void CGaussianCPD::UpdateStatisticsEM( const CPotential *pMargPot,
const CEvidence *pEvidence )
{
if( !pMargPot )
{
PNL_THROW( CNULLPointer, "evidences" )//no corresp evidences
}
intVector obsPos;
pMargPot->GetObsPositions(&obsPos);
if( obsPos.size() && (this->GetDistribFun()->GetDistributionType() != dtCondGaussian) )
{
PNL_CHECK_IS_NULL_POINTER(pEvidence);
CPotential *pExpandPot = pMargPot->ExpandObservedNodes(pEvidence, 0);
m_CorrespDistribFun->UpdateStatisticsEM(pExpandPot->GetDistribFun(), pEvidence, 1.0f,
&m_Domain.front());
delete pExpandPot;
}
else
{
m_CorrespDistribFun->UpdateStatisticsEM( pMargPot->GetDistribFun(), pEvidence, 1.0f,
&m_Domain.front() );
}
}
float CJtreeInfEngine::GetLogLik() const
{
if( m_norm == -1.0f )
{
PNL_THROW( CInvalidOperation, " can't call GetLogLik before calling inferences procedure collect" );
}
//////////////////////////////////////////////////////////////////////////
float ll = 0.0f;
intVector obsDomains;
GetObservedDomains(m_pEvidence, &obsDomains);
const CStaticGraphicalModel *pGrModel = GetModel();
int i;
for( i = 0; i < obsDomains.size(); i++ )
{
ll += pGrModel->GetFactor(obsDomains[i])->GetLogLik(m_pEvidence);
}
CPotential *pPot;
int root = GetJTreeRootNode();
pPot=m_pJTree->GetNodePotential(root);
if( pPot->GetDistribFun()->GetDistributionType() == dtGaussian )
{
CGaussianDistribFun *pDistr = static_cast<CGaussianDistribFun *>(pPot->GetDistribFun());
pDistr->UpdateMomentForm();
float koeff = pDistr->GetCoefficient(0);
ll += (float)log( koeff );
}
else
{
ll += m_norm < FLT_EPSILON ? (float)log( FLT_EPSILON ) : (float)log( m_norm );
}
return ll;
}
void CJtreeInfEngine::
DivideJTreeNodePotByDistribFun( int clqPotNum, const int *domain,
const CDistribFun *pDistrFun )
{
// bad-args check
PNL_CHECK_RANGES( clqPotNum, 0, m_pJTree->GetNumberOfNodes() - 1 );
PNL_CHECK_IS_NULL_POINTER(domain);
PNL_CHECK_IS_NULL_POINTER(pDistrFun);
// bad-args check end
CPotential *pNodePot = m_pJTree->GetNodePotential(clqPotNum);
int nodePotDomSz;
const int *nodePotDomain;
pNodePot->GetDomain( &nodePotDomSz, &nodePotDomain );
pNodePot->GetDistribFun()->DivideInSelfData( nodePotDomain, domain,
pDistrFun );
}
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;
}
//-----------------------------------------------------------------------------
CPotential* CSoftMaxCPD::ConvertWithEvidenceToGaussianPotential(
const CEvidence* pEvidence,
floatVector MeanContParents,
C2DNumericDenseMatrix<float>* CovContParents,
const int *parentIndices,
int flagSumOnMixtureNode ) const
{
int SoftMaxSize = GetSoftMaxSize();
if (SoftMaxSize != 2)
{
PNL_THROW(CNotImplemented, "It is not sigmoid");
}
else
{
if (m_CorrespDistribFun->GetDistributionType() == dtSoftMax)
{
CPotential* pot = ConvertToGaussianPotential(pEvidence,
m_CorrespDistribFun, MeanContParents, CovContParents);
CPotential *pot2 = NULL;
int domSize = pot->GetDomainSize();
bool IsAllContUnobserved = true;
const pConstNodeTypeVector* ntVec = pot->GetDistribFun()->GetNodeTypesVector();
for( int i = 0; i < domSize-1; i++ )
{
intVector Domain;
pot->GetDomain(&Domain);
int curNode = Domain[i];
if( (pEvidence->IsNodeObserved(curNode)))
{
if( !(*ntVec)[i]->IsDiscrete() )
{
IsAllContUnobserved = false;
}
}
}
if ((pot->GetDomainSize() >= 3)&&(!IsAllContUnobserved))
{
pot2 = pot->ShrinkObservedNodes(pEvidence);
}
else
{
intVector Domain;
pot->GetDomain(&Domain);
pot2 = pot->Marginalize(&(Domain[0]), 1);
}
delete pot;
return pot2;
}
else //it means m_CorrespDistribFun->GetDistributionType == dtCondSoftMax
{
int i;
const CSoftMaxDistribFun* dtSM;
dtSM =
static_cast<CCondSoftMaxDistribFun*>(m_CorrespDistribFun)->
GetDistribution(parentIndices);
intVector pObsNodes;
pConstValueVector pObsValues;
pConstNodeTypeVector pNodeTypes;
pEvidence->GetObsNodesWithValues(&pObsNodes, &pObsValues, &pNodeTypes);
int r = -1;
for (i = 0; i < pObsNodes.size(); i++)
{
