MatrixXd MultivariateFNormalSufficient::get_PW() const
{
if (!flag_PW_)
{
////PW
timer_.start(SOLVE);
MatrixXd PW(M_,M_);
if (N_==1)
{
IMP_LOG(TERSE, "MVN: W=0 => PW=0" << std::endl);
PW.setZero();
} else {
IMP_LOG(TERSE, "MVN: solving for PW" << std::endl);
if (use_cg_)
{
if (first_PW_)
{
PW = compute_PW_direct();
(*const_cast<bool *>(&first_PW_))=false;
} else {
PW = compute_PW_cg();
}
} else {
PW = compute_PW_direct();
}
}
const_cast<MultivariateFNormalSufficient *>(this)->set_PW(PW);
timer_.stop(SOLVE);
}
return PW_;
}
MatrixXd MultivariateFNormalSufficient::compute_PW_cg() const
{
//compute PW using CG. Preconditionner is Sigma^-1 and initial guess
//is previous value of PW. Do M steps (theoretically sufficient) and if the
//residuals are too big do the inversion.
//
timer_.start(PW_CG_SUCCESS);
//static unsigned numtries=0;
//static unsigned numfail=0;
cg_->set_A(get_Sigma());
cg_->set_B(get_W());
cg_->set_X0(PW_);
cg_->set_tol(cg_tol_);
MatrixXd PW(cg_->optimize(precond_, M_));
if (cg_->info()>0) timer_.stop(PW_CG_SUCCESS);
double resid = (get_Sigma()*PW-get_W()).norm();
if (resid > cg_tol_)
{
//numfail++;
PW = compute_PW_direct();
}
//numtries++;
//std::cout << "CG: numtries="<<numtries<<" numfail="<<numfail<<std::endl;
return PW;
}
MatrixXd MultivariateFNormalSufficient::get_PW() const
{
if (!flag_PW_)
{
////PW
MatrixXd PW(M_,M_);
if (N_==1)
{
LOG( "MVN: W=0 => PW=0" << std::endl);
PW.setZero();
} else {
LOG( "MVN: solving for PW" << std::endl);
PW = compute_PW_direct();
}
const_cast<MultivariateFNormalSufficient *>(this)->set_PW(PW);
}
return PW_;
}
