2、Logistic Regression求解classification问题 c代码

青旅半醒 2022-08-20 12:18 10阅读 0赞

\#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 = ![sum.wiii.gif][]

Instead of [threshold, and fire/not fire][threshold_ and fire_not fire],  
we could have *continuous* output y according to the *sigmoid* function:设定了一个sigmoid函数

> ![sigmoid.3.gif][]

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

![sigmoid.gif][]

[sum.wiii.gif]: http://computing.dcu.ie/~humphrys/Notes/Neural/Bitmaps/sum.wiii.gif
[threshold_ and fire_not fire]: http://computing.dcu.ie/~humphrys/Notes/Neural/single.neural.html#rule
[sigmoid.3.gif]: http://computing.dcu.ie/~humphrys/Notes/Neural/Bitmaps/sigmoid.3.gif
[e]: http://en.wikipedia.org/wiki/E_%28mathematical_constant%29
[properties]: http://en.wikipedia.org/wiki/E_%28mathematical_constant%29#Properties
[sigmoid.gif]: http://computing.dcu.ie/~humphrys/Notes/Neural/Bitmaps/sigmoid.gif