CBNet* Learn_process(const CBNet* pBnet)
{
//start learning for this model
//create WS BNet with different matrices
std::cout<<"Learning procedure \n ";
CGraph *pGraph = CGraph::Copy( pBnet->GetGraph() );
CModelDomain *pMD = pBnet->GetModelDomain();
CBNet* pLearnBNet = CBNet::CreateWithRandomMatrices( pGraph, pMD );
//loading data from file
const char * fname = "Data/casesForWS";
pEvidencesVector evVec;
if( ! CEvidence::Load(fname, &evVec, pMD) )
{
printf("can't open file with cases");
exit(1);
getchar();
}
int numOfSamples = evVec.size();
std::cout<<"Number of cases for learning = "<<numOfSamples<<std::endl;
//create learning engine
CEMLearningEngine *pLearn = CEMLearningEngine::Create( pLearnBNet );
//set data for learning
pLearn->SetData( numOfSamples, &evVec.front() );
pLearn->Learn();
//compare information from learned model with initial model
//both BNet have the same topology and node types
//- we need only to compare CPDs
//need to set tolerance
float epsilon = 1e-1f;
int isEqual = IsTheModelEqual( pBnet, pLearnBNet, epsilon );
std::cout << " The model was learned. The learning was " << std::endl;
if( isEqual )
{
std::cout << " successful " << std::endl;
}
else
{
std::cout << " unsuccessful " << std::endl;
}
int ev;
for( ev = 0; ev < evVec.size(); ev++ )
{
delete evVec[ev];
}
delete pLearn;
delete pBnet;
return pLearnBNet;
}
void CBICLearningEngine::Learn()
{
CEMLearningEngine *pLearn = NULL;
float resultBIC = -FLT_MAX;
CBNet *pResultBNet = NULL;
intVector resultOrder;
pEvidencesVector pEv(m_Vector_pEvidences.size(), NULL );
CModelDomain *pMD = m_pGrModel->GetModelDomain();
int nnodes = m_pGrModel->GetNumberOfNodes();
nodeTypeVector varTypes;
pMD->GetVariableTypes(&varTypes);
intVector varAss( pMD->GetVariableAssociations(), pMD->GetVariableAssociations() + nnodes );
intVector currentAssociation(nnodes);
intVector currentObsNodes(nnodes);
int i;
for( i = 0; i < nnodes; i++ )
{
currentObsNodes[i] = i;
}
CGraph *pGraph = CGraph::Create(nnodes, NULL, NULL, NULL);
CBNet *pBNet;
int lineSz = int( nnodes * ( nnodes - 1 ) / 2 );
intVecVector connect;
intVector indexes(lineSz, 0);
int startNode, endNode;
int ind;
for( ind = 0; ind < lineSz ; )
{
if( indexes[ind] == 1 )
{
FindNodesByNumber(&startNode, &endNode, nnodes, ind);
pGraph->RemoveEdge(startNode, endNode );
indexes[ind] = 0;
ind++;
}
else
{
FindNodesByNumber(&startNode, &endNode, nnodes, ind);
pGraph->AddEdge(startNode, endNode, 1 );
indexes[ind] = 1;
ind = 0;
connect.clear();
pGraph->GetConnectivityComponents(&connect);
if( connect.size() == 1 )
{
do
{
CGraph *pCopyGraph = CGraph::Copy(pGraph);
int j;
for( j = 0; j < nnodes; j++ )
{
currentAssociation[j] = varAss[currentObsNodes[j]];
}
pBNet = CBNet::Create(nnodes, varTypes, currentAssociation, pCopyGraph);
pBNet->AllocFactors();
for( j = 0; j < nnodes; j++ )
{
pBNet->AllocFactor( j );
pBNet->GetFactor(j)->CreateAllNecessaryMatrices();
}
int dimOfModel = DimOfModel(pBNet);
int k;
for( k = 0; k < pEv.size(); k++ )
{
valueVector vls;
m_Vector_pEvidences[k]->GetRawData(&vls);
pEv[k] = CEvidence::Create( pBNet->GetModelDomain(),currentObsNodes, vls );
}
pLearn = CEMLearningEngine::Create(pBNet);
pLearn->SetData(pEv.size(), &pEv.front());
pLearn->Learn();
int nsteps;
const float *score;
pLearn->GetCriterionValue(&nsteps, &score);
float log_lik = score[nsteps-1];
float BIC = log_lik - 0.5f*float( dimOfModel*log(float(pEv.size())) );
if( BIC >= resultBIC )
{
delete pResultBNet;
