void CAlgorithmParabola::caseA()
{
m_sigma = qAbs(deltaH(QPoint(m_x,m_y)))-qAbs(deltaD(QPoint(m_x,m_y)));
if(m_sigma<=0)
{
m_x++;
m_delta+= 2* qAbs(m_p);
}
else{
caseV();
}
}
/**
* The update rule for the encoding matrix H.
* The function takes in all the matrices and only changes the
* value of the H matrix.
*
* @param V Input matrix to be factorized.
* @param W Basis matrix.
* @param H Encoding matrix to be updated.
*/
inline void HUpdate(const arma::sp_mat& /* V */,
const arma::mat& W,
arma::mat& H)
{
arma::mat deltaH(H.n_rows, 1);
deltaH.zeros();
size_t currentUserIndex = it->col();
size_t currentItemIndex = it->row();
deltaH += (**it - arma::dot(W.row(currentItemIndex),
H.col(currentUserIndex))) * arma::trans(W.row(currentItemIndex));
if (kh != 0) deltaH -= kh * H.col(currentUserIndex);
H.col(currentUserIndex) += u * deltaH;
}
inline void HUpdate(const MatType& V,
const arma::mat& W,
arma::mat& H)
{
arma::mat deltaH(H.n_rows, 1);
deltaH.zeros();
for(size_t i = 0;i < n;i++)
{
double val;
if((val = V(i, currentUserIndex)) != 0)
deltaH += (val - arma::dot(W.row(i), H.col(currentUserIndex))) *
arma::trans(W.row(i));
}
if(kh != 0) deltaH -= kh * H.col(currentUserIndex);
H.col(currentUserIndex++) += u * deltaH;
currentUserIndex = currentUserIndex % m;
}
inline void SVDIncompleteIncrementalLearning::
HUpdate<arma::sp_mat>(const arma::sp_mat& V,
const arma::mat& W,
arma::mat& H)
{
arma::mat deltaH(H.n_rows, 1);
deltaH.zeros();
for(arma::sp_mat::const_iterator it = V.begin_col(currentUserIndex);
it != V.end_col(currentUserIndex);it++)
{
double val = *it;
size_t i = it.row();
if((val = V(i, currentUserIndex)) != 0)
deltaH += (val - arma::dot(W.row(i), H.col(currentUserIndex))) *
arma::trans(W.row(i));
}
if(kh != 0) deltaH -= kh * H.col(currentUserIndex);
H.col(currentUserIndex++) += u * deltaH;
currentUserIndex = currentUserIndex % m;
}
