lipatov_ilya_lab_1
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## Лабораторная работа №1
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### Работа с типовыми наборами данных и различными моделями
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## Выполнил студент группы ПИбд-41 Липатов Илья
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### Как запустить лабораторную работу:
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* установить python, numpy, matplotlib, sklearn
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* запустить проект (стартовая точка класс lab1)
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### Какие технологии использовались:
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* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
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* Среда разработки `PyCharm`
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### Что делает лабораторная работа:
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* Генерирует набор данных типа с помощью make_circles(noise=0.2, factor=0.5, random_state=4)
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* Сравнивает три типа моделей: линейную, полиномиальную (степень 4) и персептрон
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### Примеры работы:
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#### Результаты:
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* Линейная регрессия, оценка качества: 0.0494206358498015
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* Полиноминальная регрессия, оценка качества: 0.4480860719638978
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* Персептрон, оценка качества: 0.52
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#### Самый лучший результат показал персептрон - 0.52
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#### График линейной, полиномиальной, персептрон:
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import StandardScaler
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from sklearn.linear_model import Perceptron
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from sklearn.datasets import make_circles
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import matplotlib.pyplot as plt
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import numpy as np
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def polynomial(x_train, y_train):
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model = PolynomialFeatures(degree=4).fit(x_train, y_train)
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x_poly = model.fit_transform(x_train)
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lin = LinearRegression()
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lin.fit(x_poly, y_train)
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plt.scatter(x_train, y_train, color='green')
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plt.plot(x_train, lin.predict(x_poly), color='red')
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plt.show()
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print('Полиноминальная регрессия')
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print('Оценка качества:', lin.score(x_poly, y_train))
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def lineal(x, y, x_train, y_train):
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model = LinearRegression().fit(x_train, y_train)
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plt.scatter(x, y, color='green')
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plt.plot(x, model.predict(x), color='red')
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plt.show()
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print('Линейная регрессия')
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print('Оценка качества:', model.score(x_train, y_train))
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def perceptron(x_test, x_train, y_train):
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sc = StandardScaler()
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sc.fit(x_train)
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x_train_std = sc.transform(x_train)
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x_test_std = sc.transform(x_test)
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model = Perceptron(eta0=0.1, random_state=1).fit(x_train_std, y_train)
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plt.scatter(x_train, y_train, color='green')
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plt.plot(x_test_std, model.predict(x_test_std), color='red')
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plt.show()
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print('Персептрон')
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print('Оценка качества:', model.score(x_train, y_train))
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x, y = make_circles(noise=0.2, factor=0.5, random_state=10)
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x = x[:, np.newaxis, 1]
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x = StandardScaler().fit_transform(x)
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.5, random_state=42)
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lineal(x_test, y_test, x_train, y_train)
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polynomial(x_train, y_train)
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perceptron(x_test, x_train, y_train)
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