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;
}
void CEMLearningEngine::Learn()
{
CStaticGraphicalModel *pGrModel = this->GetStaticModel();
PNL_CHECK_IS_NULL_POINTER(pGrModel);
PNL_CHECK_LEFT_BORDER(GetNumEv() - GetNumberProcEv() , 1);
CInfEngine *pInfEng = NULL;
if (m_pInfEngine)
{
pInfEng = m_pInfEngine;
}
else
{
if (!m_bAllObserved)
{
pInfEng = CJtreeInfEngine::Create(pGrModel);
m_pInfEngine = pInfEng;
}
}
float loglik = 0.0f;
int nFactors = pGrModel->GetNumberOfFactors();
const CEvidence *pEv;
CFactor *pFactor;
int iteration = 0;
int ev;
bool IsCastNeed = false;
int i;
for( i = 0; i < nFactors; i++ )
{
pFactor = pGrModel->GetFactor(i);
EDistributionType dt = pFactor->GetDistributionType();
if ( dt == dtSoftMax ) IsCastNeed = true;
}
float ** full_evid = NULL;
if (IsCastNeed)
{
BuildFullEvidenceMatrix(&full_evid);
}
if (IsAllObserved())
{
int i;
float **evid = NULL;
EDistributionType dt;
CFactor *factor = NULL;
for (i = 0; i < nFactors; i++)
{
factor = pGrModel->GetFactor(i);
dt = factor->GetDistributionType();
if (dt != dtSoftMax)
{
factor->UpdateStatisticsML(&m_Vector_pEvidences[GetNumberProcEv()],
GetNumEv() - GetNumberProcEv());
}
else
{
intVector family;
family.resize(0);
pGrModel->GetGraph()->GetParents(i, &family);
family.push_back(i);
CSoftMaxCPD* SoftMaxFactor = static_cast<CSoftMaxCPD*>(factor);
SoftMaxFactor->BuildCurrentEvidenceMatrix(&full_evid,
&evid,family,m_Vector_pEvidences.size());
SoftMaxFactor->InitLearnData();
SoftMaxFactor->SetMaximizingMethod(m_MaximizingMethod);
SoftMaxFactor->MaximumLikelihood(evid, m_Vector_pEvidences.size(),
0.00001f, 0.01f);
SoftMaxFactor->CopyLearnDataToDistrib();
for (int k = 0; k < factor->GetDomainSize(); k++)
{
delete [] evid[k];
}
delete [] evid;
}
}
m_critValue.push_back(UpdateModel());
}
else
{
bool bContinue;
const CPotential * pot;
/* bool IsCastNeed = false;
int i;
for( i = 0; i < nFactors; i++ )
{
pFactor = pGrModel->GetFactor(i);
EDistributionType dt = pFactor->GetDistributionType();
if ( dt == dtSoftMax ) IsCastNeed = true;
}
float ** full_evid;
if (IsCastNeed)
{
BuildFullEvidenceMatrix(full_evid);
}*/
do
{
ClearStatisticData();
iteration++;
for( ev = GetNumberProcEv(); ev < GetNumEv() ; ev++ )
{
bool bInfIsNeed = !GetObsFlags(ev)->empty();
pEv = m_Vector_pEvidences[ev];
if( bInfIsNeed )
{
pInfEng->EnterEvidence(pEv, 0, 0);
}
int i;
for( i = 0; i < nFactors; i++ )
{
pFactor = pGrModel->GetFactor(i);
int nnodes;
const int * domain;
pFactor->GetDomain( &nnodes, &domain );
if( bInfIsNeed && !IsDomainObserved(nnodes, domain, ev ) )
{
pInfEng->MarginalNodes( domain, nnodes, 1 );
pot = pInfEng->GetQueryJPD();
if ( (!(m_Vector_pEvidences[ev])->IsNodeObserved(i)) && (IsCastNeed) )
{
Cast(pot, i, ev, &full_evid);
}
EDistributionType dt;
dt = pFactor->GetDistributionType();
if ( !(dt == dtSoftMax) )
pFactor->UpdateStatisticsEM( /*pInfEng->GetQueryJPD */ pot, pEv );
}
else
{
if ((pFactor->GetDistributionType()) != dtSoftMax)
pFactor->UpdateStatisticsML( &pEv, 1 );
}
}
}
int i;
/*
printf ("\n My Full Evidence Matrix");
for (i=0; i<nFactors; i++)
{
for (j=0; j<GetNumEv(); j++)
{
printf ("%f ", full_evid[i][j]);
}
printf("\n");
}
*/
float **evid = NULL;
EDistributionType dt;
CFactor *factor = NULL;
// int i;
for (i = 0; i < nFactors; i++)
{
factor = pGrModel->GetFactor(i);
dt = factor->GetDistributionType();
if (dt == dtSoftMax)
{
intVector family;
family.resize(0);
pGrModel->GetGraph()->GetParents(i, &family);
family.push_back(i);
CSoftMaxCPD* SoftMaxFactor = static_cast<CSoftMaxCPD*>(factor);
SoftMaxFactor->BuildCurrentEvidenceMatrix(&full_evid,
&evid,family,m_Vector_pEvidences.size());
SoftMaxFactor->InitLearnData();
SoftMaxFactor->SetMaximizingMethod(m_MaximizingMethod);
// SoftMaxFactor->MaximumLikelihood(evid, m_numberOfLastEvidences,
SoftMaxFactor->MaximumLikelihood(evid, m_Vector_pEvidences.size(),
0.00001f, 0.01f);
SoftMaxFactor->CopyLearnDataToDistrib();
for (int k = 0; k < factor->GetDomainSize(); k++)
{
delete [] evid[k];
}
delete [] evid;
}
}
loglik = UpdateModel();
if( GetMaxIterEM() != 1)
{
bool flag = iteration == 1 ? true :
(fabs(2*(m_critValue.back()-loglik)/(m_critValue.back() + loglik)) > GetPrecisionEM() );
bContinue = GetMaxIterEM() > iteration && flag;
}
else
{
bContinue = false;
}
m_critValue.push_back(loglik);
}while(bContinue);
}
SetNumProcEv( GetNumEv() );
if (IsCastNeed)
{
int NumOfNodes = pGrModel->GetGraph()->GetNumberOfNodes();
for (i=0; i<NumOfNodes; i++)
{
delete [] full_evid[i];
}
delete [] full_evid;
}
}
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;
}
