void CNodeValues::ToggleNodeStateBySerialNumber(int nNodes, const int *nodeNumbers)
{
/*to change the status of node from really observed
to potentially observed and backwards*/
int i,j;
if( ( nNodes<0 ) || ( nNodes>m_numberObsNodes ) )
{
PNL_THROW(COutOfRange,
"number of nodes to toggle must be less than m_numberObsNodes")/**/
}
else
{
int flagAllRight = 1;/*all nodes from nodeIndices are potentially
observed (if it is so - all numbers of this nodes
are in m_ObsNodes*/
int thereIsSuchNode = 0;/*check-up flag for node - is it
from m_ObsNodes?*/
int *nodeIndices = new int[nNodes];
PNL_CHECK_IF_MEMORY_ALLOCATED( nodeIndices );
/*checking up all input data*/
for ( i=0; i < nNodes; i++ )
{
for ( j=0; j < m_numberObsNodes; j++ )
{
if( nodeNumbers[i] == j )
{
thereIsSuchNode = 1;
nodeIndices[i] = j;
break;
}
}
if( !thereIsSuchNode )
{
flagAllRight = 0;
break;
}
}
if ( flagAllRight )
{
for( i=0; i < nNodes; i++ )
{
m_isObsNow[nodeIndices[i]] = 1-m_isObsNow[nodeIndices[i]];
//fixme !!! is it enougth to use offset? - I think yes
}
}
else
{
PNL_THROW( COutOfRange,
"some node has number which is not in m_obsNodes" ) /**/
}
delete []nodeIndices;
}
}
bool C2DBitwiseMatrix::AddOneRowWithAnother(int nSourceRow, int nDestinationRow)
{
if(nSourceRow <0 || nSourceRow>= m_nHeight)
{
PNL_THROW(COutOfRange, "Row out of range!");
return false;
}
if(nDestinationRow <0 || nDestinationRow>= m_nHeight)
{
PNL_THROW(COutOfRange, "Row out of range!");
return false;
}
int nLength = m_nWidth / BITWISEMATRIX_BYTESIZE;
if(nLength*BITWISEMATRIX_BYTESIZE < m_nWidth)
nLength ++;
for(int i=0; i<nLength; i++)
{
m_pMatrix[nDestinationRow][i] = (unsigned char)(m_pMatrix[nDestinationRow][i] | m_pMatrix[nSourceRow][i]);
}
return true;
}
void CMNet::AttachFactor(CFactor *param)
{
/* this function is used for attaching factors that have been created
already with the call of function Create() for some type of fator */
/* bad-args check */
if( ! param )
{
PNL_THROW( CNULLPointer, " param == NULL " );
}
if( param->GetFactorType() != ftPotential )
{
PNL_THROW( CInconsistentType, "can attach only potential to MNet" );
}
int cliqueNumber;
int numberOfNodesInDomain;
const int *domain;
param->GetDomain( &numberOfNodesInDomain, &domain );
cliqueNumber = FindCliqueNumberByDomain( numberOfNodesInDomain, domain );
if ( cliqueNumber == -1 )
{
PNL_THROW( CInvalidOperation, " no clique matching domain " );
/* there is no clique to match the domain */
}
//make calling AllocFactors() optional
if( !m_pParams )
{
AllocFactors();
}
m_paramInds[cliqueNumber] = m_pParams->AddFactor(param);
}
void CTreeCPD::UpdateStatisticsEM( const CPotential *pMargPot,
const CEvidence *pEvidence )
{
if( !pMargPot )
{
PNL_THROW( CNULLPointer, "evidences" )//no corresp evidences
}
intVector obsPos;
pMargPot->GetObsPositions(&obsPos);
m_CorrespDistribFun->UpdateStatisticsEM( pMargPot->GetDistribFun(), pEvidence, 1.0f,
&m_Domain.front() );
}
void CMNet::CreateTabularPotential( const intVector& domain,
const floatVector& data )
{
AllocFactor( domain.size(), &domain.front() );
pFactorVector factors;
int numFactors = GetFactors( domain.size(), &domain.front(), &factors );
if( numFactors != 1 )
{
PNL_THROW( CInconsistentSize,
"domain must be the same as corresponding domain size got from graph" );
