void ShowResultsForInference(const CPotential * pQueryPot, int slice)
{
int nnodes;
const int* domain;
pQueryPot->GetDomain( &nnodes, &domain );
std::cout<<" probability distribution for nodes [ ";
int i;
for( i = 0; i < nnodes; i++ )
{
std::cout<<domain[i]<<" ";
}
std::cout<<"] at slice "<<slice<<std::endl;
CMatrix<float>* pMat = pQueryPot->GetMatrix(matTable);
// graphical model hase been created using dense matrix
// so, the marginal is also dense
EMatrixClass type = pMat->GetMatrixClass();
if( ! ( type == mcDense || type == mcNumericDense || type == mc2DNumericDense ) )
{
assert(0);
}
int nEl;
const float* data;
static_cast<CNumericDenseMatrix<float>*>(pMat)->GetRawData(&nEl, &data);
for( i = 0; i < nEl; i++ )
{
std::cout<<" "<<data[i];
}
std::cout<<std::endl;
}
void ShowCPD( const CCPD* pCPD )
{
int nnodes;
const int* domain;
pCPD->GetDomain( &nnodes, &domain );
std::cout<<" node "<<domain[nnodes -1]<<" hase the parents ";
int i;
for( i = 0; i < nnodes - 1; i++ )
{
std::cout<<domain[i]<<" ";
}
std::cout<<std::endl;
CMatrix<float>* pMat = pCPD->GetMatrix(matTable);
// graphical model hase been created using dense matrix
EMatrixClass type = pMat->GetMatrixClass();
if( ! ( type == mcDense || type == mcNumericDense || type == mc2DNumericDense ) )
{
assert(0);
}
std::cout<<" conditional probability distribution \n";
int nEl;
const float* data;
static_cast<CNumericDenseMatrix<float>*>(pMat)->GetRawData(&nEl, &data);
for( i = 0; i < nEl; i++ )
{
std::cout<<" "<<data[i];
}
std::cout<<std::endl<<std::endl;
}
CPNLBase *
CPersistCondGaussianDistribFun::Load(CContextLoad *pContext)
{
int nNode;
bool bFactor;
bool isUnitFun;
CCondGaussianDistribFun *pDF;
const CNodeType *const* ppNodeType;
LoadForDistribFun(&isUnitFun, &nNode, &ppNodeType, pContext);
pContext->GetAttribute(&nNode, "NumberOfNodes");
pContext->GetAttribute(&bFactor, "IsFactor");
CMatrix<CGaussianDistribFun*> *mat = static_cast<CMatrix<CGaussianDistribFun*>*>(
pContext->Get("DistributionMatrix"));
bool isDense(mat->GetMatrixClass() == mcDense);
//dynamic_cast<CSparseMatrix<CGaussianDistribFun*>*>(mat) == 0;
pDF = CCondGaussianDistribFun::Create(bFactor ? 1:0, nNode, ppNodeType, isDense);
CMatrixIterator<CGaussianDistribFun*> *it = mat->InitIterator();
intVector index;
for(; mat->IsValueHere(it); mat->Next(it))
{
mat->Index(it, &index);
pDF->SetDistribFun(*mat->Value(it), &index.front());
}
delete it;
return pDF;
}
void CSamplingInfEngine::Normalization(CPotential *pot)
{
if( pot->GetDistributionType() != dtTabular )
{
const pConstNodeTypeVector *nt = pot->GetDistribFun()->GetNodeTypesVector();
CDistribFun *pUnitFun = CGaussianDistribFun::
CreateUnitFunctionDistribution(nt->size(), &nt->front());
pot->SetDistribFun(pUnitFun);
delete pUnitFun;
}
else
{
CMatrix< float > *pMatToSample;
pMatToSample = static_cast<CTabularDistribFun*>(pot->GetDistribFun())
->GetMatrix(matTable);
EMatrixClass mc = pMatToSample->GetMatrixClass();
if( mc != mcNumericDense || mc != mc2DNumericDense )
{
CMatrixIterator<float>* iter = pMatToSample->InitIterator();
for( iter; pMatToSample->IsValueHere( iter ); pMatToSample->Next(iter) )
{
*const_cast<float*>(pMatToSample->Value( iter )) = 1.0f;;
}
delete iter;
}
else
{
floatVector *data = const_cast< floatVector *>(
static_cast<CNumericDenseMatrix<float>*>(pMatToSample)->GetVector());
floatVector::iterator it1 = data->begin();
floatVector::iterator it2 = data->end();
for( ; it1 != it2; it1++ )
{
*it1 = 1.0f;
}
}
}
}
void Infer_Process(const CBNet* pBnet)
{
//create simple evidence for node 0 from BNet
CEvidence* pEvidForWS = CreateEvidenceForBNet(pBnet);
//create Naive inference for BNet
CNaiveInfEngine* pNaiveInf = CNaiveInfEngine::Create( pBnet );
//enter evidence created before
pNaiveInf->EnterEvidence( pEvidForWS );
//set the query node
int numQueryNds = 1;//*<-
int queryNds[] = { 3 };//*<-
//get a marginal for query set of nodes
pNaiveInf->MarginalNodes( queryNds, numQueryNds );
const CPotential* pMarg = pNaiveInf->GetQueryJPD();
//display the evidence node and such velue of BNet
intVector obsNds;
pConstValueVector obsVls;
pEvidForWS->GetObsNodesWithValues(&obsNds, &obsVls);
int i;
for( i = 0; i < obsNds.size(); i++ )
{
std::cout<<" observed value for node "<<obsNds[i];
std::cout<<" is "<<obsVls[i]->GetInt()<<std::endl;
}
//display the query node and such velue of BNet
int nnodes;
const int* domain;
pMarg->GetDomain( &nnodes, &domain );
std::cout<<" inference results: \n";
std::cout<<" probability distribution for nodes [ ";
for( i = 0; i < nnodes; i++ )
{
std::cout<<domain[i]<<" ";
}
std::cout<<"]"<<std::endl;
CMatrix<float>* pMat = pMarg->GetMatrix(matTable);
// graphical model hase been created using dense matrix
// so, the marginal is also dense
EMatrixClass type = pMat->GetMatrixClass();
if( ! ( type == mcDense || type == mcNumericDense || type == mc2DNumericDense ) )
{
assert(0);
}
int nEl;
const float* data;
static_cast<CNumericDenseMatrix<float>*>(pMat)->GetRawData(&nEl, &data);
for( i = 0; i < nEl; i++ )
{
std::cout<<" "<<data[i];
}
std::cout<<std::endl;
delete pEvidForWS;
delete pNaiveInf;
}
