old, and he had the right to decide for himself what was best for him. The grandfather did not insist, he only warned that he would definitely help him with money. And Juhyuk sometimes flew to Seoul just for Taehyung. And it was only during these periods that the younger brother truly felt good, as if nothing terrible had happened in his short life.
+“Beautiful,” Taehyung says with a smile, looking at the alpha.
+The elder lets out a quiet chuckle and leaves the suitcases against the wall, moving closer to the omega.
+- I'm glad you think so.
+“It’s still the same comfort as before,” he sighs in fascination, continuing to look around. - Still the same warm atmosphere...
+There was no such warmth in Seoul.
+“I was afraid you wouldn’t like it,” says Juhyuk. “I tried not to change anything too much, so as not to deprive this house of its past.”
+His words make Taehyung feel warmer.
+“You really have preserved the past in this little magical house.”
+“Our memories will not disappear from here.”
+The younger Kim turns his head towards the soft cream sofa and pays attention to the small glass table next to it. You don’t have to look at him for long; he quickly notices his own photograph in a small frame. It's a selfie, and my brother actually printed it out, framed it, and put it in a prominent place. Taehyung smiles widely in the photo, because he then took a photo specifically for Juhyuk, who was suffocating without him in Botswana, and the omega quickly turned on the front camera and improved his brother’s mood with his smile.
+“Juhyuk,” the alpha hums questioningly, looking at Taehyung, who has approached  slowly striding towards the sofa and keeping his gaze on the framed photo. “I want to stay,” he says with fear, fearing the elder’s reaction. “I’m not drawn back to Seoul,” he bites his lip until it hurts and looks at his brother.
+The alpha sighs and approaches him, taking the angelic face into his large palms and starting to examine every millimeter. So pale. The skin is light, not the same as before.
+Korea took away one of his highlights. A few years ago, the color was dark, tanned due to the hot African sun, but as soon as he went to another continent, he immediately turned pale. But still he did not stop shining with beauty. His facial features are unique, unusual, and to some extent rough. Juhyuk will never get tired of telling his brother how handsome he is, but he will continue to be shy and kick him with his fists so that the alpha will stop. Juhyuk is too gentle and kind to him, but also strict, like a real big brother.
+Only now I really want to argue with him and insist on my desire.
+“How many more times do we have to raise this topic, Taehyung?” — Juhyuk asks, stroking the omega’s cheekbone with his thumb.
+- Until you agree...
+“Stop it,” a little harsher.
+Taehyung chews his lips and looks him straight in the eye, after which the alpha pulls away and sits on the sofa.
+The elder Kim does not accept his brother's ardent, real desire to stay at home. It is unbearable.
+It is unbearable to live where the heart does not lie.
+“Juhyuk...” he whispers pleadingly, sitting down next to him.
+“I’ve told you the answer many times,” the alpha says calmly.
+“And every time I don’t like him.”
+“But he won’t change,” he says sharply, slightly pursing his lips.
+Taehyung reaches out to his brother and puts his head on his chest, lifting his legs onto the sofa. Curls up like a ball.
+“You’re unfair to me,” he frowns at the alpha, weakly hugging him.
+He always refuses Taehyung's request to stay. Omega has been begging for the third year, but he still doesn’t get the words he needs. Juhyuk becomes gloomy when he hears such words from him, changes his tone of voice and literally breaks Taehyung’s little dreams. He is still surprised that the alpha allowed him to fly to his homeland at least for a while, in order to relax and take his mind off his studies. Omega tried very hard: he sobbed into the phone like crazy and shouted at his brother, trying to convey how much he missed him. His whole face was so swollen from crying that Juhyuk, seeing him so disappointed on his phone screen, decided to just give up after a couple of days and allowed him to visit South Africa four years later.
+“You’re the one who’s being unfair to yourself,” Joohyuk says and looks straight into his honey eyes. “You have no future here, so you better think about your studies, which I will never allow you to quit,” Taehyung shrinks a little from such a tone. “You came here to relax and will definitely come back.”
+Omega, with every living cell of his body, does not want to fly to Korea. He lives there with his grandfather in a huge mansion, and, probably, any resident of Africa would dream of being in such a place, but not Taehyung. Loves his grandfather, but his heart is definitely drawn to Botswana. Here he is much more comfortable and warmer in every sense. Not only the soul took a long time to get used to the new environment, but also the body itself. Due to climate change, Taehyung was often sick and sat at home with a cold and terrible pain in his bones.
+“I could fly to Seoul only for exams, but I would study here around the clock...” he says with another concern, already realizing that the alpha will not like this.
+Juhyuk gives him an angry look, narrowed pupils threatening and outlining his cheekbones. Omega purses his lips and still does not move away from his brother, only hugs him tighter and frowns slightly, knowing about another refusal.
+Taehyung went to an African school early, so he graduated from a Korean school at the age of seventeen, which is why he was the youngest in his class. And now, being in his first year of journalism, the omega remains the smallest guy among his classmates. This is not scary, since Taehyung tried to find a common language with the guys, wanted to join their team, and he really managed to do this in a couple of months. The relationship between them was so good that some of the guys, including Taehyung himself, agreed to fly to Beijing in November. And when the trip was already approaching, Kim heard the long-awaited words from his older brother, who pleased him that the omega could fly to Botswana for a while. Taehyung didn’t spend a minute thinking and immediately chose South Africa, throwing China out of his mind.
+“No,” kisses the younger alpha on the forehead.
+“You’re disgusting, Juhyuk,” Taehyung snorts, rolling his eyes and rubbing his head against someone else’s chest.
+He laughs hoarsely.
+- Nasty?
+“That’s right,” he immediately confirms. “I don’t want to leave you here alone,” he sighs, closing his eyelids. — I should be in Botswana.
+My parents have not been around for four years, and the two of them must live together, and not be scattered across different continents.
+“Botswana is not the same anymore, angel,” he hugs his younger brother.
+Taehyung doesn’t understand and looks at Joohyuk questioningly. But instead of answering, the alpha only twitches the corner of his lips and touches his knuckles cheeks.
+This movement quickly drives away bad thoughts about home from the guy.