}
factors[0]->AllocMatrix( &data.front(), matTable );
}
void CMNet::AllocFactor(int cliqueNumber)
{
/* bad-args check */
if( ( cliqueNumber < 0 ) || ( cliqueNumber >= m_cliques.size() ) )
{
PNL_THROW( COutOfRange,
" cliqueNumber < 0 or >= numberOfCliques " );
}
/* bad-args check end */
_AllocFactor( cliqueNumber, m_cliques[cliqueNumber].size(),
&m_cliques[cliqueNumber].front(), ftPotential );
}
void CMlDynamicStructLearn::Learn()
{
RearrangeEvidences();
int nNodesSlice = m_pGrModel->GetNumberOfNodes();
intVector vAncestor, vDescent;
for(int i=0; i<nNodesSlice; i++)
{
vAncestor.push_back(i);
vDescent.push_back(i+nNodesSlice);
}
//currently only the hill climbing algorithm available
CMlStaticStructLearn* pSSL = CMlStaticStructLearnHC::Create(m_pGrModel->GetStaticModel(), itStructLearnML,
StructLearnHC, BIC, m_nMaxFanIn,
vAncestor, vDescent, m_nRestarts);
pSSL->SetData(m_vEvidences.size(), &m_vEvidences.front());
pSSL->SetMaxIterIPF(m_nMaxIters);
static_cast<CMlStaticStructLearnHC*>(pSSL) ->SetMinProgress(m_minProgress);
pSSL->Learn();
const CDAG* p2SDAG = pSSL->GetResultDAG();
// p2SDAG->Dump();
if(!m_LearnPriorSlice)
{
m_pResultDag = const_cast<CDAG*>(p2SDAG)->Clone();
delete pSSL;
return;
}
intVector vA, vD;
CStaticGraphicalModel* pGrModel0 = m_pGrModel->CreatePriorSliceGrModel();
CMlStaticStructLearn* pSSL0 = CMlStaticStructLearnHC::Create(pGrModel0, itStructLearnML,
StructLearnHC, BIC, m_nMaxFanIn,
vA, vD, m_nRestarts);
pSSL0->SetData(m_vEvidence0.size(), &m_vEvidence0.front());
pSSL0->SetMaxIterIPF(m_nMaxIters / 2);
static_cast<CMlStaticStructLearnHC*>(pSSL0) ->SetMinProgress(m_minProgress);
pSSL0->Learn();
const CDAG* p0SDAG = pSSL0->GetResultDAG();
// p0SDAG->Dump();
CDAG* pDAG = const_cast<CDAG*>(p2SDAG)->Clone();
if(pDAG->SetSubDag(vAncestor, const_cast<CDAG*>(p0SDAG)))
{
m_pResultDag = pDAG->Clone();
delete pDAG;
}
else
PNL_THROW(CInternalError, "InternalError, can not generate a DAG");
delete pSSL;
delete pSSL0;
}
void TestsPnlHigh::TestDefaultDistribution()
{
printf("TestDefaultDistribution\n");
BayesNet *net = SimpleCGM1();
//1 is equal to "Cont1"
WCondGaussianDistribFun *pCGDF = dynamic_cast<WCondGaussianDistribFun *>(net->m_pNet->m_paDistribution->Distribution(1));
//pCGDF->m_pDistrib->Dump();
Vector<int> aIndex(pCGDF->desc()->nNode()+3, 0);
aIndex[0] = 0;
aIndex[4] = 0;
aIndex[5] = 0;
if (pCGDF->GetAValue(pnl::matMean, aIndex) != 1.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "Wrong default distribution");
};
if (pCGDF->GetAValue(pnl::matCovariance, aIndex) != 1.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "Wrong default distribution");
};
if (pCGDF->GetAValue(pnl::matWeights, aIndex) != 0.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "Wrong default distribution");
};
aIndex[5] = 1;
if (pCGDF->GetAValue(pnl::matWeights, aIndex) != 0.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "Wrong default distribution");
};
aIndex[0] = 1;
if (pCGDF->GetAValue(pnl::matWeights, aIndex) != 0.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "Wrong default distribution");
};
aIndex[5] = 0;
if (pCGDF->GetAValue(pnl::matWeights, aIndex) != 0.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "Wrong default distribution");
};
