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#include "ranksvmtn.h"
using namespace std;
using namespace Eigen;
const int maxiter = 10;
const double prec=1e-3;
// Calculate objfunc gradient & support vectors
int objfunc_linear(const VectorXd &w,const double C,const VectorXd &pred,const VectorXd &grad, double &obj,MatrixXd &sv)
{
pred = pred.cwiseMax(Matrix::Zero(pred.rows(),pred.cols()));
obj = (pred.cwiseProduct(pred)*(C/2)) + w.transpose()*w/2;
grad = w - (((pred*C).transpose()*A)*w).transpose();
for (int i=0;i<pred.cols();++i)
if (pred(i)>0)
sv(i,i)=1;
else
sv(i,i)=0;
}
// line search
int line_search(const VectorXd &w,const double C,const VectorXd &step,VectorXd &pred,double &t)
{
}
int RSVMTN::train(DataSet &D, Labels &label){
int iter = 0;
MatrixXd A;
int n=D.rows();
LOG(INFO) << "training with feature size:" << fsize << " Data size:" << n;
MatrixXd sv=MatrixXd::Identity(n, n);
VectorXd grad(fsize);
VectorXd step(fsize);
VectorXd pred(n);
double obj,t;
pred=VectorXd::Ones(n) - (A*(D*model.weight));
while (true)
{
iter+=1;
if (iter> maxiter)
{
LOG(INFO)<< "Maxiter :"<<maxiter<<" reached";
break;
}
// Generate support vector matrix sv & gradient
objfunc_linear(D,1,pred,grad,obj,sv);
model.weight=model.weight+step*t;
// When dec is small enough
if (-step.dot(grad) < prec * obj)
break;
}
return 0;
};
int RSVMTN::predict(DataSet &D, Labels &res){
res = model.weight * D;
for (int i=0;i<res.cols();++i)
res[i] = (res[i] + model.beta);
return 0;
};
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