int main()
{
Mycode::Vector<int> intVector(2);
Mycode::Vector<char> charVector(2);
Mycode::Vector<double> doubleVector(2);
Mycode::Vector<float> floatVector(2);
//Mycode::print_vector(intVector);
std::cout << charVector;
// calling the variadic.
//printVectors(intVector, charVector, doubleVector, floatVector);
Mycode::Vector<int> copy(intVector);
// printVectors(copy);
Mycode::LessThan<int> t(42);
if(!t(43))
{
std::cout << "Obviously 42 is not less than 43." << std::endl;
}
std::cout << Mycode::countLessThan(intVector, t) << std::endl;
}
void CSamplingInfEngine::
CreateSamplingPotentials( potsPVector* potsToSampling )
{
CModelDomain *pMD = GetModel()->GetModelDomain();
intVector ndsForSampling;
GetNdsForSampling( &ndsForSampling );
potsToSampling->resize( ndsForSampling.size() );
CPotential *tmpPot;
int i;
for( i = 0; i < ndsForSampling.size(); i++ )
{
const CNodeType* nt = pMD->GetVariableType( ndsForSampling[i]);
if( nt->IsDiscrete() )
{
tmpPot = CTabularPotential::
Create( &ndsForSampling[i], 1, pMD, &floatVector(nt->GetNodeSize(), 1.0f).front() );
}
else
{
tmpPot = CGaussianPotential::
CreateUnitFunctionDistribution( &ndsForSampling[i], 1, pMD );
}
(*potsToSampling)[i] = tmpPot;
}
}
int main(int argc, char **argv)
{
vector3<float> floatVector(1.0f, 2.0f, 1.0f);
vector3<double> doubleVector(2.0f, 2.0f, 2.0f);
vector3<float> b = floatVector;
// reduce typing
typedef vector3<float> vector3f;
vector3f a = b;
return 0;
}
int GibbsForScalarGaussianBNet( float eps)
{
std::cout<<std::endl<<" Scalar gaussian BNet (5 nodes)"<< std::endl;
CBNet *pBnet;
pEvidencesVector evidences;
CGibbsSamplingInfEngine *pGibbsInf;
const CPotential *pQueryPot1, *pQueryPot2;
int i, ret;
////////////////////////////////////////////////////////////////////////
//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 nnodes = 5;
int numnt = 1;
CNodeType *nodeTypes = new CNodeType[numnt];
nodeTypes[0] = CNodeType(0,1);
intVector nodeAssociation = intVector(nnodes,0);
int nbs0[] = { 2 };
int nbs1[] = { 2 };
int nbs2[] = { 0, 1, 3, 4 };
int nbs3[] = { 2 };
int nbs4[] = { 2 };
ENeighborType ori0[] = { ntChild };
ENeighborType ori1[] = { ntChild };
ENeighborType ori2[] = { ntParent, ntParent, ntChild, ntChild };
ENeighborType ori3[] = { ntParent };
ENeighborType ori4[] = { ntParent };
int *nbrs[] = { nbs0, nbs1, nbs2, nbs3, nbs4 };
ENeighborType *orient[] = { ori0, ori1, ori2, ori3, ori4 };
intVector numNeighb = intVector(5,1);
numNeighb[2] = 4;
CGraph *graph;
graph = CGraph::Create(nnodes, &numNeighb.front(), nbrs, orient);
pBnet = CBNet::Create( nnodes, numnt, nodeTypes, &nodeAssociation.front(),graph );
pBnet->AllocFactors();
for( i = 0; i < nnodes; i++ )
{
pBnet->AllocFactor(i);
}
//now we need to create data for factors - we'll create matrices
floatVector smData = floatVector(1,0.0f);
floatVector bigData = floatVector(1,1.0f);
intVector ranges = intVector(2, 1);
ranges[0] = 1;
smData[0] = 1.0f;
CNumericDenseMatrix<float> *mean0 = CNumericDenseMatrix<float>::Create( 2, &ranges.front(), &smData.front());
bigData[0] = 4.0f;
