线性回归&&code
1 # -*- coding: utf-8 -*- 2 3 import numpy as np 4 import matplotlib.pyplot as plt 5 from certifi import __main__ 6 7 def cost(x,y,theta=np.zeros((2,1))): 8 m=len(y); 9 J=1.0/(2*m)*sum((x.dot(theta).flatten()-y)**2); 10 return J; 11 12 def gradientDesc(x,y,theat=np.zeros((2,1)),alpha=0.001,iterations=1500): 13 m=len(y) 14 J=[] 15 for i in xrange(iterations): 16 a=theat[0][0]-alpha*(1.0/m)*sum((x.dot(theat).flatten()-y)*x[:,0]); 17 b=theat[1][0]-alpha*(1.0/m)*sum((x.dot(theat).flatten()-y)*1); 18 theat[0][0],theat[1][0]=a,b 19 print theat[0][0], theat[1][0] 20 print cost(x, y, theat); 21 22 return theat; 23 24 if __name__=="__main__": 25 x=np.array([[9,1],[15,1],[25,1],[14,1],[10,1],[18,1]]); 26 y=np.array([39,56,93,61,50,75]); 27 ans=gradientDesc(x, y); 28 xx=[1,30] 29 yy=[ans[0][0]*1+ans[1][0],ans[0][0]*30+ans[1][0]] 30 plt.plot(xx,yy) 31 plt.scatter(x[:,0],y) 32 plt.show() 33 34 print 'end' 35 36 #显示数据 37 ''' 38 plt.scatter(x,y); 39 plt.show(); 40 '''
结果显示
