void CJtreeInfEngine::
MarginalizeCliqueToQuery( int clqNum, int querySz, const int *query,
int notExpandJPD )
{
// bad-args check
PNL_CHECK_RANGES( clqNum, 0, m_pJTree->GetNumberOfNodes() - 1 );
PNL_CHECK_RANGES( querySz, 1, m_pGraphicalModel->GetNumberOfNodes() );
PNL_CHECK_IS_NULL_POINTER(query);
// bad-args check end
// Note: cant call expand() for potentials, which contain continuous
// observed nodes in domain, cause those are to be expanded to
// mixture of gaussians, which we dont support right now.
delete m_pQueryJPD;
m_pQueryJPD = NULL;
delete m_pPotMPE;
m_pPotMPE = NULL;
delete m_pEvidenceMPE;
m_pEvidenceMPE = NULL;
bool bExpandAllowed = true;
CPotential* clqPotWithQuery = m_pJTree->GetNodePotential(clqNum);
EDistributionType dtClqWithQuery = clqPotWithQuery->GetDistributionType();
if( std::find_first_of( query, query + querySz,
m_actuallyObsNodes.begin(), m_actuallyObsNodes.end() )
!= ( query + querySz ) )
{
const int *queryIt = query, *query_end = query + querySz;
for( ; queryIt != query_end; ++queryIt )
{
if( std::find( m_actuallyObsNodes.begin(),
m_actuallyObsNodes.end(), *queryIt )
!= m_actuallyObsNodes.end() )
{
int shrNodebDiscrete =
m_pGraphicalModel->GetNodeType(*queryIt)->IsDiscrete();
if(((dtClqWithQuery == dtTabular)&&( !shrNodebDiscrete ))
||(( dtClqWithQuery == dtGaussian )&&( shrNodebDiscrete )))
{
bExpandAllowed = false;
break;
}
}
}
}
if( ( bExpandAllowed == false ) && ( notExpandJPD == false ) )
{
PNL_THROW( CAlgorithmicException,
" JPD expansion not possible technically " );
}
bExpandAllowed = notExpandJPD ? false : bExpandAllowed;
CPotential *pMargJPot = clqPotWithQuery->Marginalize( query, querySz,
m_bMaximize );
if( bExpandAllowed )
{
CPotential *pExpObsJPot = pMargJPot->ExpandObservedNodes(m_pEvidence);
if( m_bMaximize )
{
if( pMargJPot->GetDistributionType() == dtScalar )
{
m_pPotMPE = pExpObsJPot->GetNormalized();
m_pEvidenceMPE = m_pPotMPE->GetMPE();
}
else
{
m_pPotMPE = pMargJPot->GetNormalized();
m_pEvidenceMPE = m_pPotMPE->GetMPE();
}
}
else
{
m_pQueryJPD = pExpObsJPot->GetNormalized();
}
delete pExpObsJPot;
}
else
{
if( m_bMaximize )
{
m_pPotMPE = pMargJPot->GetNormalized();
m_pEvidenceMPE = m_pPotMPE->GetMPE();
}
else
{
m_pQueryJPD = pMargJPot->GetNormalized();
}
}
if((!m_bMaximize)&&(m_pQueryJPD->GetDistributionType() == dtGaussian))
{
static_cast<CGaussianDistribFun*>(
m_pQueryJPD->GetDistribFun())->UpdateMomentForm();
}
if((m_bMaximize)&&(m_pPotMPE->GetDistributionType() == dtGaussian))
{
static_cast<CGaussianDistribFun*>(
m_pPotMPE->GetDistribFun())->UpdateMomentForm();
}
delete pMargJPot;
}
bool CSamplingInfEngine::
ConvertingFamilyToPot( int node, const CEvidence* pEv )
{
bool ret = false;
CPotential* potToSample = m_potsToSampling[node];
Normalization(potToSample);
int i;
if( !IsAllNdsTab() )
{
if( GetModel()->GetModelType() == mtBNet )
{
for( i = 0; i < m_environment[node].size(); i++ )
{
int num = m_environment[node][i];
CPotential *pot1 = static_cast< CCPD* >( m_currentFactors[ num ] )
->ConvertWithEvidenceToPotential(pEv);
CPotential *pot2 = pot1->Marginalize(&node, 1);
delete pot1;
*potToSample *= *pot2;
delete pot2;
}
}
else
{
for( i = 0; i < m_environment[node].size(); i++ )
{
int num = m_environment[node][i];
CPotential *pot1 = static_cast< CPotential* >( m_currentFactors[ num ] )
->ShrinkObservedNodes(pEv);
CPotential *pot2 = pot1->Marginalize(&node, 1);
delete pot1;
*potToSample *= *pot2;
delete pot2;
}
}
}
else
{
CMatrix< float > *pMatToSample;
pMatToSample = static_cast<CTabularDistribFun*>(potToSample->GetDistribFun())
->GetMatrix(matTable);
intVector dims;
intVector vls;
intVector domain;
for( i = 0; i < m_environment[node].size(); i++ )
{
int num = m_environment[node][i];
m_currentFactors[ num ]->GetDomain(&domain);
GetObsDimsWithVls( domain, node, pEv, &dims, &vls);
CMatrix< float > *pMat;
pMat = static_cast<CTabularDistribFun*>(m_currentFactors[ num ]->
GetDistribFun())->GetMatrix(matTable);
pMat->ReduceOp( &dims.front(), dims.size(), 2, &vls.front(),
pMatToSample, PNL_ACCUM_TYPE_MUL );
