64 lines
2.7 KiB
Python
64 lines
2.7 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, Perceptron
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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_circles
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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_circles():
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x, y = make_circles(noise=0.2, factor=0.5, random_state=0)
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X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
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linear_regression(X_train, X_test, y_train, y_test)
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perceptron(X_train, X_test, y_train, y_test)
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ridge_regression(X_train, X_test, y_train, y_test)
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def linear_regression(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 perceptron(x_train, x_test, y_train, y_test):
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model = Perceptron()
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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('Персептрон')
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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=0.8)
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plt.plot(x_test, y_predict, color='red', linewidth=1)
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plt.show()
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print('Персептрон')
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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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def ridge_regression(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_circles()
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