N久没写过东西了..写个最近在研究的程序

 import numpy as np
 import matplotlib.pyplot as plt
 #a = np.matrix([[1,1.15],[1,1.9],[1,3.06],[1,4.66],[1,6.84],[1,7.95]])
 #b = np.matrix([[1.37],[2.4],[3.02],[3.06],[4.22],[5.42]])
 a = np.matrix([[1,2.104],  [1,1.416],      [1,1.534],      [1,0.852]])
 b = np.matrix([[0.460], [0.232], [0.315], [0.178]])
 xdata = a[:,1]
 ydata = b
 tempp =[]
 def gradient(X_norm,y,theta,alpha,m,num_it):
     temp=np.matrix([[float(0)],[float(0)]])
     for i in range(0,num_it):
         h=np.dot(X_norm,theta)
         temp[0]=theta[0]-alpha/m*(np.sum(h-y))
         temp[1]=theta[1]-alpha/m*((h-y).T*X_norm[:,1])
         theta=temp
         if i%100 == 0:
             ypredict = xdata * temp[1] + temp[0]
             tempp.append(plt.plot(xdata,ypredict))
     return theta
 c = gradient(a,b,np.matrix([[float(0)],[float(0)]]),float(0.2),float(4),1000)
 #ypredict=xdata*c[0]+c[1]
 print c
 plt.scatter(xdata,ydata)
 plt.legend(tempp)
 plt.show()

程序之后，就是结果：