void CBayesLearningEngine::Learn()
{
if(!m_pGrModel)
{
PNL_THROW( CNULLPointer, "no graphical model")
}
CStaticGraphicalModel *grmodel = this->GetStaticModel();
CFactor *factor = NULL;
int numberOfFactors = grmodel->GetNumberOfFactors();
int domainNodes;
if(m_numberOfLearnedEvidences == m_numberOfAllEvidences)
{
PNL_THROW(COutOfRange, "number of unlearned evidences must be positive")
}
int currentEvidNumber;
const CEvidence* pCurrentEvid;
//below code is intended to work on tabular CPD and gaussian CPD
//later we will generalize it for other distribution types
if ((grmodel->GetFactor(0))->GetDistributionType() == dtTabular)
{
for( int ev = m_numberOfLearnedEvidences; ev < m_numberOfAllEvidences; ev++)
{
currentEvidNumber = ev;
pCurrentEvid = m_Vector_pEvidences[currentEvidNumber];
if( !pCurrentEvid)
{
PNL_THROW(CNULLPointer, "evidence")
}
for( domainNodes = 0; domainNodes < numberOfFactors; domainNodes++ )
{
factor = grmodel->GetFactor( domainNodes );
int DomainSize;
const int *domain;
factor->GetDomain( &DomainSize, &domain );
const CEvidence *pEvidences[] = { pCurrentEvid };
CTabularDistribFun* pDistribFun = (CTabularDistribFun*)(factor->GetDistribFun());
pDistribFun->BayesUpdateFactor(pEvidences, 1, domain);
}
}
}
else
{
for( domainNodes = 0; domainNodes < numberOfFactors; domainNodes++ )
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;
}
float CMlStaticStructLearn::ComputeFamilyScore(intVector vFamily)
{
int nFamily = vFamily.size();
CCPD* iCPD = this->CreateRandomCPD(nFamily, &vFamily.front(), m_pGrModel);
CTabularDistribFun *pDistribFun;
int ncases = m_Vector_pEvidences.size();
const CEvidence * pEv;
float score;
float pred = 0;
EDistributionType NodeType;
switch (m_ScoreMethod)
{
case MaxLh :
if ( !((iCPD->GetDistribFun()->GetDistributionType() == dtSoftMax)
|| (iCPD->GetDistribFun()->GetDistributionType() == dtCondSoftMax)))
{
iCPD->UpdateStatisticsML( &m_Vector_pEvidences.front(), ncases );
score = iCPD->ProcessingStatisticalData(ncases);
}
else
{
float **evid = NULL;
float **full_evid = NULL;
BuildFullEvidenceMatrix(&full_evid);
CSoftMaxCPD* SoftMaxFactor = static_cast<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();
if (SoftMaxFactor->GetDistribFun()->GetDistributionType() == dtSoftMax)
{
score = ((CSoftMaxDistribFun*)SoftMaxFactor->GetDistribFun())->CalculateLikelihood(evid,ncases);
}
else
{
score = ((CCondSoftMaxDistribFun*)SoftMaxFactor->GetDistribFun())->CalculateLikelihood(evid,ncases);
};
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;
};
break;
case PreAs :
int i;
NodeType = iCPD->GetDistributionType();
switch (NodeType)
{
case dtTabular :
for(i = 0; i < ncases; i++)
{
pConstEvidenceVector tempEv(0);
tempEv.push_back(m_Vector_pEvidences[i]);
iCPD->UpdateStatisticsML(&tempEv.front(), tempEv.size());
iCPD->ProcessingStatisticalData(tempEv.size());
pred += log(((CTabularCPD*)iCPD)->GetMatrixValue(m_Vector_pEvidences[i]));
}
break;
case dtGaussian :
for(i = 0; i < ncases; i += 1 )
{
pConstEvidenceVector tempEv(0);
tempEv.push_back(m_Vector_pEvidences[i]);
iCPD->UpdateStatisticsML(&tempEv.front(), tempEv.size());
float tmp = 0;
if (i != 0)
{
tmp =iCPD->ProcessingStatisticalData(1);
pred +=tmp;
}
}
break;
case dtSoftMax:
PNL_THROW(CNotImplemented,
"This type score method has not been implemented yet");
break;
default:
PNL_THROW(CNotImplemented,
"This type score method has not been implemented yet");
break;
};
score = pred;
break;
case MarLh :
{
//проверка того, что потенциал дискретный
if (iCPD->GetDistributionType() != dtTabular)
{
PNL_THROW(CNotImplemented,
"This type of score method has been implemented only for discrete nets");
}
int DomainSize;
const int * domain;
switch(m_priorType)
{
case Dirichlet:
iCPD->GetDomain(&DomainSize, &domain);
pDistribFun = static_cast<CTabularDistribFun *>(iCPD->GetDistribFun());
pDistribFun->InitPseudoCounts();
for (i=0; i<ncases; i++)
{
pEv = m_Vector_pEvidences[i];
const CEvidence *pEvidences[] = { pEv };
pDistribFun->BayesUpdateFactor(pEvidences, 1, domain);
}
score = pDistribFun->CalculateBayesianScore();
break;
case K2:
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);
}
score = pDistribFun->CalculateBayesianScore();
break;
case BDeu:
iCPD->GetDomain(&DomainSize, &domain);
pDistribFun = static_cast<CTabularDistribFun *>(iCPD->GetDistribFun());
pDistribFun->InitPseudoCounts();
for (i=0; i<ncases; i++)
{
pEv = m_Vector_pEvidences[i];
const CEvidence *pEvidences[] = { pEv };
pDistribFun->BayesUpdateFactor(pEvidences, 1, domain);
}
score = pDistribFun->CalculateBayesianScore() / iCPD->GetNumberOfFreeParameters();
break;
default:
PNL_THROW(CNotImplemented,
"This type of prior has not been implemented yet");
break;
}
break;
}
default :
PNL_THROW(CNotImplemented,
"This type score method has not been implemented yet");
break;
}
int dim = iCPD->GetNumberOfFreeParameters();
switch (m_ScoreType)
{
case BIC :
score -= 0.5f * float(dim) * float(log(float(ncases)));
break;
case AIC :
score -= 0.5f * float(dim);
break;
case WithoutFine:
break;
case VAR :
PNL_THROW(CNotImplemented,
"This type score function has not been implemented yet");
break;
default:
PNL_THROW(CNotImplemented,
"This type score function has not been implemented yet");
break;
}
delete iCPD;
return score;
}