CPotential *CGaussianCPD::ConvertToPotential() const
{
CDistribFun *gauFactData = (m_CorrespDistribFun)->ConvertCPDDistribFunToPot();
CGaussianPotential *resFactor = CGaussianPotential::Create( &m_Domain.front(), m_Domain.size(), GetModelDomain() );
resFactor->SetDistribFun( gauFactData );
delete gauFactData;
return resFactor;
}
//-----------------------------------------------------------------------------
CPotential *CSoftMaxCPD::ConvertToGaussianPotential(const CEvidence* pEvidence,
CDistribFun *df,
floatVector MeanContParents,
C2DNumericDenseMatrix<float>* CovContParents) const
{
int i;
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 = static_cast<CSoftMaxDistribFun*>(m_CorrespDistribFun)->
ConvertCPDDistribFunToPotential(MeanContParents, CovContParents, r);
CGaussianPotential *resFactor = CGaussianPotential::Create( &m_Domain.front(),
m_Domain.size(), GetModelDomain());
resFactor->SetDistribFun( gauFactData );
//resFactor->Dump();
//resFactor = resFactor->ShrinkObservedNodes(pEvidence);
//resFactor->Dump();
delete gauFactData;
return resFactor;
}
int testSEGaussian()
{
PNL_USING
int i;
int ret = TRS_OK;
float eps = 1e-4f;
//create Gaussian distribution (inside the potential) and try to multiply
//is by Delta
//create Model Domain
int nSimNodes = 3;
CNodeType simNT = CNodeType(0, 2);
CModelDomain* pSimDomain = CModelDomain::Create( nSimNodes, simNT );
//create 2 potentials
intVector dom;
dom.assign(3,0);
dom[1] = 1;
dom[2] = 2;
floatVector mean;
mean.assign(6, 0);
floatVector cov;
cov.assign(36, 0.1f);
for( i = 0; i < 6; i++ )
{
mean[i] = 1.1f*i;
cov[i*7] = 2.0f;
}
CGaussianPotential* pPot = CGaussianPotential::Create( dom, pSimDomain, 1,
mean, cov, 1.0f );
//create gaussian CPD with gaussian parent
const pConstNodeTypeVector* pTypes = pPot->GetArgType();
//create data weigth
floatVector weights1;
weights1.assign(4, 1.0f);
floatVector weights2;
weights2.assign(4, 2.0f);
const float* weights[] = { &weights1.front(), &weights2.front()};
CGaussianDistribFun* pGauCPD = CGaussianDistribFun::CreateInMomentForm( 0,
3, &pTypes->front(), &mean.front(), &cov.front(), weights );
pPot->Dump();
pPot->Normalize();
//try to get multiplied delta
intVector pos;
floatVector valuesDelta;
intVector offsets;
pPot->GetMultipliedDelta(&pos, &valuesDelta, &offsets);
if( pos.size() != 0 )
{
ret = TRS_FAIL;
}
//enter evidence to the pot and after that expand and compare results
//create evidence object
/* valueVector obsVals;
obsVals.assign(6,Value(0) );
obsVals[0].SetFlt(1.0f);
obsVals[1].SetFlt(1.5f);
obsVals[2].SetFlt(2.0f);
obsVals[3].SetFlt(2.5f);
obsVals[4].SetFlt(3.0f);
obsVals[5].SetFlt(3.5f);
CEvidence* pEvid = CEvidence::Create( pSimDomain, 3, &dom.front(), obsVals );
CPotential* pShrPot = pPot->ShrinkObservedNodes( pEvid );*/
CGaussianPotential* pCanonicalPot = CGaussianPotential::Create( dom,
pSimDomain, 0, mean, cov, 1.0f );
CMatrix<float>* matrK = pCanonicalPot->GetMatrix(matK);
intVector multIndex;
multIndex.assign(2,5);
matrK->SetElementByIndexes( 3.0f, &multIndex.front() );
CMatrix<float>* matrH = pCanonicalPot->GetMatrix(matH);
multIndex[0] = 0;
matrH->SetElementByIndexes(0.0f, &multIndex.front());
pPot->ConvertToSparse();
pPot->ConvertToDense();
//create other Gaussian potential for division
