コード例 #1
0
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;
                
                const float *pMeanDataForSending;
                const float *pCovDataForSending;
                
                matMeanForSending = static_cast<C2DNumericDenseMatrix<float>*>
                    ((parameter->GetDistribFun())->GetStatisticalMatrix(stMatMu));               
                
                matMeanForSending->GetRawData(&dataLengthM, &pMeanDataForSending);
                
                matCovForSending = static_cast<C2DNumericDenseMatrix<float>*>
                    ((parameter->GetDistribFun())->GetStatisticalMatrix(stMatSigma));               
                
                matCovForSending->GetRawData(&dataLengthC, &pCovDataForSending);
                
                float *pMeanDataRecv = new float[dataLengthM];
                float *pCovDataRecv = new float[dataLengthC];
                MPI_Status status;                         
                
                MPI_Allreduce((void*)pMeanDataForSending, pMeanDataRecv, dataLengthM, MPI_FLOAT, MPI_SUM,
                    MPI_COMM_WORLD);
                MPI_Allreduce((void*)pCovDataForSending, pCovDataRecv, dataLengthC, MPI_FLOAT, MPI_SUM,
                    MPI_COMM_WORLD);
                
                memcpy((void*)pMeanDataForSending,pMeanDataRecv,dataLengthM*sizeof(float));
                
                memcpy((void*)pCovDataForSending,pCovDataRecv,dataLengthC*sizeof(float));
            }                        

            loglik = UpdateModel();
            
            if( GetMaxIterEM() != 1)
            {
                bool flag = iteration == 1 ? true : 
                (fabs(2*(m_critValue.back()-loglik)/(m_critValue.back() + loglik)) > GetPrecisionEM() );
                
                bContinue = GetMaxIterEM() > iteration && flag;
            }
            else
            {
                bContinue = false;
            }
            m_critValue.push_back(loglik);
            
        }while(bContinue);
    }
    SetNumProcEv( GetNumEv() );
}
コード例 #2
0
void CEMLearningEngine::Learn()
{
    CStaticGraphicalModel *pGrModel =  this->GetStaticModel();
    PNL_CHECK_IS_NULL_POINTER(pGrModel);
    PNL_CHECK_LEFT_BORDER(GetNumEv() - GetNumberProcEv() , 1);
    
    CInfEngine *pInfEng = NULL;
    if (m_pInfEngine)
    {
        pInfEng = m_pInfEngine;
    }
    else
    {
        if (!m_bAllObserved)
        {
            pInfEng = CJtreeInfEngine::Create(pGrModel);
            m_pInfEngine = pInfEng;
        }
    }
    
    float loglik = 0.0f;
    
    int nFactors = pGrModel->GetNumberOfFactors();
    const CEvidence *pEv;
    CFactor *pFactor;
    
    int iteration = 0;
    int ev;

    bool IsCastNeed = false;
    int i;
    for( i = 0; i < nFactors; i++ )
    {
        pFactor = pGrModel->GetFactor(i);
        EDistributionType dt = pFactor->GetDistributionType();
        if ( dt == dtSoftMax ) IsCastNeed = true;
    }

    float ** full_evid = NULL;
    if (IsCastNeed)
    {
        BuildFullEvidenceMatrix(&full_evid);
    }

    
    if (IsAllObserved())
    {
        int i;
        float **evid = NULL;
        EDistributionType dt;
        CFactor *factor = NULL;
        for (i = 0; i < nFactors; i++)
        {
            factor = pGrModel->GetFactor(i);
            dt = factor->GetDistributionType();
            if (dt != dtSoftMax)
            {
                factor->UpdateStatisticsML(&m_Vector_pEvidences[GetNumberProcEv()], 
                    GetNumEv() - GetNumberProcEv());
            }
            else
            {
                
                intVector family;
				family.resize(0);
                pGrModel->GetGraph()->GetParents(i, &family);
                family.push_back(i);
                CSoftMaxCPD* SoftMaxFactor = static_cast<CSoftMaxCPD*>(factor);
                SoftMaxFactor->BuildCurrentEvidenceMatrix(&full_evid, 
					&evid,family,m_Vector_pEvidences.size());
				SoftMaxFactor->InitLearnData();
                SoftMaxFactor->SetMaximizingMethod(m_MaximizingMethod);
                SoftMaxFactor->MaximumLikelihood(evid, m_Vector_pEvidences.size(),
                    0.00001f, 0.01f);
                SoftMaxFactor->CopyLearnDataToDistrib();
                for (int k = 0; k < factor->GetDomainSize(); k++)
                {
                    delete [] evid[k];
                }
                delete [] evid;
            }
        }
        m_critValue.push_back(UpdateModel());
    }
    else
    {
        bool bContinue;
        const CPotential * pot;
        
/*        bool IsCastNeed = false;
        int i;
        for( i = 0; i < nFactors; i++ )
        {
            pFactor = pGrModel->GetFactor(i);
            EDistributionType dt = pFactor->GetDistributionType();
            if ( dt == dtSoftMax ) IsCastNeed = true;
        }

        float ** full_evid;
        if (IsCastNeed)
        {
            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;
				    family.resize(0);
                    pGrModel->GetGraph()->GetParents(i, &family);
                    family.push_back(i);
                    CSoftMaxCPD* SoftMaxFactor = static_cast<CSoftMaxCPD*>(factor);
					SoftMaxFactor->BuildCurrentEvidenceMatrix(&full_evid, 
						&evid,family,m_Vector_pEvidences.size());
                    SoftMaxFactor->InitLearnData();
                    SoftMaxFactor->SetMaximizingMethod(m_MaximizingMethod);
                    //        SoftMaxFactor->MaximumLikelihood(evid, m_numberOfLastEvidences, 
                    SoftMaxFactor->MaximumLikelihood(evid, m_Vector_pEvidences.size(),
                        0.00001f, 0.01f);
                    SoftMaxFactor->CopyLearnDataToDistrib();
                    for (int k = 0; k < factor->GetDomainSize(); k++)
                    {
                        delete [] evid[k];
                    }
                    delete [] evid;
                }
            }
                        
            loglik = UpdateModel();
            
            if( GetMaxIterEM() != 1)
            {
                bool flag = iteration == 1 ? true : 
                (fabs(2*(m_critValue.back()-loglik)/(m_critValue.back() + loglik)) > GetPrecisionEM() );
                
                bContinue = GetMaxIterEM() > iteration && flag;
            }
            else
            {
                bContinue = false;
            }
            m_critValue.push_back(loglik);
            
        }while(bContinue);
    }
    SetNumProcEv( GetNumEv() );
   
    if (IsCastNeed)
    {
        int NumOfNodes = pGrModel->GetGraph()->GetNumberOfNodes();
        for (i=0; i<NumOfNodes; i++)
        {
            delete [] full_evid[i];
        }
        delete [] full_evid;
    }

}