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from sklearn.linear_model import ElasticNet |
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from sklearn.preprocessing import PolynomialFeatures |
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import numpy as np |
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import matplotlib.pyplot as plt |
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############################### |
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#Datos originales |
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############################### |
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m = 100 |
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X = 6 * np.random.rand(m, 1) - 3 |
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y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1) |
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plt.plot(X,y,".", label = "Datos originales") |
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############################### |
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poly_features = PolynomialFeatures(degree=2, include_bias=False) |
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X_pol = poly_features.fit_transform(X) |
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elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5) |
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elastic_net.fit(X_pol, y) |
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yout=elastic_net.predict(X_pol) |
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plt.plot(X,yout,"*", label = "Predicciones") |
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# naming the x axis |
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plt.xlabel('Eje X') |
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# naming the y axis |
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plt.ylabel('Eje Y') |
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# giving a title to my graph |
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plt.legend() |
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plt.show() |