i = 1;
floatVector meanSmall;
meanSmall.assign(2, 1.0f);
floatVector covSmall;
covSmall.assign(4, 0.0f);
covSmall[0] = 1.0f;
covSmall[3] = 1.0f;
CGaussianPotential* pSmallPot = CGaussianPotential::Create( &i, 1,
pSimDomain, 1, &meanSmall.front(), &covSmall.front(), 1.0f );
//divide by distribution in moment form
(*pCanonicalPot) /= (*pSmallPot);
//create big unit function distribution and marginalize it
CGaussianPotential* pBigUnitPot =
CGaussianPotential::CreateUnitFunctionDistribution(dom, pSimDomain, 1);
CGaussianPotential* pCloneBigUniPot = static_cast<CGaussianPotential*>(
pBigUnitPot->CloneWithSharedMatrices());
if( !pCloneBigUniPot->IsFactorsDistribFunEqual(pBigUnitPot, eps) )
{
ret = TRS_FAIL;
}
CPotential* pMargUniPot = pBigUnitPot->Marginalize(&dom.front(), 1, 0);
(*pBigUnitPot) /= (*pSmallPot);
(*pBigUnitPot) *= (*pSmallPot);
//check if there are some problems
static_cast<CGaussianDistribFun*>(pBigUnitPot->GetDistribFun())->CheckCanonialFormValidity();
if( pBigUnitPot->IsDistributionSpecific() != 1 )
{
ret = TRS_FAIL;
}
(*pBigUnitPot) /= (*pCanonicalPot);
(*pBigUnitPot) *= (*pCanonicalPot);
static_cast<CGaussianDistribFun*>(pBigUnitPot->GetDistribFun())->CheckCanonialFormValidity();
if( pBigUnitPot->IsDistributionSpecific() != 1 )
{
ret = TRS_FAIL;
}
static_cast<CGaussianDistribFun*>(pSmallPot->GetDistribFun())->
UpdateCanonicalForm();
(*pPot) /= (*pSmallPot);
pSmallPot->SetCoefficient(0.0f,1);
pSmallPot->Dump();
//create canonical potential without coefficient
i = 0;
CDistribFun* pCopyPotDistr = pPot->GetDistribFun()->ConvertCPDDistribFunToPot();
CGaussianPotential* pCanSmallPot = CGaussianPotential::Create( &i, 1,
pSimDomain, 0, &meanSmall.front(), &covSmall.front(), 1.0f );
CGaussianPotential* pCanPotCopy =
static_cast<CGaussianPotential*>(pCanSmallPot->Clone());
CGaussianPotential* pCanPotCopy1 =
static_cast<CGaussianPotential*>(pCanPotCopy->CloneWithSharedMatrices());
(*pPot) /= (*pCanSmallPot);
(*pPot) *= (*pCanSmallPot);
//can compare results, if we want
CDistribFun* pMultDivRes = pPot->GetDistribFun();
float diff = 0;
if( !pMultDivRes->IsEqual(pCopyPotDistr, eps,1, &diff) )
{
ret = TRS_FAIL;
std::cout<<"the diff is "<<diff<<std::endl;
}
delete pCopyPotDistr;
//create delta distribution
floatVector deltaMean;
deltaMean.assign( 2, 1.5f );
i = 0;
CGaussianPotential* pDeltaPot = CGaussianPotential::CreateDeltaFunction(
&i, 1, pSimDomain, &deltaMean.front(), 1 );
CGaussianDistribFun* pDeltaDistr = static_cast<CGaussianDistribFun*>(
pDeltaPot->GetDistribFun());
pDeltaDistr->CheckMomentFormValidity();
pDeltaDistr->CheckCanonialFormValidity();
pDeltaPot->Dump();
//multiply some potential by delta
(*pCanSmallPot) *= (*pDeltaPot);
(*pPot) *= (*pDeltaPot);
//(*pPot) *= (*pDeltaPot);
pPot->GetMultipliedDelta(&pos, &valuesDelta, &offsets);
(*pCanonicalPot) *= (*pDeltaPot);
//marginalize this distribFun multiplied by delta
intVector margDims;
margDims.assign(2,1);
margDims[1] = 2;
CPotential* pMargPot = pPot->Marginalize( &margDims.front(), 2 );
i = 0;
CPotential* pSmallMargPot = pPot->Marginalize(&i, 1);
//marginalize in canonical form
CPotential* pMargCanPot = pCanonicalPot->Marginalize( &margDims.front(), 2 );
CPotential* pSmallCanPot = pCanonicalPot->Marginalize(&i,1);
