|
|
@ -0,0 +1,21 @@ |
|
|
|
|
|
from sklearn.linear_model import Ridge |
|
|
|
|
|
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,".") |
|
|
|
|
|
############################### |
|
|
|
|
|
poly_features = PolynomialFeatures(degree=2, include_bias=False) |
|
|
|
|
|
X_pol = poly_features.fit_transform(X) |
|
|
|
|
|
ridge_reg = Ridge(alpha=1, solver="cholesky") |
|
|
|
|
|
ridge_reg.fit(X_pol, y) |
|
|
|
|
|
yout=ridge_reg.predict(X_pol) |
|
|
|
|
|
print(ridge_reg.predict(1.5,2)) |
|
|
|
|
|
plt.plot(X,yout,"*") |
|
|
|
|
|
plt.show() |
