lipatov_ilya_lab_1

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lipatov_ilya_lab_1/README.md

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+## Лабораторная работа №1
+
+### Работа с типовыми наборами данных и различными моделями
+
+## Выполнил студент группы ПИбд-41 Липатов Илья
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (стартовая точка класс lab1)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* Генерирует набор данных типа с помощью make_circles(noise=0.2, factor=0.5, random_state=4)
+* Сравнивает три типа моделей: линейную, полиномиальную (степень 4) и персептрон
+
+### Примеры работы:
+
+#### Результаты:
+* Линейная регрессия, оценка качества: 0.0494206358498015
+* Полиноминальная регрессия, оценка качества: 0.4480860719638978
+* Персептрон, оценка качества: 0.52
+
+#### Самый лучший результат показал персептрон - 0.52
+
+#### График линейной, полиномиальной, персептрон:
+
+![Lineal](lineal.png)
+![Polynomial](polynomial.png)
+![Perceptron](perceptron.png)

lipatov_ilya_lab_1/lab1.py

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+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import Perceptron
+from sklearn.datasets import make_circles
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def polynomial(x_train, y_train):
+    model = PolynomialFeatures(degree=4).fit(x_train, y_train)
+    x_poly = model.fit_transform(x_train)
+    lin = LinearRegression()
+    lin.fit(x_poly, y_train)
+    plt.scatter(x_train, y_train, color='green')
+    plt.plot(x_train, lin.predict(x_poly), color='red')
+    plt.show()
+    print('Полиноминальная регрессия')
+    print('Оценка качества:', lin.score(x_poly, y_train))
+
+
+def lineal(x, y, x_train, y_train):
+    model = LinearRegression().fit(x_train, y_train)
+    plt.scatter(x, y, color='green')
+    plt.plot(x, model.predict(x), color='red')
+    plt.show()
+    print('Линейная регрессия')
+    print('Оценка качества:', model.score(x_train, y_train))
+
+
+def perceptron(x_test, x_train, y_train):
+    sc = StandardScaler()
+    sc.fit(x_train)
+    x_train_std = sc.transform(x_train)
+    x_test_std = sc.transform(x_test)
+    model = Perceptron(eta0=0.1, random_state=1).fit(x_train_std, y_train)
+    plt.scatter(x_train, y_train, color='green')
+    plt.plot(x_test_std, model.predict(x_test_std), color='red')
+    plt.show()
+    print('Персептрон')
+    print('Оценка качества:', model.score(x_train, y_train))
+
+
+x, y = make_circles(noise=0.2, factor=0.5, random_state=10)
+x = x[:, np.newaxis, 1]
+x = StandardScaler().fit_transform(x)
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.5, random_state=42)
+
+lineal(x_test, y_test, x_train, y_train)
+polynomial(x_train, y_train)
+perceptron(x_test, x_train, y_train)