CNumericDenseMatrix<float> *cov0 = CNumericDenseMatrix<float>::Create( 2, &ranges.front(), &bigData.front());
pBnet->GetFactor(0)->AttachMatrix(mean0, matMean);
pBnet->GetFactor(0)->AttachMatrix(cov0, matCovariance);
float val = 1.0f;
CNumericDenseMatrix<float> *mean1 = CNumericDenseMatrix<float>::Create( 2, &ranges.front(), &val );
CNumericDenseMatrix<float> *cov1 = CNumericDenseMatrix<float>::Create( 2, &ranges.front(), &val );
pBnet->GetFactor(1)->AttachMatrix(mean1, matMean);
pBnet->GetFactor(1)->AttachMatrix(cov1, matCovariance);
smData[0] = 0.0f;
CNumericDenseMatrix<float> *mean2 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &smData.front());
smData[0] = 2.0f;
CNumericDenseMatrix<float> *w21 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &smData.front());
bigData[0] = 2.0f;
CNumericDenseMatrix<float> *cov2 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &bigData.front());
bigData[0] = 1.0f;
CNumericDenseMatrix<float> *w20 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &bigData.front());
pBnet->GetFactor(2)->AttachMatrix( mean2, matMean );
pBnet->GetFactor(2)->AttachMatrix( cov2, matCovariance );
pBnet->GetFactor(2)->AttachMatrix( w20, matWeights,0 );
pBnet->GetFactor(2)->AttachMatrix( w21, matWeights,1 );
val = 0.0f;
CNumericDenseMatrix<float> *mean3 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &val);
val = 4.0f;
CNumericDenseMatrix<float> *cov3 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &val);
smData[0] = 1.1f;
CNumericDenseMatrix<float> *w30 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &smData.front());
pBnet->GetFactor(3)->AttachMatrix( mean3, matMean );
pBnet->GetFactor(3)->AttachMatrix( cov3, matCovariance );
pBnet->GetFactor(3)->AttachMatrix( w30, matWeights,0 );
smData[0] = -0.8f;
CNumericDenseMatrix<float> *mean4 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &smData.front());
bigData[0] = 1.2f;
CNumericDenseMatrix<float> *cov4 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &bigData.front());
bigData[0] = 2.0f;
CNumericDenseMatrix<float> *w40 = CNumericDenseMatrix<float>::Create(2, &ranges.front(), &bigData.front());
pBnet->GetFactor(4)->AttachMatrix( mean4, matMean );
pBnet->GetFactor(4)->AttachMatrix( cov4, matCovariance );
pBnet->GetFactor(4)->AttachMatrix( w40, matWeights,0 );
evidences.clear();
pBnet->GenerateSamples( &evidences, 1 );
const int ndsToToggle2[] = { 0, 1, 2 };
evidences[0]->ToggleNodeState( 3, ndsToToggle2 );
const int *flags1 = evidences[0]->GetObsNodesFlags();
std::cout<<"observed nodes"<<std::endl;
for( i = 0; i < pBnet->GetNumberOfNodes(); i++ )
{
if ( flags1[i] )
{
std::cout<<"node "<<i<<"; ";
}
}
std::cout<<std::endl<<std::endl;
const int querySz2 = 1;
const int query2[] = { 0 };
CNaiveInfEngine *pNaiveInf = CNaiveInfEngine::Create(pBnet);
pNaiveInf->EnterEvidence( evidences[0] );
pNaiveInf->MarginalNodes( query2,querySz2 );
pGibbsInf = CGibbsSamplingInfEngine::Create( pBnet );
pGibbsInf->SetNumStreams( 1 );
pGibbsInf->SetMaxTime( 10000 );
pGibbsInf->SetBurnIn( 1000 );
intVecVector queries(1);
queries[0].clear();
queries[0].push_back( 0 );
