int testSetStatistics()
{
int ret = TRS_OK;
float eps = 0.1f;
int seed = pnlTestRandSeed();
pnlSeed( seed );
CBNet *pBNet = pnlExCreateCondGaussArBNet();
CModelDomain *pMD = pBNet->GetModelDomain();
CGraph *pGraph = CGraph::Copy(pBNet->GetGraph());
CBNet *pBNet1 = CBNet::CreateWithRandomMatrices( pGraph, pMD );
pEvidencesVector evidences;
int nEvidences = pnlRand( 3000, 4000);
pBNet->GenerateSamples( &evidences, nEvidences );
int i;
for( i = 0; i < nEvidences; i++)
{
//evidences[i]->MakeNodeHiddenBySerialNum(0);
}
CEMLearningEngine *pLearn = CEMLearningEngine::Create(pBNet1);
pLearn->SetData( nEvidences, &evidences.front() );
pLearn->SetMaxIterEM();
pLearn->Learn();
for( i = 0; i < pBNet->GetNumberOfFactors(); i++ )
{
if( ! pBNet->GetFactor(i)->IsFactorsDistribFunEqual(pBNet1->GetFactor(i), eps))
{
ret = TRS_FAIL;
pBNet->GetFactor(i)->GetDistribFun()->Dump();
pBNet1->GetFactor(i)->GetDistribFun()->Dump();
}
}
CDistribFun *pDistr;
const CMatrix<float>* pMat;
CFactor *pCPD;
pDistr = pBNet1->GetFactor(0)->GetDistribFun();
pMat = pDistr->GetStatisticalMatrix(stMatTable);
pCPD = pBNet->GetFactor(0);
pCPD->SetStatistics(pMat, stMatTable);
pCPD->ProcessingStatisticalData(nEvidences);
if( ! pCPD->IsFactorsDistribFunEqual(pBNet1->GetFactor(0), 0.0001f) )
{
ret = TRS_FAIL;
}
pDistr = pBNet1->GetFactor(1)->GetDistribFun();
int parentVal;
pCPD = pBNet->GetFactor(1);
parentVal = 0;
pCPD->SetStatistics(pMat, stMatCoeff);
pMat = pDistr->GetStatisticalMatrix(stMatMu, &parentVal);
pCPD->SetStatistics(pMat, stMatMu, &parentVal);
pMat = pDistr->GetStatisticalMatrix(stMatSigma, &parentVal);
pCPD->SetStatistics(pMat, stMatSigma, &parentVal);
parentVal = 1;
pMat = pDistr->GetStatisticalMatrix(stMatMu, &parentVal);
pCPD->SetStatistics(pMat, stMatMu, &parentVal);
pMat = pDistr->GetStatisticalMatrix(stMatSigma, &parentVal);
pCPD->SetStatistics(pMat, stMatSigma, &parentVal);
pCPD->ProcessingStatisticalData(nEvidences);
if( ! pCPD->IsFactorsDistribFunEqual(pBNet1->GetFactor(1), eps) )
{
ret = TRS_FAIL;
}
for( i = 0; i < nEvidences; i++)
{
delete evidences[i];
}
delete pLearn;
delete pBNet1;
delete pBNet;
return trsResult( ret, ret == TRS_OK ? "No errors" :
"Bad test on SetStatistics");
}
bool CStaticStructLearnSEM::LearnOneStep()
{
intVecVector decompsition;
CGraph* graph = m_pCurrBNet->GetGraph();
graph->GetConnectivityComponents( &decompsition );
CEMLearningEngine* pEMLearn;
if(decompsition.size() > 1)
{
CExInfEngine< CJtreeInfEngine, CBNet, PNL_EXINFENGINEFLAVOUR_DISCONNECTED > *pInf =
CExInfEngine< CJtreeInfEngine, CBNet, PNL_EXINFENGINEFLAVOUR_DISCONNECTED >::
Create( m_pCurrBNet );
pEMLearn = CEMLearningEngine::Create(m_pCurrBNet, pInf);
}
else
{
CJtreeInfEngine *pInf = CJtreeInfEngine::Create(m_pCurrBNet);
