int CMNet::GetFactors( int numberOfNodes, const int *nodes,
pFactorVector *params ) const
{
// the function is to find all the factors which are set on
// the domains which contain "nodes" as a subset
// bad-args check
PNL_CHECK_LEFT_BORDER( numberOfNodes, 1 );
PNL_CHECK_IS_NULL_POINTER(nodes);
PNL_CHECK_IS_NULL_POINTER(params);
// bad-args check end
params->clear();
int numOfClqs;
const int *clqsNums;
GetClqsNumsForNode( *nodes, &numOfClqs, &clqsNums );
assert( numOfClqs > 0 );
intVector factsNums( clqsNums, clqsNums + numOfClqs );
const int *ndsIt = nodes + 1,
*nds_end = nodes + numberOfNodes;
for( ; ndsIt != nds_end; ++ndsIt )
{
GetClqsNumsForNode( *ndsIt, &numOfClqs, &clqsNums );
intVector::iterator factsNumsIt = factsNums.end() - 1;
intVector::const_iterator factsNums_before_begin
= factsNums.begin() - 1;
for( ; factsNumsIt != factsNums_before_begin; --factsNumsIt )
{
if( std::find( clqsNums, clqsNums + numOfClqs, *factsNumsIt )
== clqsNums + numOfClqs )
{
factsNums.erase(factsNumsIt);
}
if( factsNums.empty() )
{
return 0;
}
}
}
intVector::const_iterator factsNumsIt = factsNums.begin(),
factsNums_end = factsNums.end();
for( ; factsNumsIt != factsNums_end; ++factsNumsIt )
{
params->push_back(GetFactor(*factsNumsIt));
}
return 1;
}
CSoftMaxCPD* CSoftMaxCPD::Create(const int *domain, int nNodes,
CModelDomain* pMD)
{
PNL_CHECK_IS_NULL_POINTER(domain);
PNL_CHECK_IS_NULL_POINTER(pMD);
PNL_CHECK_LEFT_BORDER(nNodes, 1);
int i;
int NumContPar = 0;
for(i = 0; i<nNodes; i++)
{
if (!pMD->GetVariableType(domain[i])->IsDiscrete())
{
NumContPar++;
}
}
if(NumContPar == 0 )
{
PNL_THROW(CInconsistentType,
"SoftMax node does not have continuous parents");
}
CSoftMaxCPD *pNewParam = new CSoftMaxCPD(domain, nNodes, pMD);
PNL_CHECK_IF_MEMORY_ALLOCATED(pNewParam);
return pNewParam;
}
PNL_USING
PNL_BEGIN
/////////////////////////////////////////////////////////////////////////////
//float C2DNumericDenseMatrix
C2DNumericDenseMatrix<float> *C2DNumericDenseMatrix<float>::Create( const int *lineSizes,
const float *data, int Clamp )
{
PNL_CHECK_IS_NULL_POINTER( lineSizes );
PNL_CHECK_LEFT_BORDER( Clamp, 0 );
PNL_CHECK_IS_NULL_POINTER( data );
int i = 0;
for( i = 0; i < 2; i++ )
{
if( lineSizes[i] < 0 )
{
PNL_THROW( COutOfRange, "range is negative" )
}
}
PNL_CHECK_IS_NULL_POINTER( data );
C2DNumericDenseMatrix<float> *pxMatrix =
new C2DNumericDenseMatrix<float>( 2, lineSizes, data, (Clamp>0));
return pxMatrix;
}
CGaussianCPD* CGaussianCPD::Create( const int *domain, int nNodes,
CModelDomain* pMD )
{
PNL_CHECK_IS_NULL_POINTER( domain );
PNL_CHECK_IS_NULL_POINTER( pMD );
PNL_CHECK_LEFT_BORDER( nNodes, 1 );
CGaussianCPD *pNewParam = new CGaussianCPD( domain, nNodes, pMD);
PNL_CHECK_IF_MEMORY_ALLOCATED( pNewParam );
return pNewParam;
}
CMNet* CMNet::ConvertFromBNetUsingEvidence( const CBNet *pBNet,