//WGaussianDistribFun *pGDF = dynamic_cast<WGaussianDistribFun *>(net->m_pNet->m_paDistribution->Distribution(2));
//pGDF->m_pDistrib->Dump();
delete net;
};
const CEvidence* CBKInfEngine::GetMPE()
{
if( m_ProcedureType != ptViterbi )
{
PNL_THROW( CInvalidOperation,
" you have not been computing the MPE ");
}
const CEvidence *pEv = m_QuerryJTree->GetMPE();
intVector queryNodes;
GetQueryNodes(&queryNodes);
SetEvidenceMPE( CEvidence::
Create(pEv, queryNodes.size(), &queryNodes.front(), GrModel()->GetModelDomain()));
return GetEvidenceMPE();
}
PNL_USING
CParEMLearningEngine* CParEMLearningEngine::Create(
CStaticGraphicalModel *pGrModel)
{
if(!pGrModel)
{
PNL_THROW(CNULLPointer, "Graphical Model");
}
CParEMLearningEngine *pNewEngine =
new CParEMLearningEngine(pGrModel, NULL, itParamLearnEM);
return pNewEngine;
}
void CNodeValues::SetData(const valueVector& data)
{
/*we set all data - if all nodes are really observed and we need to know
all values*/
if ( m_numberObsNodes )
{
/*put new data (values of noes as char array) instead of old data*/
if( m_rawValues.size() != data.size() )
{
PNL_THROW( CInconsistentSize,
"input data size must corresponds node types of observed nodes" );
}
m_rawValues.assign( data.begin(), data.end() );
}
}
void TokIdNode::Alias(TokId const &id)
{
TokIdNode *nd;
for(nd = h_prev; nd; nd = nd->h_prev)
{
if(nd->Match(id))
{
PNL_THROW(pnl::CBadArg, "attempt to make ambiguous alias, brothers cannot have matching aliases");
}
}
for(nd = h_next; nd; nd = nd->h_next)
{
if(nd->Match(id))
{
PNL_THROW(pnl::CBadArg, "attempt to make ambiguous alias, brothers cannot have matching aliases");
}
}
this->id.push_back(id);
for(nd = v_prev; nd; nd = nd->v_prev)
{
nd->desc.insert(std::make_pair(id, this));
}
}
PNL_USING
CGibbsSamplingInfEngine * CGibbsSamplingInfEngine::Create(
const CStaticGraphicalModel *pGraphicalModel )
{
if( !pGraphicalModel )
{
PNL_THROW( CNULLPointer, "graphical model" );
return NULL;
}
else
{
CGibbsSamplingInfEngine* newInf = new CGibbsSamplingInfEngine( pGraphicalModel );
return newInf;
}
}
CTreeCPD::CTreeCPD( const CTreeCPD& TreeCPD )
:CCPD( dtTree, ftCPD, TreeCPD.GetModelDomain() )
{
//m_CorrespDistribFun = TreeCPD.m_CorrespDistribFun->CloneDistribFun();
if( TreeCPD.m_CorrespDistribFun->GetDistributionType() == dtTree )
{
delete m_CorrespDistribFun;
m_CorrespDistribFun = CTreeDistribFun::Copy(
static_cast<CTreeDistribFun*>(TreeCPD.m_CorrespDistribFun ));
}
else
{
PNL_THROW( CInconsistentType, "distribution must be tree" );
}
m_Domain = intVector( TreeCPD.m_Domain );
}
PNL_USING
CLWSamplingInfEngine * CLWSamplingInfEngine::Create(
const CStaticGraphicalModel *pGraphicalModel, int particleCount )
{
if( !pGraphicalModel )
{
PNL_THROW( CNULLPointer, "graphical model" );
return NULL;
}
else
{
CLWSamplingInfEngine* newInf = new CLWSamplingInfEngine( pGraphicalModel, particleCount);
return newInf;
}
}
void
CPersistGaussianDistribFun::TraverseSubobject(CPNLBase *pObj, CContext *pContext)
{
CGaussianDistribFun *pDF = dynamic_cast<CGaussianDistribFun*>(pObj);
if(pDF->IsDistributionSpecific() == 3)
{
PNL_THROW(CInvalidOperation, "DistribFun is Gaussian multiplied by Delta");
}
TraverseDistribFunSubobjects(pDF, pContext);
if(pDF->IsDistributionSpecific() == 1)
{
return;
}