arutunyan_dmitry_lab_7/texts/text-ru.txt

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+Сегодня во дворе вновь не стоит покой, ведь хищный зверь уже не спит.
+Каждый, находящийся на ядовитой территории, прикусывает свой язык, точно ждёт, когда их всех в клочья разорвут: жестоко и кровожадно. Оборачиваются по сторонам: судорожно и пугливо. В их венах кровь стынет, сердце глухо бьётся в груди, и черти перед глазами пляшут.
+Никто им не позволяет сдвинуться с места и укрыться хотя бы за высокими деревьями поблизости. Их могут и там обнаружить. Они слышат очень тихие шаги, и всё это лишь воображение, построенное на страхе и испуге, которые пульсируют острой болью в висках. Шепчут слова богам, в которых они верят и которым поклоняются, и раз эти люди все ещё живы на этой земле, то, вероятно, их слышат. Но им самим кажется, что если двинутся лишний раз слишком резко, то их смогут найти и не оставят ничего похожего на человека. Мысль подобная будоражит и сводит с ума всё сильнее с каждой никчемной минутой, пропитанной трусостью.
+Единственный хозяин территории, где находятся запуганные, несмелые, изредка очерчивает их силуэты холодным взглядом и восхищается тем, сколько в них страха живёт только из-за одного живого существа. Они ничего не знают о смелости. Ему порой бывает тошно от подобного поведения, но винить этих людей ни в чём не собирается, так как он один тут знает язык не только человеческий. Он вырос в таких условиях, был воспитан по-другому: необычно и дико для остальных жителей планеты. Почти с рождения учился тем вещам, из-за которых могут посчитать его сумасшедшим, безумцем на этой ядовитой территории. А яд протекает по земле только из-за образа жизни, взглядов, принципов, традиций.
+Альфа смотрит на вооруженных охранников его дома — те воевали, видели множество смертей, но по-прежнему имеют страх в груди, — теперь выразительные глаза держит на оранжевом заходящем солнце, которое своим тусклым светом освещает всю Ботсвану, и позже возвращает к своим рукам с двумя большими острыми ножами. Их он звонко точит друг об друга, замечает яркий блеск и отражение своего лица. Видит того, кого боятся многие люди на этой земле. Сам себе построил ужасающую репутацию.
+Мужчина вновь отвлекается от своего занятия, откладывает холодное оружие на каменистую землю и поворачивает голову. Слышит приближающиеся шаги и полностью переключается на этот звук. Темнокожий альфа лет двенадцати всем телом трясётся, и по вискам его капельки пота катятся. Поднос, который он держит в своих детских руках, шумит вместе с его сердцем. Боится, но пытается скрыть это перед хозяином ядовитой территории. А тот всё видит, не злится, только лишь чувствует, как пробуждается в нём интерес. Этот парнишка, двигающийся в его сторону, до ужаса сейчас напуган, страшится живого существа, но продолжает идти к нему, периодически осматривая огромный двор, откуда может выскочить он. С таким же страхом оглядываются и охранники, стоящие у высоких железных решетчатых ворот.
+Они все дети. Они все одинаковые.
+Мальчишка приближается и сразу опускает запуганный взгляд, жуя губы и понятия не имея, с чего ему нужно начать. Здесь он новенький: не прошла и неделя с его прибытия в этот огромный дом.
+— Сэр… — совсем негромко слышится его детский голос, а старший только выгибает бровь. — Чон-Рено, — вспоминает, как папа говорил ему, что хозяин дома больше предпочитает, чтобы его звали по фамилии. — Ваш… — он протягивает поднос чуть вперёд и понимает, что снова забыл, как называется алкоголь, наполняющий небольшой хрустальный стакан. — Ваш… — повторяет и проглатывает слюну. — То, что вы просили.
+— Ром, — помогает ему Чон-Рено. — Это ром, Ези, — берёт стакан с напитком и кивает. Мальчик поднимает на него удивлённые глаза, не рассчитывая, что мужчина будет помнить его имя. — Почему ты так боишься? — сужает свои ярко-серые глаза, немного склонив голову.
+Ези хлопает густыми чёрными ресницами. 
+Неужто старший альфа действительно не знает, почему всем на этой территории сейчас страшно? Даже сам Бог об этом догадывается.
+Должен и львиный Бог догадаться… 
+— Вы выпустили Диего, — произносит это так, точно пытается донести до Рено всю глубину его безумия.
+— Он не монстр, — стальной голос пугает.
+— Он хищник, сэр…
+Уголки сухих губ старшего немного дёргаются, и безразличный отводится взгляд в сторону.
+Его хищник.
+Диего поблизости не видно. Его мальчик развлекается, довольствуется хождением по земле, чистым вечерним воздухом и большим оранжевым солнцем, которое дарит свой теплый свет. Зверь его лучи ловит и рычит. Тоже живое существо, и оно точно так же, как и люди, желает быть свободным и отпущенным, но в отличие от жалких людей, он не трус и не боится созданного Богом. Сильнее, мудрее и опаснее. Этого могущественного хищника наконец слышно: звуки, которые он издаёт, привлекают драгоценное внимание Рено, а темнокожего мальчишку заставляют вздрогнуть и обернуться, огромными напуганными глазами шастая по территории. Страх у него танцует в крови, и сердце бьётся загнанной в угол жалкой пташкой. Он содрогается от противоречивых ощущений, что замечает и мужчина рядом с ним.
+Чон-Рено бесцветно смотрит на Белло Ези, на двенадцатилетнего альфу из бедной семьи, работающей в этом большом особняке. Мужчина знаком с ним пару дней, и тот ещё никак не привыкнет к обычному образу жизни хозяина этого очага ужаса и безумия.
+— Ези, — холодно призывает и получает должное внимание к себе. — Пойди в дом, попроси положить мясо для Диего, — младший быстро кивает и собирается уйти. — И принесёшь мне его сам, — а эти слова уже казались намного страшнее. 
+— Что?.. — переспрашивает, не веря. 
+— Ты меня услышал. 
+Но кажется, что ослышался.
+В груди копошится ужасное чувство трусости и обиды, но Ези никак не может возразить или высказать своё нежелание выходить во двор во второй раз, да и ещё с сырым куском мяса для хищника. Ему остаётся только невнятно опять кивнуть старшему и торопливо пойти к лестнице, ведущей к дверям особняка.
+А сам владелец дома хмыкает на пугливую реакцию мальчишки. Он, не сделав глотка, опускает стакан рома на землю и поднимается с небольшого кресла. Разминая ладонью шею, на которой детально прорисована морда льва, альфа точно готовится к жестокому кровопролитному бою. Опять. Его серые глаза яркие, спокойные, однако заинтересованность в предстоящем через пару минут в зрачках мелко блестит. Проводя кончиком языка по нижней губе, Чон-Рено взглядом бегает по обширному двору, где пока не виднеется любимая фигура — она слышится так чётко и громко, что невозможно скрыть своё восхищение и искреннюю любовь. Но эти звуки нисколько не могут успокоить охранников у ворот. Они к ним не привыкли. Никогда не привыкнут, потому что не воспитали его, как это сделал Чонгук.
+Когда из дома наконец-то выходит Ези, держа в руках небольшой поднос с куском мяса, Рено вновь усмехается и заводит руки за спину. Ожидает его, смотрит и издалека слышит, как отчаянно ломает сердце грудную клетку маленькому альфе. Тот всё ближе к нему, часто оглядывается по сторонам и что-то шепчет себе под нос. Неужели молитвы? Мужчина сгибает бровь и поворачивает голову, слыша ещё один громкий рёв, которым он мог только гордиться. Его мальчик очень подрос, стал крепким, поэтому всех так пугает. Может быть, у Чон-Рено его звуки вызывают довольную улыбку, но у Ези они вызывают желание спрятаться за одну из колонн. Только его ждёт сам Рено, и он не может себе позволить выполнить желаемое, иначе правда получит заслуженное наказание. А этого сейчас хочется меньше всего.
+Уже каждый житель Ботсваны наслышан о жёстких методах расплаты за непослушание.
+— Сэр, вот мясо, — шепчет Ези, становясь напротив мужчины и чувствуя, как слёзы подступают к выразительным глазам. — Можно я уже пойду в дом?..
+Детское сердце так и просится к папе.
+— Нет, — мороз и холод.
+Мальчик от подобного ответа сильнее сжимается и прикусывает до боли губу, а затем послушно переводит взгляд туда, куда смотрит Чонгук.
+Теперь глаза Диего появляются в полутьме. 
+Страх бьется в груди вторым сердцем, в горле резко пересыхает. Ези хочет убежать отсюда, когда видит, что хищное животное выходит из-за угла особняка, показывает, что здесь является тоже хозяином. Он смотрит на темнокожего мальчика и стоящего рядом с ним высокого мужчину, показывающего гордую улыбку на сухих губах. Шаги его, медленные, уверенные, ведь он хорошо знает себе цену и как величественно он сейчас выглядит под оранжевым светом закатного неба. Гуляет смело и грациозно, пронзает острым взглядом, чуя еду где-то рядом с собой. Взгляд — дикий, свободный, всесильный. Полоски, плавно расположенные на его бархатной шерсти, всегда могли привлекать чужой взгляд, сводить с ума и молить о помиловании. Тёмные полосы — тени от высоких деревьев, а между ними горят желтые блики африканского солнца. Весь вид зверя умиротворённый, но от него почему-то дрожь бежит кошмарно по коже.
+Так красив и молод. Рено от него заинтересованный взор отвести не в состоянии, как и застывший бедный Ези. Он впервые видит зверя так хорошо и чётко, до этого был лишь наслышан о могущественном Диего, чей внешний вид вынуждает каждого поверить в Бога. Даже обыкновенные шаги гипнотизируют всех на этой территории.
+Чонгук опускает глаза на Белло Ези и хмыкает.
+— Поднеси ему поближе его закуску, — мальчик глотает нервно слюну и ошарашенно смотрит снизу вверх на Чон-Рено. — Единственный способ спастись от хищника — скормить ему что-нибудь другое, — читает поучительно ему то, во что Ези не верит, и старший опускается на корточки перед ним, заглядывая в стеклянные округлившиеся глаза. — Ты не хочешь быть съеденным им, ведь так? — тот молчит. — Тогда дай ему то, что он хочет, и он тебя не тронет.
+— Сэр, пожалуйста, — молит его дрожащим голосом. — Он тигр, я никогда к ним не подходил… — говорит ему очевидные вещи, надеясь на жалость. — Это опасно…
+— Отнеси Диего это мясо, Ези, — твёрдо и неоспоримо.
+В миг мальчику становится страшнее от Чон-Рено Чонгука, нежели от огромного молодого тигра, медленно движущегося в их сторону.
+— Я не могу… — тихо шепчет, смотря лишь в серые глаза напротив.
+А кто бы смог? Видимо, только сам хозяин этого зверя.
+— Ты не должен так говорить, — Чонгук немного наклоняет голову к плечу. — Ты ведь даже не приблизился к нему, значит, и не знаешь точно, можешь или же нет, — Ези поджимает губы. — Вперёд, — приказывает, кивнув в сторону Диего.
+Бенгальский тигр всё ближе к ним, продолжает двигаться и держит свои жёлтые глаза только на незнакомом ему человеке. Впервые видит, поэтому так хищно анализирует, заставляя Ези затаить дыхание и сделать наконец-то напряженный шаг вперёд. К Диего.
+За ним внимательно наблюдает и Рено, который спокойно встаёт обратно на ноги и выпрямляется в спине. Мальчик правда хорошо держится. Чонгук уже рассчитывал, что он расплачется и просто унесётся отсюда. За ним бы не пошёл: не ему судить подростка за вполне существенный и логичный страх. Не каждый здесь решается подходить к Диего и кормить его. А правильнее, так ещё никто не делал. Только Чон-Рено к нему настолько близко находится: живёт с ним в одной комнате и не смеет даже бояться этого зверя, которого сам спас, сам и воспитал. Тигр его лишь в шутку может укусить, не причиняя сильной боли. Но Ези кажется, что настал тот день, когда ему придётся попрощаться со своей короткой жизнью. Уж точно не думал, что умрёт от клыков тигра. Подобные ужасы ему в голову никогда не лезли.
+Диего, замечая и чуя кусок мяса, ускоряет свой шаг и издает негромкий рык, что может только сильнее напугать мальчика, который по-прежнему надеется на разумные мысли Чонгука, стоящего позади, но останавливаться не собирается.
+Раз суждено умереть сегодня, пусть так и будет.
+Ези уже смело смотрит в глаза приближающемуся к нему тигру и останавливается в паре метров от Чон-Рено, опускаясь на корточки и держа свой взгляд на безумно красивом звере. Этому смелому шагу старший альфа даже немного удивляется, наблюдая за происходящим и слегка ухмыляясь. Младший прикусывает нижнюю губу и опускает взор на алый кусок мяса на серебряном подносе, который он кладёт на каменистую землю, делая неуверенный маленький шаг назад. Он до боли жмурит глаза и сжимает крепко пальцы в кулак, уже начиная чувствовать несуществующую боль, принесенную ему в скором будущем голодным тигром. За эти секунды успевает попрощаться со своими родителями, шепотом просит прощения у них за все свои оплошности, слушая приближающиеся шаги. У него, кажется, сердце остановится раньше, чем зверь подойдёт.
+Но… 
+Солнцем целованный тигр — Диего — рычит на весь двор, величественно разгоняя птиц на деревьях и на крыше особняка. Ези испуганно распахивает веки и видит перед собой злую морду и огромную пасть животного, которое всаживает в следующую секунду клыки в кусок сырого мяса. Он не прекращает рычать на мальчика, точно пытается что-то ему сказать этим. Но подросток только ошарашенно смотрит в жёлтые глаза и забывает дышать, падая пятой точкой на землю и открывая рот. Диего лишь забирает мясо и отбегает в сторону, швыряет свою закуску подальше от мальчишки, ложится рядом с ней и спокойно начинает облизывать и поедать любимое лакомство, лишая Ези мысли о возможной смерти. С удовольствием и рычанием отрывает маленькие кусочки, сразу же их проглатывая и иногда поглядывая на своего хозяина.
+Чонгук слабо улыбается, смотря на тигра и пряча обе руки в карманы чёрных брюк-карго. Он медленно переводит с него взгляд на мальчика, лежащего на земле и глядевшего с прежним шоком на тихого Диего, который больше не обращает на него никакого внимания. Зверь занят своей настоящей едой, а не человеком, который поднёс её ему. Наверное, только Чонгук до последних секунд был убеждён, что Диего не тронет Ези.
+В своём тигре и в своих львах никогда не сомневается. 
+Каждое их движение наперёд знает.
+Темнокожий мальчик и многие иностранцы рассчитывают, что каждый африканец разговаривает с животными на равных, понимает их мысли и язык. Но во всей Ботсване мало таких. Единицы. И среди них на вершине стоит Чон-Рено Чонгук — львиный Бог. Только Белло Ези сейчас кажется, что мужчина в свои двадцать восемь лет стал ещё и покровителем тигров.
+У молодого альфы с трудом получается подняться с земли, отвести взгляд от Диего и обернуться к его хозяину, смотревшему так остро и пронзительно на него. Теперь Чонгук к ним приближается: спокойно и медленно. Но Ези больше не боится, будто тигр одарил его невероятной храбростью из-за близости с ним. Альфа верит в подобные сказки, услышанные из чужих уст. Верит и в то ужасающее чувство, что хранится в груди мужчины, у кого на шее чёрными чернилами изображена морда льва. Истинный львиный Бог. Этот Бог становится в метре от уже не напуганного мальчика, а восхищенного увиденным две минуты назад. Конечно же, его поразило то, как близко к нему был молодой бенгальский тигр. Эту историю друзьям и одноклассникам рассказывать побежал бы, но те ни за что не поверят, поэтому важное воспоминание поселит глубоко в сердце.
+— Сэр… — шокировано начинает Ези, глотнув слюну. — Диего не тронул меня, — смотрит на Чонгука, хмыкнувшего себе под нос. — Почему? Он ведь мог…
+Хватило бы пары секунд, чтобы зверь кровожадно растерзал его.
+— Ты скормил ему что-то другое и остался нетронутым, — раздаётся басистый тяжелый голос. — Клыки и когти не всадились в тебя лишь по этой причине. Он хищник, а не убийца, — Ези немного расслабляется и коротко кивает, запоминая его слова. — Ему нужна еда — не чья-то смерть, — Рено, закончив, смотрит уже на своего полосатого мальчика.
+— Он очень красивый, — тихо признается Ези, тоже взглянув в сторону зверя. 
+Чонгук опускает на него глаза и усмехается. 
+— Невероятно красивый, — вслух подтверждает.
+Ези улыбается и в конечном итоге не таит в себе вопрос:
+ — Могу я теперь пойти?..
+Рено кивает без слов.
+Ези широко улыбается и берёт с земли поднос, после чего бежит в огромный особняк, желая рассказать о случившемся своему отцу, который отвечает за кухню.
+На полосатой шерсти сказочно играют яркие лучи заходящего африканского солнца. Чонгук в данный момент очарован этим видом перед собой. Диего сильно подрос. Ему только полтора года, но размеры у него, как у взрослого тигра. Сложно поверить, что не так давно зверь был зверьком, совсем крохотным, и постоянно вился рядом с Чон-Рено, не отходил толком от него: лез, игриво рычал и кусал хозяина за ноги, желая привлечь должное внимание к себе. Привык к Чонгуку слишком быстро. После того, как Чонгук забрал его раненным взрослыми зверями к себе, тигрёнку хватило две недели, чтобы довериться полностью и тайно залезать на грудь альфы по ночам. А когда не мог уснуть, царапал и кусал за подбородок, вынуждая Чонгука проснуться. И тот просыпался, играл, кормил и возвращался обратно в постель, а за ним и маленький Диего своей величественной походкой.
+По сей день тигр делает то же самое.
+Чонгук с улыбкой на губах медленно подходит к животному, которое с диким аппетитом закидывает в рот оставшийся маленький кусок мяса. Диего сам подрывается с места и бежит к своему хозяину, присевшему на корточки. Тигр сразу лезет на него, будто с настоящими человеческими объятиями, игриво начинает издавать рыки, отчего Рено шире улыбается, хлопая и гладя большой ладонью зверя по животу. Тот не успокаивается, уже языком проводит по шее альфы — показывает свою истинную любовь к нему. Чонгук опускается на землю и двумя руками ведет по мягкой шерсти тигра, уделяя положенное внимание месту за ушами, как Диего и любит. С подобной лаской становится котёнком. Крутится, облизывает, где хочет, и тычется носом в подбородок Чон-Рено, который смеётся над таким поведением. Его за такое никогда никто не посмеет поругать.
+— С каждым днём тяжелее и тяжелее становишься, Диего, — хмыкает Чонгук и продолжает гладить его. — Своим видом всех вокруг пугаешь, большой мальчик, — тигр отстраняется и обходит мужчину, уже прижимаясь к его спине и проводя языком по загривку русых волос. — Одного меня никогда не вспугнёшь, даже не рассчитывай на мой страх, — Рено поворачивает голову к Диего. Тот на него тихо рычит, словно отвечает на сказанные слова. — Мы с тобой два хищника, и одному Богу известно, кто из нас опаснее.
+А Чон-Рено уже знает, кто опаснее на этой земле.
+Так и жил: шалил в мечтах со скуки, терпел муки и разочарования. В который раз дал он себе слово скорее вернуться домой. Лишь там обретёт спасение, покой в сердце, повезет — кусочек счастья.
+Это место заставит его забыть все печали и всю боль, разрешит чувствовать себя так, будто он выпил бутылку вина.
+Очередной щелчок и очередной сохраненный божественный кадр на флешке дорогого нового фотоаппарата. Медовые глаза мгновенно цепляются за сделанную фотографию, что высвечивается на небольшом экране. Здешние пейзажи до конца жизни будут вынуждать молодого парня отчетливо ощущать очарование, восторг и восхищение. Взглянув на эти красоты, человек в состоянии избавиться от любой заразы и боли. Здесь ослепшие станут зрячими, оглохшие познают мир звуков. Чистое голубое небо, тёплое ноябрьское солнце, высокие и низкорослые деревья, бегающие по территории дикие животные, всё — всё вынуждало улыбнуться и расслабиться.
+Молодой альфа за рулем чёрного джипа, на котором было легко передвигаться по неровным дорогам, поворачивает голову к сидящему на пассажирском сидении омеге, который рассматривает с легкой улыбкой на губах сделанную фотографию. Среди пепельных шелковистых волос путаются чистые лучи необъятного солнца. Скользя ниже, они решаются трогать открытые участки светлой кожи, точно специально освещают заметные следы на теле.
+Водителя с первых секунд привлекла одна яркая черта этого парня. Это быстро бросилось в глаза. Всю дорогу мужчина не находит сил выкинуть из головы любопытство: то следит за дорогой, то переводит взгляд на пассажира, тайно рассматривая не только симпатичное лицо. 
+Прежде не удавалось подобное увидеть.
+Это поневоле устрашает, пугает, вынуждает задуматься и свои теории в мыслях построить, а спросить африканец вряд ли осмелится. Возможно, омега не хочет говорить об этом, скрывает что-то и чувствует боль, когда посторонние люди обращают внимание.
+Альфа жуёт нижнюю губу и смотрит на дорогу, несильно нажимая на педаль. Омега хлопает ресницами и медленно поворачивает голову, взглянув на водителя. Рассчитывал, что ему кажется, что парень его детально рассматривает по довольно ясной причине, но теперь окончательно убедился в этом. Тот сразу паникует, стыдясь своего бессовестного поведения. А омега тем временем просто смущается, покрывается легким румянцем и тянет глубокий вырез белой футболки вверх, слегка прикрывая острые ключицы, которые только что внимательно исследовали. Обоим от этого действия становится ещё более неловко.
+У Ким Тэхёна толком ничего не получается скрыть. Остаётся смириться — этим он занимается на протяжении семи лет.
+— Извините, — тихо говорит водитель и поджимает губы. — Я не должен был.
+— Вы не первый и не последний, — тянет губы в легкой улыбке Тэхён и смотрит по-доброму на альфу. — Я должен уже привыкнуть к подобному вниманию, не переживайте, — хмыкает и переводит взгляд на экран фотоаппарата, настраивая цвета.
+— Это не некрасиво, — решается на комплимент и боится реакции парня, слыша тихий смех. — Правда, — кивает несколько раз он, следя за дорогой. — Скорее, это очень необычно, загадочно…
+— В этом нет никакой загадки, — отвечает Тэхён и мягко улыбается.
+Всё элементарно.
+Но все продолжают задавать вопрос: «Что это?» С кем бы ни познакомился — обязательно спросят, а он спокойно ответит. Ему не надоело. С каждым разом всё сильнее и сильнее поражает своей историей.
+Как выжил?
+Темнокожий альфа больше не затрагивает эту тему, да и Тэхён просто направляет камеру на невероятные пейзажи. До сих пор дух захватывает: слишком красивым, слишком нереальным всё вокруг выглядит. Он делает сразу несколько фотографий для своего архива и лишь через некоторое время понимает, что они совсем скоро прибудут к нужному месту. Эта мысль заставляет зажечься пламя не только в медовых глазах, но и в самом сердце. Парень и отсюда фотографирует любимый вид, рядом с ним точно не стоят городские улицы. Это не Гонконг, который быстро полюбился. Это не Нью-Йорк, куда помчался на неделю. Это даже не Сеул, где начал строить свою новую жизнь. Это нечто поистине красивое, настоящее, божественное, не тронутое современным взглядом. Тут можно увидеть прошлое.
+Водитель сразу замечает подобную реакцию и приподнимает уголок губ, смотря на омегу. На его лицо. Больше не переходит черту и не опускает взгляд куда не положено. Анализирует лишь глаза, глядевшие кропотливо в даль, из-за которой у Тэхёна не получается сдержать в себе желание ещё шире улыбнуться. Он спешно отключает свой фотоаппарат и просто довольствуется видом. Чем дальше они едут, тем больше видят диких животных, отдыхающих и гуляющих по бескрайней травянистой местности. Тэхён выглядывает в открытое окно, замечая не слишком далеко любопытную зебру, которая хлопает густыми ресницами и глядит на проезжающий мимо джип. Когда они оказываются уже дальше от этого зверя, омега всё же оборачивается и смеётся, видя, что зебра до сих пор смотрит на него.
+Как же скучал… 
+От любви к этому месту в груди цветы раскрываются, точно после долгого сна.
+— Вы впервые в Ботсване? — спрашивает водитель. 
+Тэхён, расплываясь в счастливой улыбке, поворачивается к нему.
+— Почему вы так подумали?
+— Вы очарованы местным видом.
+Тут любой в восторг от увиденного придёт.
+Птицы над ними летают и громко поют. Вокруг одни животные: родители и их детеныши. На одном из высоких деревьев можно обнаружить гепарда, прячущегося от палящего африканского солнца. Пока ехали по неровной дороге, Тэхёну удалось увидеть семейство слонов, пьющих спокойно воду. Их омега запечатлел на фотографии, выйдя из джипа на пару минут. 
+— Это мой дом, — с гордостью отвечает Тэхён, приподнимая немного подбородок и впитывая глазами вид перед собой через лобовое стекло. — Я здесь родился, — на губах сверкает широкая белоснежная улыбка. — Я приехал на Родину.
+Родился в волшебном и до жути красивом мире. Первые шаги были проделаны на этой земле, в Ботсване омега начал лепетать невнятные слова, именно здесь многому научился, и по сей день он помнит каждую мелочь, которую в голову вбивали не только его родители, но и другие жители государства в Южной Африке. Тэхён хранил в голове и сердце все детали, унёс их вместе с собой в другую страну и вернулся обратно, ничего не позабыв. И сейчас только понимает, насколько сильно он скучал по родному континенту, по всем знакомым, одноклассникам и друзьям, с которыми рос. 
+Но больше всего Тэхён соскучился по своей кровинке. По старшему брату, что сейчас, видимо, на улице стоит и ждёт его возвращения домой спустя четыре года.
+Тэхён часы считал до долгожданной поездки. И вот она, перед глазами вся красота, снящаяся ему сладкими ночами.
+Альфа действительно удивляется озвученному факту. Нисколько не ожидал услышать, что этот светлый паренёк родом из Южной Африки, поскольку таковым не выглядит, и считал его туристом. Альфа ведь обыкновенный таксист, и Тэхён у него был первым клиентом сегодня. Рано утром с широкой улыбкой и фотоаппаратом в руках запрыгнул в чёрный джип, диктуя адрес и сразу предлагая деньги. 
+— Признаюсь честно, я не мог и подумать, что вы местный, — дергает бровями альфа и усмехается.
+— Взглянув на азиата, никто бы не решил, что он ботсванец, — пожимает плечами Тэхён, ни на секунду не прекращая улыбаться.
+— Я не имел в виду вашу расу, — сразу поправляет его добрым и мягким тоном водитель.
+Омега выгибает бровь и в неясности смотрит на темнокожего альфу, постепенно погружаясь в свои запутанные мысли. Сказанные в его сторону слова странным образом повлияли на него: вынудили задуматься и невольно проглотить слюну.
+Больше всего боялся, что в другой стране прекратит быть тем, кем истинно является. Всеми силами держал в себе истоки, традиции и нравы африканских земель, то, чему его с рождения учили. Родители из своих сыновей делали добрых, миролюбивых, честных людей, лишая обоих алчности и злобы. Независимо от настоящей нации, они пытались взрастить в них душу африканца.
+Тэхён чувствует, что и ныне таков. Четыре года жизни в Южной Корее не отняли у него сердце вместе с душой.
+Ким Тэхён был ботсванцем и будет являться им даже на другом свете.
+Только через некоторое время взгляд омеги падает на собственную дорогую белую обувь и такого же цвета джинсовые шорты чуть выше колен. На правом запястье сверкает бриллиантовый браслет, подаренный дедом, от которого отказаться не мог, хотя до сих пор стыдится его носить. Узнать боится, какова цена этого украшения. Он его получил два месяца назад в качестве подарка на свой восемнадцатый день рождения. Отнекивался, умолял убрать с глаз подобный браслет, уверяя, что и без него обойдется и будет жить счастливо, однако отец папы уж чересчур щедр по отношению к своему младшему внуку, с которым четыре года жил под одной крышей. Тэхён получал и, вероятнее всего, будет получать и в Ботсване от деда вещи, украшения, крупные суммы на карту. Ему этого совершенно не хочется на африканской земле, если только не собирается снимать эти деньги для нуждающихся.
+А ему самому хватило и того, что он просто, наконец-то, выбрался из Сеула. Смог вернуться на родную землю. Он хочет вновь пахнуть Африкой, а Африка — им. 
+Поглощенный своими мыслями и тревогами, Тэхён не успевает заметить, как они приехали к нужному месту. Месту, где он родился и где рос до четырнадцати лет. Джип на низкой скорости въезжает в маленькое поселение с довольно большим количеством аккуратных простых домов. Здесь спокойно и по-домашнему уютно. Здесь есть шанс спастись. Здесь у Тэхёна, окруженного своим прошлым, получится дышать полной грудью. Он даже постарается забыть тот ужас, случившийся с ним семь лет назад.
+Простит Ботсване всю моральную и физическую боль.
+Машина останавливается где-то посередине пустой улицы, и именно в этот момент сердце Тэхёна начинает биться в два раза быстрее. Водитель быстро глушит мотор и выходит, направляясь к багажнику, дабы помочь омеге с его чемоданами, а Тэхён так и остаётся сидеть на сиденье. Страшно выходить, в какой-то степени даже стыдно. Он такое долгое время не ступал на эту землю, что сейчас считает себя недостойным вновь шагать по этим африканским улицам, точно предал их и обменял на сеульские.
+Брат так не поступил. Тэхён — да, и от этого тошно. Но на тот момент решения за него принимали другие.
+Губы растягиваются в нежной и слабой улыбке, а рука тянется к ручке двери, которую он через пару секунд осмеливается открыть. Свои светлые глаза останавливает на уже виднеющейся фигуре. Парень, что старше Тэхёна на восемь лет, стоит со скрещёнными на груди руками, улыбаясь уголком губ и разглядывая лицо своего младшего брата. Омега закрывает за собой дверь и поправляет ремешок от фотоаппарата, что висит у него на шее. Смотрят друг на друга и лишь улыбаются широко, не веря собственным сердцам, которые сообщают им, что вновь на этой территории они вместе. Тэхён всё ещё чувствует себя во сне. Он так желал этого. Сотни раз парень просился на родную землю и столько же раз ему отказывали, что вскоре омега начал сомневаться в возможности возвращения домой.
+— Джухёк... — на выдохе произносит. 
+Тэхён закидывает фотоаппарат на спину и подбегает к брату первым, врезаясь в него. Альфа хрипло смеётся и все силы вкладывает в объятия, заодно целуя в пепельную макушку со всей своей любовью к этому человеку. Омега рядом с ним совсем крохотный, как это было в детстве. Нисколько не изменился. Вечно на носочки встаёт, когда пытается обнять Джухёка, который умиляется и поглаживает ладонью спину. Тэхён ярко улыбается, когда альфа легко поднимает его с земли и сжимает крепко в руках, оставляя короткие поцелуи на плече. Омега слегка отстраняется и смотрит на него, излучая одно тепло и искреннее счастье, которого старшему Киму так не хватало шесть месяцев.
+Полгода в разлуке. Полгода в мучениях друг без друга находились.
+— Тэхён, — наконец-то слышит его голос омега. Джухёк опускает парня на землю и целует в лоб. — Ангел, — младший поднимает на него глаза и морщит нос, улыбаясь. 
+Так нежен и ласков с ним бывает мужчина. 
+— Я ужасно ждал этой поездки.
+— Мне кажется, я вряд ли найду силы, чтобы вновь отпустить тебя.
+Омега хитро смеётся и делает шаг в сторону, чтобы увидеть стоящий маленький, но красивый и аккуратный дом.
+— И не ищи, — отвечает Тэхён, взглянув на брата. — Вдруг я больше не вернусь в Сеул, Джухёк? — хмыкает и оборачивается, замечая таксиста с двумя яркими чемоданами, принадлежащими Тэхёну.
+Старший Ким окидывает младшего подозрительным взглядом, после чего подходит к водителю чёрного джипа и берёт у него чемоданы, тихо поблагодарив. Тэхён со своего места кивает таксисту и улыбается, благодаря. Омега снова поворачивается к родному дому, отчего в уголках медовых глаз собираются слёзы. Всем телом расслабляется и вбирает в легкие тёплый воздух, оглядывая дом перед собой. Он-то точно поменялся за четыре года, пока Тэхёна здесь не было. Стал красивее, хотя и раньше был таким, но сейчас в нём чётко заметны изменения снаружи, видимо, и внутри всё стало по-другому. Старший брат определённо хорошо постарался улучшить здесь всё. И дело касается не только лишь их очага, но и соседних домов. 
+Немалые карманные деньги Тэхёна точно пошли на пользу. Не зря он их тайно перечислял Джухёку.
+— В каком смысле не вернешься обратно? — серьёзно спрашивает альфа, когда подходит ближе. 
+— Просто мечтаю… — прикусывая губу. 
+— Мечтаешь? 
+— А ты запрещаешь? — весело хмыкая, забирает у брата чемодан и уходит к дому.
+Мужчина щурит глаза и смотрит внимательно на отдаляющуюся спину Тэхёна, задумываясь и идя вскоре за ним. Омега первым входит в дом, затаивая дыхание и поджимая до легкой боли губы. Джухёк же не торопится, поглядывает на младшего и почему-то переживает из-за его реакции. Тот осторожно ступает по новому деревянному полу и очень медленно оглядывается по сторонам, заинтересованно хлопая чёрными ресницами.
+Четыре года…
+Тэхён правда уже потерял надежду, что вернется опять в этот дом. Одна его сторона просилась обратно, вторая — боялась. Раньше они жили вчетвером, сейчас их только двое.
+Джухёк закрывает двери за собой, ни на секунду не отводя от брата глаз. Полностью на нём сосредотачивается. Ему важно знать, что Тэхёну до сих пор приятно здесь находиться, независимо от некоторых изменений. Толком ничего не поменялось: стало красивее и чище. Джухёк очень постарался навести порядок в своём доме, окончательно погрузился в это дело. Отвлекался физическим трудом, не хотел подпускать к себе боль и грусть после того, как остался совсем один в Южной Африке. После того, как родной дед забрал младшего брата к себе в Сеул.
+Отец папы рассчитывал, что четырнадцатилетнему подростку будет в Корее намного лучше, нежели в Ботсване. Там молодой омега попытался бы думать о других вещах, уделил бы время различным творческим занятиям, так как в Сеуле есть такая возможность, в Ботсване — нет. Тэхён так и поступил, точнее, просто послушался деда и пошёл в школу, которая была забита учениками, в отличие от африканских школ. В школе на родине не было настолько много детей, и половины даже. Поэтому поначалу Тэхён был очень скован рядом со сверстниками: привыкал к таким большим изменениям довольно долго, порой и боялся находиться в настолько больших компаниях. Они не были похожи на жителей Ботсваны. Абсолютно разные люди: у них другой образ жизни, отличные взгляды и странное поведение.
+Но в какой-то момент Тэхён понял, что это у него просто другое сердце.
+Полгода был очень тихим. По ночам под одеялом плакал, дрожал всем телом и шевелил мокрыми персиковыми губами, просясь к брату. Дед пытался забрать к себе и Джухёка, но альфа категорически отказывался от подобного предложения. На тот момент ему было двадцать два года, и он вправе был решать сам, что для него лучше. Дед не стал настаивать, лишь предупредил, что обязательно будет помогать ему с деньгами. А Джухёк временами прилетал в Сеул только ради Тэхёна. И единственно в эти периоды младший брат поистине чувствовал себя хорошо, словно ничего ужасного не произошло в его короткой жизни.
+— Красиво, — с улыбкой проговаривает Тэхён, посмотрев на альфу. 
+Старший издает тихий смешок и оставляет чемоданы у стены, подходя ближе к омеге. 
+— Я рад, что ты так считаешь.
+— Тут по-прежнему тот же уют, что и раньше, — очарованно вздыхает, продолжая осматриваться. — Всё та же теплая атмосфера…
+В Сеуле такого тепла не было.
+— Боялся, что тебе не понравится, — говорит Джухёк. — Я пытался особо ничего не менять, дабы не лишить этот дом прошлого.
+От его слов Тэхёну теплее становится. 
+— Ты правда сохранил прошлое в этом маленьком волшебном доме.
+— Наши воспоминания отсюда не исчезнут. 
+Младший Ким поворачивает голову в сторону мягкого кремового дивана, обращает внимание на маленький стеклянный столик рядом с ним. Долго оглядывать его не приходится, быстро замечает свою собственную фотографию в небольшой рамке. Это селфи, и брат действительно распечатал его, наградив рамкой и поставив на видное место. Тэхён на фотографии широко улыбается, ведь он тогда сфотографировался именно для Джухёка, который задыхался без него в Ботсване, а омега быстро включил фронтальную камеру и улучшил настроение брата своей улыбкой.
+— Джухёк, — альфа вопросительно мычит, смотря на Тэхёна, подошедшего медленным шагом к дивану и удерживающего взгляд на фото в рамке. — Я хочу остаться, — со страхом произносит, опасаясь реакции старшего. — Меня не тянет обратно в Сеул, — прикусывает до боли губу и глядит на брата.
+Альфа вздыхает и подходит к нему, взяв ангельское лицо в свои большие ладони и начав рассматривать каждый миллиметр. Такой бледный. Кожа светлая, не такая, как раньше. 
+Корея отняла у него одну из его изюминок. Несколько лет назад цвет был смуглым, загорелым из-за жаркого африканского солнца, но стоило ему отправиться на другой континент, так сразу побледнел. Но всё равно не прекратил блистать красотой. Черты его лица уникальные, необычные, в какой-то степени грубые. Джухёку никогда не надоест повторять брату, насколько он красив, а тот так и продолжит стесняться и пинать его кулаками, чтобы альфа прекратил. Джухёк слишком нежен и добр к нему, но также и строг, как настоящий старший брат. 
+Только сейчас хочется действительно с ним поспорить и настоять на своем желании.
+— Сколько раз нам ещё поднимать эту тему, Тэхён? — спрашивает Джухёк, оглаживая большим пальцем скулу омеги. 
+— Пока ты не согласишься…
+— Прекрати, — чуть жёстче.
+Тэхён жует губы и смотрит ему прямо в глаза, после чего альфа отстраняется и садится на диван.
+Старший Ким не принимает ярое настоящее желание брата остаться дома. Это невыносимо.
+Невыносимо жить там, где сердце не лежит.
+— Джухёк… — с мольбой шепчет, присаживаясь рядом.
+— Я много раз говорил тебе ответ, — спокойно произносит альфа. 
+— И каждый раз он мне не нравится.
+— Но он не поменяется, — резко бросает, несильно поджав губы.
+Тэхён тянется к брату и кладёт голову на его грудь, поднимая ноги на диван. Сворачивается, как клубочек. 
+— Ты несправедлив ко мне, — хмуро смотрит на альфу, слабо обнимая его.
+Он всё время отказывает Тэхёну в просьбе остаться. Омега третий год умоляет, но нужных слов так и не получает. Джухёк мрачнеет, когда слышит от него подобные слова, меняет тон голоса и буквально ломает маленькие мечты Тэхёна. Тот до сих пор удивлен, что альфа разрешил ему прилететь хотя бы на некоторое время на Родину, дабы отдохнуть и отвлечься от учёбы. Омега очень усердно старался: рыдал в трубку, как ненормальный, и кричал на брата, пытаясь донести, насколько же сильно скучает по нему. Всё лицо у него тогда от слёз опухло так, что Джухёк, увидев его таким разочарованным на экране своего телефона, через пару дней решил просто сдаться и позволил посетить Южную Африку спустя четыре года.
+— Это ты несправедлив к себе, — говорит Джухёк и смотрит прямо в медовые глаза. — У тебя здесь нет будущего, поэтому лучше думай о своей учёбе, которую я тебе ни за что не позволю бросить, — Тэхён от подобного тона немного сжимается. — Приехал ты сюда отдохнуть и обязательно вернешься обратно.
+Омега каждой живой клеткой своего организма не желает улетать в Корею. Он живёт там с дедом в огромном особняке, и, наверное, любой житель Африки мечтал бы оказаться в подобном месте, но только не Тэхён. Любит своего деда, но сердце определенно тянет в Ботсвану. Здесь ему куда комфортнее и теплее во всех смыслах. Не только душа долго привыкала к новой обстановке, но и само тело. Из-за смены климата Тэхён часто болел и сидел дома с простудой и жуткой болью в костях.
+— Я мог бы летать в Сеул только на экзамены, а занимался бы круглосуточно тут… — с очередным беспокойством произносит, уже понимая, что это альфе не понравится.
+Джухёк кидает на него злобный взгляд, угрожая суженными зрачками и очерчивая скулы. Омега поджимает губы и всё равно не отстраняется от брата, только крепче обнимает его и слегка хмурит брови, зная об очередном отказе.
+Тэхён пошёл рано в африканскую школу, поэтому и корейскую закончил в семнадцать лет, из-за чего был самым младшим в классе. И сейчас, находясь на первом курсе журналистики, омега так и остаётся самым мелким парнишкой среди своих одногруппников. Это нестрашно, так как Тэхён попытался найти общий язык с ребятами, хотел влиться в их коллектив, и это действительно удалось сделать за пару месяцев. Отношения настолько хорошо между ними сложилось, что некоторые из ребят, в том числе и сам Тэхён, договорились слетать в Пекин в ноябре. И когда поездка была уже на носу, Ким услышал долгожданные слова от старшего брата, который обрадовал его тем, что омега может прилететь на время в Ботсвану. Тэхён и минуты не уделил размышлениям и сразу выбрал Южную Африку, выкинув из головы Китай.
+— Нет, — целует в лоб младшего альфа.
+— Противный ты, Джухёк, — фыркает Тэхён, закатывая глаза и потираясь головой о чужую грудь. 
+Тот хрипло смеётся.
+— Противный?
+— Верно, — сразу подтверждает. — Не хочу оставлять тебя здесь одного, — вздыхает, прикрывая веки. — Я должен находиться в Ботсване.
+Родителей рядом нет четыре года, и они вдвоём обязаны жить вместе, а не быть раскинутыми по разным континентам.
+— Ботсвана уже не та, ангел, — обнимает младшего брата.
+Тэхён не понимает и поднимает на Джухёка вопросительный взгляд. Но вместо ответа альфа лишь дергает уголком губ и касается костяшками пальцев его щеки.
+Это движение быстро отгоняет от парня плохие мысли о доме.