pEMLearn = CEMLearningEngine::Create(m_pCurrBNet, pInf);
}
int i;
for(i=0; i<decompsition.size(); i++)
decompsition[i].clear();
decompsition.clear();
ConvertToCurrEvidences(m_pCurrBNet);
pEMLearn->SetData(m_numberOfAllEvidences, &m_vCurrEvidences.front());
pEMLearn->SetMaxIterEM(m_IterEM);
// pEMLearn->ClearStatisticData();
pCPDVector vNeighborCPDs;
floatVector vNeighborLLs;
EDGEOPVECTOR vValidMoves;
intVector vRevCorrespDel;
CreateNeighborCPDs(m_pCurrBNet, &vNeighborCPDs, &vValidMoves, &vRevCorrespDel);
pEMLearn->LearnExtraCPDs(m_nMaxFanIn+1, &vNeighborCPDs, &vNeighborLLs);
// m_pCurrBNet = static_cast<CBNet*>(pEMLearn->GetStaticModel());
const float* familyLL = pEMLearn->GetFamilyLogLik();
floatVector familyScores(m_nNodes,0);
int j, freeparams;
float logebase = (float)log(float(m_numberOfAllEvidences));
float total_score = 0.0f;
CFactor* pCPD;
for(i=0; i<m_nNodes; i++)
{
pCPD = m_pCurrBNet->GetFactor(i);
freeparams = pCPD->GetNumberOfFreeParameters();
familyScores[i] = familyLL[i] - 0.5f * float(freeparams) * logebase;
total_score += familyScores[i];
}
int nMoves = vValidMoves.size();
floatVector neighborScores(nMoves, 0);
for(i=0; i<nMoves; i++)
{
pCPD = static_cast<CFactor*>(vNeighborCPDs[i]);
freeparams = pCPD->GetNumberOfFreeParameters();
neighborScores[i] = vNeighborLLs[i] - 0.5f * float(freeparams) * logebase;
}
int start, end, max_position=0;
float tmp_score, best_score = -1e37f;
EDGEOP move;
for(i=0; i<nMoves; i++)
{
move = vValidMoves[i];
switch (move.DAGChangeType)
{
case DAG_DEL :
end = move.originalEdge.endNode;
tmp_score = neighborScores[i] - familyScores[end];
if( best_score<tmp_score )
{
best_score = tmp_score;
max_position = i;
}
break;
case DAG_ADD :
end = move.originalEdge.endNode;
tmp_score = neighborScores[i] - familyScores[end];
if( best_score<tmp_score )
{
best_score = tmp_score;
max_position = i;
}
break;
case DAG_REV :
end = move.originalEdge.startNode;
tmp_score = neighborScores[i] - familyScores[end];
end = move.originalEdge.endNode;
tmp_score += neighborScores[vRevCorrespDel[i]] - familyScores[end];
if( best_score<tmp_score )
{
best_score = tmp_score;
max_position = i;
}
break;
}
}
move = vValidMoves[max_position];
start = move.originalEdge.startNode;
end = move.originalEdge.endNode;
EDAGChangeType changeType = move.DAGChangeType;
CCPD *addCPD=0, *delCPD=0;
switch (changeType)
{
case DAG_DEL :
delCPD = static_cast<CCPD*>((vNeighborCPDs[max_position])->Clone());
break;
case DAG_ADD :
addCPD = static_cast<CCPD*>((vNeighborCPDs[max_position])->Clone());
break;
case DAG_REV :
addCPD = static_cast<CCPD*>((vNeighborCPDs[max_position])->Clone());
delCPD = static_cast<CCPD*>((vNeighborCPDs[vRevCorrespDel[max_position]])->Clone());
break;
}