if(pDF->IsDistributionSpecific() == 2)
{
pContext->Put(pDF->GetMatrix(matMean), "MatMean");
return;
}
if(pDF->GetCanonicalFormFlag())
{
pContext->Put(pDF->GetMatrix(matH), "MatH");
pContext->Put(pDF->GetMatrix(matK), "MatK");
}
if(pDF->GetMomentFormFlag())
{
pContext->Put(pDF->GetMatrix(matMean), "MatMean");
pContext->Put(pDF->GetMatrix(matCovariance), "MatCovariance");
if(!pDF->GetFactorFlag())
{
int nParent = pDF->GetNumberOfNodes() - 1;
for(int i = 0; i < nParent; ++i)
{
std::stringstream name;
name << "MatWeight" << i;
pContext->Put(pDF->GetMatrix(matWeights, i), name.str().c_str());
}
}
}
}
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);
}
}
const CPotential* CBKInfEngine::GetQueryJPD()
{
if( m_ProcedureType != ptFiltering &&
m_ProcedureType != ptSmoothing &&
m_ProcedureType != ptFixLagSmoothing )
{
PNL_THROW( CInvalidOperation,
" you have not been computing the Query JPD ");
}
if( !GetQueryPot() )
{
const CPotential* pQueryPot = m_QuerryJTree->GetQueryJPD();
/*
const CDistribFun *pQueryDistribFun = pQueryPot->GetDistribFun();
switch( pQueryDistribFun->GetDistributionType() )
{
case dtTabular:
SetQueryPot( CTabularPotential::Create( m_queryNodes,
GrModel()->GetModelDomain(), NULL) );
break;
case dtGaussian:
SetQueryPot( CGaussianPotential::Create( m_queryNodes,
GrModel()->GetModelDomain(), NULL) );
break;
case dtScalar:
SetQueryPot( CScalarPotential::Create( m_queryNodes,
GrModel()->GetModelDomain() ) );
break;
default:
PNL_THROW(CNotImplemented, "type of potential");
}
GetQueryPot()->SetDistribFun( pQueryDistribFun );
*/
intVector obsPos;
pQueryPot->GetObsPositions(&obsPos);
intVector queryNodes;
GetQueryNodes(&queryNodes);
SetQueryPot(static_cast<CPotential*>( CFactor::
CopyWithNewDomain(pQueryPot, queryNodes, GrModel()->GetModelDomain(), obsPos)));
}
return GetQueryPot();
}
void CStaticStructLearnSEM::ConvertToCurrEvidences(CBNet* pBNet)
{
if(!pBNet)
PNL_THROW(CInvalidOperation, "need to create a new BNet first");
int i,oldNode,newNode,m;
intVector ObsNodes;
if( m_vCurrEvidences.size() != 0 )
{
for(i=0; i<m_numberOfAllEvidences; i++)
delete m_vCurrEvidences[i];
m_vCurrEvidences.clear();
}
CModelDomain* pMD = pBNet->GetModelDomain();
CModelDomain* pGrMD = this->GetStaticModel()->GetModelDomain();
const CNodeType* nt;
const Value* value;
valueVector vValues;
for(i=0; i<m_numberOfAllEvidences; i++)
{
const CEvidence* pEvidence = m_Vector_pEvidences[i];
for(newNode=0; newNode<m_nNodes; newNode++)
{
oldNode = m_vGlobalRenaming[newNode];
if( pEvidence->IsNodeObserved(oldNode) )
{
ObsNodes.push_back(newNode);
value = pEvidence->GetValue(oldNode);
nt = pGrMD->GetVariableType(oldNode);
if(nt->IsDiscrete())
{
vValues.push_back(*value);
}
else
{
for(m=0; m<nt->GetNodeSize(); m++)
vValues.push_back(*(value+m));
}
}
}
CEvidence* pEv = CEvidence::Create(pMD, ObsNodes, vValues);
m_vCurrEvidences.push_back(pEv);
vValues.clear();
ObsNodes.clear();
}
}
void CJtreeInfEngine::EnterEvidence( const CEvidence *pEvidence,
int maximize, int sumOnMixtureNode )
{
// bad-args check
PNL_CHECK_IS_NULL_POINTER(pEvidence);
PNL_CHECK_RANGES( maximize, 0, 2 );
if( pEvidence->GetModelDomain() != m_pGraphicalModel->GetModelDomain() )
{
PNL_THROW( CInvalidOperation,
"evidence and the Graphical Model must be on one Model Domain" );