basharin_sevastyan_lab_2/README.md

@@ -0,0 +1,42 @@
+## Лабораторная работа 2. Вариант 5.
+### Задание 
+Выполнить ранжирование признаков. Отобразить получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Провести анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? 
+
+Модели:
+
+- Гребневая регрессия `Ridge`, 
+- Рекурсивное сокращение признаков `Recursive Feature Elimination – RFE`, 
+- Сокращение признаков Случайными деревьями `Random Forest Regressor`
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+```
+python main.py
+```
+
+### Используемые технологии
+- `numpy` (псевдоним `np`): NumPy - это библиотека для научных вычислений в Python.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `LinearRegression` - линейная регрессия - это алгоритм машинного обучения, используемый для задач бинарной классификации.
+  - `Ridge` - инструмент работы с моделью "Гребневая регрессия"
+  - `RFE` - инструмент оценки важности признаков "Рекурсивное сокращение признаков"
+  - `RandomForestRegressor` - инструмент работы с моделью "Регрессор случайного леса"
+
+### Описание работы
+1. Программа генерирует данные для обучения моделей, содержащие матрицу признаков X и вектор целевой переменной y.
+1. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную.
+1. Разделяет данные на матрицу признаков X и вектор целевой переменной y
+1. Создает список обученных моделей для ранжирования признаков: гребневой регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями.
+1. Создает словарь model_scores для хранения оценок каждой модели.
+1. Выводит оценки признаков каждой модели и их средние оценки.
+1. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения.
+
+### Результат работы
+![](ridge.png "Гребневая регрессия")
+![](rfe.png "Рекурсивное сокращение признаков")
+![](rfr.png "Сокращение признаков Случайными деревьями")
+![](res.png "Четыре самых важных")
+
+### Вывод
+Четыре наиболее важных признака, определенных на основе средних оценок, включают 
+Признак 1, Признак 3, Признак 12 и Признак 6.

basharin_sevastyan_lab_2/main.py

@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Ridge, LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import RFE
+from sklearn.preprocessing import MinMaxScaler
+
+''' Задание
+Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с 
+помощью указанных по вариантумоделей. Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и
+среднюю  оценку. Проведите анализ получившихся результатов. Какие четырепризнака оказались самыми важными по среднему 
+значению? (Названия\индексы  признаков и будут ответом на задание).
+
+Вариант 5.
+Гребневая регрессия (Ridge), Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE),
+Сокращение признаков Случайными деревьями (Random Forest Regressor).
+'''
+
+# создание данных
+random_state = np.random.RandomState(2)
+X, y = make_regression(n_samples=750, n_features=15, noise=0.1, random_state=random_state)
+data = pd.DataFrame(X, columns=[f'Признак {i}' for i in range(X.shape[1])])
+data['Целевая переменная'] = y
+X = data.drop('Целевая переменная', axis=1)
+y = data['Целевая переменная']
+
+ridge = Ridge(alpha=1)  # Гребневая регрессия
+ridge.fit(X, y)
+
+recFE = RFE(LinearRegression(), n_features_to_select=1)  # Рекурсивное сокращение признаков
+recFE.fit(X, y)
+
+rfr = RandomForestRegressor()  # Сокращение признаков Случайными деревьями
+rfr.fit(X, y)
+
+models = [('Ridge', ridge),
+          ('RFE', recFE),
+          ('RFR', rfr)]
+model_scores = []
+
+for name, model in models:
+    if name == 'Ridge':
+        coef = model.coef_
+        normalized_coef = MinMaxScaler().fit_transform(coef.reshape(-1, 1))
+        model_scores.append((name, normalized_coef.flatten()))
+    elif name == 'RFE':
+        rankings = model.ranking_
+        normalized_rankings = 1 - (rankings - 1) / (np.max(rankings) - 1)
+        model_scores.append((name, normalized_rankings))
+    elif name == 'RFR':
+        feature_importances = model.feature_importances_
+        normalized_importances = MinMaxScaler().fit_transform(feature_importances.reshape(-1, 1))
+        model_scores.append((name, normalized_importances.flatten()))
+
+for name, scores in model_scores:
+    print(f"{name} оценки признаков:")
+    for feature, score in enumerate(scores, start=1):
+        print(f"Признак {feature}: {score:.2f}")
+    print(f"Средняя оценка: {np.mean(scores):.2f}")
+
+all_feature_scores = np.mean(list(map(lambda x: x[1], model_scores)), axis=0)
+sorted_features = sorted(enumerate(all_feature_scores, start=1), key=lambda x: x[1], reverse=True)
+top_features = sorted_features[:4]
+print("Четыре наиболее важных признака:")
+for feature, score in top_features:
+    print(f"Признак {feature}: {score:.2f}")

