STK_MixtureAlgoPredict.cpp

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2016  Serge Iovleff, Université Lille 1, Inria
 
    This program is free software; you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as
    published by the Free Software Foundation; either version 2 of the
    License, or (at your option) any later version.
 
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.
 
    You should have received a copy of the GNU Lesser General Public
    License along with this program; if not, write to the
    Free Software Foundation, Inc.,
    59 Temple Place,
    Suite 330,
    Boston, MA 02111-1307
    USA
 
    Contact : S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
*/
 
/*
 * Project:  stkpp::Clustering
 * created on: 16 oct. 2012
 * Author:   iovleff, S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
 * Originally created by Parmeet bhatia <b..._DOT_p..._AT_gmail_Dot_com>
 **/
 
#include <Sdk.h>
 
#include <Clustering/include/MixtureAlgo/STK_MixtureAlgoPredict.h>
#include <Clustering/include/STK_IMixtureComposer.h>
 
namespace STK
{
 
bool EMPredict::run()
{
  if (p_model_->computeNbMissingValues() == 0) { return predictBayesClassifier();}
#ifdef STK_MIXTURE_VERBOSE
  stk_cout << _T("-------------------------------\n");
  stk_cout << _T("Entering EMPredict::run() with:\n")
           << _T("nbIterBurn_ = ") << nbIterBurn_ << _T("\n")
           << _T("nbIterLong_ = ") << nbIterLong_ << _T("\n");
#endif
  try
  {
    p_model_->initializeStep();
    if (!burnStep())
    {
      p_model_->mapStep();
      p_model_->finalizeStep();
      return false;
    }
    Real currentLnLikelihood = p_model_->lnLikelihood();
    int iter;
    for (iter = 0; iter < nbIterLong_; iter++)
    {
      p_model_->imputationStep();
      p_model_->eStep();
      Real lnLikelihood = p_model_->lnLikelihood();
      if ( (lnLikelihood - currentLnLikelihood) < epsilon_) // no abs as the likelihood should increase
      {
#ifdef STK_MIXTURE_VERY_VERBOSE
        stk_cout << _T("Terminating EMPredict::run() with:\n")
                 << _T("iter = ") << iter << _T("\n")
                 << _T("delta = ") << lnLikelihood - currentLnLikelihood << _T("\n");
#endif
        break;
      }
      currentLnLikelihood = lnLikelihood;
    }
#ifdef STK_MIXTURE_VERBOSE
    stk_cout << _T("In EMPredict::run() iteration ") << iter << _T("terminated.\n")
             << _T("p_model_->lnLikelihood = ") << p_model_->lnLikelihood() << _T("\n");
#endif
  }
  catch (Clust::exceptions const& error)
  {
    msg_error_ = Clust::exceptionToString(error);
#ifdef STK_MIXTURE_VERBOSE
    stk_cout << _T("An error occur in EMPredict::run():\n") << msg_error_ << _T("\n");
#endif
    p_model_->mapStep();
    p_model_->finalizeStep();
    return false;
  }
  catch (Exception const& error)
  {
    msg_error_ = error.error();
#ifdef STK_MIXTURE_VERBOSE
    stk_cout << _T("An error occur in EMPredict::run():\n") << msg_error_ << _T("\n");
#endif
    p_model_->mapStep();
    p_model_->finalizeStep();
    return false;
  }
  p_model_->mapStep();
  p_model_->finalizeStep();
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("Terminating EMPredict::run()\n");
  stk_cout << _T("----------------------------\n");
#endif
  return true;
}
 
bool SemiSEMPredict::run()
{
  if (p_model_->computeNbMissingValues() == 0)
  { return predictBayesClassifier();}
 
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("------------------------------------\n");
  stk_cout << _T("Entering SemiSEMPredict::run() with:\n");
  stk_cout << _T("nbIterBurn_ = ") << nbIterBurn_ << _T("\n")
           << _T("nbIterLong_ = ") << nbIterLong_
           << _T("p_model_->lnLikelihood = ") << p_model_->lnLikelihood() << _T("\n");
#endif
  try
  {
    p_model_->initializeStep();
    if (!burnStep())
    {
      p_model_->mapStep();
      p_model_->finalizeStep();
      return false;
    }
    int iter;
    for (iter = 0; iter < nbIterLong_; ++iter)
    {
      p_model_->samplingStep();
      p_model_->eStep();
      p_model_->storeIntermediateResults(iter+1); // store current parameters
    }
  }
  catch (Clust::exceptions const& error)
  {
    msg_error_ = Clust::exceptionToString(error);
#ifdef STK_MIXTURE_VERBOSE
  stk_cout << _T("An exception occur in SemiSEMPredict::run(): ") << msg_error_ << _T("\n");
#endif
    p_model_->setParametersStep();
    p_model_->mapStep();
    p_model_->finalizeStep();
    return false;
  }
  catch (Exception const& error)
  {
    msg_error_ = error.error();
#ifdef STK_MIXTURE_VERBOSE
    stk_cout << _T("An error occur in SemiSEMPredict::run():\n") << msg_error_ << _T("\n");
#endif
    p_model_->setParametersStep();
    p_model_->mapStep();
    p_model_->finalizeStep();
    return false;
  }
#ifdef STK_MIXTURE_VERBOSE
    stk_cout << _T("In SemiSEMPredict::run() iterations terminated.\n")
             << _T("p_model_->lnLikelihood = ") << p_model_->lnLikelihood() << _T("\n");
#endif
  // set averaged parameters
  p_model_->setParametersStep();
  p_model_->mapStep();
  p_model_->finalizeStep();
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("Terminating SemiSEMPredict::run()\n");
  stk_cout << _T("---------------------------------\n");
#endif
  return true;
}
 
 
} // namespace STK