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29 lines
795 B
29 lines
795 B
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()
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