const CEvidence *pEvidence )
{
/* bad-args check */
PNL_CHECK_IS_NULL_POINTER(pBNet);
PNL_CHECK_IS_NULL_POINTER(pEvidence);
/* bad-args check end */
CMNet *pMNet = new CMNet( pBNet, pEvidence );
PNL_CHECK_IF_MEMORY_ALLOCATED(pMNet);
return pMNet;
}
int CMRF2::GetFactors( int numberOfNodes, const int *nodes,
pFactorVector *params ) const
{
/* bad-args check */
PNL_CHECK_RANGES( numberOfNodes, 1, 2 );
PNL_CHECK_IS_NULL_POINTER(nodes);
PNL_CHECK_IS_NULL_POINTER(params);
/* bad-args check end */
params->clear();
int numOfClqsFrstNode;
const int *clqsFrstNode;
GetClqsNumsForNode( *nodes, &numOfClqsFrstNode, &clqsFrstNode );
if( numberOfNodes == 1 )
{
const int *clqsIt = clqsFrstNode,
*clqs_end = clqsFrstNode + numOfClqsFrstNode;
for( ; clqs_end - clqsIt; ++clqsIt )
{
params->push_back(GetFactor(*clqsIt));
}
}
else
{
int numOfClqsScndNode;
const int *clqsScndNode;
GetClqsNumsForNode( *(nodes + 1), &numOfClqsScndNode, &clqsScndNode );
const int *pClqNum = std::find_first_of( clqsFrstNode,
clqsFrstNode + numOfClqsFrstNode, clqsScndNode,
clqsScndNode + numOfClqsScndNode );
if( pClqNum == clqsFrstNode + numOfClqsFrstNode )
{
return 0;
}
params->push_back(GetFactor(*pClqNum));
}
assert( params->size() != 0 );
return 1;
}
void CBKInfEngine::ForwardFirst(const CEvidence *pEvidence, int maximize )
{
PNL_CHECK_FOR_NON_ZERO(GetTime());
m_JTreeInfIter = m_CRingJtreeInf.begin();
PNL_CHECK_IS_NULL_POINTER(*m_JTreeInfIter);
PNL_CHECK_IS_NULL_POINTER(pEvidence);
(*m_JTreeInfIter)->ShrinkObserved( pEvidence, maximize );
(*m_JTreeInfIter)->CollectEvidence();
m_CurrentTime++;
}
void
CGaussianCPD::AllocDistribution(const float* pMean, const float* pCov,
float normCoeff, const float* const* pWeights,
const int* parentCombination )
{
PNL_CHECK_IS_NULL_POINTER( pMean );
PNL_CHECK_IS_NULL_POINTER( pCov );
if( m_CorrespDistribFun->GetDistributionType() == dtGaussian )
{
SetCoefficient( normCoeff, 0 );
AllocMatrix( pMean, matMean );
AllocMatrix( pCov, matCovariance );
if( pWeights )
{
int numParents = m_Domain.size() - 1;
for( int i = 0; i < numParents; i++ )
{
if( pWeights[i] )
{
m_CorrespDistribFun->AllocMatrix( pWeights[i],
matWeights, i );
}
}
}
}
else
{
PNL_CHECK_IS_NULL_POINTER( parentCombination );
SetCoefficient( normCoeff, parentCombination );
AllocMatrix( pMean, matMean, -1, parentCombination );
AllocMatrix( pCov, matCovariance, -1, parentCombination );
if( pWeights )
{
intVector contParentsIndex;
static_cast<CCondGaussianDistribFun*>(m_CorrespDistribFun)->
GetContinuousParentsIndices(&contParentsIndex);
int numParents = contParentsIndex.size();
for( int i = 0; i < numParents; i++ )
{
if( pWeights[i] )
{
m_CorrespDistribFun->AllocMatrix( pWeights[i],
matWeights, i, parentCombination );
}
}
}
}
}
void CLWSamplingInfEngine::
