SVM svm_predict_values详解

void svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values)

{

if(model->param.svm_type == ONE_CLASS ||

  model->param.svm_type == EPSILON_SVR ||

  model->param.svm_type == NU_SVR)

{

double *sv_coef = model->sv_coef[0];

double sum = 0;

for(int i=0;il;i++)

sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param);

sum -= model->rho[0];

*dec_values = sum;

}

else

{

int i;

int nr_class = model->nr_class;

int l = model->l;

double *kvalue = Malloc(double,l);

for(i=0;i

{

kvalue[i] = Kernel::k_function(x,model->SV[i],model->param);

// printf("kvalue[%d]: %lf \n",i,kvalue[i]) ;

}

int *start = Malloc(int,nr_class);

start[0] = 0;

for(i=1;i

start[i] = start[i-1]+model->nSV[i-1];   //正负支持向量的个数

int p=0;

for(i=0;i

for(int j=i+1;j

{

double sum = 0;

int si = start[i];

int sj = start[j];

int ci = model->nSV[i];

int cj = model->nSV[j];

int k;

double *coef1 = model->sv_coef[j-1];

double *coef2 = model->sv_coef[i];

for(k=0;k

sum += coef1[si+k] * kvalue[si+k];

for(k=0;k

sum += coef2[sj+k] * kvalue[sj+k];

sum -= model->rho[p];

dec_values[p] = sum;

p++;

}

free(kvalue);

free(start);

}

}

假如有n类，则上述循环每次乘的系数如下表所示，其中(1,2)代表当第一类和第二类比较时，与第一类对应的向量乘的系数，(1,2)代表当第一类和第二类比较时，与第二类对应的向量乘的系数，依次类推。且(1,2)是一个列向量，如果第一类有m个向量与之对应，则(1,2)就是一个m维的列向量。

(1,2)	(1,3)	(1,4)	(1,5)	⋯	(1,n)
(2,1)	(2,3)	(2,4)	(2,5)	⋯	(3,n)
(3,1)	(3,2)	(3,4)	(3,5)	⋯	(4,n)
(4,1)	(4,2)	(4,3)	(4,5)	⋯	(5,n)
(5,1)	(5,2)	(5,3)	(5,4)	⋯	(6,n)
⋮	⋮	⋮	⋮	⋱	⋮
(n,1)	(n,2)	(n,3)	(n,4)	⋯	(n,n-1)