//queries[0].push_back( 2 );
pGibbsInf->SetQueries( queries );
pGibbsInf->EnterEvidence( evidences[0] );
pGibbsInf->MarginalNodes( query2, querySz2 );
pQueryPot1 = pGibbsInf->GetQueryJPD();
pQueryPot2 = pNaiveInf->GetQueryJPD();
std::cout<<"result of gibbs"<<std::endl<<std::endl;
pQueryPot1->Dump();
std::cout<<"result of naive"<<std::endl;
pQueryPot2->Dump();
ret = pQueryPot1->IsFactorsDistribFunEqual( pQueryPot2, eps, 0 );
delete evidences[0];
delete pNaiveInf;
delete pGibbsInf;
delete pBnet;
return ret;
////////////////////////////////////////////////////////////////////////////////////////
}
int testPearlInfEngForMRF2()
{
int ret = TRS_OK; // of course ;)
int numOfRows = 0;
while( ( numOfRows < 1 ) || ( numOfRows > MAX_NUM_OF_ROWS ) )
{
trsiRead( &numOfRows, "3", "Number of rows in a 2-layered MRF2" );
}
int numOfCols = 0;
while( ( numOfCols < 1 ) || ( numOfCols > MAX_NUM_OF_COLS ) )
{
trsiRead( &numOfCols, "3", "Number of columns in a 2-layered MRF2" );
}
int numOfNodeVals = 0;
while( ( numOfNodeVals < 1 ) || ( numOfNodeVals > MAX_NUM_OF_NODE_VALS ) )
{
trsiRead( &numOfNodeVals, "10",
"Number of values each node in a 2-layered MRF2 can take" );
}
int numOfNodeValDims = 0;
while( ( numOfNodeValDims < 1 )
|| ( numOfNodeValDims > MAX_NUM_OF_NODE_VAL_DIMS ) )
{
trsiRead( &numOfNodeValDims, "10",
"Number of dimensions of each node value in a 2-layered MRF2" );
}
int equalValNum = -2;
while( ( equalValNum < -1 ) || ( equalValNum >= numOfNodeVals ) )
{
trsiRead( &equalValNum, "0",
"values of this number will be equal for all nodes MRF2\n"
"if you choose value -1 (negative one), there will be no\n"
"equal values for all nodes");
}
int maxNumOfIters = 0;
while( ( maxNumOfIters < 1 ) || ( maxNumOfIters > MAX_NUM_OF_ITERS ) )
{
trsiRead( &maxNumOfIters, "3",
"Number of iterations Pearl Inference engine will run for" );
}
int chosenType = 1;
char *strVal;
strVal = trsInt(chosenType);
int typeOfPotential = -1;
while( ( typeOfPotential < 0 ) )
{
trsiRead( &typeOfPotential, strVal, "Type of potential in created model" );
}
const int numOfNds = 2*numOfRows*numOfCols;
int numOfObsNds = numOfNds/2;
nodeTypeVector nodeTypes(2);
nodeTypes[0].SetType( true, numOfNodeVals );
nodeTypes[1].SetType( true, 1 );
intVector nodeAssociation( numOfNds, 0 );
std::fill( nodeAssociation.begin() + numOfNds/2,
nodeAssociation.end(), 1 );
CModelDomain* pModelDomain = CModelDomain::Create( nodeTypes,
nodeAssociation );
pnlVector< floatVecVector > nodeVals( numOfNds/2,
floatVecVector( numOfNodeVals,
floatVector(numOfNodeValDims) ) );
int i;
for( i = 0; i < numOfNds/2; ++i )
{
int j;
for( j = 0; j < numOfNodeVals; ++j )
{
if( j != equalValNum )
{
super_helper::GetRandomNodeVal( &nodeVals[i][j] );
}
else
{
super_helper::GetEqualNodeVal( &nodeVals[i][j] );
}
}
}
// for( i = 0 ; i < numOfNds/2; ++i )
// {
// for_each( nodeVals[i].begin(), nodeVals[i].end(),
// super_helper::PrintVector<float>() );
// }
CMRF2* p2LMRF2Model = SuperResolution2lMRF2( numOfRows, numOfCols,
pModelDomain, nodeVals );
intVector obsNds(numOfObsNds);
valueVector obsNdsVals(numOfObsNds);
