本文整理汇总了C++中CFactor::UpdateStatisticsEM方法的典型用法代码示例。如果您正苦于以下问题：C++ CFactor::UpdateStatisticsEM方法的具体用法？C++ CFactor::UpdateStatisticsEM怎么用？C++ CFactor::UpdateStatisticsEM使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类CFactor的用法示例。
在下文中一共展示了CFactor::UpdateStatisticsEM方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: LearnContMPI
void CParEMLearningEngine::LearnContMPI()
{
CStaticGraphicalModel *pGrModel = this->GetStaticModel();
PNL_CHECK_IS_NULL_POINTER(pGrModel);
PNL_CHECK_LEFT_BORDER(GetNumEv() - GetNumberProcEv() , 1);
CInfEngine *pInfEng = NULL;
pInfEng = CJtreeInfEngine::Create(pGrModel);
float loglik = 0.0f;
int domainNodes;
CFactor *parameter = NULL;
int numberOfParameters = pGrModel->GetNumberOfParameters();
int nFactors = pGrModel->GetNumberOfFactors();
const CEvidence *pEv;
CFactor *pFactor;
int iteration = 0;
int ev;
int i,numSelfEvidences,NumberOfProcesses, MyRank;
int start_mpi, finish_mpi;
MPI_Comm_size(MPI_COMM_WORLD, &NumberOfProcesses);
MPI_Comm_rank(MPI_COMM_WORLD, &MyRank);
if (IsAllObserved())
{
int i;
float **evid = NULL;
EDistributionType dt;
CFactor *factor = NULL;
for (i = 0; i < nFactors; i++)
{
factor = pGrModel->GetFactor(i);
factor->UpdateStatisticsML(&m_Vector_pEvidences[GetNumberProcEv()],
GetNumEv() - GetNumberProcEv());
}
m_critValue.push_back(UpdateModel());
}
else
{
bool bContinue;
const CPotential * pot;
do
{
ClearStatisticData();
iteration++;
numSelfEvidences = (GetNumEv() - GetNumberProcEv()) / NumberOfProcesses;
start_mpi = GetNumberProcEv() + numSelfEvidences * MyRank;
if (MyRank < NumberOfProcesses - 1)
finish_mpi = start_mpi + numSelfEvidences;
else
finish_mpi = GetNumEv();
for(int ev = start_mpi; ev < finish_mpi; ev++)
{
bool bInfIsNeed = !GetObsFlags(ev)->empty();
pEv = m_Vector_pEvidences[ev];
if( bInfIsNeed )
{
pInfEng->EnterEvidence(pEv, 0, 0);
}
int i;
for( i = 0; i < nFactors; i++ )
{
pFactor = pGrModel->GetFactor(i);
int nnodes;
const int * domain;
pFactor->GetDomain( &nnodes, &domain );
if( bInfIsNeed && !IsDomainObserved(nnodes, domain, ev ) )
{
pInfEng->MarginalNodes( domain, nnodes, 1 );
pot = pInfEng->GetQueryJPD();
pFactor->UpdateStatisticsEM( /*pInfEng->GetQueryJPD */ pot, pEv );
}
else
{
pFactor->UpdateStatisticsML( &pEv, 1 );
}
}
}
for(domainNodes = 0; domainNodes < numberOfParameters; domainNodes++ )
{
parameter = pGrModel->GetFactor(domainNodes);
C2DNumericDenseMatrix<float> *matMeanForSending;
C2DNumericDenseMatrix<float> *matCovForSending;
int dataLengthM,dataLengthC;
//.........这里部分代码省略.........
示例2: Learn
//.........这里部分代码省略.........
BuildFullEvidenceMatrix(full_evid);
}*/
do
{
ClearStatisticData();
iteration++;
for( ev = GetNumberProcEv(); ev < GetNumEv() ; ev++ )
{
bool bInfIsNeed = !GetObsFlags(ev)->empty();
pEv = m_Vector_pEvidences[ev];
if( bInfIsNeed )
{
pInfEng->EnterEvidence(pEv, 0, 0);
}
int i;
for( i = 0; i < nFactors; i++ )
{
pFactor = pGrModel->GetFactor(i);
int nnodes;
const int * domain;
pFactor->GetDomain( &nnodes, &domain );
if( bInfIsNeed && !IsDomainObserved(nnodes, domain, ev ) )
{
pInfEng->MarginalNodes( domain, nnodes, 1 );
pot = pInfEng->GetQueryJPD();
if ( (!(m_Vector_pEvidences[ev])->IsNodeObserved(i)) && (IsCastNeed) )
{
Cast(pot, i, ev, &full_evid);
}
EDistributionType dt;
dt = pFactor->GetDistributionType();
if ( !(dt == dtSoftMax) )
pFactor->UpdateStatisticsEM( /*pInfEng->GetQueryJPD */ pot, pEv );
}
else
{
if ((pFactor->GetDistributionType()) != dtSoftMax)
pFactor->UpdateStatisticsML( &pEv, 1 );
}
}
}
int i;
/*
printf ("\n My Full Evidence Matrix");
for (i=0; i<nFactors; i++)
{
for (j=0; j<GetNumEv(); j++)
{
printf ("%f ", full_evid[i][j]);
}
printf("\n");
}
*/
float **evid = NULL;
EDistributionType dt;
CFactor *factor = NULL;
// int i;
for (i = 0; i < nFactors; i++)
{
factor = pGrModel->GetFactor(i);
dt = factor->GetDistributionType();
if (dt == dtSoftMax)
{
intVector family;