basharin_sevastyan_lab_3/README.md

@@ -0,0 +1,93 @@
+## Лабораторная работа 3. Вариант 4.
+### Задание 
+Выполнить ранжирование признаков и решить с помощью библиотечной реализации дерева решений 
+задачу классификации на 99% данных из курсовой работы. Проверить 
+работу модели на оставшемся проценте, сделать вывод.
+
+Модель:
+- Дерево решений `DecisionTreeClassifier`.
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+``` python
+python main.py
+```
+
+### Используемые технологии
+- Библиотека `pandas`, используемая для работы с данными для анализа scv формата.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `metrics` - набор инструменов для оценки моделей
+  - `DecisionTreeClassifier` - классификатор, реализующий алгоритм дерева решений. Дерево решений - это модель машинного обучения, которая разбивает данные на рекурсивные решения на основе значений признаков. Она используется для задач классификации и регрессии.
+  - `accuracy_score` -функция из scikit-learn, которая используется для оценки производительности модели классификации путем вычисления доли правильно классифицированных примеров (точности) на тестовом наборе данных.
+  - `train_test_split` - это функция из scikit-learn, используемая для разделения набора данных на обучающий и тестовый наборы.
+  - `LabelEncoder` - это класс из scikit-learn, используемый для преобразования категориальных признаков (например, строки) в числовые значения.
+
+### Описание работы
+#### Описание набора данных
+Набор данных: набор данных о цене автомобиля в автопарке.
+
+Названия столбцов набора данных и их описание:
+
+- Id: Уникальный идентификатор для каждого автомобиля в списке.
+- Price: Ценовой диапазон автомобилей с конкретными ценниками и подсчетами. (111000 - 77500000)
+- Company Name: Название компании-производителя автомобилей с указанием процентной доли представительства каждой компании.
+- Model Name: Название модели автомобилей с указанием процентного соотношения каждой модели.
+- Model Year: Диапазон лет выпуска автомобилей с указанием количества и процентных соотношений. (1990 - 2019)
+- Location: Местоположение автомобилей с указанием регионов, где они доступны для покупки, а также их процентное соотношение.
+- Mileage: Информация о пробеге автомобилей с указанием диапазонов пробега, количества и процентов. (1 - 999999)
+- Engine Type: Описания типов двигателей с процентными соотношениями для каждого типа.
+- Engine Capacity: Мощность двигателя варьируется в зависимости от количества и процентов. (16 - 6600)
+- Color: Цветовое распределение автомобилей с указанием процентных соотношений для каждого цвета.
+- Assembly: Импорт или местный рынок.
+- Body Type: Тип кузова.
+- Transmission Type: Тип трансмиссии.
+- Registration Status: Статус регистрации.
+
+Ссылка на страницу набора на kuggle: [Ultimate Car Price Prediction Dataset](https://www.kaggle.com/datasets/mohidabdulrehman/ultimate-car-price-prediction-dataset/data)
+
+#### Оцифровка и нормализация данных
+Для нормальной работы с данными, необходимо исключить из них все нечисловые значения. 
+После этого, представить все строковые значения параметров как числовые и очистить датасет от "мусора". 
+Для удаления нечисловых значений воспользуемся функцией `.dropna()`.
+Так же мы удаляем первый столбец `Id`, так как при открытии файла в `pd` он сам нумерует строки.
+
+Все нечисловые значения мы преобразуем в числовые с помощью `LabelEncoder`:
+```python
+label_encoder = LabelEncoder()
+data['Location'] = label_encoder.fit_transform(data['Location'])
+data['Company Name'] = label_encoder.fit_transform(data['Company Name'])
+data['Model Name'] = label_encoder.fit_transform(data['Model Name'])
+data['Engine Type'] = label_encoder.fit_transform(data['Engine Type'])
+data['Color'] = label_encoder.fit_transform(data['Color'])
+data['Assembly'] = label_encoder.fit_transform(data['Assembly'])
+data['Body Type'] = label_encoder.fit_transform(data['Body Type'])
+data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type'])
+data['Registration Status'] = label_encoder.fit_transform(data['Registration Status'])
+```
+
+#### Выявление значимых параметров
+```python
+# Оценка важности признаков
+feature_importances = clf.feature_importances_
+feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+```
+
+#### Решение задачи кластеризации на полном наборе признаков 
+Чтобы решить задачу кластеризации моделью `DecisionTreeClassifier`, воспользуемся методом `.predict()`.
+```python
+clf = DecisionTreeClassifier(max_depth=5, random_state=42)
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+```
+
+#### Оценка эффективности
+Для оценки точности модели будем использовать встроенный инструмент `accuracy_score`:
+```python
+accuracy = accuracy_score(y_test, y_pred)
+```
+
+#### Результаты
+![](accuracy.png "Точность")
+
+![](important.png "Важность признаков")

basharin_sevastyan_lab_3/main.py

@@ -0,0 +1,78 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+
+''' Названия столбцов набора данных и их описание:
+Id: Уникальный идентификатор для каждого автомобиля в списке.
+Price: Ценовой диапазон автомобилей с конкретными ценниками и подсчетами. (111000 - 77500000)
+Company Name: Название компании-производителя автомобилей с указанием процентной доли представительства каждой компании.
+Model Name: Название модели автомобилей с указанием процентного соотношения каждой модели.
+Model Year: Диапазон лет выпуска автомобилей с указанием количества и процентных соотношений. (1990 - 2019)
+Location: Местоположение автомобилей с указанием регионов, где они доступны для покупки, а также их процентное соотношение.
+Mileage: Информация о пробеге автомобилей с указанием диапазонов пробега, количества и процентов. (1 - 999999)
+Engine Type: Описания типов двигателей с процентными соотношениями для каждого типа.
+Engine Capacity: Мощность двигателя варьируется в зависимости от количества и процентов. (16 - 6600)
+Color: Цветовое распределение автомобилей с указанием процентных соотношений для каждого цвета.
+'''
+
+
+# Загрузите данные из вашей курсовой работы, предположим, что у вас есть файл CSV.
+data = pd.read_csv('Data_pakwheels.csv')
+data.pop("Id")
+
+data.dropna(inplace=True)  # Удаление строки с пропущенными значениями.
+
+# Преобразуйте категориальные признаки в числовые. Используйте, например, one-hot encoding.
+# data = pd.get_dummies(data, columns=['Company Name', 'Model Name', 'Location', 'Engine Type', 'Color'])
+
+# Создайте объект LabelEncoder
+label_encoder = LabelEncoder()
+
+data['Location'] = label_encoder.fit_transform(data['Location'])
+data['Company Name'] = label_encoder.fit_transform(data['Company Name'])
+data['Model Name'] = label_encoder.fit_transform(data['Model Name'])
+data['Engine Type'] = label_encoder.fit_transform(data['Engine Type'])
+data['Color'] = label_encoder.fit_transform(data['Color'])
+data['Assembly'] = label_encoder.fit_transform(data['Assembly'])
+data['Body Type'] = label_encoder.fit_transform(data['Body Type'])
+data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type'])
+data['Registration Status'] = label_encoder.fit_transform(data['Registration Status'])
+
+# Разделение данных на обучающий набор и тестовый набор. Мы будем использовать 99% данных для обучения.
+train_data, test_data = train_test_split(data, test_size=0.01, random_state=42)
+
+# Определите целевую переменную (то, что вы пытаетесь предсказать, например, 'Price').
+X_train = train_data.drop(columns=['Price'])
+y_train = train_data['Price']
+X_test = test_data.drop(columns=['Price'])
+y_test = test_data['Price']
+
+# Создание и обучение модели DecisionTreeClassifier
+clf = DecisionTreeClassifier(random_state=42)
+clf.fit(X_train, y_train)
+
+# Оценка важности признаков
+feature_importances = clf.feature_importances_
+
+# Создание DataFrame с именами признаков и их важностью
+feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})
+
+# Сортировка признаков по убыванию важности
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+
+# Вывод ранжированных признаков
+print(feature_importance_df)
+
+clf = DecisionTreeClassifier(max_depth=5, random_state=42)
+
+# Обучите модель на обучающем наборе данных
+clf.fit(X_train, y_train)
+
+# Предсказание целевой переменной на тестовом наборе данных
+y_pred = clf.predict(X_test)
+
+# Оцените производительность модели с помощью различных метрик
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Точность модели: {accuracy}')

belyaeva_ekaterina_lab_3/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_3/README.md

@@ -0,0 +1,35 @@
+## Задание 
+
+Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы  искусственного  интеллекта»на  99%  ваших  данных.  Проверьте работу модели на оставшемся проценте, сделайте вывод
+
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача определить винрейт персонажа на ранге рекрут в зависимости от его атрибута, роли (я взяла 2 - саппорт или керри), и того, как часто его берут на рекрутах.
+
+Программа берет столбцы Herald Win Rate, Primary Attribute, Herald Picks и Roles, далее проводит фильтрацию столбца Roles и выбирает тех персонажей, у которых есть роль support или carry. Затем создает
+два новых столбца - isCarry и isSupport, так как в столбце Roles несколько значений и его нужно удалить.
+
+Затем данные делятся на обучающую и тестовую выборки и выясняется зависимость винрейта от остальных признаков.
+
+В конце программа выводит, насколько важны были выбранные признаки при определении винрейта и точность модели. 
+
+
+## Результат
+
+В результате получаем следующее:
+
+Feature Importances: [0.08035262 0.82893841 0.00453277 0.08617619]
+Score: 0.23055568233652535
+
+Вывод: самым значимым признаком при определении винрейта стал признак Primary Attribute. На фоне других признаков его значимость сильно выделяется, все остальные признаки уже играют очень маленькую роль.
+
+Точность модели вышла относительно низкой, но это легко объясняется тем, что в Доте невозможно точно предсказать винрейт персонажа, основываясь на подобных признаках. Винрейт предсказывается только лишь тем, какие персонажи сильны в данной мете, что зависит от их скиллов и изменений патча, не описанных в датасете (но и нет такого датасета, где они могли бы быть описаны).
+
+Тем не менее, данная программа дала понять, что на рекрутах на винрейт персонажа сильно влияет его главный атрибут.
+

belyaeva_ekaterina_lab_3/main.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+
+# Загрузка данных
+data = pd.read_csv("Current_Pub_Meta.csv")
+
+# Отбор нужных столбцов
+selected_columns = ['Herald Win Rate', 'Primary Attribute', 'Herald Picks', 'Roles']
+data = data[selected_columns]
+
+# Фильтрация по ролям Carry и Support
+data = data[data['Roles'].apply(lambda x: 'Carry' in x or 'Support' in x)]
+
+# Создание столбцов для каждой роли и заполнение их значениями 1 или 0
+data['IsCarry'] = data['Roles'].apply(lambda x: 1 if 'Carry' in x else 0)
+data['IsSupport'] = data['Roles'].apply(lambda x: 1 if 'Support' in x else 0)
+
+# Удаление столбца Roles
+data.drop('Roles', axis=1, inplace=True)
+
+# Замена категориальных переменных на числовые
+data['Primary Attribute'] = data['Primary Attribute'].map({'str': 0, 'all': 1, 'int': 2, 'agi': 3})
+
+# Разделение данных на обучающую и тестовую выборки
+X = data.drop('Herald Win Rate', axis=1)
+y = data['Herald Win Rate']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Обучение модели
+model = DecisionTreeRegressor()
+model.fit(X_train, y_train)
+
+# Прогноз на тестовой выборке
+y_pred = model.predict(X_test)
+
+# Вывод обработанных данных
+print("Обработанные данные:")
+print(data)
+
+# Оценка значимости признаков
+feature_importances = model.feature_importances_
+print("Feature Importances:", feature_importances)
+
+# Оценка score модели
+score = model.score(X_test, y_test)
+print("Score:", score)

belyaeva_ekaterina_lab_4/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_4/README.md

@@ -0,0 +1,31 @@
+## Задание 
+
+Использовать метод кластеризациипо варианту для данных из таблицы 1  по  варианту(таблица  9),самостоятельно  сформулировав  задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной вами задачи  
+Вариант 6 - dendogram
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, matplotlib, scipy, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача сгруппировать персонажей по их винрейту и частоте их пиков на определенных рангах.
+
+Программа берет столбцы Name, Herald Win Rate, Herald Picks, создает матрицу для анализа и вычисляет матрицу связей, а затем выводит дендограмму, где персонажи объединены по тому, как часто их пикают и какой у них винрейт.
+
+## Результат
+
+В результате получаем дендограмму, где персонажи сгруппированы по частоте пиков и винрейту. Наглядное представление оказалось очень точным и такой способ решения поставленной задачи выполнил свою работу хорошо.
+
+Например, на диаграмме ниже можно обратить внимание на то, что на ранге рекрут персонажи Phantom Asassin, Witch Doctor, Sniper и Pudge стоят вместе в правом нижнем углу. Такое наблюдение говорит о том, что датасет очень приближен к реальным данным и составлен правильно, а так же о том, что программа работает верно и выдает правильный, приближенный к реальности, результат.
+
+![heraldInfo.png](heraldInfo.png)
+
+Если же посмотреть на результат по данным для ранга титан, можно увидеть других героев, объединенных друг с другом по тому же приципу.
+
+![ImmortalInfo.png](ImmortalInfo.png)
+
+Сначала я хотела объединить героев по их винрейту на всех рангах, но такая информация не несет в себе много смысла, поэтому задача, которую я описала выше, сформулирована правильно, несет в себе смысл и решается заданным способом. 
+
+Такую статистику можно посмотреть по любому из рангов, заменив в коде слово Herald на интересующий ранг.

belyaeva_ekaterina_lab_4/main.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.cluster.hierarchy import dendrogram, linkage
+
+# Загрузка данных
+data = pd.read_csv('Current_Pub_Meta.csv')
+
+# Выбор нужных столбцов
+selected_columns = ['Name', 'Herald Picks', 'Herald Win Rate']
+data = data[selected_columns]
+
+# Создание матрицы для анализа
+matrix = data.drop('Name', axis=1).values
+
+# Вычисление матрицы связей
+linked = linkage(matrix, 'ward')
+
+# Рисование дендрограммы
+plt.figure(figsize=(10, 6))
+dendrogram(linked,
+           orientation='top',
+           labels=data['Name'].tolist(),
+           distance_sort='descending',
+           show_leaf_counts=True)
+plt.title('Dendrogram of Hero Win Percentage')
+plt.xlabel('Heroes')
+plt.ylabel('Distance')
+plt.xticks(rotation=90)
+plt.show()

belyaeva_ekaterina_lab_5/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_5/README.md

@@ -0,0 +1,42 @@
+## Задание 
+
+Использовать  регрессию по  варианту для  данных  из  таблицы  1  по варианту(таблица  10),самостоятельно  сформулировав  задачу.  Оценить, насколько хорошо она подходит для решения сформулированной вами задачи  
+Вариант 6 - полиномиальная регрессия
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, matplotlib, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача предсказать винрейт персонажа по тому, как часто его берут и по его винрейту на 
+смежных рангах (просто предсказать винрейт по тому, как часто его берут, нельзя, потому что винрейт зависит от текущей меты)
+
+Программа берет столбцы Name, Archon Picks, Archon Win Rate, Legend Picks, Legend Win Rate, Ancient Picks, Ancient Win Rate.
+Все столбцы, кроме Name и Legend Win Rate, нужны для того чтобы обучить модель. Legend Win Rate - 
+данные, которые нужно предсказать. Name - столбец для вывода результатов.
+
+Дальше все по дефолту - программа делит данные на обучающую и тестовые выборки, просиходит
+применение данных для обучения, затем обучаем модель. После этого происходит то же самое с тестовыми данными и затем выводится 
+оценка качества модели.
+
+В конце программа строит график, где показывает точки обучающей и тестовой выборки, но к тестовой выборки я решила добавить названия
+персонажей, чтобы график был более наглядным, но в то же время не перегруженным.
+
+## Результат
+
+В результате получаем график, который показывает результаты обучающей и тестовой выборок.
+![diagram.png](diagram.png)
+
+Помимо этого, программа вводит оценку качества модели:
+![R2Score.png](R2Score.png)
+
+Из чего можно сделать вывод, что модель работает очень хорошо и успешно решает поставленную задачу.
+
+Это объясняется тем, что модели было предоставлено достаточно большое количество признаков, по которым можно предсказать 
+интересующие нас данные. Кроме того, винрейт персонажей взят со смежных рангов. 
+
+Если взять винрейт персонажей на рангах, которые 
+находятся далеко от целевого, модель будет работать хуже, потому что чем больше разница в рангах, тем более разный винрейт у персонажей.
+Также, если бы было взято меньше признаков, оценка качества модели так же была бы ниже.

belyaeva_ekaterina_lab_5/main.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+# Загрузка данных
+data = pd.read_csv('Current_Pub_Meta.csv')
+
+X = data[['Archon Picks', 'Archon Win Rate', 'Legend Picks', 'Ancient Picks', 'Ancient Win Rate']]
+y = data['Legend Win Rate']
+names = data['Name']
+
+# Разбиваем данные на обучающую и тестовую выборки
+X_train, X_test, y_train, y_test, names_train, names_test = train_test_split(X, y, names, test_size=0.1, random_state=42)
+
+# Применяем полиномиальные признаки к обучающим данным
+poly_features = PolynomialFeatures(degree=2)
+X_train_poly = poly_features.fit_transform(X_train)
+
+# Создаем и обучаем модель полиномиальной регрессии
+poly_model = LinearRegression()
+poly_model.fit(X_train_poly, y_train)
+
+# Применяем полиномиальные признаки к тестовым данным и делаем предсказания
+X_test_poly = poly_features.transform(X_test)
+y_pred = poly_model.predict(X_test_poly)
+
+# Оценка качества модели на тестовых данных
+r2 = poly_model.score(X_test_poly, y_test)
+print(f"R-квадрат: {r2}")
+
+# Построение графика с именами персонажей
+plt.figure(figsize=(10, 6))
+plt.title('Корреляция между выбором персонажей и победами в ранге "Legend"')
+plt.grid(True)
+plt.scatter(X_train['Legend Picks'], y_train, color='blue', alpha=0.5, label='Обучающая выборка')
+plt.scatter(X_test['Legend Picks'], y_test, color='red', alpha=0.5, label='Тестовая выборка')
+
+# Добавляем имена персонажей на график
+for i, name in enumerate(names_test):
+    plt.annotate(name, (X_test['Legend Picks'].iloc[i], y_pred[i]), fontsize=8, alpha=0.7, color='black')
+
+plt.xlabel('Legend Picks')
+plt.ylabel('Legend Win Rate')
+plt.legend()
+plt.show()

belyaeva_ekaterina_lab_6/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_6/README.md