for( i = 0; i < numOfObsNds; ++i )
{
obsNds[i] = numOfObsNds + i;
obsNdsVals[i].SetInt(0);
}
CEvidence* pEvidence = CEvidence::Create( p2LMRF2Model,
obsNds, obsNdsVals );
CPearlInfEngine* pPearlInfEngine =
CPearlInfEngine::Create(p2LMRF2Model);
CSpecPearlInfEngine* pSpecPearlInfEngine =
CSpecPearlInfEngine::Create(p2LMRF2Model);
pPearlInfEngine->SetMaxNumberOfIterations(maxNumOfIters);
pSpecPearlInfEngine->SetMaxNumberOfIterations(maxNumOfIters);
pPearlInfEngine->EnterEvidence( pEvidence, true );
pSpecPearlInfEngine->EnterEvidence( pEvidence, true );
intVector maxIndices(numOfNds/2);
intVector specMaxIndices(numOfNds/2);
for( i = 0; i < numOfNds/2; ++i )
{
pPearlInfEngine->MarginalNodes( &i, 1 );
const CEvidence* pMPE = pPearlInfEngine->GetMPE();
pSpecPearlInfEngine->MarginalNodes( &i, 1 );
const CEvidence* pSpecMPE = pSpecPearlInfEngine->GetMPE();
intVector MPEobsNdsNums;
pConstValueVector MPEobsNdsVals;
pConstNodeTypeVector MPENodeTypes;
pMPE->GetObsNodesWithValues( &MPEobsNdsNums, &MPEobsNdsVals,
&MPENodeTypes );
maxIndices[i] = MPEobsNdsVals[0]->GetInt();
pSpecMPE->GetObsNodesWithValues( &MPEobsNdsNums, &MPEobsNdsVals,
&MPENodeTypes );
specMaxIndices[i] = MPEobsNdsVals[0]->GetInt();
}
std::cout << "Here're the numbers of node values chosen"
" by Pearl (max_indices)" << std::endl;
std::for_each( maxIndices.begin(), maxIndices.end(),
super_helper::Print<int>() );
//compare results
int numIndices = maxIndices.size();
for( i = 0; i < numIndices; i++ )
{
if( specMaxIndices[i] != maxIndices[i] )
{
ret = TRS_FAIL;
}
}
if( ( equalValNum != -1 )
&& ( std::count_if( maxIndices.begin(), maxIndices.end(),
super_helper::NotEqual<int>(equalValNum) ) > 0 ) )
{
ret = TRS_FAIL;
}
//add the code for testing specPearl
CMRF2* pModelToWorkWith = pnlExCreateBigMRF2( typeOfPotential, 15, 15, 4, 1.0f, 1.0f );
const int numNodes = pModelToWorkWith->GetNumberOfNodes();
maxNumOfIters = numNodes/2;
numOfObsNds = rand()%( numNodes - 2 );
intVector obsNdsSp(numOfObsNds);
valueVector obsNdsValsSp(numOfObsNds);
SetRndObsNdsAndVals( pModelToWorkWith->GetModelDomain(), &obsNdsSp,
&obsNdsValsSp );
CEvidence* pEvidenceSp = CEvidence::Create( pModelToWorkWith,
obsNdsSp, obsNdsValsSp );
CPearlInfEngine* pPearlEng = CPearlInfEngine::Create(pModelToWorkWith);
pPearlEng->SetMaxNumberOfIterations(maxNumOfIters);
// trsTimerStart(0);
pPearlEng->EnterEvidence(pEvidenceSp);
// trsTimerStop(0);
// double timeOfEnterEvidenceForPearl = trsTimerSec(0);
int numProvIters = pPearlEng->GetNumberOfProvideIterations();
CSpecPearlInfEngine* pPearlEng1 = CSpecPearlInfEngine::Create(pModelToWorkWith);
pPearlEng1->SetMaxNumberOfIterations(maxNumOfIters);
// trsTimerStart(0);
pPearlEng1->EnterEvidence(pEvidenceSp);
// trsTimerStop(0);
// double timeOfEnterEvidenceForPearl1 = trsTimerSec(0);
int numProvIters1 = pPearlEng1->GetNumberOfProvideIterations();
//check are the potentials the same
int potsAreTheSame = 1;
float eps = 1e-5f;
float maxDiff = 0.0f;
const CPotential* potPearl = NULL;
const CPotential* pot1Pearl = NULL;
for( i = 0; i < numNodes; i++ )
{
