Python_machinelearning/Regresionlinealmatricial.py

import numpy as np
import matplotlib.pyplot as plt

###############################
#Datos originales
###############################
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100,1)

plt.plot(X,y,".")
###############################

X_b = np.c_[np.ones((100,1)), X] #Se agrega x0=1 para cada instancia
theta_best=np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)

X_new = np.array([[0], [2]])
X_new_b = np.c_[np.ones((2, 1)), X_new] #Se agrega x0=1 para cada instancia
y_predict = X_new_b.dot(theta_best)

plt.plot(X_new, y_predict, "r-")
plt.plot(X, y, "b.")
plt.axis([0, 2, 0, 15])
plt.show()