| @ -0,0 +1,23 @@ | |||||
| 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() | |||||