import numpy as np

 import matplotlib.pyplot as plt

 __author__ = 'zhen'

 # 这里相当于是随机X维度X1，rand是随机均匀分布

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

 # 人为的设置真实的Y一列，np.random.randn(100, 1)是设置error，randn是标准正太分布

 y = 3 + 6 * X + np.random.randn(100, 1)

 # 整合X0和X1

 X_b = np.c_[np.ones((100, 1)), X] #combine聚合两数据集

 # print(X_b)

 # 常规等式求解theta

 # invert

 theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)

 print(theta_best)

 # 创建测试集里面的X1

 X_new = np.array([[0], [2]])

 X_new_b = np.c_[(np.ones((2, 1))), X_new]

 print(X_new_b)

 y_predict = X_new_b.dot(theta_best)

 print(y_predict)

 plt.plot(X_new, y_predict, 'r-')

 plt.plot(X, y, 'b.')

 plt.axis([0, 2, 0, 15])

 plt.show()

 import numpy as np

 import matplotlib.pyplot as plt

 from sklearn.linear_model import LinearRegression

 __author__ = 'zhen'

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

 y = 3 + 6 * X + np.random.randn(100, 1)

 lin_reg = LinearRegression()

 lin_reg.fit(X, y)

 print(lin_reg.intercept_, lin_reg.coef_)

 X_new = np.array([[0], [2]])

 y_predict = lin_reg.predict(X_new)

 print(y_predict)

 # 可视化

 plt.plot(X_new, y_predict, 'r-')

 plt.plot(X, y, 'b.')

 plt.axis([0, 2, 0, 15])

 plt.show()