//------------------------------------------------------------------------------
CPotential* CSoftMaxCPD::ConvertWithEvidenceToTabularPotential(
const CEvidence* pEvidence, int flagSumOnMixtureNode ) const
{
//need to convert to potential and after that add evidence
CPotential* potWithoutEv = ConvertToTabularPotential(pEvidence);
CPotential* potWithEvid = potWithoutEv->ShrinkObservedNodes(pEvidence);
delete potWithoutEv;
return potWithEvid;
}
CPotential*
CGaussianCPD::ConvertWithEvidenceToPotential(const CEvidence* pEvidence,
int flagSumOnMixtureNode )const
{
if( m_CorrespDistribFun->GetDistributionType() == dtGaussian )
{
//need to convert to potential and after that add evidence
CPotential* potWithoutEv = ConvertToPotential();
CPotential* potWithEvid = potWithoutEv->ShrinkObservedNodes(pEvidence);
delete potWithoutEv;
return potWithEvid;
}
else //it means m_CorrespDistribFun->GetDistributionType == dtCondGaussian
{
//need to enter discrete & continuous evidence separetly
//before it create node types - if all nodes discrete
//or there are only nodes of size 0 from continuous -
// - result distribution type is Tabular
//collect information for enter discrete evidence & continuous
int domSize = m_Domain.size();
intVector obsDiscreteIndex;
obsDiscreteIndex.reserve( domSize );
//observed discrete values put into int vector
intVector obsDiscrVals;
obsDiscrVals.reserve( domSize );
//collect information about Gaussian observed indices
intVector obsGauIndex;
obsGauIndex.reserve( domSize );
//continuous observed values into vector of matrices
pnlVector<C2DNumericDenseMatrix<float>*> obsGauVals;
obsGauVals.reserve( domSize );
//create matrix to store observed value of node
C2DNumericDenseMatrix<float>* obsSelfVal = NULL;
//create vectors for storage temporary objects
int i;
int isTab;
for( i = 0; i < domSize; i++ )
{
int curNum = m_Domain[i];
if( pEvidence->IsNodeObserved(curNum) )
{
const CNodeType* nt = GetModelDomain()->GetVariableType( curNum );
isTab = nt->IsDiscrete();
if( isTab )
{
obsDiscreteIndex.push_back( i );
obsDiscrVals.push_back( pEvidence->GetValue(curNum)->GetInt() );
}
else
{
int contSize = nt->GetNodeSize();
//create matrices to call Enter continuous evidence
floatVector val;
val.resize(contSize);
const Value* vFromEv = pEvidence->GetValue(curNum);
for( int j = 0; j < contSize; j++ )
{
val[j] = vFromEv[j].GetFlt();
}
intVector dims;
dims.assign( 2, 1 );
dims[0] = contSize;
if( i == domSize - 1 )
{
obsSelfVal = C2DNumericDenseMatrix<float>::Create(
&dims.front(), &val.front());
}
else
{
//store only parent indices
obsGauIndex.push_back( i );
C2DNumericDenseMatrix<float>* obsGauVal =
C2DNumericDenseMatrix<float>::Create(
&dims.front(), &val.front() );
obsGauVals.push_back( obsGauVal );
}
}
}
} // for( i = 0; i < domSize; i++ )
CModelDomain* pMD = GetModelDomain();
CPotential* resPot = NULL;
int isLastNodeObs = pEvidence->IsNodeObserved(m_Domain[m_Domain.size()-1]);
if( (obsDiscreteIndex.size() + obsGauIndex.size() == m_Domain.size()-1) && isLastNodeObs)
{
//result distribution is scalar
obsDiscreteIndex.insert(obsDiscreteIndex.end(), obsGauIndex.begin(),
obsGauIndex.end());
//child node is observed
obsDiscreteIndex.insert(obsDiscreteIndex.end(), domSize-1);
resPot = CScalarPotential::Create( m_Domain, GetModelDomain(), obsDiscreteIndex );
}
else
{
const CNodeType* nt;
//if all discrete nodes are observed then distribution will be Gaussian (Bader - comment)
int allDiscrObs = 1;
int allContObs = 1;
int i;
int isTab;
int isCon;
for( i = 0; i < domSize; i++ )
{
int curNum = m_Domain[i];