resultBIC = BIC;
m_critValue.push_back(BIC);
pResultBNet = pBNet;
resultOrder.assign( currentObsNodes.begin(), currentObsNodes.end() );
}
else
{
delete pBNet;
}
for( k = 0; k < pEv.size(); k++ )
{
delete pEv[k];
}
delete pLearn;
}while(std::next_permutation(currentObsNodes.begin(), currentObsNodes.end()));
}
}
}
delete pGraph;
m_pResultGrModel = pResultBNet;
m_resultRenaming.assign(resultOrder.begin(), resultOrder.end());
}
int main()
{
PNL_USING
//we create very small model to start inference on it
// the model is from Kevin Murphy's BNT\examples\static\belprop_polytree_gaussain
/*
Do the example from Satnam Alag's PhD thesis, UCB ME dept 1996 p46
Make the following polytree, where all arcs point down
0 1
\ /
2
/ \
3 4
*/
int i;
//create this model
int nnodes = 5;
int numnt = 2;
CNodeType *nodeTypes = new CNodeType[numnt];
nodeTypes[0] = CNodeType(0,2);
nodeTypes[1] = CNodeType(0,1);
intVector nodeAssociation = intVector(nnodes,0);
nodeAssociation[1] = 1;
nodeAssociation[3] = 1;
int nbs0[] = { 2 };
int nbs1[] = { 2 };
int nbs2[] = { 0, 1, 3, 4 };
int nbs3[] = { 2 };
int nbs4[] = { 2 };
int *nbrs[] = { nbs0, nbs1, nbs2, nbs3, nbs4 };
int numNeighb[] = {1, 1, 4, 1, 1};
ENeighborType ori0[] = { ntChild };
ENeighborType ori1[] = { ntChild };
ENeighborType ori2[] = { ntParent, ntParent, ntChild, ntChild };
ENeighborType ori3[] = { ntParent };
ENeighborType ori4[] = { ntParent };
ENeighborType *orient[] = { ori0, ori1, ori2, ori3, ori4 };
CGraph *pGraph;
pGraph = CGraph::Create(nnodes, numNeighb, nbrs, orient);
CBNet *pBNet;
pBNet = CBNet::Create( nnodes, numnt, nodeTypes, &nodeAssociation.front(), pGraph );
//Allocation space for all factors of the model
pBNet->AllocFactors();
for( i = 0; i < nnodes; i++ )
{
//Allocation space for all matrices of CPD
pBNet->AllocFactor(i);
}
//now we need to create data for CPDs - we'll create matrices
CFactor *pCPD;
floatVector smData = floatVector(2,0.0f);
floatVector bigData = floatVector(4,1.0f);
intVector ranges = intVector(2, 1);
ranges[0] = 2;
smData[0] = 1.0f;
CNumericDenseMatrix<float> *mean0 = CNumericDenseMatrix<float>::
Create( 2, &ranges.front(), &smData.front());
bigData[0] = 4.0f;
bigData[3] = 4.0f;
ranges[1] = 2;
CNumericDenseMatrix<float> *cov0 = CNumericDenseMatrix<float>::
Create( 2, &ranges.front(), &bigData.front());
pCPD = pBNet->GetFactor(0);
pCPD->AttachMatrix(mean0, matMean);
pCPD->AttachMatrix(cov0, matCovariance);
ranges[0] = 1;
ranges[1] = 1;
float val = 1.0f;
CNumericDenseMatrix<float> *mean1 = CNumericDenseMatrix<float>::
Create( 2, &ranges.front(), &val );
CNumericDenseMatrix<float> *cov1 = CNumericDenseMatrix<float>::
Create( 2, &ranges.front(), &val );
pCPD = pBNet->GetFactor(1);
pCPD->AttachMatrix(mean1, matMean);
pCPD->AttachMatrix(cov1, matCovariance);
smData[0] = 0.0f;
smData[1] = 0.0f;
ranges[0] = 2;
CNumericDenseMatrix<float> *mean2 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &smData.front());
smData[0] = 2.0f;
smData[1] = 1.0f;
CNumericDenseMatrix<float> *w21 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &smData.front());
bigData[0] = 2.0f;
bigData[1] = 1.0f;
bigData[2] = 1.0f;
bigData[3] = 1.0f;
ranges[1] = 2;
CNumericDenseMatrix<float> *cov2 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &bigData.front());
bigData[0] = 1.0f;
bigData[1] = 2.0f;
bigData[2] = 1.0f;
bigData[3] = 0.0f;
CNumericDenseMatrix<float> *w20 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &bigData.front());
pCPD = pBNet->GetFactor(2);
