Python_machinelearning/regresionpolinomial.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
###############################
#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_poly = poly_features.fit_transform(X)
lin_reg = LinearRegression()
lin_reg.fit(X_poly, y)
yout=lin_reg.predict(X_poly)
plt.plot(X,yout,"*")
plt.show()