@@ -0,0 +1,92 @@
+## Задание 
+
+Использовать  нейронную  сеть MLPClassifier для  данных  из  таблицы  1  по  
+варианту, самостоятельно  сформулировав  задачу. Интерпретировать  результаты  и оценить,  насколько  хорошо  она подходит  для  решения сформулированной вами задачи
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача понять, можно ли определить позицию персонажа (всего в игре есть 5 позиций - 
+carry, mid, offlane, support, full support), по его главному атрибуту и по тому, какие роли он выполняет в игре. Учитывая
+то, что Dota 2 имеет 124 персонажа, все они очень разные, поэтому была вероятность, что модель не установит зависимость и 
+не будет работать в принципе. Именно поэтому я посчитала данную задачу довольно интересной. В моем датасете присутствует информация о главном атрибуте персонажа и его ролях, но нет 
+информации о том, на каких позициях он играется. Поэтому для выяснения этого списка я обратилась к внешним ресурсам
+и занесла информацию об этом в программу вручную. Это можно увидеть в коде в месте, где определяются роли.
+
+![positions.png](positions.png)
+
+Программа берет столбцы Name, Roles, PrimaryAttribute из датасета. Так как в столбце Roles есть 9 значений, которые прописаны
+в разном количестве и разные у каждого персонажа, нужно было создать 9 дополнительных столбцов, где для каждого персонажа
+выставлялось 1, если такая роль присутствует в его описании и 0, если ее нет. 
+
+Пример:
+data['IsDurable'] = data['Roles'].apply(lambda x: 1 if 'Durable' in x else 0)
+
+Далее столбец Roles был удален.
+
+Так как PrimaryAttribute указан в строковом значении, он так же был переведен в числовое значение.
+
+После этого нужно было заполнить столбцы posCarry, posMid, posOfflane, posSupport, posFullSupport. Если персонаж есть в списке
+персонажей с этой позицией, там проставлялась 1, 0 - если нет.
+
+В итоге получился датасет, где есть имя персонажа, его главный атрибут в виде числа, его роли (1 - если есть, 0 - если нет)
+и то же самое с позициями.
+
+Далее датафрейм делится на признаки (все столбцы, кроме столбцов с позициями) и метки (столбцы с позициями). Метки переводятся в числовой формат с помощью LabelEncoder(), иначе программа не может с ними работать.
+Данные делятся на обучающую и тестовую выборку.  
+
+Модель создается таким образом потому, что если ставить меньшее число итераций или скрытых слоев, то она не успевала обучаться.
+model = MLPClassifier(hidden_layer_sizes=(128, 128, 128), activation='relu', max_iter=1000, random_state=42)
+
+Затем происходит предсказание позиций для тестовой выборки и оценка работы модели с помощью accuracy_score и classification_report
+
+## Результат
+
+В результате получаем следующее:
+
+![accuracy.png](accuracy.png)
+
+Оценка модели имеет относительно низкое значение. Однако, как было сказано ранее, она могла не работать в принципе, поэтому 
+я считаю это достаточно неплохим результатом и поставленная цель была выполнена - было выяснено, что позиция персонажа
+все-таки зависит от его атрибута и ролей, которые он выполняет по игре, хоть эта зависимость и не 100% явная. Если бы она
+была явная, например, все персонажи с атрибутом "сила" - это позиция offlane, тогда работа модели была бы значительно лучше.
+
+Далее мы получаем classification report:
+
+![classificationReport.png](classificationReport.png)
+
+В данном отчете представлены 5 классов, то есть позиции (0, 1, 2, 3, 4). Для каждого класса представлены значения точности, 
+полноты и F1-оценки, вычисленные с использованием соответствующих метрик. Также показана поддержка класса, которая 
+представляет собой количество образцов, принадлежащих этому классу.
+
+Precision (точность) - это метрика, которая оценивает долю правильно классифицированных объектов из всех объектов, которые модель отнесла к данному классу. Она измеряет, насколько точно модель предсказывает положительные классы. 
+
+Recall (полнота) - это метрика, которая оценивает долю правильно классифицированных объектов, отнесенных моделью к данному классу, относительно всех объектов, принадлежащих к данному классу. Она измеряет, насколько полно модель находит положительные классы.
+
+F1-мера (F1-score) - это гармоническое среднее между precision и recall. Она используется для объединения оценок точности и полноты в единую метрику. F1-мера принимает значение между 0 и 1, где 1 - это идеальное значение, означающее, что модель идеально находит и точно классифицирует объекты положительного класса
+
+micro avg - средневзвешенное значение точности, полноты и F1-оценки во всех классах, подсчитанное по общему количеству образцов.
+
+macro avg - среднее значение точности, полноты и F1-оценки по всем классам, без учета количества образцов.
+
+weighted avg - средневзвешенное значение точности, полноты и F1-оценки по всем классам, учитывая количество образцов.
+
+samples avg - средневзвешенное значение точности, полноты и F1-оценки по всем классам, учитывая количество образцов 
+класса (если образец может принадлежать нескольким классам).
+
+Из данного отчета можно сделать вывод о том, что по атрибутам и ролям в игре модель точно выявила персонажей для позиции 
+mid и offlane, но при этом, при работе с объектами, модель пропустила больше всего объектов, относящихся к этим классам,
+и занесла их в другие классы, из-за чего снизилась precision других классов. Мы сами должны выбирать, что важнее - точность или полнота,
+и в моем случае важнее точность, ведь изначально стоял вопрос о том, сможет ли модель определить, что к чему относится. Но низкие 
+значения полноты говорят о том, что низкое значение accuracy вполне оправдано, и хоть модель и может выявить, какие объекты к каким классам относятся, 
+делает она это не совсем "пОлно" и пропускает некоторые объекты.
+
+Что касается признаков micro avg, macro avg, weighted avg, samples avg - все они показывают неплохие результаты относительно 
+ожиданий по поводу работы модели. Я думаю, что для поставленной задачи значения этих показателей довольно высоки.
+
+Вывод: точность и показатели из отчета вышли достаточно хорошими относительно поставленной задачи, также был получен ответ на вопрос
+зависит ли позиция персонажа от его атрибута и роли. Следовательно, с задачей разработанная модель справилась.

belyaeva_ekaterina_lab_6/main.py

@@ -0,0 +1,76 @@
+import pandas as pd
+from sklearn.neural_network import MLPClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, classification_report
+
+# Чтение данных из файла Current_Pub_Meta.csv
+current_pub_meta = pd.read_csv('Current_Pub_Meta.csv')
+
+# Создаем пустой DataFrame для хранения данных
+data = pd.DataFrame(columns=['Name', 'Roles', 'Primary Attribute', 'IsDurable', 'IsSupport', 'IsCarry', 'IsDisabler',
+                             'IsInitiator', 'IsNuker', 'IsEscaper', 'IsPusher', 'posCarry', 'posMid',
+                           'posOfflane', 'posSupport', 'posHardSupport'])
+
+
+# Добавление новых столбцов из файла в датафрейм data
+data['Name'] = current_pub_meta['Name']
+data['Roles'] = current_pub_meta['Roles']
+data['Primary Attribute'] = current_pub_meta['Primary Attribute']
+data['Primary Attribute'] = data['Primary Attribute'].map({'str': 0, 'all': 1, 'int': 2, 'agi': 3})
+
+data['IsDurable'] = data['Roles'].apply(lambda x: 1 if 'Durable' in x else 0)
+data['IsCarry'] = data['Roles'].apply(lambda x: 1 if 'Carry' in x else 0)
+data['IsSupport'] = data['Roles'].apply(lambda x: 1 if 'Support' in x else 0)
+data['IsDisabler'] = data['Roles'].apply(lambda x: 1 if 'Disabler' in x else 0)
+data['IsInitiator'] = data['Roles'].apply(lambda x: 1 if 'Initiator' in x else 0)
+data['IsNuker'] = data['Roles'].apply(lambda x: 1 if 'Nuker' in x else 0)
+data['IsEscaper'] = data['Roles'].apply(lambda x: 1 if 'Escaper' in x else 0)
+data['IsPusher'] = data['Roles'].apply(lambda x: 1 if 'Pusher' in x else 0)
+
+#Удаление столбца Roles
+data.drop('Roles', axis=1, inplace=True)
+
+# Создаем список персонажей на каждую позицию
+roles = {
+    'posHardSupport': ['Undying', 'Pudge', 'Marci', 'Grimstroke', 'Elder Titan', 'Warlock', 'Dazzle', 'Witch Doctor', 'Vengeful Spirit', 'Ancient Apparition', 'Disruptor', 'Keeper of the Light', 'Rubick', 'Jakiro', 'Oracle', 'Visage', 'Silencer', 'Shadow Demon', 'Chen', 'Winter Wyvern', 'Bane', 'Treant Protector', 'Io', 'Enchantress', 'Naga Siren'],
+    'posSupport': ['Venomancer', 'Tusk', 'Tiny', 'Spirit Breaker', 'Techies', 'Snapfire', 'Pudge', 'Muerta', 'Marci', 'Hoodwink', 'Grimstroke', 'Earth Spirit', 'Bounty Hunter', 'Crystal Maiden', 'Lion', 'Shadow Shaman', 'Lich', 'Ogre Magi', 'Warlock', 'Dazzle', 'Witch Doctor', 'Vengeful Spirit', 'Ancient Apparition', 'Disruptor', 'Keeper of the Light', 'Rubick', 'Jakiro', 'Oracle', 'Visage', 'Silencer', 'Shadow Demon', 'Chen', 'Winter Wyvern', 'Bane', 'Treant Protector', 'Io', 'Enchantress', 'Naga Siren', 'Earthshaker', 'Skywrath Mage', 'Leshrac', 'Shadow Fiend', 'Nyx Assassin', 'Pugna', 'Lina', 'Zeus', "Nature's Prophet", 'Dark Willow'],
+    'posOfflane': ['Wraith King', 'Spirit Breaker', 'Snapfire', 'Pudge', 'Primal Beast', 'Marci', 'Dragon Knight', 'Tidehunter', 'Centaur Warrunner', 'Dark Seer', 'Beastmaster', 'Mars', 'Brewmaster', 'Timbersaw', 'Bristleback', 'Abaddon', 'Axe', 'Enigma', 'Sand King', 'Clockwerk', 'Doom', 'Underlord', 'Omniknight', 'Legion Commander', "Nature's Prophet", 'Slardar', 'Faceless Void', 'Earthshaker', 'Pangolier', 'Pugna', 'Mars', 'Batrider', 'Windranger', 'Mirana', 'Beastmaster', 'Brewmaster', 'Phoenix', 'Beastmaster', 'Dark Seer', 'Lone Druid', 'Timbersaw', 'Broodmother', "Nature's Prophet", 'Magnus', 'Necrophos', 'Bloodseeker', 'Lycan'],
+    'posMid': ['Void Spirit', 'Pudge', 'Primal Beast', 'Earth Spirit', 'Dragon Knight', 'Arc Warden', 'Invoker', 'Storm Spirit', 'Shadow Fiend', 'Templar Assassin', 'Queen of Pain', 'Puck', 'Zeus', 'Tinker', 'Lina', 'Ember Spirit', 'Outworld Destroyer', 'Morphling', 'Leshrac', 'Sniper', 'Mirana', 'Viper', 'Death Prophet', 'Razor', 'Pugna', 'Skywrath Mage', "Nature's Prophet", 'Windranger', 'Batrider', 'Lina', 'Shadow Fiend', 'Templar Assassin', 'Ember Spirit', 'Huskar', 'Kunkka', 'Puck', 'Queen of Pain', 'Invoker', 'Storm Spirit', 'Outworld Devourer', 'Death Prophet', 'Razor', 'Lina', 'Sniper', 'Medusa', 'Leshrac', 'Viper'],
+    'posCarry': ['Pudge', 'Muerta', 'Monkey King', 'Drow Ranger', 'Alchemist', 'Anti-Mage', 'Spectre', 'Juggernaut', 'Phantom Assassin', 'Faceless Void', 'Phantom Lancer', 'Lifestealer', 'Slark', 'Terrorblade', 'Medusa', 'Luna', 'Shadow Fiend', 'Morphling', 'Templar Assassin', 'Ember Spirit', 'Naga Siren', 'Troll Warlord', 'Gyrocopter', 'Lone Druid', 'Ursa', 'Riki', 'Sven', 'Phantom Lancer', 'Chaos Knight', 'Night Stalker', 'Wraith King', 'Meepo', 'Troll Warlord', 'Juggernaut', 'Lifestealer', 'Templar Assassin', 'Ursa', 'Clinkz', 'Weaver', 'Riki', 'Spectre', 'Phantom Assassin', 'Naga Siren', 'Luna', 'Gyrocopter', 'Meepo', 'Lone Druid', 'Slark', 'Morphling', 'Terrorblade', 'Medusa', 'Faceless Void']
+}
+
+# Перебираем каждого героя и добавляем значения в соответствующие столбцы
+for index, row in data.iterrows():
+    for role, characters in roles.items():
+        data.loc[index, role] = int(row['Name'] in characters)
+
+pd.set_option('display.max_columns', None)
+pd.set_option('display.max_rows', None)
+print(data)
+
+# Разделение датафрейма на признаки и метки
+X = data[['Primary Attribute', 'IsDurable', 'IsSupport', 'IsCarry', 'IsDisabler', 'IsInitiator', 'IsNuker', 'IsEscaper', 'IsPusher']]
+y = data[['posCarry', 'posMid', 'posOfflane', 'posSupport', 'posHardSupport']]
+
+# Преобразование меток в числовой формат
+label_encoder = LabelEncoder()
+y = y.apply(label_encoder.fit_transform)
+
+# Разделение выборки на обучающую и тестовую
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+
+# Создание и обучение модели
+model = MLPClassifier(hidden_layer_sizes=(128, 128, 128), activation='relu', max_iter=1000, random_state=42)
+model.fit(X_train, y_train)
+
+# Предсказание позиций для тестовой выборки
+y_pred = model.predict(X_test)
+
+# Оценка точности модели
+accuracy = accuracy_score(y_test, y_pred)
+class_report = classification_report(y_test, y_pred)
+print("Accuracy:", accuracy)
+print('Classification Report:')
+print(class_report)

belyaeva_ekaterina_lab_7/README.md

@@ -0,0 +1,54 @@
+## Задание 
+
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и обучить на нем рекуррентную 
+нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом.
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки tensorflow, numpy, их компоненты
+## Описание лабораторной (программы)
+
+Данная лабораторная работа обучает модели для обработки русского и английского текста и решает задачу генерации.
+Ниже будет описан алгоритм работы одной из моделей (вторая работает аналогично):
+1. Читается текст из файла
+2. Создается экземпляр Tokenizer для токенизации текста
+3. С помощью метода fit_on_texts токенизатор анализирует текст и строит словарь уникальных слов
+4. rus_vocab_size - длина словаря
+5. C помощью метода text_to_sequences текст преобразуется в последовательность чисел
+6. Создаются последовательности для обучения модели
+7. Рассчитывается максимальная длина последовательности 
+8. Входные последовательности выравниваются до максимальной длины
+9. С помощью функции to_categorical последовательности преобразуются в one-hot представление
+10. Переменные x_rus_train, y_rus_train инициализируются соответствующими значениями
+11. Такая же обработка текста происходит и для текста на английском языке
+12. Происходит создание модели на русском языке: 
+ - создается экземпляр модели Sequential 
+ - добавляется слой Embedding, отображающий слова в векторы фиксированной длины
+ - добавляется слой LSTM с 512 нейронами
+ - добавляется слой Dense с функцией softmax для получения вероятности каждого слова в словаре
+ - модель компилируется
+13. Происходит обучение модели через model.fit()
+14. Все то же самое происходит для модели с английским языком
+15. Определяется функция generate_text для генерации текста на основе всех заданных параметров
+16. Выводятся результаты работы моделей и сгенерированные тексты
+
+## Результат
+
+Результат сгенерированного текста на русском языке: Помню просторный грязный двор и низкие домики обнесённые забором двор стоял у самой реки и по вёснам когда спадала полая вода он был усеян щепой и ракушками а иногда и другими куда более интересными вещами так однажды мы нашли туго набитую письмами сумку а потом вода принесла и осторожно положила на берег и самого почтальона он лежал на спине закинув руки как будто заслонясь от солнца ещё совсем молодой белокурый в форменной тужурке с блестящими пуговицами должно быть отправляясь в свой последний рейс почтальон начистил их мелом мелом мелом спадала щепой мелом мелом мелом мелом мелом спадала полая вода он ракушками а
+
+Результат сгенерированного текста на английском языке: The old man was thin and gaunt with deep wrinkles in the back of his neck the brown blotches of the benevolent skin cancer the sun brings from its reflection on the tropic sea were on his cheeks the blotches ran well down the sides of his face and his hands had the deep creased scars from handling heavy fish on the cords but none of these scars were fresh they were as old as erosions in a fishless desert fishless desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert fishless
+
+Результат потерь на тренировочных данных:
+
+![res.png](res.png)
+
+Вывод: можно заметить, что в сгенерированных текстах в конце слова повторяются. Это происходит потому, что в параметрах модели 
+указано сгенерировать 100 слов, хотя в тексте, по которому модель обучается, меньше слов. Поэтому сгенерированный текст сначала
+соответствует тексту для обучения, а затем начинает выдавать рандомные слова. Но нужно отметить, что это слова, а не просто 
+набор букв и пробелы, которые получались при иных настройках моделей.
+
+Так как у английской модели меньше потерь на тренировочных данных, чем у русской, то получается, что выполненная модель
+обрабатывает английский текст чуть лучше, чем русский, но в результате обе модели выдали осмысленный текст, что связано с большим
+числом нейронов и эпох, при помощи которых обучалась модель. Ведь когда было 20 эпох, а не 200, модель выдавала очень слабо осмысленный результат.
+

belyaeva_ekaterina_lab_7/eng.txt

@@ -0,0 +1,5 @@
+The old man was thin and gaunt with deep wrinkles in the back of his neck. The
+brown blotches of the benevolent skin cancer the sun brings from its reflection on the
+tropic sea were on his cheeks. The blotches ran well down the sides of his face and his
+hands had the deep-creased scars from handling heavy fish on the cords. But none of
+these scars were fresh. They were as old as erosions in a fishless desert.

belyaeva_ekaterina_lab_7/main.py

@@ -0,0 +1,97 @@
+import tensorflow as tf
+import numpy as np
+from keras.models import Sequential
+from keras.layers import LSTM, Dense, Embedding
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+# Загрузка и предобработка данных на русском языке
+with open("rus.txt", "r", encoding="utf-8") as f:
+    rus_text = f.read()
+
+tokenizer_rus = Tokenizer()
+tokenizer_rus.fit_on_texts([rus_text])
+
+rus_vocab_size = len(tokenizer_rus.word_index) + 1
+rus_sequences = tokenizer_rus.texts_to_sequences([rus_text])[0]
+rus_input_sequences = []
+rus_output_sequences = []
+
+for i in range(1, len(rus_sequences)):
+    rus_input_sequences.append(rus_sequences[:i])
+    rus_output_sequences.append(rus_sequences[i])
+
+rus_max_sequence_len = max([len(seq) for seq in rus_input_sequences])
+rus_input_sequences = pad_sequences(rus_input_sequences, maxlen=rus_max_sequence_len)
+
+x_rus_train = rus_input_sequences
+y_rus_train = tf.keras.utils.to_categorical(rus_output_sequences, num_classes=rus_vocab_size)
+
+# Загрузка и предобработка данных на английском языке
+with open("eng.txt", "r", encoding="utf-8") as f:
+    eng_text = f.read()
+
+tokenizer_eng = Tokenizer()
+tokenizer_eng.fit_on_texts([eng_text])
+
+eng_vocab_size = len(tokenizer_eng.word_index) + 1
+eng_sequences = tokenizer_eng.texts_to_sequences([eng_text])[0]
+eng_input_sequences = []
+eng_output_sequences = []
+
+for i in range(1, len(eng_sequences)):
+    eng_input_sequences.append(eng_sequences[:i])
+    eng_output_sequences.append(eng_sequences[i])
+
+eng_max_sequence_len = max([len(seq) for seq in eng_input_sequences])
+eng_input_sequences = pad_sequences(eng_input_sequences, maxlen=eng_max_sequence_len)
+
+x_eng_train = eng_input_sequences
+y_eng_train = tf.keras.utils.to_categorical(eng_output_sequences, num_classes=eng_vocab_size)
+
+# Построение модели для русского языка
+rus_model = Sequential()
+rus_model.add(Embedding(rus_vocab_size, 256, input_length=rus_max_sequence_len))
+rus_model.add(LSTM(512))
+rus_model.add(Dense(rus_vocab_size, activation='softmax'))
+
+rus_model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+# Обучение модели для русского языка
+rus_history = rus_model.fit(x_rus_train, y_rus_train, batch_size=128, epochs=200)
+
+# Построение модели для английского языка
+eng_model = Sequential()
+eng_model.add(Embedding(eng_vocab_size, 256, input_length=eng_max_sequence_len))
+eng_model.add(LSTM(512))
+eng_model.add(Dense(eng_vocab_size, activation='softmax'))
+
+eng_model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+# Обучение модели для английского языка
+eng_history = eng_model.fit(x_eng_train, y_eng_train, batch_size=128, epochs=200)
+
+def generate_text(model, tokenizer, max_sequence_len, seed_text):
+    output_text = seed_text
+    for _ in range(100):  # Генерируем 100 слов
+        encoded_text = tokenizer.texts_to_sequences([output_text])[0]
+        pad_encoded = pad_sequences([encoded_text], maxlen=max_sequence_len, truncating='pre')
+        pred_word_index = np.argmax(model.predict(pad_encoded), axis=-1)
+        pred_word = tokenizer.index_word[pred_word_index[0]]
+        output_text += " " + pred_word
+    return output_text
+
+# Генерация текста для русской и английской моделей
+rus_output_text = generate_text(rus_model, tokenizer_rus, rus_max_sequence_len, "Помню просторный")
+eng_output_text = generate_text(eng_model, tokenizer_eng, eng_max_sequence_len, "The old man")
+
+# Вывод результатов
+print("Русская модель:")
+print("Потери на тренировочных данных:", rus_history.history['loss'][-1])
+print("Сгенерированный текст:")
+print(rus_output_text)
+
+print("Английская модель:")
+print("Потери на тренировочных данных:", eng_history.history['loss'][-1])
+print("Сгенерированный текст:")
+print(eng_output_text)

belyaeva_ekaterina_lab_7/rus.txt

@@ -0,0 +1 @@
+Помню просторный грязный двор и низкие домики, обнесённые забором. Двор стоял у самой реки, и по вёснам, когда спадала полая вода, он был усеян щепой и ракушками, а иногда и другими, куда более интересными вещами. Так, однажды мы нашли туго набитую письмами сумку, а потом вода принесла и осторожно положила на берег и самого почтальона. Он лежал на спине, закинув руки, как будто заслонясь от солнца, ещё совсем молодой, белокурый, в форменной тужурке с блестящими пуговицами: должно быть, отправляясь в свой последний рейс, почтальон начистил их мелом.

