CCPD*
CMlStaticStructLearn::ComputeFactor(intVector vFamily, CGraphicalModel* pGrModel, CEvidence** pEvidences)
{
int nFamily = vFamily.size();
int DomainSize;
const int * domain;
const CEvidence * pEv;
int i;
CTabularDistribFun *pDistribFun;
CCPD* iCPD = this->CreateRandomCPD(nFamily,
&vFamily.front(), pGrModel);
int ncases = m_Vector_pEvidences.size();
if ( !(iCPD->GetDistributionType() == dtSoftMax))
{
if (m_ScoreMethod != MarLh)
{
iCPD->UpdateStatisticsML( pEvidences, ncases );
iCPD->ProcessingStatisticalData(ncases);
}
else
{
iCPD->GetDomain(&DomainSize, &domain);
pDistribFun = static_cast<CTabularDistribFun *>(iCPD->GetDistribFun());
pDistribFun->InitPseudoCounts(m_K2alfa);
for (i=0; i<ncases; i++)
{
pEv = m_Vector_pEvidences[i];
const CEvidence *pEvidences[] = { pEv };
pDistribFun->BayesUpdateFactor(pEvidences, 1, domain);
}
pDistribFun->PriorToCPD();
}
}
else
{
float **evid = NULL;
float **full_evid = NULL;
BuildFullEvidenceMatrix(&full_evid);
CSoftMaxCPD* SoftMaxFactor = (CSoftMaxCPD*)iCPD;
SoftMaxFactor->BuildCurrentEvidenceMatrix(&full_evid, &evid,vFamily,
m_Vector_pEvidences.size());
SoftMaxFactor->InitLearnData();
SoftMaxFactor->SetMaximizingMethod(mmGradient);
SoftMaxFactor->MaximumLikelihood(evid, m_Vector_pEvidences.size(),
0.00001f, 0.01f);
SoftMaxFactor->CopyLearnDataToDistrib();
for (int k = 0; k < SoftMaxFactor->GetDomainSize(); k++)
{
delete [] evid[k];
}
delete [] evid;
int i;
intVector obsNodes;
(m_Vector_pEvidences[0])->GetAllObsNodes(&obsNodes);
for (i=0; i<obsNodes.size(); i++)
{
delete [] full_evid[i];
}
delete [] full_evid;
};
return iCPD;
}