EnterEvidenceProbability( floatVecVector *pEvidenceProbIn )
{
PNL_CHECK_IS_NULL_POINTER(pEvidenceProbIn);
int i, j;
float w;
int iSamples = pEvidenceProbIn->size();
int iSampleSize = m_currentEvVec.size();
if(iSamples != iSampleSize) return;
for( i = 0; i < iSamples; i++)
{
w = 1;
for( j = 0; j < pEvidenceProbIn[i].size(); j++)
{
w = w * (*pEvidenceProbIn)[i][j];
}
m_particleWeight[i] = w;
}
NormalizeWeight();
}
void CLWSamplingInfEngine::
EnterEvidence( const CEvidence *pEvidenceIn , int maximize , int sumOnMixtureNode )
{
PNL_CHECK_IS_NULL_POINTER(pEvidenceIn);
LWSampling(pEvidenceIn);
// Given evidencs, calculate particle weight by CPD
float w;
int iSampleSize = m_currentEvVec.size();
const int* ObsNodes = pEvidenceIn->GetAllObsNodes();
int NumberObsNodes = pEvidenceIn->GetNumberObsNodes();
int i, j;
for( i = 0; i < iSampleSize; i++)
{
w = 0;
for( j = 0; j < NumberObsNodes; j++)
{
if(pEvidenceIn->IsNodeObserved(ObsNodes[j]))
{
CFactor* pFactor = m_pGraphicalModel->GetFactor(ObsNodes[j]);
w = w + pFactor->GetLogLik( m_currentEvVec[i]);
}
}
m_particleWeight[i] = (float)exp(w);
}
NormalizeWeight();
}
void
CGraphPersistence::Save(CPNLBase *pObj, CContextSave *pContext)
{
CGraph *pG = dynamic_cast<CGraph*>(pObj);
std::stringstream buf;
PNL_CHECK_IS_NULL_POINTER(pG);
int nNode = pG->GetNumberOfNodes();
intVector neig;
neighborTypeVector neigType;
pContext->AddAttribute("SavingType", "ByEdges");
{
char buf2[12];
sprintf(buf2, "%i", nNode);
pContext->AddAttribute("NumberOfNodes", buf2);
}
for(int i = 0; i < nNode; ++i)
{
pG->GetNeighbors(i, &neig, &neigType);
buf << i << ":";
for(int j = neig.size(); --j >= 0;)
{
buf << neig[j] << "_(" << neighTypeSymbols[neigType[j]] << ") ";
}
buf << '\n';
}
pContext->AddText(buf.str().c_str());
}
CJtreeInfEngine* CJtreeInfEngine::Create( const CStaticGraphicalModel
*pGraphicalModel,
CJunctionTree *pJTree)
{
/* bad-args check */
PNL_CHECK_IS_NULL_POINTER(pGraphicalModel);
PNL_CHECK_IS_NULL_POINTER(pJTree);
/* bad-args check end */
CJtreeInfEngine *pJTreeInfEngine = new CJtreeInfEngine( pGraphicalModel,
pJTree );
PNL_CHECK_IF_MEMORY_ALLOCATED(pJTreeInfEngine);
return pJTreeInfEngine;
}
void CMNet::ConstructClqsFromBNetsFamiliesAndInternalGraph(const CBNet *pBNet)
{
// bad-args check
PNL_CHECK_IS_NULL_POINTER(pBNet);
// bad-args check end
}
inline void CReferenceCounter::Release(void* pObject)
{
PNL_CHECK_IS_NULL_POINTER(pObject);
omp_set_lock(&m_release_lock);
std::list<void*>::iterator location = std::find( m_refList.begin(),
m_refList.end(), pObject );
assert( location != m_refList.end() );
while( location != m_refList.end() )
{
location = std::find( m_refList.erase(location), m_refList.end(),
pObject );
}
if( m_refList.empty() )
{
omp_unset_lock(&m_release_lock);
delete this;
}
else
{
omp_unset_lock(&m_release_lock);