gusev_vladislav_lab_7/README.md

@@ -0,0 +1,41 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и 
+обучить на нем рекуррентную нейронную сеть для решения задачи генерации. 
+Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. 
+Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, 
+как архитектура товарища справляется с вашим текстом. 
+В завершении подобрать компромиссную архитектуру, справляющуюся достаточно хорошо с обоими видами 
+текстов.
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_7.py, решение будет в консоли.
+
+### Технологии
+Keras - это библиотека для Python, позволяющая легко и быстро создавать нейронные сети.
+NumPy - библиотека для работы с многомерными массивами.
+
+### По коду
+1) Читаем файл с текстом
+2) Создаем объект tokenizer для превращение текста в числа для нейронной сети.
+3) Создаем модель нейронной сети с следующими аргументами:
+
+- Embedding - это слой, который обычно используется для векторного представления категориальных данных, таких как слова или символы. Он позволяет нейронной сети изучать эмбеддинги, то есть отображение слов (или символов) в вектора низкой размерности. Это позволяет сети понимать семантические отношения между словами.
+- LSTM - это слой, представляющий собой рекуррентный нейрон, который способен учитывать зависимости в последовательных данных. Он хорошо подходит для обработки последовательных данных, таких как текст.
+- Dense - это полносвязный слой, который принимает входные данные и применяет весовые коэффициенты к ним. Этот слой часто используется в конце нейронных сетей для решения задачи классификации или регрессии.
+
+4) Обучаем модель на 100 эпохах (итерациях по данным) и генерируем текст.
+
+
+
+![img.png](img.png)
+Английский 100 эпох
+![img_1.png](img_1.png)
+
+![img_3.png](img_3.png)
+Русский 100 эпох
+![img_2.png](img_2.png)
+Русский 17 эпох
+![img_4.png](img_4.png)
+### По консоли
+- Английский текст генерировался на 100 эпохах, начало получилось осмысленным, но чем ближе к концу тем хуже.
+- Русский текст также генерировался на 100 эпохах, с многочисленными ошибками в словах. Русский текст,сгенерированный на 17 эпохах по ошибкам в словах оказался лучше, но всё равно не идеально.

gusev_vladislav_lab_7/gusev_vladislav_lab_7.py

@@ -0,0 +1,61 @@
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Embedding, LSTM, Dense
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+# Загрузка текста из файла
+with open('text_ru.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+# Создание экземпляра Tokenizer
+tokenizer = Tokenizer(char_level=True)
+tokenizer.fit_on_texts(text)
+
+# Преобразование текста в последовательность чисел
+sequences = tokenizer.texts_to_sequences(text)
+
+# Подготовка обучающих данных
+seq_length = 100
+dataX, dataY = [], []
+for i in range(0, len(sequences) - seq_length):
+    seq_in = sequences[i:i + seq_length]
+    seq_out = sequences[i + seq_length]
+    dataX.append(seq_in)
+    dataY.append(seq_out)
+
+dataX = np.array(dataX)
+dataY = np.array(dataY)
+
+# Создание модели
+vocab_size = len(tokenizer.word_index) + 1
+embedding_dim = 256
+rnn_units = 1024
+
+model = Sequential()
+model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=seq_length))
+model.add(LSTM(units=rnn_units))
+model.add(Dense(units=vocab_size, activation='softmax'))
+
+model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
+
+# Обучение модели
+batch_size = 64
+model.fit(dataX, dataY, epochs=17, batch_size=batch_size)
+def generate_text(seed_text, gen_length):
+    generated_text = seed_text
+
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_char = tokenizer.index_word[predicted_index]
+        generated_text += predicted_char
+        seed_text += predicted_char
+        seed_text = seed_text[1:]
+
+    return generated_text
+# Пример использования
+generated_text = generate_text("Мультфильмы", 250)
+print(generated_text)

gusev_vladislav_lab_7/text_eng.txt

@@ -0,0 +1,21 @@
+Do you like watching cartoons? Probably you do! But how did they come to be? Who invented them?
+
+This is actually a very tough question. The first cartoons were created long before the TV.
+For example, shadow play was a very popular form of entertainment in ancient China. Such shows looked almost like modern cartoons!
+
+A toy called a flip book was made in the late 19th century. It was a small soft book with pictures.
+Each picture was drawn in a slightly different5 way. When you bend this book and release the pages one by one, the images start to move.
+Strictly speaking, they don’t, but our eyes see it like that anyway. The first real cartoons were made using this trick, too!
+
+In 1895 brothers Louis and Auguste Lumière created a cinematograph.
+It was a camera and a film projector in one device. Using this device, many aspiring film directors started to create their own cartoons.
+
+This developed into a full industry by 1910. Many cartoons of that era are forgotten now, but some are still with us.
+For example, Felix the Cat was created by Otto Messmer in 1919, and he’s still with us, more than a hundred years later.
+Currently the rights to the character are held by DreamWorks Animation.
+
+One of the pioneers in the industry was famous Walt Disney.
+He was not afraid to experiment to make a cartoon, and his Snow White film was among the firsts to use a multiplane camera.
+With its help the characters were able to move around the objects, creating an illusion of a 3D world.
+
+Today most of the cartoons are made with computer animation. The last traditional Disney cartoon to date was Winnie the Pooh (2011).

gusev_vladislav_lab_7/text_ru.txt

@@ -0,0 +1,21 @@
+Вам нравится смотреть мультфильмы? Вероятно, так оно и есть! Но как они появились на свет? Кто их изобрел?
+
+На самом деле это очень сложный вопрос. Первые мультфильмы были созданы задолго до появления телевидения.
+Например, игра с тенью была очень популярной формой развлечения в Древнем Китае. Такие шоу выглядели почти как современные мультфильмы!
+
+Игрушка под названием книжка-перевертыш была изготовлена в конце 19 века. Это была маленькая мягкая книжка с картинками.
+Каждая картинка была нарисована немного по-разному. Когда вы сгибаете эту книгу и отпускаете страницы одну за другой, изображения начинают двигаться.
+Строго говоря, это не так, но наши глаза все равно видят это именно так. Первые настоящие мультфильмы тоже были сделаны с использованием этого трюка!
+
+В 1895 году братья Луи и Огюст Люмьер создали кинематограф.
+Это была камера и кинопроектор в одном устройстве. Используя это устройство, многие начинающие режиссеры начали создавать свои собственные мультфильмы.
+
+К 1910 году это развилось в полноценную индустрию. Многие мультфильмы той эпохи сейчас забыты, но некоторые все еще с нами.
+Например, кот Феликс был создан Отто Мессмером в 1919 году, и он все еще с нами, более ста лет спустя.
+В настоящее время правами на персонажа владеет DreamWorks Animation.
+
+Одним из пионеров в этой отрасли был знаменитый Уолт Дисней.
+Он не боялся экспериментировать при создании мультфильма, и его фильм "Белоснежка" был одним из первых, в котором использовалась многоплановая камера.
+С его помощью персонажи смогли передвигаться по объектам, создавая иллюзию трехмерного мира.
+
+Сегодня большинство мультфильмов создано с использованием компьютерной анимации. Последним традиционным диснеевским мультфильмом на сегодняшний день был "Винни-Пух" (2011).

kurmyza_pavel_lab_5/README.md

@@ -0,0 +1,53 @@
+# Лабораторная работа №5
+
+## ПИбд-41, Курмыза Павел
+
+Датасет по варианту: https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand.
+
+Данный набор данных содержит информацию о бронировании городской и курортной гостиниц и включает в себя такие
+сведения, как время бронирования, продолжительность пребывания, количество взрослых, детей и/или младенцев, количество
+свободных парковочных мест и т.д.
+
+## Как запустить ЛР
+
+- Запустить файл main.py
+
+## Используемые технологии
+
+- Язык программирования Python
+- Библиотеки: sklearn, numpy, pandas
+
+## Что делает программа
+
+Программа решает задачу кластеризации на выбранном датасете: выделение наиболее прибыльных посетителей отелей на основе
+их времени прибывания и средней цены одной ночи пребывания в отели. Решение достигается в несколько этапов:
+
+- Предобработка данных
+- Стандартизация данных и приведение их к виду, удобном для работы с моделями ML
+- Использование модели кластеризации K-средних
+- Визуализация полученных результатов и вывод
+
+## Тестирование
+
+Теперь мы рассмотрели задачу кластеризации K-средних, и проанализируем результаты каждого
+кластера, чтобы определить наиболее прибыльных клиентов в нашем наборе данных на основе времени выполнения заказа и ADR.
+Первая проблема, с которой мы сталкиваемся, когда хотим использовать кластеризацию с помощью K-средних, - это
+определение оптимального количества кластеров, которые мы хотим получить в качестве результатов. Поэтому сначала для
+определения количества кластеров мы использовали метод локтя:
+
+![Кластеры](clusters.jpg)
+
+Для определения оптимального количества кластеров необходимо выбрать значение k, после которого искажение начинает
+линейно уменьшаться. Таким образом, мы пришли к выводу, что оптимальное количество кластеров для данных равно 4. Поэтому
+мы запустили алгоритм K-средних на основе lead_time и ADR с количеством кластеров, равным 4, и вывели центры кластеров:
+
+![Центры кластеров](centers.jpg)
+
+## Вывод
+
+Наиболее прибыльными считаются клиенты с наименьшим временем пребывания и наибольшим ADR, т.е. клиенты, попавшие в
+зеленый кластер. В то время как красная категория показывает самый низкий ADR и самое высокое (наименее выгодное) время
+пребывания. В нашем случае после визуализации графика мы можем задать такие вопросы, как: почему у
+одних клиентов время пребывания меньше, чем у других? и есть ли вероятность, что клиенты в определенных странах
+соответствуют этому профилю? и т.д. На все эти вопросы алгоритм кластеризации K-средних может и не ответить напрямую,
+но сведение данных в отдельные кластеры обеспечивает надежную основу для постановки подобных вопросов.

kurmyza_pavel_lab_5/main.py

@@ -0,0 +1,81 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import datetime as dt
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import LabelEncoder
+import sklearn.cluster as cluster
+
+# Чтение данных датасета
+df = pd.read_csv('hotel_bookings.csv')
+
+# Удаление строк, содержащих отсутствующие значения
+df = df[df['children'].notna()]
+df = df[df['country'].notna()]
+
+# Объединение столбцов 'arrival_date_year', 'arrival_date_month', 'arrival date day_of_month' в столбец
+# 'arrival_date', содержащий день, месяц и год приезда клиента в формате datetime
+df["arrival_date_month"] = pd.to_datetime(df['arrival_date_month'], format='%B').dt.month
+df["arrival_date"] = pd.to_datetime({"year": df["arrival_date_year"].values,
+                                     "month": df["arrival_date_month"].values,
+                                     "day": df["arrival_date_day_of_month"].values})
+df = df.drop(columns=['arrival_date_year', 'arrival_date_month', 'arrival_date_day_of_month'])
+
+# Преобразование типа столбца reservation_status_date в datetime
+df["reservation_status_date"] = pd.to_datetime(df["reservation_status_date"], format='%Y-%m-%d')
+
+# Заполнение нулевых значений в столбцах средним значением каждого столбца
+for column in ['agent', 'company', 'arrival_date']:
+    df[column] = df[column].fillna(df[column].mean())
+
+# Удаляем повторяющиеся значения
+df.drop_duplicates(inplace=True)
+
+# Преобразование категориальных переменных в числовые переменные для того, чтобы модель могла с ними работать
+categoricalV = ["hotel", "meal", "country", "market_segment", "distribution_channel", "reserved_room_type",
+                "assigned_room_type", "deposit_type", "customer_type"]
+df[categoricalV[1:11]] = df[categoricalV[1:11]].astype('category')
+
+df[categoricalV[1:11]] = df[categoricalV[1:11]].apply(lambda x: LabelEncoder().fit_transform(x))
+
+df['hotel_Num'] = LabelEncoder().fit_transform(df['hotel'])
+
+df['numerical_larrival_date'] = df['arrival_date'].map(dt.datetime.toordinal)
+df['numerical_reservation_status_date'] = df['reservation_status_date'].map(dt.datetime.toordinal)
+
+df["is_canceled"].replace({'not canceled': 0, 'canceled': 1}, inplace=True)
+df["reservation_status"].replace({'Canceled': 0, 'Check-Out': 1, 'No-Show': 2}, inplace=True)
+
+# Определение входных и выходных значений
+usefull_columns = df.columns.difference(['hotel', 'hotel_Num', 'arrival_date', 'reservation_status_date'])
+X = df[usefull_columns]
+Y = df["hotel_Num"].astype(int)
+
+# Деление данных на тестовую и обучающую выборки
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=150)
+
+# Определение оптимального количества кластеров
+df_Short = df[['lead_time', 'adr']]
+
+K = range(1, 12)
+wss = []
+for k in K:
+    kmeans = cluster.KMeans(n_clusters=k, init="k-means++")
+    kmeans = kmeans.fit(df_Short)
+    wss_iter = kmeans.inertia_
+    wss.append(wss_iter)
+
+mycenters = pd.DataFrame({'Clusters': K, 'WSS': wss})
+
+sns.scatterplot(x='Clusters', y='WSS', data=mycenters, marker="+")
+
+# Решение задачи кластеризации с использованием K-Means
+kmeans = cluster.KMeans(n_clusters=4, init="k-means++")
+kmeans = kmeans.fit(df[['lead_time', 'adr']])
+df['Clusters'] = kmeans.labels_
+
+# Визуализируем кластеры
+sns.lmplot(x="lead_time", y="adr", hue='Clusters', data=df)
+plt.ylim(0, 600)
+plt.xlim(0, 800)
+plt.show()

kurmyza_pavel_lab_6/README.md

@@ -0,0 +1,51 @@
+# Лабораторная работа №6
+
+## ПИбд-41, Курмыза Павел
+
+Датасет по варианту: https://www.kaggle.com/datasets/jessemostipak/hotel-booking-demand.
+
+Данный набор данных содержит информацию о бронировании городской и курортной гостиниц и включает в себя такие
+сведения, как время бронирования, продолжительность пребывания, количество взрослых, детей и/или младенцев, количество
+свободных парковочных мест и т.д.
+
+## Как запустить ЛР
+
+- Запустить файл main.py
+
+## Используемые технологии
+
+- Язык программирования Python
+- Библиотеки: sklearn, numpy, pandas, xgboost, matplotlib, seaborn
+
+## Что делает программа
+
+Программа решает задачу классификации на выбранном датасете: определение гостиничного класса отеля (городской отель или
+курортный отель). Решение достигается в несколько этапов:
+
+- Предобработка данных
+- Балансировка данных
+- Стандартизация данных и приведение их к виду, удобном для работы с моделью ML
+- Использование модели классификации MLPClassifier
+- Оценка точности и специфичности данной модели классификации
+
+## Тестирование
+
+Для решения задачи классификации были выбрана модель MLPClassifier.
+
+Оценка точности модели: 0.9778297119757453
+
+![Отчет классификации](classification_report.jpg)
+
+Оценка способности модели MLPClassifier предсказывать истинные положительные результаты (TP / (TP + FN)), также
+известные как коэффициент чувствительности, и истинные отрицательные результаты (TN / (TN + FP)), также известный как
+коэффициент специфичности через матрицу неточностей:
+
+![Матрица неточностей](confusion_matrix.jpg)
+
+Матрица неточностей подтверждает приведенную ранее оценку модели MLPClassifier. Кроме того, она указывает на
+то, что помимо высокой точности, модель также имеет высокую специфичность.
+
+## Вывод
+
+По итогу тестирования было выявлено, что модель MLPClassifier подходит для решения поставленной задачи, на что указывают
+высокая оценка точности (97%) и специфичности данной модели.

