void RecursiveRLSCholUpdateWrapper<T>::update(const gMat2D<T> &X, const gMat2D<T> &y)
{
if(!this->trainedModel())
throw gException("Error, Train Model First");
RLSPrimalRecUpdateCholesky<T> optimizer;
const unsigned long d = X.getSize();
const unsigned long t = y.getSize();
gMat2D<T>X_mat(1, d);
copy(X_mat.getData(), X.getData(), d);
gMat2D<T>y_mat(1, t);
copy(y_mat.getData(), y.getData(), t);
GurlsOptionsList* ret = optimizer.execute(X_mat, y_mat, *(this->opt));
this->opt->removeOpt("optimizer");
this->opt->addOpt("optimizer", ret);
}
T GurlsWrapper<T>::eval(const gVec<T> &X, unsigned long *index)
{
if(!trainedModel())
throw gException("Error, Train Model First");
gMat2D<T>X_mat(1, X.getSize());
copy(X_mat.getData(), X.getData(), X.getSize());
gMat2D<T>* pred_mat = eval(X_mat);
const T* pred = pred_mat->getData();
const unsigned long size = pred_mat->getSize();
const T* max = std::max_element(pred, pred+size);
T ret = *max;
if(index != NULL)
*index = max-pred;
delete pred_mat;
return ret;
}
void RecursiveRLSWrapper<T>::update(const gVec<T> &X, const gVec<T> &y)
{
if(!this->trainedModel())
throw gException("Error, Train Model First");
RLSPrimalRecUpdate<T> optimizer;
const unsigned long d = X.getSize();
const unsigned long t = y.getSize();
gMat2D<T>X_mat(1, d);
copy(X_mat.getData(), X.getData(), d);
gMat2D<T>y_mat(1, t);
copy(y_mat.getData(), y.getData(), t);
GurlsOptionsList* ret = optimizer.execute(X_mat, y_mat, *(this->opt));
this->opt->removeOpt("optimizer");
this->opt->addOpt("optimizer", ret);
++nTot;
gMat2D<T>* xtx = new gMat2D<T>(d,d);
gMat2D<T>* xty = new gMat2D<T>(d,t);
dot(X.getData(), X.getData(), xtx->getData(), 1, d, 1, d, d, d, CblasTrans, CblasNoTrans, CblasColMajor);
dot(X.getData(), y.getData(), xty->getData(), 1, d, 1, t, d, t, CblasTrans, CblasNoTrans, CblasColMajor);
GurlsOptionsList* kernel = this->opt->template getOptAs<GurlsOptionsList>("kernel");
const gMat2D<T>& XtX = kernel->getOptValue<OptMatrix<gMat2D<T> > >("XtX");
const gMat2D<T>& Xty = kernel->getOptValue<OptMatrix<gMat2D<T> > >("Xty");
axpy(d*d, (T)1.0, XtX.getData(), 1, xtx->getData(), 1);
axpy(d*t, (T)1.0, Xty.getData(), 1, xty->getData(), 1);
kernel->removeOpt("XtX");
kernel->addOpt("XtX", new OptMatrix<gMat2D<T> >(*xtx));
kernel->removeOpt("Xty");
kernel->addOpt("Xty", new OptMatrix<gMat2D<T> >(*xty));
unsigned long proportion = static_cast<unsigned long>(gurls::round(1.0/this->opt->getOptAsNumber("hoproportion")));
if(nTot % proportion == 0)
{
const gMat2D<T>& Xva = kernel->getOptValue<OptMatrix<gMat2D<T> > >("Xva");
const gMat2D<T>& yva = kernel->getOptValue<OptMatrix<gMat2D<T> > >("yva");
const unsigned long nva = Xva.rows();
const unsigned long nva_new = nva+1;
gMat2D<T>* Xva_new = new gMat2D<T>(nva_new, d);
const T* old_it = Xva.getData();
T* new_it = Xva_new->getData();
for(const T* end = new_it+(nva_new*d); new_it< end; old_it+=nva, new_it +=nva_new)
copy(new_it, old_it, nva);
copy(Xva_new->getData()+nva, X.getData(), d, nva_new, 1);
kernel->removeOpt("Xva");
kernel->addOpt("Xva", new OptMatrix<gMat2D<T> >(*Xva_new));
gMat2D<T>* yva_new = new gMat2D<T>(nva_new, t);
old_it = yva.getData();
new_it = yva_new->getData();
for(const T* end = new_it+(nva_new*t); new_it< end; old_it+=nva, new_it +=nva_new)
copy(new_it, old_it, nva);
copy(yva_new->getData()+nva, y.getData(), t, nva_new, 1);
kernel->removeOpt("yva");
kernel->addOpt("yva", new OptMatrix<gMat2D<T> >(*yva_new));
}
}
