regression.cpp

模糊聚類分析源碼。包含教學文件

/*
  
  Context       : Fuzzy Clustering Algorithms

  Author        : Frank Hoeppner, see also AUTHORS file 

  Description   : implementation of class module Regression
                  
  History       :
    

  Comment       : 
    This file was generated automatically. DO NOT EDIT.

  Copyright     : Copyright (C) 1999-2000 Frank Hoeppner

    This program is free software; you can redistribute it and/or modify
    it under the terms of the GNU 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 General Public License for more details.

    You should have received a copy of the GNU 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
*/
/*
  The University of Applied Sciences Oldenburg/Ostfriesland/Wilhelmshaven
  hereby disclaims all copyright interests in the program package `fc' 
  (tool package for fuzzy cluster analysis) written by Frank Hoeppner.
  
  Prof. Haass, President of Vice, 2000-Mar-10
*/

#ifndef Regression_SOURCE
#define Regression_SOURCE

/* configuration include */
#ifdef HAVE_CONFIG_H
/*
//FILETREE_IFDEF HAVE_CONFIG_H
*/
#include "config.h"
/*
//FILETREE_ENDIF
*/
#endif



// necessary includes
#include "Regression.hpp"
#include "TransMatrix.hpp"

// data


// implementation
template < class ANALYSIS >
Regression< ANALYSIS >::Regression
  (
  Algorithm<ANALYSIS>* ap_alg
  )
  : mp_succ_alg(ap_alg)
  
  
  {
  
  }
template < class ANALYSIS >
Regression< ANALYSIS >::~Regression
  (
  )
  {
  FUNCLOG("~Regression");
  
  delete mp_succ_alg;
  }
template < class ANALYSIS >
void
Regression< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis
  )
  {
  FUNCLOG("Regression");
  
  //const int s( a_analysis.option().output_dimension() );
  const int s(1);
  const int p( a_analysis.option().data_dimension() );
  const int c( a_analysis.option().number_prototypes() );

  matrix_type yx[c];
  matrix_type xx[c];
  int i;

  for (i=0;i<c;++i)
    {
    xx[i].adjust(p,p);
    yx[i].adjust(p,s);
    matrix_set_scalar(xx[i],0);
    matrix_set_scalar(yx[i],0);
    }
  typename ANALYSIS::link_iter i_link(a_analysis.links().begin());
  for (
      typename ANALYSIS::data_iter i_data(a_analysis.data().begin());
      i_data != a_analysis.data().end();
      ++i_data
      )
    {
    i=0;
    for (
        typename ANALYSIS::prot_iter i_prot(a_analysis.prototypes().begin());
        i_prot != a_analysis.prototypes().end();
        ++i_prot
        )
      {
      const real_type u ( (*i_link).pow_membxweight() );
      if (u!=0) // non-zero membership and weight
        {
        matrix_inc_scaled_product(xx[i],
          u,(*i_data).datum(),transposed((*i_data).datum()));
  /*      matrix_inc_scaled_product(yx[i],
          u,(*i_data).result()[0],(*i_data).datum());
  */
        matrix_inc_scaled(yx[i],
          u*(*i_data).result()[0],(*i_data).datum());
        }
      ++i_link; ++i;
      }
    }
    i=0;
    for (
        typename ANALYSIS::prot_iter i_prot(a_analysis.prototypes().begin());
        i_prot != a_analysis.prototypes().end();
        ++i_prot
        )
      {
      gauss_jordan(xx[i]);
      matrix_set_product((*i_prot).coefficient(),xx[i],yx[i]);
      ++i;
      } 
  
  }

// template instantiation



#endif // Regression_SOURCE