};
}
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 );
}
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() );
}
}
PNL_USING
CStaticStructLearnSEM::CStaticStructLearnSEM( CBNet* pBNet,
ELearningTypes LearnType,
int nMaxFanIn,
intVector* vAncestor,
intVector* vDescent ):
CStaticLearningEngine( pBNet, LearnType )
{
PNL_CHECK_IS_NULL_POINTER(pBNet);
int i;
m_nMaxFanIn = nMaxFanIn;
m_nNodes = pBNet->GetNumberOfNodes();
if(vAncestor)
m_vAncestor.assign(vAncestor->begin(), vAncestor->end());
if(vDescent)
m_vDescent.assign(vDescent->begin(), vDescent->end());
m_pCurrBNet = pBNet->Copy(pBNet);
m_vGlobalRenaming.assign(m_nNodes, 0);
for(i=0; i<m_nNodes; i++)
{
m_vGlobalRenaming[i] = i;
}
m_IterEM = 8;
m_maxLoop = 50;
m_minProgress = (float)1e-3;
}
CBKInfEngine* CBKInfEngine::
Create(const CDynamicGraphicalModel *pGrModel, intVecVector& clusters)
{
PNL_CHECK_IS_NULL_POINTER(pGrModel);
std::string description;
if( !pGrModel->IsValid( &description ))
{
PNL_THROW( CBadArg, description );
}
if( pGrModel->GetModelType() != mtDBN )
{
PNL_THROW( CInconsistentType, " input model is invalid " );
}
intVector interfNds;
pGrModel->GetInterfaceNodes( &interfNds );
if( !CheckClustersValidity(clusters, interfNds) )
{
PNL_THROW( CBadArg, " clusters are invalid " );
}
return new CBKInfEngine(pGrModel, clusters);
}
void CSoftMaxCPD::AllocDistribution(const float* pWeights,
const float* pOffsets, const int* parentCombination)
{
PNL_CHECK_IS_NULL_POINTER(pWeights);
PNL_CHECK_IS_NULL_POINTER(pOffsets);
////////////////////////////////////////////////
const CNodeType *nt;
nt = GetModelDomain()->GetVariableType( m_Domain[m_Domain.size()-1] );
int SoftMaxSize = nt->GetNodeSize();
if (SoftMaxSize == 2)
{
int matSize = 0;
int i;
for (i = 0; i < m_Domain.size(); i++)
{
nt = GetModelDomain()->GetVariableType( m_Domain[i] );
if(!(nt->IsDiscrete()))
{
matSize ++;
}
}
//matSize = matSize;
for (i = 0; i < 2*matSize-1; i+=2)
{
if (pWeights[i] - pWeights[i+1] == 0)
PNL_THROW(CNotImplemented, "sigmoid must have distinct weights");
}
}
////////////////////////////////////////////////
if (m_CorrespDistribFun->GetDistributionType() == dtSoftMax)
{
AllocMatrix(pWeights, matWeights);
static_cast<CSoftMaxDistribFun*>(m_CorrespDistribFun)->
AllocOffsetVector(pOffsets);
}
else
{
PNL_CHECK_IS_NULL_POINTER(parentCombination);
AllocMatrix(pWeights, matWeights, -1, parentCombination);
static_cast<CCondSoftMaxDistribFun*>(m_CorrespDistribFun)->
AllocOffsetVector(pOffsets, parentCombination);
}
}
inline void CReferenceCounter::AddRef(void* pObject)
{
PNL_CHECK_IS_NULL_POINTER(pObject);
omp_set_lock(&m_release_lock);
m_refList.push_back(pObject);
omp_unset_lock(&m_release_lock);
}
CTreeCPD* CTreeCPD::Copy( const CTreeCPD* pTreeCPD )
{
PNL_CHECK_IS_NULL_POINTER( pTreeCPD );
