STK_IMixtureLearner.cpp

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2016  Serge Iovleff
 
    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
 * Author:   iovleff, S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
 **/
 
#include <Clustering/include/STK_IMixtureLearner.h>
 
namespace STK
{
 
/* Constructor.
 *  @param nbCluster,nbSample number of clusters and samples
 **/
IMixtureLearner::IMixtureLearner( int nbSample, int nbCluster)
                                : IMixtureStatModel(nbSample, nbCluster)
                                , ziPred_(tik_.rows())
                                , state_(Clust::modelCreated_)
{}
/* copy constructor.
 *  @param model the model to clone
 **/
IMixtureLearner::IMixtureLearner( IMixtureLearner const& model)
                                : IMixtureStatModel(model), state_(model.state_)
{}
/* destructor */
IMixtureLearner::~IMixtureLearner() {}
 
/* Compute ziPred using the Map estimate. */
void IMixtureLearner::mapStep()
{
  CPointX lnComp_(tik_.cols());
  int kmax;
  Real max;
  for (int i=tik_.beginRows(); i < tik_.endRows(); i++)
  {
    // get maximal element of ln(x_i,\theta_k) + ln(p_k)
    for (int k=tik_.beginCols(); k< tik_.endCols(); k++)
    { lnComp_[k] = std::log(pk_[k])+lnComponentProbability(i,k);}
    if (lnComp_.isInfinite().any()) { throw(Clust::mapStepFail_);}
    max = lnComp_.maxElt(kmax);
    // set ziPred_
    ziPred_[i] = kmax;
    // return  max + sum_k p_k exp{lnCom_k - lnComp_kmax}
    Real sum =  (tik_.row(i) = (lnComp_ - max).exp()).sum();
    tik_.row(i) /= sum;
  }
}
 
} // namespace STK