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- #!/usr/bin/python
- # -*- coding: UTF-8 -*-
- import sys
- reload(sys)
- sys.setdefaultencoding('utf-8')
- import random
- import math
- '''
- 逻辑回归的mse即KL距离
- '''
- def logistic(x,w):
- d=sum([ s1*s2 for [s1,s2] in zip(x,w)])
- r=1.0/(1+math.exp(-1*d))
- return r
- def cal_entropy(s1,s2):
- s2=min(0.999,max(s2,0.001))
- if s1==0:
- return math.log(1.0/(1-s2))
- else:
- return math.log(1.0/s2)
- def cal_error(data,w):
- y2=[ logistic(x,w) for [x,_] in data]
- y=[s[1][0] for s in data]
- mse=sum([ (s1-s2)*(s1-s2) for [s1,s2] in zip(y,y2)])/len(data)
- entropy=sum([ cal_entropy(s1,s2) for [s1,s2] in zip(y,y2)])/len(data)
- return mse,entropy
- def read_data(path):
- with open(path) as f :
- lines=f.readlines()
- lines=[eval(line.strip()) for line in lines]
- return lines
- data=read_data("train_data")
- results=[]
- num=100
- step=0.2
- for i in range(-num,num):
- w1=1.87+step*i
- #for j in range(-num,num):
- w2=-1.87#+step*j
- e1,e2=cal_error(data,[w1,w2])
- results.append("{},{},{}".format(w1,e1,e2))
- print "{},{},{}".format(w1,e1,e2)
- # with open("mse_entropy.csv","w") as f :
- # f.writelines("\n".join(results))
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