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