float CMlLearningEngine::_LearnPotentials()
{
int iteration = 1;
float log_lik = 0.0f;
CStaticGraphicalModel *grmodel = this->GetStaticModel();
CFactor *parameter = NULL;
float epsilon = m_precisionIPF;
const CPotential *joint_prob = NULL;
CPotential *clique_jpd = NULL;
CMatrix<float> *itogMatrix;
CInfEngine *m_pInfEngine =
CNaiveInfEngine::Create(grmodel);
intVector obsNodes(0);
valueVector obsValues(0);
CEvidence *emptyEvidence = CEvidence::Create(grmodel->GetModelDomain(), obsNodes, obsValues);
m_pInfEngine -> EnterEvidence( emptyEvidence );
int querySize = grmodel->GetNumberOfNodes();
int *query;
query = new int [querySize];
int i;
for( i = 0; i < querySize; i++ )
{
query[i] = i;
}
m_pInfEngine -> MarginalNodes( query, querySize );
joint_prob = m_pInfEngine->GetQueryJPD();
CPotential *itog_joint_prob =
static_cast<CPotential *>(joint_prob ->Marginalize(query, querySize));
int DomainSize;
const int *domain;
potsPVector learn_factors;
CPotential *tmp_factor;
for (i = 0; i < grmodel -> GetNumberOfFactors(); i++)
{
factor = grmodel -> GetFactor(i);
factor -> GetDomain( &DomainSize, &domain );
CDistribFun *correspData= factor -> GetDistribFun();
CMatrix<float> *learnMatrix = correspData ->
GetStatisticalMatrix(stMatTable);
CPotential *factor = CTabularPotential::Create( domain, DomainSize,
parameter->GetModelDomain());
learn_factors.push_back(factor);
learn_factors[i] -> AttachMatrix(learnMatrix->NormalizeAll(), matTable);
}
int data_length;
float *old_itog_data = NULL;
const float *itog_data;
delete [] query;
int convergence = 0;
while( !convergence && (iteration <= m_maxIterIPF))
{
iteration++;
itogMatrix = (itog_joint_prob->GetDistribFun())
-> GetMatrix(matTable);
static_cast<CNumericDenseMatrix<float>*>(itogMatrix)->
GetRawData(&data_length, &itog_data);
old_itog_data = new float[data_length];
for( i = 0; i < data_length; i++)
{
old_itog_data[i] = itog_data[i];
}
for( int clique = 0; clique < grmodel->GetNumberOfFactors(); clique++)
{
factor = grmodel -> GetFactor(clique);
factor -> GetDomain( &DomainSize, &domain );
clique_jpd = static_cast<CPotential *>
(itog_joint_prob -> Marginalize( domain, DomainSize ));
tmp_factor = itog_joint_prob -> Multiply(learn_factors[clique]);
delete (itog_joint_prob);
itog_joint_prob = tmp_factor;
tmp_factor = itog_joint_prob -> Divide(clique_jpd);
delete (itog_joint_prob);
delete (clique_jpd);
itog_joint_prob = tmp_factor;
}
itogMatrix = (itog_joint_prob->GetDistribFun())
-> GetMatrix(matTable);
static_cast<CNumericDenseMatrix<float>*>(itogMatrix)->
GetRawData(&data_length, &itog_data);
convergence = true;
for (int j = 0; j < data_length; j++)
{
if( fabs( itog_data[j] - old_itog_data[j] ) > epsilon)
{
convergence = false;
break;
}
}
delete []old_itog_data;
}
if(iteration > m_maxIterIPF)
{
PNL_THROW(CNotConverged,
"maximum number of iterations for IPF procedure")
}
for(int clique = 0; clique < grmodel -> GetNumberOfFactors(); clique++)
{
CMatrix<float> *matrix = NULL;
factor = grmodel -> GetFactor(clique);
int DomainSize;
const int *domain;
int data_length;
const float *data;
factor -> GetDomain( &DomainSize, &domain );
matrix = itog_joint_prob->Marginalize( domain, DomainSize )
->GetDistribFun()-> GetMatrix( matTable );
static_cast<CNumericDenseMatrix<float>*>(matrix)->GetRawData(&data_length, &data);
CNumericDenseMatrix<float>* matLearn =
static_cast<CNumericDenseMatrix<float>*>(
parameter->GetDistribFun()->GetStatisticalMatrix(stMatTable));
for(int offset = 0; offset < data_length; offset++)
{
float prob = float( ( data[offset] < FLT_EPSILON ) ? -FLT_MAX : log( data[offset] ) );
log_lik += matLearn->GetElementByOffset(offset)*prob -
m_Vector_pEvidences.size();
}
factor ->AttachMatrix(matrix, matTable);
delete (learn_factors[clique]);
}
delete (itog_joint_prob);
learn_factors.clear();
return log_lik;
}
