STK_DiagGaussian_s.h

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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.org (see copyright for ...)
*/
 
/*
 * Project:  stkpp::Clustering
 * created on: Oct 24, 2013
 * Author:   Serge Iovleff
 **/
 
#ifndef STK_DIAGGAUSSIAN_S_H
#define STK_DIAGGAUSSIAN_S_H
 
#include "../DiagGaussianModels/STK_DiagGaussianBase.h"
 
namespace STK
{
 
// forward declaration
template<class Array>class DiagGaussian_s;
 
namespace hidden
{
template<class Array_>
struct MixtureTraits< DiagGaussian_s<Array_> >
{
  typedef Array_ Array;
  typedef ModelParameters<Clust::Gaussian_s_> Parameters;
};
 
} // namespace Clust
 
template<class Array>
class DiagGaussian_s: public DiagGaussianBase<DiagGaussian_s<Array> >
{
  public:
    typedef DiagGaussianBase<DiagGaussian_s<Array> > Base;
    using Base::param_;
    using Base::p_data;
 
    DiagGaussian_s( int nbCluster): Base(nbCluster) {}
    DiagGaussian_s( DiagGaussian_s const& model): Base(model) {}
    ~DiagGaussian_s() {}
    void randomInit( CArrayXX const* const& p_tik, CPointX const* const& p_tk) ;
    bool run( CArrayXX const* const& p_tik, CPointX const* const& p_tk) ;
    inline int computeNbFreeParameters() const
    { return this->nbCluster()*p_data()->sizeCols()+1;}
};
 
/* Initialize randomly the parameters of the Gaussian mixture. The centers
 *  will be selected randomly among the data set and the standard-deviation
 *  will be set to 1.
 */
template<class Array>
void DiagGaussian_s<Array>::randomInit( CArrayXX const* const& p_tik, CPointX const* const& p_tk)
{
  this->randomMean(p_tik);
  // compute the standard deviation
  Real variance = 0.0;
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    variance += (  p_tik->col(k).transpose()
                 * (*p_data() - (Const::Vector<Real>(this->nbSample()) * param_.mean_[k])
                   ).square()
                ).sum();
  }
  param_.sigma_ = std::sqrt(variance/(this->nbSample()*p_data()->sizeCols()));
#ifdef STK_MIXTURE_VERY_VERBOSE
  stk_cout << _T("Gaussian_s<Array>::randomInit( CArrayXX const* const& p_tik, CPointX const* const& p_tk)  done\n");
#endif
}
 
/* Compute the weighted mean and the common standard deviation. */
template<class Array>
bool DiagGaussian_s<Array>::run( CArrayXX const* const& p_tik, CPointX const* const& p_tk)
{
  // compute the means
  if (!this->updateMean(p_tik)) return false;
  // compute the standard deviation
  Real variance = 0.0;
  for (int k= p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    variance += ( p_tik->col(k).transpose()
                 * (*p_data() - (Const::Vector<Real>(this->nbSample()) * param_.mean_[k])
                   ).square()
                ).sum();
  }
  param_.sigma_ = std::sqrt(variance/(this->nbSample()*p_data()->sizeCols()));
#ifdef STK_MIXTURE_DEBUG
    if( param_.sigma_ <= 0  )
    {
      stk_cout << _T("DiagGaussian_s::run() failed\n");
      stk_cout << _T("param_.mean_ =") << param_.mean_;
      stk_cout << _T("param_.sigma_=") << param_.sigma_ << _T("\n");
    }
#endif
  //if ((variance<=0) || !Arithmetic<Real>::isFinite(variance)) return false;
  return true;
}
 
} // namespace STK
 
#endif /* STK_DiagGaussian_S_H */