CTreeCPD *retCPD = new CTreeCPD( *pTreeCPD );
PNL_CHECK_IF_MEMORY_ALLOCATED( retCPD );
return retCPD;
}
PNL_USING
void CGraphicalModel::GetNodeTypes(nodeTypeVector* nodeTypesOut)
{
PNL_CHECK_IS_NULL_POINTER(nodeTypesOut);
m_pMD->GetVariableTypes(nodeTypesOut);
};
CGaussianCPD* CGaussianCPD::Copy( const CGaussianCPD* pGauCPD )
{
PNL_CHECK_IS_NULL_POINTER( pGauCPD );
CGaussianCPD *retCPD = new CGaussianCPD( *pGauCPD );
PNL_CHECK_IF_MEMORY_ALLOCATED( retCPD );
return retCPD;
}
CSoftMaxCPD* CSoftMaxCPD::Copy(const CSoftMaxCPD* pSMCPD)
{
PNL_CHECK_IS_NULL_POINTER(pSMCPD);
CSoftMaxCPD *retCPD = new CSoftMaxCPD(*pSMCPD);
PNL_CHECK_IF_MEMORY_ALLOCATED(retCPD);
return retCPD;
}
CGaussianCPD*
CGaussianCPD::CreateUnitFunctionCPD(const int *domain, int nNodes, CModelDomain* pMD)
{
PNL_CHECK_IS_NULL_POINTER( domain );
PNL_CHECK_IS_NULL_POINTER( pMD );
PNL_CHECK_LEFT_BORDER( nNodes, 1 );
CGaussianCPD* resCPD = new CGaussianCPD( domain, nNodes, pMD );
intVector dom = intVector( domain, domain + nNodes );
pConstNodeTypeVector ntVec;
pMD->GetVariableTypes( dom, &ntVec );
CGaussianDistribFun* UniData =
CGaussianDistribFun::CreateUnitFunctionDistribution( nNodes,
&ntVec.front(), 0, 0);
delete (resCPD->m_CorrespDistribFun);
resCPD->m_CorrespDistribFun = UniData;
return resCPD;
}
PNL_USING
CMNet* CMNet::Create( int numberOfCliques, const int *cliqueSizes,
const int **cliques, CModelDomain* pMD )
{
/* bad-args check */
PNL_CHECK_LEFT_BORDER( numberOfCliques, 1 );
PNL_CHECK_IS_NULL_POINTER(cliqueSizes);
PNL_CHECK_IS_NULL_POINTER(cliques);
PNL_CHECK_IS_NULL_POINTER( pMD );
/* bad-args check end */
/* creating the model */
CMNet *pMNet = new CMNet( numberOfCliques, cliqueSizes, cliques, pMD);
PNL_CHECK_IF_MEMORY_ALLOCATED(pMNet);
return pMNet;
}
void CFactor::AttachMatrix(CMatrix<float> *matrix,
EMatrixType mType, int numberOfMatrix,
const int* discrParentValuesIndices)
{
/*Attach the matrix as m_Type for this type of DistribFun*/
PNL_CHECK_IS_NULL_POINTER( matrix );
m_CorrespDistribFun->AttachMatrix( matrix, mType, numberOfMatrix,
discrParentValuesIndices );
}
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 );
}
void CBKInfEngine::BackwardT()
{
PNL_CHECK_IS_NULL_POINTER(*m_JTreeInfIter);
(*m_JTreeInfIter)->DistributeEvidence();
if(m_CRingDistrOnSep.size())
{
m_CDistrOnSepIter--;
}
}
void CMNet::GetFactors( int numberOfNodes, const int *nodes,
int *numberOfFactors, CFactor ***params ) const
{
// bad-args check
PNL_CHECK_LEFT_BORDER( numberOfNodes, 1 );
PNL_CHECK_IS_NULL_POINTER(nodes);
PNL_CHECK_IS_NULL_POINTER(numberOfFactors);
PNL_CHECK_IS_NULL_POINTER(params);
// bad-args check end
if( GetFactors( numberOfNodes, nodes, &m_paramsForNodes ) )
{
*numberOfFactors = m_paramsForNodes.size();
*params = &m_paramsForNodes.front();
}
else
{
*numberOfFactors = 0;
*params = NULL;
}
}