}
// bad-args check end
int i;
ShrinkObserved( pEvidence, maximize, sumOnMixtureNode );
CollectEvidence();
DistributeEvidence();
for (i = 0; i < m_pJTree->GetNumberOfNodes(); i++)
{
EDistributionType dt = m_pJTree->GetNodePotential(i)->GetDistribFun()->
GetDistributionType();
if(dt == dtGaussian)
{
static_cast<CGaussianDistribFun*>(m_pJTree->GetNodePotential(i)->
GetDistribFun())->UpdateCanonicalCoefficient();
}
}
if (GetModel()->GetModelType() == mtBNet)
{
for (i = 0; i < GetModel()->GetNumberOfNodes(); i++)
{
if (GetModel()->GetFactor(i)->GetDistributionType() == dtSoftMax)
{
GetModel()->GetModelDomain()->ChangeNodeType(i, 0);
}
}
}
}
void CStaticLearningEngine::AppendData(int dim, const CEvidence* const* pEvidences)
{
//create vector consists of points to evidences
int i = 0;
for( i = 0; i < dim; i++ )
{
if (!pEvidences[i])
{
PNL_THROW(CNULLPointer,"evidence")
}
m_Vector_pEvidences.push_back( pEvidences[i] );
}
m_numberOfAllEvidences = m_Vector_pEvidences.size();
ClearStatisticData();
}
const CPotential* CJtreeInfEngine::GetQueryJPD() const
{
/* operation validity check */
/* if( m_lastOpDone != opsMargNodes )
{
PNL_THROW( CInvalidOperation,
" cannot return query jpd, marginalization not done " );
}
*/ /* operation validity check end */
if( !m_pQueryJPD )
{
PNL_THROW( CInvalidOperation,
" can't call GetQueryJPD() before calling MarginalNodes() " );
}
return m_pQueryJPD;
}
void CMlStaticStructLearnHC::Learn()
{
if(m_Vector_pEvidences.size() == 0)
PNL_THROW(CInconsistentState, "should set the data first");
CGraph* iGraph = m_pGrModel->GetGraph();
CDAG* iDAG = CDAG::Create(*iGraph);
CDAG* pDAG;
CDAG* pBestDAG = NULL;
float BestScore = (float)-1e37;
int irestarts = m_nRestarts;
int i, istart;
float score;
for(istart=0; istart<irestarts; istart++)
{
if(istart>0)
{
delete iDAG;
intVector vDiscrete, vContinuous;
const CNodeType* nt;
for(i=0; i<m_nNodes; i++)
{
nt = m_pGrModel->GetNodeType(i);
if( nt->IsDiscrete() )
vDiscrete.push_back(i);
else
vContinuous.push_back(i);
}
iDAG = CDAG::RandomCreateADAG(m_nNodes, m_nMaxFanIn, vDiscrete, vContinuous);
}
LearnInOneStart(iDAG, &pDAG, &score);
if(score > BestScore)
{
delete pBestDAG;
pBestDAG = pDAG->Clone();
BestScore = score;
}
delete pDAG;
}
delete iDAG;
m_pResultDAG = pBestDAG->Clone();
m_critValue.push_back(BestScore);
delete pBestDAG;
}
PNL_USING
/////////////////////////////////////////////////////////////////////////////
CSpecPearlInfEngine* CSpecPearlInfEngine::Create(const CStaticGraphicalModel* pGrModel)
{
PNL_CHECK_IS_NULL_POINTER(pGrModel);
if( !IsInputModelValid(pGrModel) )
{
PNL_THROW( CInconsistentType, " input model is invalid " );
}
CSpecPearlInfEngine* pPearlInfEng = new CSpecPearlInfEngine(pGrModel);
PNL_CHECK_IF_MEMORY_ALLOCATED(pPearlInfEng);
return pPearlInfEng;
}
void CMlLearningEngine::Learn()
{
/*
function takes an information from m_pEvidences and learns factors
of graphical model using prior probabilities or not
*/
float logLikTmp = 0;
if(!m_pGrModel)
{
PNL_THROW( CNULLPointer, "no graphical model")
}
CStaticGraphicalModel *grmodel = this->GetStaticModel();
CFactor *parameter = NULL;
int numberOfDomains = grmodel -> GetNumberOfFactors();
for( int domainNodes = 0; domainNodes < numberOfDomains; domainNodes++ )
