STK_Categorical_pk.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
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    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.,
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    Contact : S..._DOT_I..._AT_stkpp.org (see copyright for ...)
*/
 
/*
 * Project:  stkpp::Clustering
 * created on: Oct 24, 2013
 * Author:   Serge Iovleff
 **/
 
#ifndef STK_CATEGORICAL_PK_H
#define STK_CATEGORICAL_PK_H
 
#include "../CategoricalModels/STK_CategoricalBase.h"
 
namespace STK
{
 
//forward declaration, to allow for recursive template
template<class Array>class Categorical_pk;
 
namespace hidden
{
template<class Array_>
struct MixtureTraits< Categorical_pk<Array_> >
{
  typedef Array_ Array;
  typedef ModelParameters<Clust::Categorical_pk_> Parameters;
};
 
} // namespace hidden
 
template<class Array>
class Categorical_pk: public CategoricalBase<Categorical_pk<Array> >
{
  public:
    typedef CategoricalBase<Categorical_pk<Array> > Base;
    using Base::param_;
    using Base::p_data;
    using Base::modalities_;
    Categorical_pk( int nbCluster): Base(nbCluster) {}
    Categorical_pk( Categorical_pk const& model): Base(model) {}
    ~Categorical_pk() {}
    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()*(this->modalities_.size()-1);}
};
 
/* Initialize randomly the parameters of the Categorical mixture. The centers
 *  will be selected randomly among the data set and the standard-deviation
 *  will be set to 1.
 */
template<class Array>
void Categorical_pk<Array>::randomInit( CArrayXX const* const& p_tik, CPointX const* const& p_tk)
{
  for (int k = p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    param_.proba_[k].randUnif();
    param_.proba_[k] /= param_.proba_[k].sum();
  }
}
 
/* Compute the weighted mean and the common variance. */
template<class Array>
bool Categorical_pk<Array>::run( CArrayXX const* const& p_tik, CPointX const* const& p_tk)
{
  for (int k = p_tik->beginCols(); k < p_tik->endCols(); ++k)
  {
    param_.proba_[k] = 0.;
    for (int j = p_data()->beginCols(); j < p_data()->endCols(); ++j)
    {
      for (int i = p_tik->beginRows(); i < p_tik->endRows(); ++i)
      { param_.proba_[k][(*p_data())(i, j)] += (*p_tik)(i, k);}
    }
    Real sum = param_.proba_[k].sum();
    if (sum<=0.) return false;
    param_.proba_[k] /= sum;
  }
  return true;
}
 
} // namespace STK
 
#endif /* STK_CATEGORICAL_PK_H */