nt = GetModelDomain()->GetVariableType( curNum );
isTab = nt->IsDiscrete();
isCon = !(isTab);
if( isTab )
if( !(pEvidence->IsNodeObserved(curNum)) )
allDiscrObs = 0;
if( isCon )
if( !(pEvidence->IsNodeObserved(curNum)) )
allContObs = 0;
}
if (allContObs && (!allDiscrObs) )
{
CCondGaussianDistribFun* withDiscrEv =
(static_cast<CCondGaussianDistribFun*>(m_CorrespDistribFun))->
EnterDiscreteEvidence(obsDiscreteIndex.size(),
&obsDiscreteIndex.front(), &obsDiscrVals.front(),
pMD->GetObsTabVarType() );
CTabularDistribFun* resDistr = withDiscrEv->
EnterFullContinuousEvidence( obsGauIndex.size(),
&obsGauIndex.front(), obsSelfVal, &obsGauVals.front(),
pMD->GetObsGauVarType() );
//need to unite gaussian and tabular observed index
obsDiscreteIndex.insert(obsDiscreteIndex.end(), obsGauIndex.begin(),
obsGauIndex.end());
//child node is observed
obsDiscreteIndex.insert(obsDiscreteIndex.end(), domSize-1);
resPot = CTabularPotential::Create(
&m_Domain.front(), m_Domain.size(), GetModelDomain(), NULL,
obsDiscreteIndex );
resPot->SetDistribFun( resDistr );
delete withDiscrEv;
delete resDistr;
}
else
{
if (allDiscrObs && !allContObs)
{
intVector discParents;
for ( i = 0; i < m_Domain.size(); i++)
{
nt = GetModelDomain()->GetVariableType( m_Domain[i] );
if (nt->IsDiscrete())
discParents.push_back(m_Domain[i]);
}
int *parentComb = new int [discParents.size()];
intVector pObsNodes;
pConstValueVector pObsValues;
pConstNodeTypeVector pNodeTypes;
pEvidence->GetObsNodesWithValues(&pObsNodes, &pObsValues, &pNodeTypes);
int j;
int location;
for ( j = 0; j < discParents.size(); j++)
{
location =
std::find(pObsNodes.begin(), pObsNodes.end(), discParents[j])
- pObsNodes.begin();
parentComb[j] = pObsValues[location]->GetInt();
}
const CGaussianDistribFun* resDistr =
static_cast<CCondGaussianDistribFun*>(m_CorrespDistribFun)->GetDistribution(parentComb);
CDistribFun* newResDistr = resDistr->ConvertCPDDistribFunToPot();
obsGauIndex.insert(obsGauIndex.end(), obsDiscreteIndex.begin(),
obsDiscreteIndex.end());
intVector gauSubDomain;
for( j = 0; j < m_Domain.size(); j++)
{
nt = GetModelDomain()->GetVariableType( m_Domain[j] );
if(!(nt->IsDiscrete()))
gauSubDomain.push_back(m_Domain[j]);
}
resPot = CGaussianPotential::Create( &gauSubDomain.front(),
gauSubDomain.size(), GetModelDomain());
resPot->SetDistribFun( newResDistr );
delete newResDistr;
delete [] parentComb;
}
else
{
//can enter discrete evidence first if all continuous nodes observed
//need to check if all them observed!
CCondGaussianDistribFun* withDiscrEv =
(static_cast<CCondGaussianDistribFun*>(m_CorrespDistribFun))->
EnterDiscreteEvidence(obsDiscreteIndex.size(),
&obsDiscreteIndex.front(), &obsDiscrVals.front(),
pMD->GetObsTabVarType() );
//need to enter continuous evidence
CTabularDistribFun* resDistr = withDiscrEv->
EnterFullContinuousEvidence( obsGauIndex.size(),
&obsGauIndex.front(), obsSelfVal, &obsGauVals.front(),
pMD->GetObsGauVarType() );
//need to unite gaussian and tabular observed index
obsDiscreteIndex.insert(obsDiscreteIndex.end(), obsGauIndex.begin(),
obsGauIndex.end());
//child node is observed
obsDiscreteIndex.insert(obsDiscreteIndex.end(), domSize-1);
resPot = CTabularPotential::Create(
&m_Domain.front(), m_Domain.size(), GetModelDomain(), NULL,
obsDiscreteIndex );
resPot->SetDistribFun( resDistr );
delete withDiscrEv;
delete resDistr;
}
}
}
delete obsSelfVal;
for( i = 0; i < obsGauVals.size(); i++ )
{
delete obsGauVals[i];
}
return resPot;
}
}
//-----------------------------------------------------------------------------
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;
}
}
}