66 lines
2.9 KiB
Python
66 lines
2.9 KiB
Python
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn.linear_model import LinearRegression, Ridge
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.datasets import make_moons
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from sklearn import metrics
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cm_bright = ListedColormap(['#8B0000', '#FF0000'])
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cm_bright1 = ListedColormap(['#FF4500', '#FFA500'])
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def create_moons():
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x, y = make_moons(noise=0.3, random_state=0)
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X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.4, random_state=42)
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linear_regretion(X_train, X_test, y_train, y_test)
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polynomial_regretion(X_train, X_test, y_train, y_test)
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ridge_regretion(X_train, X_test, y_train, y_test)
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def linear_regretion(x_train, x_test, y_train, 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.title('Линейная регрессия')
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print('Линейная регрессия')
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plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
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plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7)
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plt.plot(x_test, y_predict, color='red')
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print('MAE', metrics.mean_absolute_error(y_test, y_predict[:, 1]))
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print('MSE', metrics.mean_squared_error(y_test, y_predict[:, 1]))
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plt.show()
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def polynomial_regretion(x_train, x_test, y_train, y_test):
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polynomial_features = PolynomialFeatures(degree=3)
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X_polynomial = polynomial_features.fit_transform(x_train, y_train)
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base_model = LinearRegression()
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base_model.fit(X_polynomial, y_train)
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y_predict = base_model.predict(X_polynomial)
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plt.title('Полиномиальная регрессия')
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plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
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plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7)
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plt.plot(x_train, y_predict, color='blue')
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plt.show()
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print('Полиномиальная регрессия')
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print('MAE', metrics.mean_absolute_error(y_train, y_predict))
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print('MSE', metrics.mean_squared_error(y_train, y_predict))
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def ridge_regretion(X_train, X_test, y_train, y_test):
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model = Pipeline([('poly', PolynomialFeatures(degree=3)), ('ridge', Ridge(alpha=1.0))])
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model.fit(X_train, y_train)
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y_predict = model.predict(X_test)
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plt.title('Гребневая полиномиальная регрессия')
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7)
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plt.plot(X_test, y_predict, color='blue')
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plt.show()
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print('Гребневая полиномиальная регрессия')
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print('MAE', metrics.mean_absolute_error(y_test, y_predict))
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print('MSE', metrics.mean_squared_error(y_test, y_predict))
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create_moons()
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