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