void CFactor::SetStatistics( const CMatrix<float> *pMat,
EStatisticalMatrix matrix, const int* parentsComb)
{
CDistribFun *pDistr = GetDistribFun();
PNL_CHECK_IS_NULL_POINTER(pDistr);
pDistr->SetStatistics( pMat, matrix, parentsComb );
}
//-----------------------------------------------------------------------------
CPotential *CSoftMaxCPD::ConvertToTabularPotential(const CEvidence* pEvidence) const
{
//searching discrite nodes in domain
intVector discriteNodesPosInDom;
int domSize = m_Domain.size();
int numSoftMaxNode;
int discrDomSize = 0;
int *parentIndexes;
int SoftMaxSize;
const pConstNodeTypeVector* ntVec = GetDistribFun()->GetNodeTypesVector();
int i;
for (i = 0; i < domSize; i++)
{
if ((*ntVec)[i]->IsDiscrete())
{
discriteNodesPosInDom.push_back(m_Domain[i]);
SoftMaxSize = (*ntVec)[i]->GetNodeSize();
numSoftMaxNode = i;
}
};
discrDomSize = discriteNodesPosInDom.size();
//fill parents indexes vector
parentIndexes = new int[discrDomSize-1];
for( i = 0; i < discrDomSize-1; i++ )
{
parentIndexes[i] = discriteNodesPosInDom[i];
}
// creating new evidece that contain all observed nodes in this domain
intVector pObsNodes;
pConstValueVector pObsValues;
pConstNodeTypeVector pNodeTypes;
pEvidence->GetObsNodesWithValues(&pObsNodes,&pObsValues,&pNodeTypes);
int *obsNodes;
int obsNodesSize;
obsNodesSize=pObsNodes.size();
obsNodes = new int[obsNodesSize];
for(i = 0;i < obsNodesSize; i++)
{
obsNodes[i] = pObsNodes[i];
}
CEvidence *pCopyEvidence;
valueVector cpyValVect(0);
for(i = 0; i < obsNodesSize; i++)
{
cpyValVect.push_back(*(pObsValues[i]));
}
pCopyEvidence = CEvidence::Create(pEvidence->GetModelDomain(), obsNodesSize,
obsNodes,(const valueVector&)cpyValVect);
for(i = 0; i < pObsNodes.size(); i++)
{
if((std::find(m_Domain.begin(),m_Domain.end(), pObsNodes[i])) == m_Domain.end())
{
pCopyEvidence->MakeNodeHidden(pObsNodes[i]);
}
};
//creating tabular potential
CTabularPotential *resFactor = CTabularPotential::Create(
GetModelDomain(),discriteNodesPosInDom);
if( m_DistributionType == dtSoftMax)
{
resFactor->AttachMatrix(((CSoftMaxDistribFun*)m_CorrespDistribFun)->
GetProbMatrix(pCopyEvidence),matTable);
}
else
{
if(m_DistributionType == dtCondSoftMax)
{
resFactor->AttachMatrix(((CCondSoftMaxDistribFun*)m_CorrespDistribFun)->
GetProbMatrix(pCopyEvidence),matTable);
}
else
{
PNL_THROW( CInconsistentType,
"distribution must be SoftMax or conditional SoftMax" )
}
}
delete [] parentIndexes;
delete [] obsNodes;
delete pCopyEvidence;
return resFactor;
}
void CFactor::SetModelDomain( CModelDomain* pMD, bool checkNodeTypesinMD )
{
if( pMD == m_pMD )
{
return;
}
if( checkNodeTypesinMD )
{
//check if node types are the same - check only node types in models!
int i;
int domSize = m_Domain.size();
for( i = 0; i < domSize; i++ )
{
if( *(m_pMD->GetVariableType(m_Domain[i])) != *(pMD->GetVariableType(m_Domain[i]) ) )
{
PNL_THROW( CInconsistentType, "types of variables should correspond" );
}
}
}
//need to set new ModelDomain
CDistribFun *pNewDistribFun;
pNewDistribFun = GetDistribFun();
pConstNodeTypeVector ntFromNewModelDomain;
pMD->GetVariableTypes( m_Domain, &ntFromNewModelDomain );
const pConstNodeTypeVector *nodeTypes = GetDistribFun()->GetNodeTypesVector();
const CNodeType *nt;
int i;
int numObsPos = m_obsPositions.size();
if( numObsPos )
{
for( i = 0; i < numObsPos; i++ )
{
nt = (*nodeTypes)[m_obsPositions[i]];
if( nt->IsDiscrete() )
{
ntFromNewModelDomain[m_obsPositions[i]] = pMD->GetObsTabVarType();
}
else
{
ntFromNewModelDomain[m_obsPositions[i]] = pMD->GetObsGauVarType();
}
}
}
pNewDistribFun->ResetNodeTypes( ntFromNewModelDomain );
CFactor* obj = this;
if( m_pMD->IsAFactorOwner(obj) )
{
m_pMD->ReleaseFactor(obj);
m_pMD = pMD;
int num = m_pMD->AttachFactor( obj );
m_factNumInHeap = num;
}
else
{
m_pMD = pMD;
}
}
}
}
}
else
{
for( domainNodes = 0; domainNodes < numberOfFactors; domainNodes++ )
{
if( !m_Vector_pEvidences[m_numberOfLearnedEvidences])
{
PNL_THROW(CNULLPointer, "evidence")
}
factor = grmodel->GetFactor( domainNodes );
int DomainSize;
const int *domain;
factor->GetDomain( &DomainSize, &domain );
const pConstNodeTypeVector *ntCorr = factor->GetDistribFun()->GetNodeTypesVector();
if ((*ntCorr)[DomainSize - 1]->GetNodeSize() != 1)
{
m_updateCPD = 1;
}
const CEvidence ** pEv = new const CEvidence* [m_numberOfAllEvidences - m_numberOfLearnedEvidences];
int c;
for( c = 0; c < m_numberOfAllEvidences - m_numberOfLearnedEvidences; ++c)
{
pEv[c] = m_Vector_pEvidences[m_numberOfLearnedEvidences + c];
}
const CEvidence * const* pEvidences = pEv;
CGaussianDistribFun* pDistribFun = (CGaussianDistribFun*)(factor->GetDistribFun());
pDistribFun->BayesUpdateFactor(pEvidences, m_numberOfAllEvidences - m_numberOfLearnedEvidences, domain);
}
}