kurmyza_pavel_lab_6/main.py

@@ -0,0 +1,104 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+from sklearn.feature_selection import VarianceThreshold
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import confusion_matrix, classification_report
+import seaborn as sns
+from sklearn.neural_network import MLPClassifier
+
+# Считываем датасет
+ds = pd.read_csv('hotel_bookings.csv')
+
+# Удаляем из датасета строки с пропущенными значениями столбцов country, children.
+# Выбраны именно данные столбцы, так как, по информации из kaggle, только они могут содержать пропущеные значения
+ds.dropna(axis=0, subset=['country', 'children'], inplace=True)
+
+# Усредняем значения столбца agent, чтобы убрать его влияние на результат, так как столбец содержит неважные данные
+moa = ds['agent'].mean()
+ds['agent'].fillna(value=moa, axis=0, inplace=True)
+
+# Заполняем пропущенные значения ячеек, чтобы исключить незаполненные
+ds.fillna(method='pad', inplace=True)
+ds.dropna(inplace=True, subset=['company'])
+
+# Переводим столбцы, содержащие текстовые данные в числовое представление
+hotel = LabelEncoder()
+meal = LabelEncoder()
+country = LabelEncoder()
+market_segment = LabelEncoder()
+distribution_channel = LabelEncoder()
+reserved_room_type = LabelEncoder()
+assigned_room_type = LabelEncoder()
+deposit_type = LabelEncoder()
+customer_type = LabelEncoder()
+reservation_status = LabelEncoder()
+reservation_status_date = LabelEncoder()
+
+ds['hotel_n'] = hotel.fit_transform(ds['hotel'])
+ds['arrival_date_month_n'] = hotel.fit_transform(ds['arrival_date_month'])
+ds['meal_n'] = hotel.fit_transform(ds['meal'])
+ds['country_n'] = hotel.fit_transform(ds['country'])
+ds['market_segment_n'] = hotel.fit_transform(ds['market_segment'])
+ds['distribution_channel_n'] = hotel.fit_transform(ds['distribution_channel'])
+ds['reserved_room_type_n'] = hotel.fit_transform(ds['reserved_room_type'])
+ds['assigned_room_type_n'] = hotel.fit_transform(ds['assigned_room_type'])
+ds['deposit_type_n'] = hotel.fit_transform(ds['deposit_type'])
+ds['customer_type_n'] = hotel.fit_transform(ds['customer_type'])
+ds['reservation_status_n'] = hotel.fit_transform(ds['reservation_status'])
+ds['reservation_status_date_n'] = hotel.fit_transform(ds['reservation_status_date'])
+
+# Удаляем приведенные к числовым данным столбцы, они больше не нужны
+ds.drop(
+    ['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment', 'distribution_channel', 'reserved_room_type',
+     'assigned_room_type', 'deposit_type', 'customer_type', 'reservation_status', 'reservation_status_date'], axis=1,
+    inplace=True)
+
+# Производим балансировку данных таким образом, чтобы было одинаковое количество отелей всех классов
+ds_0 = ds[ds['hotel_n'] == 0]
+ds_1 = ds[ds['hotel_n'] == 1]
+ds_0 = ds_0.sample(ds_1.shape[0])
+ds = ds_0._append(ds_1, ignore_index=True)
+
+# Полдготовка данных для выполнения модели
+x = ds.drop('hotel_n', axis=1)
+y = ds['hotel_n']
+
+threshold = VarianceThreshold()
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+x_train = threshold.fit_transform(x_train)
+x_test = threshold.transform(x_test)
+
+# Производим стандартизацию данных и приводим их к виду, с которым работают модель классификации MLPClassifier
+scaler = StandardScaler()
+
+x_train = scaler.fit_transform(x_train)
+x_test = scaler.fit_transform(x_test)
+
+y_train = y_train.to_numpy()
+y_test = y_test.to_numpy()
+
+# Инициализируем модель MLPClassifier и обучаем её
+
+mlp = MLPClassifier()
+mlp.fit(x_train, y_train)
+
+# Оценка точности моделей классификации
+
+mlp_accuracy = mlp.score(x_test, y_test)
+print(f"Оценка точности модели: {mlp_accuracy}")
+
+# Оценка коэффициента специфичности через матрицу неточностей
+
+y_pred = mlp.predict(x_test)
+
+cm = confusion_matrix(y_test, y_pred)
+plt.figure(figsize=(7, 5))
+sns.heatmap(cm, annot=True)
+plt.xlabel('Prediction')
+plt.ylabel('Actual')
+plt.show()
+
+print(classification_report(y_test, y_pred))

kutygin_andre_lab_3/README.md

@@ -0,0 +1,118 @@
+**Задание**
+***
+Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта»на 99% ваших данных. Проверьте работу модели на оставшемся проценте, сделайте вывод
+
+**Как запустить лабораторную**
+***
+Запустить файл main.py
+
+**Используемые технологии**
+***
+Библиотеки pandas, scikit-learn, matplotlib, их компоненты
+
+**Описание лабораторной (программы)**
+***
+В данном коде мы создаем и обучаем модель дерева решений для прогнозирования инцидентов с НЛО на основе набора данных.
+
+1. В первой строке кода мы загружаем данные из CSV-файла 'ufo_data_nuforc.csv' с помощью функции pd.read_csv(). Эти данные содержат информацию о различных инцидентах с НЛО.
+2. Далее мы выбираем набор признаков, в данном случае, эти признаки - населенность и время, которые будут использоваться для обучения модели, и сохраняем их в переменную features.
+3. Затем преобразуем категориальные признаки в числовой вид при помощи функции pd.get_dummies(). Это необходимо, так как модель дерева решений работает только с числовыми данными.
+4. После этого мы разделяем данные на обучающую и тестовую выборки с помощью функции train_test_split(). Обучающая выборка будет использоваться для обучения модели, а тестовая - для проверки ее точности.
+5. Создаем модель дерева решений с помощью класса DecisionTreeClassifier() из библиотеки sklearn.tree.
+6. Обучаем модель на обучающей выборке с помощью метода fit(). В процессе обучения модель настраивает параметры дерева решений, чтобы лучше предсказывать целевой признак.
+7. После обучения модели, мы производим прогнозы на тестовых данных с помощью метода predict().
+8. Оцениваем точность модели на тестовой выборке с помощью метода accuracy_score() из библиотеки sklearn.metrics. Этот метод сравнивает фактические значения целевого признака с предсказанными и возвращает точность модели.
+9. Наконец, выводим точность модели на тестовой выборке, чтобы оценить, насколько хорошо модель предсказывает инциденты с НЛО.
+10. Также, код визуализирует данные в виде графика с помощью библиотеки matplotlib.pyplot, отображая фактические значения целевого признака и предсказания модели. Это помогает наглядно оценить, насколько близки предсказания модели к реальным значениям.
+**Результат**
+***
+Точность модели на тестовой выборке: 0.1377245508982036
+Прогнозы по оставшемуся проценту данных: 'cylinder' 'circle' 'sphere' 'disk' 'disk' 'fireball' 'disk' 'oval'
+ 'circle' 'disk' 'disk' 'other' 'light' 'light' 'oval' 'fireball' 'light'
+ 'rectangle' 'chevron' 'unknown' 'sphere' 'oval' 'light' 'circle'
+ 'unknown' 'unknown' 'disk' 'triangle' 'triangle' 'unknown' 'formation'
+ 'unknown' 'cigar' 'unknown' 'light' 'other' 'rectangle' 'light' 'other'
+ 'light' 'cylinder' 'delta' 'sphere' 'other' 'changing' 'fireball'
+ 'cylinder' 'cigar' 'circle' 'triangle' 'light' 'fireball' 'fireball'
+ 'sphere' 'circle' 'light' 'chevron' 'oval' 'oval' 'light' 'unknown'
+ 'triangle' 'other' 'rectangle' 'triangle' 'triangle' 'flash' 'unknown'
+ 'sphere' 'unknown' 'other' 'circle' 'oval' 'light' 'oval' 'formation'
+ 'sphere' 'triangle' 'changing' 'sphere' 'oval' 'unknown' 'circle'
+ 'circle' 'flash' 'light' 'light' 'sphere' 'other' 'other' 'egg' 'unknown'
+ 'other' 'light' 'light' 'disk' 'diamond' 'oval' 'unknown' 'light'
+ 'triangle' 'other' 'light' 'disk' 'unknown' 'light' 'changing' 'sphere'
+ 'triangle' 'circle' 'flash' 'sphere' 'light' 'unknown' 'oval' 'formation'
+ 'light' 'circle' 'unknown' 'other' 'triangle' 'other' 'light' 'disk'
+ 'formation' 'oval' 'triangle' 'triangle' 'light' 'formation' 'oval'
+ 'light' 'light' 'oval' 'disk' 'sphere' 'egg' 'unknown' 'unknown'
+ 'unknown' 'light' 'disk' 'changing' 'light' 'light' 'circle' 'circle'
+ 'formation' 'light' 'light' 'cigar' 'light' 'triangle' 'oval' 'fireball'
+ 'cylinder' 'other' 'circle' 'egg' 'changing' 'triangle' 'circle' 'other'
+ 'oval' 'disk' 'light' 'flash' 'fireball' 'circle' 'circle' 'circle'
+ 'circle' 'light' 'disk' 'fireball' 'other' 'sphere' 'light' 'changing'
+ 'cigar' 'light' 'cylinder' 'rectangle' 'chevron' 'light' 'light' 'light'
+ 'light' 'circle' 'circle' 'light' 'light' 'circle' 'sphere' 'triangle'
+ 'light' 'egg' 'circle' 'fireball' 'sphere' 'sphere' 'triangle' 'light'
+ 'other' 'cigar' 'sphere' 'sphere' 'fireball' 'light' 'light' 'disk'
+ 'oval' 'oval' 'other' 'cigar' 'triangle' 'light' 'light' 'light' 'disk'
+ 'light' 'light' 'light' 'light' 'other' 'light' 'teardrop' 'triangle'
+ 'teardrop' 'fireball' 'sphere' 'cylinder' 'fireball' 'circle' 'egg'
+ 'sphere' 'disk' 'chevron' 'triangle' 'light' 'other' 'light' 'circle'
+ 'rectangle' 'fireball' 'formation' 'light' 'light' 'circle' 'light'
+ 'light' 'formation' 'light' 'triangle' 'light' 'oval' 'light' 'unknown'
+ 'fireball' 'diamond' 'light' 'circle' 'light' 'triangle' 'oval' 'oval'
+ 'cylinder' 'circle' 'light' 'disk' 'light' 'sphere' 'circle' 'light'
+ 'triangle' 'light' 'fireball' 'triangle' 'light' 'flash' 'triangle' 'egg'
+ 'disk' 'oval' 'circle' 'flash' 'light' 'oval' 'sphere' 'light' 'triangle'
+ 'other' 'chevron' 'other' 'circle' 'unknown' 'unknown' 'sphere' 'light'
+ 'cigar' 'light' 'fireball' 'circle' 'diamond' 'fireball' 'triangle'
+ 'diamond' 'sphere' 'circle' 'chevron' 'cylinder' 'light' 'circle'
+ 'fireball' 'unknown' 'light' 'circle' 'fireball' 'light' 'fireball'
+ 'fireball' 'fireball' 'light' 'sphere' 'light' 'sphere' 'sphere'
+ 'formation' 'light' 'fireball' 'fireball' 'disk' 'disk' 'circle'
+ 'rectangle' 'unknown' 'disk' 'unknown' 'disk' 'triangle' 'other' 'sphere'
+ 'diamond' 'light' 'light' 'unknown' 'sphere' 'circle' 'disk' 'circle'
+ 'oval' 'changing' 'other' 'other' 'disk' 'unknown' 'unknown' 'disk'
+ 'rectangle' 'disk' 'light' 'oval' 'unknown' 'sphere' 'light' 'changing'
+ 'disk' 'disk' 'other' 'other' 'disk' 'cylinder' 'disk' 'rectangle'
+ 'light' 'disk' 'disk' 'light' 'fireball' 'formation' 'cigar' 'oval'
+ 'fireball' 'unknown' 'disk' 'light' 'light' 'triangle' 'triangle' 'light'
+ 'sphere' 'triangle' 'sphere' 'circle' 'light' 'oval' 'oval' 'circle'
+ 'oval' 'rectangle' 'disk' 'oval' 'light' 'light' 'other' 'cigar'
+ 'triangle' 'disk' 'cigar' 'other' 'triangle' 'egg' 'unknown' 'triangle'
+ 'light' 'triangle' 'disk' 'changing' 'triangle' 'disk' 'disk' 'rectangle'
+ 'other' 'triangle' 'triangle' 'formation' 'triangle' 'egg' 'sphere'
+ 'fireball' 'triangle' 'rectangle' 'light' 'triangle' 'triangle' 'other'
+ 'light' 'light' 'disk' 'fireball' 'light' 'disk' 'oval' 'triangle'
+ 'other' 'fireball' 'light' 'light' 'triangle' 'unknown' 'cigar' 'light'
+ 'unknown' 'chevron' 'formation' 'disk' 'cigar' 'light' 'sphere' 'cigar'
+ 'unknown' 'triangle' 'other' 'light' 'light' 'triangle' 'diamond' 'light'
+ 'triangle' 'oval' 'changing' 'light' 'flash' 'circle' 'oval' 'other'
+ 'sphere' 'circle' 'triangle' 'unknown' 'teardrop' 'unknown' 'fireball'
+ 'light' 'light' 'cigar' 'cigar' 'light' 'fireball' 'other' 'egg' 'light'
+ 'other' 'unknown' 'unknown' 'changing' 'circle' 'light' 'other' 'unknown'
+ 'unknown' 'light' 'other' 'light' 'unknown' 'cylinder' 'triangle'
+ 'circle' 'light' 'circle' 'circle' 'circle' 'light' 'light' 'changing'
+ 'changing' 'circle' 'circle' 'triangle' 'triangle' 'light' 'light'
+ 'light' 'light' 'other' 'changing' 'triangle' 'cylinder' 'light'
+ 'unknown' 'circle' 'disk' 'sphere' 'oval' 'formation' 'teardrop'
+ 'triangle' 'chevron' 'light' 'unknown' 'unknown' 'other' 'egg' 'circle'
+ 'oval' 'cigar' 'unknown' 'chevron' 'oval' 'cigar' 'fireball' 'circle'
+ 'unknown' 'light' 'sphere' 'fireball' 'changing' 'light' 'circle'
+ 'unknown' 'fireball' 'light' 'sphere' 'light' 'formation' 'circle'
+ 'fireball' 'formation' 'formation' 'formation' 'light' 'other' 'light'
+ 'light' 'circle' 'diamond' 'oval' 'circle' 'oval' 'triangle' 'light'
+ 'disk' 'light' 'other' 'triangle' 'triangle' 'cylinder' 'disk' 'cylinder'
+ 'light' 'oval' 'cigar' 'circle' 'disk' 'light' 'unknown' 'circle' 'other'
+ 'light' 'light' 'light' 'unknown' 'triangle' 'other' 'disk' 'cylinder'
+ 'triangle' 'oval' 'disk' 'light' 'triangle' 'circle' 'light' 'other'
+ 'light' 'other' 'circle' 'disk' 'other' 'triangle' 'oval' 'unknown'
+ 'light' 'triangle' 'unknown' 'circle' 'unknown' 'light' 'fireball'
+ 'fireball' 'rectangle' 'light' 'formation' 'unknown' 'light' 'light'
+ 'formation' 'fireball' 'light' 'light' 'other' 'unknown' 'light'
+ 'triangle' 'fireball' 'triangle' 'triangle' 'flash' 'circle' 'triangle'
+ 'disk' 'light' 'unknown' 'light' 'light' 'fireball' 'circle' 'unknown'
+ 'unknown' 'circle' 'disk' 'chevron' 'disk' 'disk' 'triangle' 'light'
+ 'light' 'disk'
+
+***Вывод:*** Наша модель дерева решений показала низкую точность предсказаний (Точность модели на тестовой выборке: 0.1377245508982036), что означает, что она не очень хорошо предсказывает форму НЛО на основе выбранных признаков (население и время). Из-за чего можно сделать вывод, что возможно, эти признаки недостаточно информативны или недостаточно связаны с формой НЛО.

kutygin_andre_lab_3/main.py

@@ -0,0 +1,39 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+# Загрузка данных
+data = pd.read_csv('ufo_sighting_data.csv')
+
+# Выбор признаков
+features = [ 'length_of_encounter_seconds', 'latitude', 'longitude']
+target = 'UFO_shape'
+# Удаление строк содержащих NaN
+data.dropna(inplace=True)
+
+# Удаление столбцов содержащих NaN
+data.dropna(axis='columns', inplace=True)
+
+# Разделение данных на обучающую и тестовую выборки
+train_data, test_data, train_labels, test_labels = train_test_split(data[features], data[target], test_size=0.2, random_state=42)
+
+# Создание и обучение модели дерева решений
+model = DecisionTreeClassifier()
+model.fit(train_data, train_labels)
+
+# Прогнозирование на тестовой выборке
+predictions = model.predict(test_data)
+
+# Оценка точности модели
+accuracy = accuracy_score(test_labels, predictions)
+print('Точность модели на тестовой выборке:', accuracy)
+
+# Прогнозирование на оставшемся проценте данных
+remaining_data = data.drop(test_data.index)
+remaining_predictions = model.predict(remaining_data[features])
+
+# Вывод результатов
+print('Прогнозы по оставшемуся проценту данных:', remaining_predictions)
+
+# Сделайте необходимые выводы

lipatov_ilya_lab_4/README.md

@@ -0,0 +1,26 @@
+## Лабораторная работа №4
+
+### Кластеризация
+
+## Выполнил студент группы ПИбд-41 Липатов Илья
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (стартовая точка класс lab4)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* Кластеризирует данные о домах в Бостоне исходя из уровня преступности на душу населения в разбивке по городам и процента более низкого статуса населения. Ожидаем, что разбиение домов будет на три кластера.
+
+### Примеры работы:
+
+#### Результаты:
+* Кластеризация разбила наши дома в Бостоне на три большие группы, как мы этого и ожидали, значит алгоритм с задачей справился.
+
+![Result](result.png)

lipatov_ilya_lab_4/boston.csv

@@ -0,0 +1,507 @@
+CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
+0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98,24.00
+0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60
+0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70
+0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40
+0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20
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lipatov_ilya_lab_4/lab4.py

