48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
from matplotlib import pyplot as plt
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from sklearn.linear_model import LinearRegression
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from sklearn.linear_model import Ridge
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import PolynomialFeatures
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def lin(x_train, x_test, y_train, y_test):
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plt.scatter(x_test, y_test)
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model = LinearRegression().fit(x_train, y_train)
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y_predict = model.intercept_ + model.coef_ * x_test
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plt.plot(x_test, y_predict, color='red')
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plt.title('Линейная регрессия')
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plt.savefig('images/linear.png')
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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 polynom(x_train, y_train):
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plt.scatter(x_train, y_train)
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x_poly = PolynomialFeatures(degree=4).fit_transform(x_train)
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pol_reg = LinearRegression()
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model = pol_reg.fit(x_poly, y_train)
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y_predict = pol_reg.predict(x_poly)
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plt.plot(x_train, y_predict, color='green')
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plt.title('Полиномиальная регрессия')
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plt.savefig('images/polynomial.png')
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plt.show()
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print('Полиномиальная регрессия')
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print('Оценка качества:', model.score(x_poly, y_train))
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def greb_polynom(x_train, x_test, y_train, y_test):
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plt.scatter(x_test, y_test)
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pipeline = Pipeline([("polynomial_features", PolynomialFeatures(degree=4)), ("ridge", Ridge(alpha=1.0))])
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model = pipeline.fit(x_train, y_train)
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y_predict = pipeline.predict(x_test)
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plt.plot(x_test, y_predict, color='blue')
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plt.title('Гребневая полиномиальная регрессия')
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plt.savefig('images/greb_polynom.png')
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plt.show()
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print('Гребневая полиномиальная регрессия')
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print('Оценка качества:', model.score(x_train, y_train))
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