//create unit function distribution in canonical form
i = 0;
CGaussianPotential* pUnitPot =
CGaussianPotential::CreateUnitFunctionDistribution( &i, 1, pSimDomain,
1);
pUnitPot->Dump();
CGaussianDistribFun* pUnitDistr = static_cast<CGaussianDistribFun*>(
pUnitPot->GetDistribFun());
pUnitDistr->CheckCanonialFormValidity();
pUnitDistr->CheckMomentFormValidity();
(*pPot) *= (*pUnitPot);
if( pUnitPot->IsFactorsDistribFunEqual(pBigUnitPot, eps, 1) )
{
ret = TRS_FAIL;
}
deltaMean.resize(6);
deltaMean[2] = 2.5f;
deltaMean[3] = 2.5f;
deltaMean[4] = 3.5f;
deltaMean[5] = 3.5f;
CGaussianPotential* pBigDeltaPot = CGaussianPotential::CreateDeltaFunction(
dom, pSimDomain, deltaMean, 1 );
CGaussianPotential* pCloneBigDeltaPot = static_cast<CGaussianPotential*>(
pBigDeltaPot->CloneWithSharedMatrices());
//we can shrink observed nodes in this potential
valueVector vals;
vals.resize(2);
vals[0].SetFlt(1.5f);
vals[1].SetFlt(1.5f);
i = 0;
CEvidence* pDeltaEvid = CEvidence::Create( pSimDomain, 1, &i,vals );
CGaussianPotential* pShrGauDeltaPot = static_cast<CGaussianPotential*>(
pBigDeltaPot->ShrinkObservedNodes( pDeltaEvid ));
delete pDeltaEvid;
pShrGauDeltaPot->Dump();
delete pShrGauDeltaPot;
CPotential* pSmallDeltaMarg = pBigDeltaPot->Marginalize( &dom.front(), 1, 0 );
pSmallDeltaMarg->Normalize();
pDeltaPot->Normalize();
if( !pSmallDeltaMarg->IsFactorsDistribFunEqual( pDeltaPot, eps, 1 ) )
{
ret = TRS_FAIL;
}
//call operator = for delta distributions
(*pSmallDeltaMarg) = (*pDeltaPot);
//call operator = for canonical and delta distribution
(*pCanPotCopy) = (*pUnitPot);
//call operator = for canonical and unit distribution
(*pCanPotCopy1) = (*pDeltaPot);
(*pUnitPot) = (*pUnitPot);
(*pPot) *= (*pBigDeltaPot);
(*pCloneBigDeltaPot) *= (*pDeltaPot);
if( !pCloneBigDeltaPot->IsFactorsDistribFunEqual(pBigDeltaPot, eps) )
{
ret = TRS_FAIL;
}
//we can create the matrix which will be almost delta distribution,
//but covariance matrix will contain almost zeros
floatVector almostDeltaCov;
almostDeltaCov.assign(4, 0.0f);
almostDeltaCov[0] = 0.00001f;
almostDeltaCov[3] = 0.00001f;
i = 0;
CGaussianPotential* pAlmostDeltaPot = CGaussianPotential::Create( &i, 1,
pSimDomain, 1, &deltaMean.front(), &almostDeltaCov.front(), 1.0f );
if( !pDeltaPot->IsFactorsDistribFunEqual( pAlmostDeltaPot, eps, 0 ) )
{
ret = TRS_FAIL;
}
if( !pAlmostDeltaPot->IsFactorsDistribFunEqual( pDeltaPot, eps, 0 ) )
{
ret = TRS_FAIL;
}
(*pCloneBigUniPot ) = ( *pPot );
if( !(pCloneBigUniPot->IsFactorsDistribFunEqual(pPot, eps)) )
{
ret = TRS_FAIL;
}
(*pCloneBigDeltaPot) = (*pPot);
if( !(pCloneBigDeltaPot->IsFactorsDistribFunEqual(pPot, eps)) )
{
ret = TRS_FAIL;
}
delete pCanPotCopy;
delete pCanPotCopy1;
delete pAlmostDeltaPot;
delete pMargUniPot;
delete pCloneBigUniPot;
delete pBigUnitPot;
delete pCanSmallPot;
delete pDeltaPot;
delete pUnitPot;
delete pCloneBigDeltaPot;
delete pBigDeltaPot;
delete pMargCanPot;
delete pSmallCanPot;
delete pMargPot;
delete pSmallMargPot;
delete pCanonicalPot;
//delete pShrPot;
//delete pEvid;
delete pGauCPD;
delete pPot;
delete pSimDomain;
return trsResult( ret, ret == TRS_OK ? "No errors" :
"Bad test on SEGaussian");
}
//-----------------------------------------------------------------------------
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;
}
}
}