pCPD->AttachMatrix( mean2, matMean );
pCPD->AttachMatrix( cov2, matCovariance );
pCPD->AttachMatrix( w20, matWeights,0 );
pCPD->AttachMatrix( w21, matWeights,1 );
val = 0.0f;
ranges[0] = 1;
ranges[1] = 1;
CNumericDenseMatrix<float> *mean3 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &val);
val = 1.0f;
CNumericDenseMatrix<float> *cov3 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &val);
ranges[1] = 2;
smData[0] = 1.0f;
smData[1] = 1.0f;
CNumericDenseMatrix<float> *w30 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &smData.front());
pCPD = pBNet->GetFactor(3);
pCPD->AttachMatrix( mean3, matMean );
pCPD->AttachMatrix( cov3, matCovariance );
pCPD->AttachMatrix( w30, matWeights,0 );
ranges[0] = 2;
ranges[1] = 1;
smData[0] = 0.0f;
smData[1] = 0.0f;
CNumericDenseMatrix<float> *mean4 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &smData.front());
ranges[1] = 2;
bigData[0] = 1.0f;
bigData[1] = 0.0f;
bigData[2] = 0.0f;
bigData[3] = 1.0f;
CNumericDenseMatrix<float> *cov4 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &bigData.front());
bigData[2] = 1.0f;
CNumericDenseMatrix<float> *w40 = CNumericDenseMatrix<float>::
Create(2, &ranges.front(), &bigData.front());
pCPD = pBNet->GetFactor(4);
pCPD->AttachMatrix( mean4, matMean );
pCPD->AttachMatrix( cov4, matCovariance );
pCPD->AttachMatrix( w40, matWeights,0 );
//Generate random evidences for the modes
int nEv = 1000;
pEvidencesVector evid;
pBNet->GenerateSamples( &evid, nEv );
/////////////////////////////////////////////////////////////////////
//Create copy of initial model with random matrices
CGraph *pGraphCopy = CGraph::Copy(pGraph);
CBNet *pLearnBNet = CBNet::CreateWithRandomMatrices(pGraphCopy, pBNet->GetModelDomain() );
// Creating learning process
CEMLearningEngine *pLearn = CEMLearningEngine::Create(pLearnBNet);
pLearn->SetData(nEv, &evid.front());
pLearn->Learn();
CNumericDenseMatrix<float> *pMatrix;
int length = 0;
const float *output;
///////////////////////////////////////////////////////////////////////
std::cout<<" results of learning (number of evidences = "<<nEv<<std::endl;
for (i = 0; i < nnodes; i++ )
{
int j;
std::cout<<"\n matrix mean for node "<<i;
std::cout<<"\n initial BNet \n";
pMatrix = static_cast<CNumericDenseMatrix<float>*>
(pBNet->GetFactor(i)->GetMatrix(matMean));
pMatrix->GetRawData(&length, &output);
for ( j = 0; j < length; j++ )
{
std::cout<<" "<<output[j];
}
std::cout<<"\n BNet with random matrices after learning \n ";
pMatrix = static_cast<CNumericDenseMatrix<float>*>
(pLearnBNet->GetFactor(i)->GetMatrix(matMean));
pMatrix->GetRawData(&length, &output);
for ( j = 0; j < length; j++)
{
std::cout<<" "<<output[j];
}
std::cout<<"\n \n matrix covariance for node "<<i<<'\n';
std::cout<<"\n initial BNet \n";
pMatrix = static_cast<CNumericDenseMatrix<float>*>
(pBNet->GetFactor(i)->GetMatrix(matCovariance));
pMatrix->GetRawData(&length, &output);
for (j = 0; j < length; j++ )
{
std::cout<<" "<<output[j];
}
std::cout<<"\n BNet with random matrices after learning \n ";
pMatrix = static_cast<CNumericDenseMatrix<float>*>
(pLearnBNet->GetFactor(i)->GetMatrix(matCovariance));
pMatrix->GetRawData(&length, &output);
for ( j = 0; j < length; j++ )
{
std::cout<<" "<<output[j];
}
std::cout<<"\n ___________________________\n";
}
for( i = 0; i < nEv; i++)
{
delete evid[i];
}
delete pLearn;
delete pLearnBNet;
delete pBNet;
return 1;
}
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;
}