{
factor = grmodel->GetFactor( domainNodes );
factor ->UpdateStatisticsML( &m_Vector_pEvidences.front(),
m_Vector_pEvidences.size() );
PNL_CHECK_LEFT_BORDER(m_numberOfAllEvidences, 1);
logLikTmp += parameter->ProcessingStatisticalData(m_numberOfAllEvidences);
}
switch( grmodel -> GetModelType() )
{
case mtBNet:
{
break;
}
case mtMRF2:
case mtMNet:
{
logLikTmp = _LearnPotentials();
break;
}
default:
{
PNL_THROW(CBadConst, "model type" )
break;
}
}
m_critValue.push_back(logLikTmp);
}
CMRF2::CMRF2( int numberOfCliques, const int *cliqueSizes,
const int **cliques, CModelDomain* pMD )
:CMNet( numberOfCliques, cliqueSizes, cliques, pMD )
{
int i;
/* clique validity check */
for( i = 0; i < numberOfCliques; i++ )
{
if( cliqueSizes[i] != 2 )
{
PNL_THROW( CInconsistentType,
" not all the cliques are of two nodes " );
}
}
/* clique validity check */
m_modelType = mtMRF2;
}
PNL_USING
CDynamicGraphicalModel::
CDynamicGraphicalModel( EModelTypes modelType, CStaticGraphicalModel *pGrModel):
CGraphicalModel(pGrModel->GetModelDomain())
{
// Check the validity of the model:
if( !pGrModel->IsValidAsBaseForDynamicModel() )
{
PNL_THROW( CInconsistentType,
"static model doesn't valid for creation dynamic model" )
}
m_pGrModel = pGrModel;
m_modelType = modelType;
m_nnodesPerSlice = pGrModel->GetNumberOfNodes() / 2;
FindInterfaceNodes();
}
void CGibbsSamplingInfEngine::
EnterEvidence( const CEvidence *pEvidenceIn, int maximize, int sumOnMixtureNode )
{
if( !m_queryes.size() )
{
PNL_THROW( CAlgorithmicException, "Possible queryes must be defined");
}
PNL_CHECK_IS_NULL_POINTER(pEvidenceIn);
m_pEvidence = pEvidenceIn;
m_bMaximize = maximize;
DestroyCurrentEvidences();
DestroyQueryFactors();
if(GetModel()->GetModelType() == mtBNet)
{
static_cast< const CBNet* >(GetModel())->
GenerateSamples( GetCurrentEvidences(), GetNumStreams(), pEvidenceIn );
}
else
{
static_cast< const CMNet* >(GetModel())->
GenerateSamples( GetCurrentEvidences(), GetNumStreams(), pEvidenceIn );
}
CreateQueryFactors();
boolVector sampleIsNeed;
if( m_bUsingDSep )
{
ConsDSep( m_queryes, &sampleIsNeed, m_pEvidence );
}
else
{
FindCurrentNdsForSampling( &sampleIsNeed );
}
SetSamplingNdsFlags(sampleIsNeed);
Sampling( 0, GetMaxTime() );
}
void CBayesLearningEngine::AppendData( int dim,
const CEvidence* const* pEvidencesIn )
{
//create vector consists of points to evidences
PNL_CHECK_LEFT_BORDER(dim, 1);
int i = 0;
for( i = 0; i < dim; i++ )
{
if (!pEvidencesIn[i])
{
PNL_THROW(CNULLPointer,"evidence")
}
if( IsInfNeed(pEvidencesIn[i]) )
{
PNL_THROW(CNotImplemented,"all nodes should be observed")
}
m_Vector_pEvidences.push_back( pEvidencesIn[i] );
}
m_numberOfAllEvidences = m_Vector_pEvidences.size();
}
void TokArr::Init(char const *s)
{
int num_ampersands;
resize(0);
for(;;)
{
for(num_ampersands = 0; isspace(*s) || *s == '&'; ++s)
{
num_ampersands += *s == '&';
}
if(*s == 0)
{
while(num_ampersands--)
{
push_back("");
}
return;
}
if(size())
{
while(--num_ampersands > 0)
{
push_back("");
}
}
else
{
while(num_ampersands--)
{
push_back("");
}
}
if(!(isalnum(*s) || strchr("-^+.", *s)))
{
PNL_THROW(pnl::CBadArg, "alien symbol inside TokArr, one can use alphanumerics or + - . ^ & only");
}
push_back(Tok::root);
s = back().Init(s);
}
}
