线性回归&&code

 # -*- coding: utf-8 -*-

 import numpy as np
 import matplotlib.pyplot as plt
 from certifi import __main__

 def cost(x,y,theta=np.zeros((2,1))):
    m=len(y);
    J=1.0/(2*m)*sum((x.dot(theta).flatten()-y)**2);
    return J;

 def gradientDesc(x,y,theat=np.zeros((2,1)),alpha=0.001,iterations=1500):
     m=len(y)
     J=[]
     for i in xrange(iterations):
         a=theat[0][0]-alpha*(1.0/m)*sum((x.dot(theat).flatten()-y)*x[:,0]);
         b=theat[1][0]-alpha*(1.0/m)*sum((x.dot(theat).flatten()-y)*1);
         theat[0][0],theat[1][0]=a,b
         print theat[0][0], theat[1][0]
         print cost(x, y, theat);

     return theat;

 if __name__=="__main__":
     x=np.array([[9,1],[15,1],[25,1],[14,1],[10,1],[18,1]]);
     y=np.array([39,56,93,61,50,75]);
     ans=gradientDesc(x, y);
     xx=[1,30]
     yy=[ans[0][0]*1+ans[1][0],ans[0][0]*30+ans[1][0]]
     plt.plot(xx,yy)
     plt.scatter(x[:,0],y)
     plt.show()

     print 'end'

 #显示数据
 '''
 plt.scatter(x,y);
 plt.show();
 '''

结果显示