@@ -0,0 +1,36 @@
+from sklearn.cluster import AgglomerativeClustering
+from scipy.cluster.hierarchy import dendrogram
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+FILE_PATH = "boston.csv"
+FEATURES = ['LSTAT', 'CRIM']
+
+def plot_dendrogram(model, **kwargs):
+    counts = np.zeros(model.children_.shape[0])
+    n_samples = len(model.labels_)
+    for i, merge in enumerate(model.children_):
+        current_count = 0
+        for child_idx in merge:
+            if child_idx < n_samples:
+                current_count += 1
+            else:
+                current_count += counts[child_idx - n_samples]
+        counts[i] = current_count
+
+    linkage_matrix = np.column_stack(
+        [model.children_, model.distances_, counts]
+    ).astype(float)
+
+    dendrogram(linkage_matrix, **kwargs)
+
+
+data = pd.read_csv(FILE_PATH)
+X = data[FEATURES]
+model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
+model = model.fit(X)
+plt.title("Hierarchical Clustering Dendrogram for Boston House Prices")
+
+plot_dendrogram(model, truncate_mode="level", p=2)
+plt.show()

lipatov_ilya_lab_5/README.md

@@ -0,0 +1,51 @@
+## Лабораторная работа №5
+
+### Регрессия
+
+## Выполнил студент группы ПИбд-41 Липатов Илья
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (стартовая точка класс lab5)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* С помощью полиномиальной регрессии предсказывает среднюю стоимость домов в 1000 долларах [тыс. долларов] исходя из среднего количества комнат в жилом помещении, уровень преступности на душу населения в разбивке по городам и индекса доступности к радиальным магистралям.
+* Выводит размер ошибки, оценку модели и полученное предсказание 
+
+### Примеры работы:
+
+### Результаты:
+Были проведены тесты с различными параметрами степени (от 1 до 6). По итогу степень ошибки большая, меньше всего она при степени равной 2 или 4 (при этом и оценка модели 0.68 и 0.55 соответственно).
+
+#### Тесты
+
+#### degree = 1
+* Оценка качества: 0.4252542186083391
+* Ошибка: 0.22653604605972913
+
+#### degree = 2
+* Оценка качества: 0.6835376807930289
+* Ошибка: 0.1625504540569756
+
+#### degree = 3
+* Оценка качества: 0.5267438865953347
+* Ошибка: 0.195302452251188
+
+#### degree = 4
+* Оценка качества: 0.5481932964142193
+* Ошибка: 0.17852746450144702
+
+#### degree = 5
+* Оценка качества: -3.372087305867348
+* Ошибка: 0.4163026401028063
+
+#### degree = 6
+* Оценка качества: -69.05174526020205
+* Ошибка: 1.3125236408458876

lipatov_ilya_lab_5/boston.csv

@@ -0,0 +1,507 @@
+CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
+0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98,24.00
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lipatov_ilya_lab_5/lab5.py

@@ -0,0 +1,20 @@
+from sklearn.metrics import mean_absolute_percentage_error
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+import pandas as pd
+
+data = pd.read_csv('boston.csv')
+X = (data[['CRIM', 'RM', 'RAD']])
+y = data['MEDV']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+lin = LinearRegression()
+polynomial_features = PolynomialFeatures(degree=1)
+pipeline = Pipeline([("Linear", polynomial_features), ("linear_regression", lin)])
+pipeline.fit(X_train, y_train)
+y_predict = lin.predict(polynomial_features.fit_transform(X_test))
+print('Предсказание: ', y_predict)
+print('Оценка качества:', pipeline.score(X_test, y_test))
+print('Ошибка:', mean_absolute_percentage_error(y_test, y_predict))

madyshev_egor_lab_5/main.py

@@ -0,0 +1,57 @@
+import numpy as np
+import pandas as pb
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Perceptron, LogisticRegression, Lasso, Ridge
+from sklearn.neural_network import MLPClassifier, MLPRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler
+from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.preprocessing import PolynomialFeatures
+
+df = pb.read_csv("StudentsPerformance.csv", sep=",", encoding="windows-1251")
+df1 = df
+print("Данные без подготовки:")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+def prepareStringData(columnName):
+    uniq = df[columnName].unique()
+    mp = {}
+    for i in uniq:
+        mp[i] = len(mp)
+    df[columnName] = df[columnName].map(mp)
+
+
+print()
+print("Данные после подготовки:")
+prepareStringData("gender")
+prepareStringData("race/ethnicity")
+prepareStringData("parental level of education")
+prepareStringData("lunch")
+prepareStringData("test preparation course")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+
+
+X = df[["gender", "race/ethnicity", "lunch", "test preparation course", "parental level of education", "reading score", "writing score"]]
+y = df["math score"]
+X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.26, random_state=42)
+lnr = LinearRegression()
+lnr = lnr.fit(X_train,y_train)
+
+poly_regression = make_pipeline(PolynomialFeatures(degree=4), LinearRegression())
+poly_regression.fit(X_train, y_train)
+
+lasso = Lasso()
+lasso.fit(X_train, y_train)
+
+ridge = Ridge()
+ridge.fit(X_train, y_train)
+
+
+print("Линейная регрессия: ", lnr.score(X_Test,y_test))
+print("Полиномиальная регрессия: ", poly_regression.score(X_Test,y_test))
+print("Лассо-регрессия: ", lasso.score(X_Test,y_test))
+print("Гребневая регрессия: ", ridge.score(X_Test,y_test))

madyshev_egor_lab_5/readme.md

@@ -0,0 +1,41 @@
+# Задание
+Использовать регрессию по варианту для данных из таблицы 1 по варианту (таблица 10), самостоятельно сформулировав задачу. Оценить, насколько хорошо она подходит для решения сформулированной вами задачи.    
+## Задание по варианту
+Полиномиальная регрессия
+## Решение
+### Запуск программы
+Для запуска программы необходимо запустить файл main.py, содержащий код программы  
+### Используемые технологии
+Программа использует следующие библиотеки:  
+- numpy - библиотека для работы с массивами и матрицами.
+- matplotlib - библиотека для создания графиков и визуализации данных.
+- sklearn - библиотека для машинного обучения и анализа данных.
+### Что делает программа
+Программа читает данные из csv файла. Подготавливает их для работы модели, приводя текстовые параметры к числам.  И пытается научиться предсказывать оценку по математике на основании остальных данных с помощью различных моделей.
+### Тесты
+Данные без подготовки:  
+   gender race/ethnicity parental level of education         lunch test preparation course  math score  reading score  writing score  
+0  female        group B           bachelor's degree      standard                    none          72             72             74  
+1  female        group C                some college      standard               completed          69             90             88  
+2  female        group B             master's degree      standard                    none          90             95             93  
+3    male        group A          associate's degree  free/reduced                    none          47             57             44  
+4    male        group C                some college      standard                    none          76             78             75  
+  
+Данные после подготовки:  
+   gender  race/ethnicity  parental level of education  lunch  test preparation course  math score  reading score  writing score  
+0       0               0                            0      0                        0          72             72             74  
+1       0               1                            1      0                        1          69             90             88  
+2       0               0                            2      0                        0          90             95             93  
+3       1               2                            3      1                        0          47             57             44  
+4       1               1                            1      0                        0          76             78             75  
+  
+Линейная регрессия:  0.8769480272687482  
+Полиномиальная регрессия:  0.736490555768213  
+Лассо-регрессия:  0.8299946331354273  
+Гребневая регрессия:  0.8768384994076267  
+
+Логическая регрессия не подошла так как требует чтобы переменная ответа была двоичной.
+Из результатов четырех моделей видно, что для решения задачи предсказания оценки по математике неплохо подходит модель Линейной регрессии.  
+Модель гребневой регрессии имеет схожие результаты. Далее идет лассо, и хуже всех полиномиальная регрессия.  
+  
+Вывод: Для решения задачи предсказания результатов экзамена по математике неплохо подходят линейные модели, а именно линейная регрессия и гребневая регрессия

madyshev_egor_lab_6/main.py

@@ -0,0 +1,49 @@
+import numpy as np
+import pandas as pb
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Perceptron, LogisticRegression, Lasso, Ridge
+from sklearn.neural_network import MLPClassifier, MLPRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler
+from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.preprocessing import PolynomialFeatures
+
+df = pb.read_csv("StudentsPerformance.csv", sep=",", encoding="windows-1251")
+df1 = df
+print("Данные без подготовки:")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+def prepareStringData(columnName):
+    uniq = df[columnName].unique()
+    mp = {}
+    for i in uniq:
+        mp[i] = len(mp)
+    df[columnName] = df[columnName].map(mp)
+
+
+print()
+print("Данные после подготовки:")
+prepareStringData("gender")
+prepareStringData("race/ethnicity")
+prepareStringData("parental level of education")
+prepareStringData("lunch")
+prepareStringData("test preparation course")
+with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000):
+    print(df[:5])
+
+
+
+X = df[["gender", "race/ethnicity", "lunch", "parental level of education", "reading score", "writing score", "math score"]]
+y = df["test preparation course"]
+X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.26, random_state=42)
+
+mlpr = MLPRegressor()
+mlpc = MLPClassifier()
+mlpr.fit(X_train, y_train)
+mlpc.fit(X_train, y_train)
+
+print("MLPRegressor:", mlpr.score(X_Test, y_test))
+print("MLPClassifier:", mlpc.score(X_Test, y_test))

madyshev_egor_lab_6/readme.md

@@ -0,0 +1,38 @@
+# Задание
+Использовать нейронную сеть (четные варианты – MLPRegressor, нечетные – MLPClassifier) для данных из таблицы 1 по варианту, самостоятельно сформулировав задачу. Интерпретировать результаты и оценить, насколько хорошо она подходит для решения сформулированной вами задачи.    
+## Задание по варианту
+MLPRegressor
+## Решение
+### Запуск программы
+Для запуска программы необходимо запустить файл main.py, содержащий код программы  
+### Используемые технологии
+Программа использует следующие библиотеки:  
+- numpy - библиотека для работы с массивами и матрицами.
+- matplotlib - библиотека для создания графиков и визуализации данных.
+- sklearn - библиотека для машинного обучения и анализа данных.
+### Что делает программа
+Программа читает данные из csv файла. Подготавливает их для работы модели, приводя текстовые параметры к числам.  И пытается научиться предсказывать прохождение подготовительных курсов с помощью моделей нейронных сетей.
+### Тесты
+Данные без подготовки:  
+   gender race/ethnicity parental level of education         lunch test preparation course  math score  reading score  writing score  
+0  female        group B           bachelor's degree      standard                    none          72             72             74  
+1  female        group C                some college      standard               completed          69             90             88  
+2  female        group B             master's degree      standard                    none          90             95             93  
+3    male        group A          associate's degree  free/reduced                    none          47             57             44  
+4    male        group C                some college      standard                    none          76             78             75  
+  
+Данные после подготовки:  
+   gender  race/ethnicity  parental level of education  lunch  test preparation course  math score  reading score  writing score  
+0       0               0                            0      0                        0          72             72             74  
+1       0               1                            1      0                        1          69             90             88  
+2       0               0                            2      0                        0          90             95             93  
+3       1               2                            3      1                        0          47             57             44  
+4       1               1                            1      0                        0          76             78             75  
+  
+MLPRegressor: 0.1347847602324338  
+MLPClassifier: 0.65  
+
+Модель регрессии показала себя хуже чем модель классификации. Хотя модель классификации показала себя чуть лучше, результаты её работы всё равно не очень высоки.  
+Итоговый результат лежит в границах между 0 и 1, и в тестовых результатах является целым. Это значит, что угадывая произвольно модель в любом случае может достигнуть точности близкой к 0.5  
+
+Вывод:  Модели нейронных сетей MLPRegressor и MLPClassifier не подходят для решения поставленной задачи, предсказания прохождения курсов по остальным данным. Или на практике не существует соответствующей зависимости в данных.

malkova_anastasia_lab_1/README.md

@@ -0,0 +1,44 @@
+# Лабораторная работа №1
+
+> Работа с типовыми наборами данных и различными моделями
+
+# Задание
+
+Сгенерировать определённый тип данных, сравнить на нём разные модели и отобразить качество на графиках.
+
+Данные: make_classification (n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1) 
+Модели:
+* Линейную регрессию 
+* Персептрон 
+* Гребневую полиномиальную регрессию (со степенью 3, alpha= 1.0)
+
+### Как запустить лабораторную работу
+
+1. Установить python, numpy, sklearn, matplotlib
+2. Запустить команду `python main.py` в корне проекта
+
+### Использованные технологии
+
+* Язык программирования `python`
+* Библиотеки `numpy, sklearn, matplotlib`
+* Среда разработки `PyCharm`
+
+### Что делает программа?
+
+Генерирует набор данных для классификации с помощью make_classification.
+Обучает на них 3 модели:
+
+- Линейную регрессию
+- Персептрон
+- Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
+
+Собирает итоговые оценки моделей:
+
+- Линейная регрессия - коэффициент детерминации R2
+- Персептрон - средняя точность по заданным тестовым данным
+- Гребневая полиномиальная регрессия - Перекрёстная проверка
+
+![plots screen](plots.jpg)
+
+Лучший результат показала модель персептрона
+

malkova_anastasia_lab_1/dataset.py

@@ -0,0 +1,16 @@
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+
+
+def generate_dataset():
+    x, y = make_classification(n_samples=500, n_features=2, n_redundant=0,
+                               n_informative=2, random_state=0, n_clusters_per_class=1)
+    random = np.random.RandomState(2)
+    x += 2.5 * random.uniform(size=x.shape)
+    return x, y
+
+
+def split_dataset(x, y):
+    return train_test_split(
+        x, y, test_size=.05, random_state=42)

malkova_anastasia_lab_1/main.py

@@ -0,0 +1,19 @@
+from dataset import generate_dataset, split_dataset
+from models import launch_linear_regression, launch_perceptron, launch_ridge_poly_regression
+from plots import show_plot
+
+x, y = generate_dataset()
+
+x_train, x_test, y_train, y_test = split_dataset(x, y)
+
+my_linear_model, linear_model_score = launch_linear_regression(
+    x_train, x_test, y_train, y_test)
+my_perceptron_model, perceptron_model_score = launch_perceptron(
+    x_train, x_test, y_train, y_test)
+my_polynomial_model, polynomial_model_score = launch_ridge_poly_regression(
+    x_train, x_test, y_train, y_test)
+
+show_plot(x, x_train, x_test, y_train, y_test,
+          my_linear_model, linear_model_score,
+          my_perceptron_model, perceptron_model_score,
+          my_polynomial_model, polynomial_model_score)

malkova_anastasia_lab_1/models.py

@@ -0,0 +1,37 @@
+from sklearn.linear_model import LinearRegression, Perceptron, Ridge
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.model_selection import cross_val_score
+from sklearn.pipeline import Pipeline
+
+
+def launch_linear_regression(x_train, x_test, y_train, y_test):
+    my_linear_model = LinearRegression()
+    my_linear_model.fit(x_train, y_train)
+    linear_model_score = my_linear_model.score(
+        x_test, y_test)
+    print('linear_model_score: ', linear_model_score)
+    return my_linear_model, linear_model_score
+
+
+# Perceptron
+def launch_perceptron(x_train, x_test, y_train, y_test):
+    my_perceptron_model = Perceptron()
+    my_perceptron_model.fit(x_train, y_train)
+    perceptron_model_score = my_perceptron_model.score(
+        x_test, y_test)
+    print('perceptron_model_score: ', perceptron_model_score)
+    return my_perceptron_model, perceptron_model_score
+
+
+# RidgePolyRegression
+def launch_ridge_poly_regression(x_train, x_test, y_train, y_test):
+    my_polynomial_model = PolynomialFeatures(degree=3, include_bias=False)
+    ridge = Ridge(alpha=1)
+    pipeline = Pipeline(
+        [("polynomial_features", my_polynomial_model), ("ridge_regression", ridge)])
+    pipeline.fit(x_train, y_train)
+    scores = cross_val_score(pipeline, x_test, y_test,
+                             scoring="neg_mean_squared_error", cv=5)
+    polynomial_model_score = -scores.mean()
+    print('mean polynomial_model_score: ', polynomial_model_score)
+    return my_polynomial_model, polynomial_model_score

malkova_anastasia_lab_1/plots.py

@@ -0,0 +1,71 @@
+import numpy as np
+from matplotlib.colors import ListedColormap
+from matplotlib.axes import Axes
+from matplotlib import pyplot as plt
+
+TRAIN_DATA_ROW_LENGTH = 3
+TEST_DATA_ROW_LENGTH = 6
+LINEAR_REGRESSION_PLOT_INDEX = 6
+PERCEPTRON_REGRESSION_PLOT_INDEX = 7
+RIDGE_POLY_REGRESSION_REGRESSION_PLOT_INDEX = 8
+
+
+def show_plot(x, x_train, x_test, y_train, y_test, my_linear_model, linear_model_score, my_perceptron_model, perceptron_model_score, pipeline, polynomial_model_score):
+    h = .02  # шаг регулярной сетки
+    x0_min, x0_max = x[:, 0].min() - .5, x[:, 0].max() + .5
+    x1_min, x1_max = x[:, 1].min() - .5, x[:, 1].max() + .5
+    xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h),
+                           np.arange(x1_min, x1_max, h))
+    cm = plt.cm.RdBu
+
+    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
+
+    for i in range(9):
+        current_subplot = plt.subplot(3, 3, i+1)
+        if i < TRAIN_DATA_ROW_LENGTH:
+            current_subplot.scatter(
+                x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
+        elif i < TEST_DATA_ROW_LENGTH:
+            current_subplot.scatter(
+                x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+        else:
+            if i == LINEAR_REGRESSION_PLOT_INDEX:
+                show_gradient(my_linear_model, current_subplot=current_subplot,
+                              title='LinearRegression', score=linear_model_score, xx0=xx0, xx1=xx1, cm=cm)
+
+            elif i == PERCEPTRON_REGRESSION_PLOT_INDEX:
+                show_gradient(my_perceptron_model, current_subplot=current_subplot,
+                              title='Perceptron', score=perceptron_model_score, xx0=xx0, xx1=xx1, cm=cm)
+
+            elif i == RIDGE_POLY_REGRESSION_REGRESSION_PLOT_INDEX:
+                current_subplot.set_title('RidgePolyRegression')
+                show_gradient(pipeline, current_subplot=current_subplot,
+                              title='RidgePolyRegression', score=polynomial_model_score, xx0=xx0, xx1=xx1, cm=cm)
+
+            current_subplot.scatter(
+                x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
+            current_subplot.scatter(
+                x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+
+    plt.show()
+
+
+def show_gradient(model, current_subplot: Axes, title: str, score: float, xx0, xx1, cm):
+    current_subplot.set_title(title)
+    if hasattr(model, "decision_function"):
+        Z = model.decision_function(np.c_[xx0.ravel(), xx1.ravel()])
+    elif hasattr(model, "predict_proba"):
+        Z = model.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1]
+    elif hasattr(model, "predict"):
+        Z = model.predict(np.c_[xx0.ravel(), xx1.ravel()])
+    else:
+        return
+
+    Z = Z.reshape(xx0.shape)
+    current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8)
+    current_subplot.set_xlim(xx0.min(), xx0.max())
+    current_subplot.set_ylim(xx0.min(), xx1.max())
+    current_subplot.set_xticks(())
+    current_subplot.set_yticks(())
+    current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score),
+                         size=15, horizontalalignment='left')