import numpy as np

X = 2 * np.random.rand(100, 1)

y = 4 + 3 * X + np.random.randn(100, 1)

X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance

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] # add x0 = 1 to each instance

y_predict = X_new_b.dot(theta_best)

y_predict

scikit learn庫

from sklearn.linear_model import LinearRegression

lin_reg = LinearRegression()

lin_reg.fit(X, y)

lin_reg.intercept_, lin_reg.coef_

lin_reg.predict(X_new)