delete pEMLearn;
for(i=0; i<vNeighborCPDs.size(); i++)
{
delete vNeighborCPDs[i];
}
vNeighborCPDs.clear();
for(i=0; i<m_numberOfAllEvidences; i++)
{
delete m_vCurrEvidences[i];
}
m_vCurrEvidences.clear();
vValidMoves.clear();
float score_gate = (float)fabs(m_minProgress * total_score);
if(best_score <= score_gate)
{
if(changeType == DAG_REV)
{
delete addCPD;
delete delCPD;
}
if(changeType == DAG_ADD)delete addCPD;
if(changeType == DAG_DEL)delete delCPD;
return false;
}
total_score += best_score;
CDAG* pDAG = CDAG::Create(*(m_pCurrBNet->GetGraph()));
int node, node1, newnode;
if(!(pDAG->DoMove(start, end, changeType)))
{
PNL_THROW(CInternalError, "There are some internal errors");
}
intVector vRenaming, Old2New;
CDAG* iDAG;
int TopologicSorted = pDAG->IsTopologicallySorted();
if( TopologicSorted )
{
iDAG = pDAG->Clone();
for(i=0; i<m_nNodes; i++) vRenaming.push_back(i);
}
else
iDAG = pDAG->TopologicalCreateDAG(vRenaming);
pDAG->Dump();
intVector gRename;
for(i=0; i<m_nNodes; i++)
{
node = vRenaming[i];
node1 = m_vGlobalRenaming[node];
gRename.push_back(node1);
}
m_vGlobalRenaming.assign(gRename.begin(), gRename.end());
int pos;
for(i=0; i<m_nNodes; i++)
{
pos = std::find(vRenaming.begin(), vRenaming.end(), i) - vRenaming.begin();
Old2New.push_back(pos);
}
const int* oldNodeAsso = m_pCurrBNet->GetNodeAssociations();
intVector newNodeAsso(m_nNodes,0);
for(i=0; i<m_nNodes; i++)
{
newNodeAsso[i] = oldNodeAsso[vRenaming[i]];
}
nodeTypeVector vpnt;
m_pCurrBNet->GetNodeTypes(&vpnt);
CBNet* pBNet = CBNet::Create(m_nNodes, vpnt.size(), &vpnt.front(),
&newNodeAsso.front(), static_cast<CGraph*>(iDAG));
CModelDomain* pMDnew = pBNet->GetModelDomain();
pBNet->AllocFactors();
intVector domainNew, domainOld;
const CFactor* factor=0;
CFactor* curFactor;
for(i=0; i<m_nNodes; i++)
{
domainNew.clear();
newnode = Old2New[i];
if( (i != start) && (i != end) )
{
factor = m_pCurrBNet->GetFactor(i);
}
else
{
if(changeType == DAG_REV)
{
if(i == start)
factor = addCPD->Clone();
if(i == end)
factor = delCPD->Clone();
}
if(changeType == DAG_DEL)
{
if(i == start)
factor = m_pCurrBNet->GetFactor(i);
if(i == end)
factor = delCPD->Clone();
}
if(changeType == DAG_ADD)
{
if(i == start)
factor = m_pCurrBNet->GetFactor(i);
if(i == end)
factor = addCPD->Clone();
}
}
factor->GetDomain(&domainOld);
for(j=0; j<domainOld.size(); j++)
{
domainNew.push_back(Old2New[domainOld[j]]);
}
curFactor = CFactor::CopyWithNewDomain(factor, domainNew, pMDnew);
pBNet->AttachFactor(curFactor);
}
if(changeType == DAG_REV)
{
delete addCPD;
delete delCPD;
}
if(changeType == DAG_ADD)delete addCPD;
if(changeType == DAG_DEL)delete delCPD;
delete m_pCurrBNet;
delete pDAG;
m_pCurrBNet = pBNet;
m_critValue.push_back(total_score);
return true;
}