python实现最小二乘法线性拟合

import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegression n = 101 x = np.linspace(0,10,n)noise = np.random.randn(n)y = 2.5 * x + 0.8 + 2.0 * noise def self_func(steps=100, alpha=0.01):  w = 0.5  b = 0  alpha = 0.01  for i in range(steps):    y_hat = w*x + b    dy = 2.0*(y_hat - y)    dw = dy*x    db = dy    w = w - alpha*np.sum(dw)/n    b = b - alpha*np.sum(db)/n    e = np.sum((y_hat-y)**2)/n    #print (i,'W=',w,'/tb=',b,'/te=',e)  print ('self_func:/tW =',w,'/n/tb =',b)  plt.scatter(x,y)  plt.plot(np.arange(0,10,1), w*np.arange(0,10,1) + b, color = 'r', marker = 'o', label = 'self_func(steps='+str(steps)+', alpha='+str(alpha)+')') def skl_func():  lr = LinearRegression()  lr.fit(x.reshape(-1,1),y)  y_hat = lr.predict(np.arange(0,10,0.75).reshape(-1,1))  print('skl_fun:/tW = %f/n/tb = %f'%(lr.coef_,lr.intercept_))  plt.plot(np.arange(0,10,0.75), y_hat, color = 'g', marker = 'x', label = 'skl_func')  self_func(10000)skl_func()plt.legend(loc='upper left')plt.show()