if (m_Domain[m_Domain.size()-1] == pObsNodes[i])
{
r = pObsValues[i]->GetInt();
break;
}
}
if (r == -1)
{
PNL_THROW(CNotImplemented, "Not exist evidence");
}
CDistribFun *gauFactData = const_cast<CSoftMaxDistribFun*>(dtSM)->
ConvertCPDDistribFunToPotential(MeanContParents, CovContParents, r);
intVector gauSubDomain;
const CNodeType *nt;
for(i = 0; i < m_Domain.size(); i++)
{
nt = GetModelDomain()->GetVariableType( m_Domain[i] );
if(!(nt->IsDiscrete()))
{
gauSubDomain.push_back(m_Domain[i]);
}
}
intVector obsIndex;
for( i = 0; i < gauSubDomain.size(); i++ )
{
if( pEvidence->IsNodeObserved(gauSubDomain[i]) )
{
obsIndex.push_back( i );
}
}
CGaussianPotential *resFactor = CGaussianPotential::Create(&gauSubDomain.front(),
gauSubDomain.size(), GetModelDomain());
resFactor->SetDistribFun( gauFactData );
CPotential *pot = NULL;
int domSize = resFactor->GetDomainSize();
bool IsAllContUnobserved = true;
const pConstNodeTypeVector* ntVec = resFactor->GetDistribFun()->GetNodeTypesVector();
for( i = 0; i < domSize-1; i++ )
{
intVector Domain;
resFactor->GetDomain(&Domain);
int curNode = Domain[i];
if( (pEvidence->IsNodeObserved(curNode)))
{
if( !(*ntVec)[i]->IsDiscrete() )
{
IsAllContUnobserved = false;
}
}
}
if ((resFactor->GetDomainSize() >= 3)&&(!IsAllContUnobserved))
{
pot = resFactor->ShrinkObservedNodes(pEvidence);
}
else
{
intVector Domain;
resFactor->GetDomain(&Domain);
pot = resFactor->Marginalize(&(Domain[0]), 1);
}
delete resFactor;
delete gauFactData;
return pot;
}
}
}
bool pnl::EqualResults(CJtreeInfEngine& eng1, CJtreeInfEngine& eng2,
float epsilon, int doPrint, int doFile, float *maxDiff)
{
CJunctionTree *JTree1, *JTree2;
JTree1 = eng1.GetJTree();
JTree2 = eng2.GetJTree();
int NumOfNds1 = JTree1->GetNumberOfNodes();
int NumOfNds2 = JTree2->GetNumberOfNodes();
int numOfNdsInClq;
const int *clique;
const floatVector *myVector;
int node;
#if 0
FILE *out;
if (doFile)
{
out = fopen( "jtree1.out", "w" );
for(node = 0; node < NumOfNds1; node++)
{
JTree1->GetNodeContent(node, &numOfNdsInClq, &clique);
fprintf(out, "Nodes of clique %d :\n", node);
for (int i = 0; i < numOfNdsInClq; i++)
fprintf(out, "%d ", clique[i]);
CMatrix<float>* mat = NULL;
CPotential* p = JTree1->GetNodePotential(node);
mat = p->GetDistribFun()->GetMatrix(matTable);
CNumericDenseMatrix<float>* myMatrix =
static_cast<CNumericDenseMatrix<float>*>(mat->ConvertToDense());
fprintf(out,"\nMatrix of potential of clique %d:\n", node);
myVector = (myMatrix)->GetVector();
for(int j = 0; j < myVector->size(); j++)
{
fprintf(out,"%f ",(*myVector)[j]);
}
fprintf(out,"\n\n");
}
fclose( out );
out = fopen( "jtree2.out", "w" );
for(node = 0; node < NumOfNds2; node++)
{
JTree2->GetNodeContent(node, &numOfNdsInClq, &clique);
fprintf(out, "Nodes of clique %d :\n", node);
for (int i = 0; i < numOfNdsInClq; i++)
fprintf(out, "%d ", clique[i]);
CMatrix<float>* mat = JTree2->GetNodePotential(node)->
GetDistribFun()->GetMatrix(matTable);
CNumericDenseMatrix<float>* myMatrix =
static_cast<CNumericDenseMatrix<float>*>(mat->ConvertToDense());
fprintf(out,"\nMatrix of potential of clique %d:\n", node);
const floatVector *myVector = (myMatrix)->GetVector();
for(int j = 0; j < myVector->size(); j++)
{
fprintf(out,"%f ",(*myVector)[j]);
}
fprintf(out,"\n\n");
}
fclose( out );
}
#endif
bool res = 1;
if (NumOfNds1 != NumOfNds2)
res = 0;
CDistribFun* distrib1;
if (maxDiff)
{
*maxDiff = 0;
}
float maxDifference;
for(node = 0; node < NumOfNds1; node++)
{
distrib1 = JTree1->GetNodePotential(node)->GetDistribFun();
if (!(distrib1->IsEqual(JTree2->GetNodePotential(node)->
GetDistribFun(), epsilon, 1, &maxDifference)))
{
res = 0;
if (maxDiff && (*maxDiff < maxDifference))
{
*maxDiff = maxDifference;
}
#if 0
if (doPrint)
printf("clique %d: notOK maxDiff = %.6f\n", node, maxDifference);
#endif
}
else
{
#if 0
if (doPrint)
printf("clique %d: OK\n", node);
#endif
}
}
return res;
}