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lipatov_ilya_lab_1/README.md

@@ -1,35 +0,0 @@
-## 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

@@ -1,52 +0,0 @@
-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)