pPearlEng->MarginalNodes(&i, 1);
potPearl = pPearlEng->GetQueryJPD();
pPearlEng1->MarginalNodes(&i, 1);
pot1Pearl = pPearlEng1->GetQueryJPD();
if( !potPearl->IsFactorsDistribFunEqual(pot1Pearl, eps, 0, &maxDiff ) )
{
potsAreTheSame = 0;
}
}
std::cout<<"num iterations pearl: "<<numProvIters<<std::endl;
std::cout<<"num iterations spec pearl: "<<numProvIters1<<std::endl;
// std::cout<<"time pearl: "<<timeOfEnterEvidenceForPearl<<std::endl;
// std::cout<<"time spec pearl: "<<timeOfEnterEvidenceForPearl1<<std::endl;
delete pPearlEng;
delete pPearlEng1;
delete pModelToWorkWith;
delete pEvidenceSp;
//create other model
//add the code for testing specPearl
CMRF2* pOtherModel= pnlExCreateBigMRF2( 5, 5, 5, 6, 1.0f, 1.0f );
const int numOtherNodes = pOtherModel->GetNumberOfNodes();
numOfObsNds = rand()%( numOtherNodes - 2 );
intVector obsNdsOtherSp(numOfObsNds);
valueVector obsNdsValsOtherSp(numOfObsNds);
SetRndObsNdsAndVals( pOtherModel->GetModelDomain(), &obsNdsOtherSp,
&obsNdsValsOtherSp );
CEvidence* pEvidenceOtherSp = CEvidence::Create( pOtherModel,
obsNdsOtherSp, obsNdsValsOtherSp );
CPearlInfEngine* pOtherPearlEng = CPearlInfEngine::Create(pOtherModel);
pOtherPearlEng->SetMaxNumberOfIterations(maxNumOfIters);
pOtherPearlEng->EnterEvidence(pEvidenceOtherSp);
CSpecPearlInfEngine* pOtherPearlEng1 = CSpecPearlInfEngine::Create(pOtherModel);
pOtherPearlEng1->SetMaxNumberOfIterations(maxNumOfIters);
pOtherPearlEng1->EnterEvidence(pEvidenceOtherSp);
//check are the potentials the same
potsAreTheSame = 1;
maxDiff = 0.0f;
for( i = 0; i < numOtherNodes; i++ )
{
pOtherPearlEng->MarginalNodes(&i, 1);
potPearl = pOtherPearlEng->GetQueryJPD();
pOtherPearlEng1->MarginalNodes(&i, 1);
pot1Pearl = pOtherPearlEng1->GetQueryJPD();
if( !potPearl->IsFactorsDistribFunEqual(pot1Pearl, eps, 0, &maxDiff ) )
{
potsAreTheSame = 0;
}
}
delete pEvidenceOtherSp;
delete pOtherPearlEng;
delete pOtherPearlEng1;
delete pOtherModel;
delete pEvidence;
//CPearlInfEngine::Release(&pPearlInfEngine);
delete pPearlInfEngine;
delete pSpecPearlInfEngine;
delete p2LMRF2Model;
delete pModelDomain;
return ret;
}
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;
}
#ifdef PNL_RTTI
#include "pnlpnlType.hpp"
#endif
PNL_BEGIN
#ifdef SWIG
%rename(CreateUnitF) CTabularCPD::CreateUnitFunctionCPD( const intVector& domainIn, CModelDomain* pMD);
#endif
class PNL_API CTabularCPD : public CCPD
{
public:
static CTabularCPD* Copy( const CTabularCPD* pTabCPD );
#ifndef SWIG
static CTabularCPD* Create( const intVector& domainIn, CModelDomain* pMD,
const floatVector& dataIn = floatVector() );
#endif
static CTabularCPD* Create( CModelDomain* pMD, const intVector& domainIn,
CMatrix<float>* dataIn = NULL);
static CTabularCPD* CreateUnitFunctionCPD( const intVector& domainIn,
CModelDomain* pMD);
float GetMatrixValue(const CEvidence *pEv);
virtual void CreateAllNecessaryMatrices(int typeOfMatrices = 1);
//typeOfMatrices = 1 - all matrices are random
//only Gaussian covariance matrix is matrix unit
//for ConditionalGaussianDistribution
//the matrix of Gaussian distribution functions is dense
