2、Logistic Regression求解classification问题 c代码

#include "stdio.h"

#include "math.h"

double x[6][4]={{1.0,47.0,76.0,24.0}, //include x0=1

{1.0,46.0,77.0,23.0},

{1.0,48.0,74.0,22.0},

{1.0,34.0,76.0,21.0},

{1.0,35.0,75.0,24.0},

{1.0,34.0,77.0,25.0},

};

double y[]={1.0,1.0,1.0,0.0,0.0,0.0};

double theta[]={1.0,1.0,1.0,1.0}; // include theta0

int i,j,k;

double ez,gz,gh;

double fun_g(double z){//重点是定义函数g(z),之后可以调用

ez= pow(2.718,z);

gz=ez/(1+ez);

return gz;

}

int main(){

double fun_g(double z);

for(i=1;i<1000;i++){

for(j=0;j<6;j++){

double h=0.0;

for(k=0;k<4;k++){//记住j,k都从0开始

h=h+theta[k]*x[j][k];

}

gh=fun_g(h) ;

double error=0.0;

error=y[j]-gh;

for(k=0;k<4;k++){

theta[k]=theta[k]+0.01*error*x[j][k];

}

printf("%lf %lf %lf %lf\t",theta[0],theta[1],theta[2],theta[3]);

}

}

}

logistics回归和线性回归的参数迭代学习规则形式完全一样，但意义不同，logistics本质上是梯度上升，logistics回归是凹函数，找局部最大值，一定是全局最大值

logistics回归用来处理分类问题，处理离散数据
http://blog.csdn.net/pennyliang/article/details/7045372
【这是别人Logistic Regression求解classification问题 的代码】

Given Summed Input:

x =


Instead of threshold, and fire/not fire,

we could have continuous output y according to the sigmoid function:设定了一个sigmoid函数



Note e and its properties.

As x goes to minus infinity, y goes to 0 (tends not to fire).

As x goes to infinity, y goes to 1 (tends to fire):

At x=0, y=1/2