LR 逻辑回归代码 （梯度下降）

这几天一直在推导逻辑回归的公式，就自己写了个，发现动手写和公式还是有点区别的。

公式推导就不赘述了，反正没有解析解，就只能梯度下降呢，不过后面会优化成随机梯度和牛顿法来处理，毕竟批梯度下降对于大数据那就是不行的呢。

代码如下，这里稍微参考了下别人的代码，主要是关于权重更新，矩阵的运算，开始参考公式，老是有问题。

#coding=utf-8
#!/usr/bin/python
import pprint
from numpy import *
import matplotlib.pyplot as plt
def load_data(path):
'''
:param path:传递路径,返回样例的数据和标签,格式采用矩阵,便于进行矩阵运算
:return:
'''
data_set=[]
label_set=[]
file_object=open(path)
for line in file_object.readlines():
lineArr = line.strip().split()
lineArr = [float(x) for x in lineArr ]
label_set.append(int(lineArr[-1])) #最后一列默认为标记
lineArr[-1]=1#这里添加1,是因为又一个常数变量w和b合并在一起.
data_set.append(lineArr)
#归一化操作,暂时不动,后续添加
#data_set[0]=(data_set[0]-min(data_set[0]))/(max(data_set[0])-min(data_set[0]))
return data_set,label_set

class myLRregression(object):
def __init__(self):
'''
:这里先初始化数据和权重
'''
self.data_set=[]
self.label_set=[]
self.weight=[]

def sigmoid(self, inX):
'''
:param inX:sigmod 函数
:return:
'''
return 1.0/(1+exp(-inX))

def load_data(self,path):
'''
:param path:传递路径,返回样例的数据和标签,格式采用矩阵,便于进行矩阵运算
:return:
'''
file_object=open(path)
for line in file_object.readlines():
lineArr = line.strip().split()
lineArr = [float(x) for x in lineArr ]
self.label_set.append(int(lineArr[-1])) #最后一列默认为标记
lineArr[-1]=1#这里添加1,是因为又一个常数变量w和b合并在一起.
self.data_set.append(lineArr)
def get_data_set(self):
pprint.pprint(self.data_set)
def train(self,train_fun):
'''
:return:返回训练好的权重
'''
max_iter=20000 #最大迭代次数
alpha=0.01   #设置变量为0.01
data_set=mat(self.data_set)   #转换为矩阵,进行计算
lable_set=mat(self.label_set).transpose()
weights = mat(ones((3,1))) #权重初始化为1

print weights

if(train_fun=="gradDescent"):
for i in range(max_iter):
loss=lable_set-self.sigmoid(data_set*weights) #这的矩阵运算稍微注意下
weights=weights+alpha*data_set.transpose()*loss
print weights

return weights,data_set,lable_set
#self.weight=list(weights)

if __name__ == '__main__':
print("-------start load data-----")
path="./LR/testSet.txt"
LR=myLRregression()
LR.load_data(path)
weights,data_set,lable_set=LR.train(train_fun='gradDescent')
pprint.pprint( LR.weight)
pprint.pprint( LR.data_set)
pprint.pprint(LR.label_set)

测试数据如下：

-0.017612 14.053064
0

-1.395634 4.662541
1

-0.752157 6.538620
0

-1.322371 7.152853
0

0.423363 11.054677
0

0.406704 7.067335
1

0.667394 12.741452
0

-2.460150 6.866805
1

0.569411 9.548755
0

-0.026632 10.427743
0

0.850433 6.920334
1

1.347183 13.175500
0

1.176813 3.167020
1

-1.781871 9.097953
0

-0.566606 5.749003
1

0.931635 1.589505
1

-0.024205 6.151823
1

-0.036453 2.690988
1

-0.196949 0.444165
1

1.014459 5.754399
1

1.985298 3.230619
1

-1.693453 -0.557540
1

-0.576525 11.778922
0

-0.346811 -1.678730
1

-2.124484 2.672471
1

1.217916 9.597015
0

-0.733928 9.098687
0

-3.642001 -1.618087
1

0.315985 3.523953
1

1.416614 9.619232
0

-0.386323 3.989286
1

0.556921 8.294984
1

1.224863 11.587360
0

-1.347803 -2.406051
1

1.196604 4.951851
1

0.275221 9.543647
0

0.470575 9.332488
0

-1.889567 9.542662
0

-1.527893 12.150579
0

-1.185247 11.309318
0

-0.445678 3.297303
1

1.042222 6.105155
1

-0.618787 10.320986
0

1.152083 0.548467
1

0.828534 2.676045
1

-1.237728 10.549033
0

-0.683565 -2.166125
1

0.229456 5.921938
1

-0.959885 11.555336
0

0.492911 10.993324
0

0.184992 8.721488
0

-0.355715 10.325976
0

-0.397822 8.058397
0

0.824839 13.730343
0

1.507278 5.027866
1

0.099671 6.835839
1

-0.344008 10.717485
0

1.785928 7.718645
1

-0.918801 11.560217
0

-0.364009 4.747300
1

-0.841722 4.119083
1

0.490426 1.960539
1

-0.007194 9.075792
0

0.356107 12.447863
0

0.342578 12.281162
0

-0.810823 -1.466018
1

2.530777 6.476801
1

1.296683 11.607559
0

0.475487 12.040035
0

-0.783277 11.009725
0

0.074798 11.023650
0

-1.337472 0.468339
1

-0.102781 13.763651
0

-0.147324 2.874846
1

0.518389 9.887035
0

1.015399 7.571882
0

-1.658086 -0.027255
1

1.319944 2.171228
1

2.056216 5.019981
1

-0.851633 4.375691
1

-1.510047 6.061992
0

-1.076637 -3.181888
1

1.821096 10.283990
0

3.010150 8.401766
1

-1.099458 1.688274
1

-0.834872 -1.733869
1

-0.846637 3.849075
1

1.400102 12.628781
0

1.752842 5.468166
1

0.078557 0.059736
1

0.089392 -0.715300
1

1.825662 12.693808
0

0.197445 9.744638
0

0.126117 0.922311
1

-0.679797 1.220530
1

0.677983 2.556666
1

0.761349 10.693862
0

-2.168791 0.143632
1

1.388610 9.341997
0

0.317029 14.739025
0