svker.cpp

用VC++编写的若干个机器学习中的核函数

#include"stdio.h"
#include"malloc.h"//dynamic  storing
#include"stdlib.h"
#include"math.h"

#define M 5
#define N 2

#define linear L
#define poly   P
#define rbf    R


void svkernel(double **,double **,double **,double **,char ch,int p0,int Dx,int Dy);
void linear_op(double **,double **,double **,double **,int Dx,int Dy);
void poly_op(double **,double **,double **,double **,int p1,int Dx,int Dy);
void rbf_op(double **,double **,double **,double **,int p1,int Dx,int Dy);


main()
{
double **u,**v,**VT,**K;
int i,j,p;
int Width=0,Height=0;

u=(double **)malloc(M*sizeof(double *));//apply for 2d memory space 
for(i=0;i<M;i++)
	u[i]=(double*)malloc(N*sizeof(double));

v=(double **)malloc(M*sizeof(double *));
for(i=0;i<M;i++)
	v[i]=(double*)malloc(N*sizeof(double));

VT=(double **)malloc(N*sizeof(double *));
for(i=0;i<N;i++)
	VT[i]=(double*)malloc(M*sizeof(double));

K=(double **)malloc(M*sizeof(double *));
for(i=0;i<M;i++)
	K[i]=(double*)malloc(M*sizeof(double));


Width=N;
Height=M;

for(i=0;i<Height;i++){
	for(j=0;j<Width;j++){
		u[i][j]=rand()%(10)+1;
		v[i][j]=rand()%(20)+1;
	}
}
p=2;

svkernel(u,v,VT,K,'P',p,Width,Height);
free(u);
free(v);
free(VT);
free(K);
}

void svkernel(double **u,double **v,double **VT,double **K,char ch,int p0,int Dx,int Dy)//kernel operation
{


	switch(ch)
	{
	case 'L':
		linear_op(u,v,VT,K,Dx,Dy);//linear kernel
		break;
	case 'P':
		poly_op(u,v,VT,K,p0,Dx,Dy);//polynomial kernel
		break;
	case 'R':
		rbf_op(u,v,VT,K,p0,Dx,Dy);//rbf kernel
		break;

	}

}


void linear_op(double **u ,double **v ,double **VT,double **K,int Dx,int Dy)//linear kernel
{
int i,j,k;
double dot_m=0.0;

for(i=0;i<Dy;i++)//transpose
{
	for(j=0;j<Dx;j++)
	{
		VT[j][i]=v[i][j];
	}
}

for(i=0;i<Dy;i++)
{
	for(j=0;j<Dy;j++)//dot product
	{
		for(k=0;k<Dx;k++)
		{
			dot_m+=u[i][k]*VT[k][j];
		}
		K[i][j]=dot_m;
		printf("%20f",K[i][j]);

	}
}

}


void poly_op(double **u,double **v,double **VT,double **K,int p1,int Dx,int Dy)//polynomial kernel
{
int i,j,k,l;
double dot_m_p=0.0;

linear_op(u,v,VT,K,Dx,Dy);//linear kernel is used


for(l=0;l<p1;l++)
{

for(i=0;i<Dy;i++)//dot product
{
	for(j=0;j<Dy;j++)
	{
		for(k=0;k<Dy;k++)
		{
			dot_m_p+=K[i][k]*K[k][j];
		}
		K[i][j]=dot_m_p;
		printf("%20f",K[i][j]);
	}
}
}

}


void rbf_op(double **u,double **v,double **VT,double **K,int p1,int Dx,int Dy)//rbf kernel 
{
int i,j,k;
double dot_m_r=0.0;
double **UV,**UVT;

UV=(double **)malloc(Dy*sizeof(double *));//apply for 2d memory space 
for(i=0;i<Dy;i++)
	UV[i]=(double*)malloc(Dx*sizeof(double));

UVT=(double **)malloc(Dx*sizeof(double *));
for(i=0;i<Dx;i++)
	UVT[i]=(double*)malloc(Dy*sizeof(double));

for(i=0;i<Dy;i++)//the deduction of two kinds of data
{ 
	for(j=0;j<Dx;j++)
	{
		UV[i][j]=u[i][j]-v[i][j];
	}
}

for(i=0;i<Dy;i++)//transpose
{
	for(j=0;j<Dx;j++)
	{
		UVT[j][i]=UV[i][j];
	}
}

for(i=0;i<Dy;i++)
{
	for(j=0;j<Dy;j++)
	{
		for(k=0;k<Dx;k++)//dot product
		{
			dot_m_r+=UV[i][k]*UVT[k][j];
		}
		K[i][j]=exp(-dot_m_r)/(2*p1*p1);
		printf("%20f",K[i][j]);
	}
}
free(UV);
free(UVT);
}