dims.clear();
vls.clear();
domain.clear();
}
}
//check for non zero elements
CMatrix<float> *pMat;
if( potToSample->GetDistributionType()==dtTabular )
{
pMat = potToSample->GetDistribFun()->GetMatrix(matTable);
}
else
{
CGaussianDistribFun* pDistr = static_cast<CGaussianDistribFun*>(potToSample->GetDistribFun());
if(pDistr->GetMomentFormFlag())
{
pMat = pDistr->GetMatrix(matCovariance);
}
else
{
pMat = pDistr->GetMatrix(matK);
}
}
CMatrixIterator<float>* iter = pMat->InitIterator();
for( iter; pMat->IsValueHere( iter ); pMat->Next(iter) )
{
if(*(pMat->Value( iter )) > FLT_EPSILON)
{
ret = true;
break;
}
}
delete iter;
return ret;
}
int CJtreeInfEngine::GetDataForMargAndMult(const int source, const int sink,
pnl::CNumericDenseMatrix< float > **sorceMatrix, int **dims_to_keep,
int &num_dims_to_keep, pnl::CNumericDenseMatrix< float > **sepMatrix,
pnl::CNumericDenseMatrix< float > **sinkMatrix, int **dims_to_mul,
int &num_dims_to_mul)
{
// bad-args check
PNL_CHECK_RANGES(source, 0, m_pJTree->GetNumberOfNodes() - 1);
PNL_CHECK_RANGES(sink, 0, m_pJTree->GetNumberOfNodes() - 1);
// bad-args check end
if (source == sink)
{
PNL_THROW(CInvalidOperation, " source and sink should differ ");
}
if (!m_pJTree->GetGraph()->IsExistingEdge(source, sink))
{
PNL_THROW(CInvalidOperation, " there is no edge between source and sink");
}
CPotential *potSource = m_pJTree->GetNodePotential(source),
*potSink = m_pJTree->GetNodePotential(sink);
int numNdsInSourceDom, numNdsInSinkDom;
const int *sourceDom, *sinkDom;
potSource->GetDomain(&numNdsInSourceDom, &sourceDom);
potSink->GetDomain(&numNdsInSinkDom, &sinkDom);
CPotential *potSep = m_pJTree->GetSeparatorPotential(source, sink);
int numNdsInSepDom;
const int *sepDom;
potSep->GetDomain(&numNdsInSepDom, &sepDom);
EDistributionType sepDistType = potSep->GetDistributionType();
num_dims_to_keep = numNdsInSepDom;
*dims_to_keep = new int [num_dims_to_keep];
int* pEquivPos;
for (int i = 0; i < numNdsInSepDom; i++)
{
pEquivPos = (int*)std::find(sourceDom, sourceDom + numNdsInSourceDom, sepDom[i]);
if (pEquivPos != sourceDom + numNdsInSourceDom)
{
(*dims_to_keep)[i] = (pEquivPos - sourceDom);
}
else
{
PNL_THROW( CInconsistentSize, "small domain isn't subset of domain")
return 0;
}
//check that pSmallDom is m_Domain's subset
}
switch (sepDistType)
{
case dtTabular:
{
CDistribFun *sepDistrFun = potSep -> GetDistribFun();
CDistribFun *sourceDistrFun = potSource -> GetDistribFun();
CDistribFun *sinkDistrFun = potSink -> GetDistribFun();
if (!sourceDistrFun->IsValid())
{
PNL_THROW( CInconsistentType, "MarginalizeData is invalid" )
}
//check if distribution of potSource is Unit Function - do nothing
if(sourceDistrFun->IsDistributionSpecific())
{
return 0;
}
if ( sepDistrFun->IsDistributionSpecific() )
{
sepDistrFun->SetUnitValue(0);
}
*sorceMatrix = static_cast<CNumericDenseMatrix<float> *>(sourceDistrFun->
GetMatrix(matTable));
*sepMatrix = static_cast<CNumericDenseMatrix<float> *>(sepDistrFun->
GetMatrix(matTable));
EDistributionType dtsink = sinkDistrFun->GetDistributionType();
if ((dtsink != dtTabular) && (dtsink != dtScalar))
{
PNL_THROW(CInvalidOperation, "we can multiply only tabulars")
}
int location;
num_dims_to_mul = numNdsInSepDom;
*dims_to_mul = new int [num_dims_to_mul];
for (int i = 0; i < numNdsInSepDom; i++)
{
location =
std::find(sinkDom, sinkDom + numNdsInSinkDom, sepDom[i]) - sinkDom;
if (location < numNdsInSinkDom)
{
(*dims_to_mul)[i] = location;
}
}
if(sinkDistrFun->IsDistributionSpecific())
{
sinkDistrFun->SetUnitValue(0);
floatVector *aValue =
(floatVector *)((CDenseMatrix<float>*)sinkDistrFun->
GetMatrix(matTable))->GetVector();
aValue->assign(aValue->size(), 1.0f);
}
*sinkMatrix = static_cast<CNumericDenseMatrix<float>*>(sinkDistrFun->
GetMatrix(matTable));
break;
}
case dtScalar:
{
// propagate isn't need
return 0;
}
default:
{
PNL_THROW(CNotImplemented, "we have only Tabular now");
return 0;
}
}
if (numNdsInSepDom == 0)
{
PNL_THROW(COutOfRange, "domain size should be positive");
}
return 1;
}
