meancovariance.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 MeanCovariance
                  
  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 MeanCovariance_SOURCE
#define MeanCovariance_SOURCE

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



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

// data
#define COV_EPSILON 1E-12

// implementation
template < class ANALYSIS >
MeanCovariance< ANALYSIS >::MeanCovariance
  (
  Algorithm<ANALYSIS> *ap_alg
  )
  : mp_succ_alg(ap_alg)
  
  
  {
  
  }
template < class ANALYSIS >
MeanCovariance< ANALYSIS >::~MeanCovariance
  (
  )
  {
  FUNCLOG("~MeanCovariance");
  
  delete mp_succ_alg;
  }
template < class ANALYSIS >
void
MeanCovariance< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis
  )
  {
  FUNCLOG("MeanCovariance");
  
  for (
      typename ANALYSIS::prot_iter i_prot(a_analysis.prototypes().begin());
      i_prot != a_analysis.prototypes().end();
      ++i_prot
      )
    {
    matrix_set_scalar( (*i_prot).covmatrix(), 0);
    } 
  static tuple_type diff;
  diff.adjust(a_analysis.option().data_dimension());

  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
      )
    {
    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_set_diff(diff,(*i_data).datum(),(*i_prot).center());
        matrix_inc_scaled_product((*i_prot).covmatrix(),u, 
          diff,transposed(diff));
        }
      ++i_link;
      }
    }
  for (
      typename ANALYSIS::prot_iter i_prot(a_analysis.prototypes().begin());
      i_prot != a_analysis.prototypes().end();
      ++i_prot
      )
    {
    matrix_scale( 
      (*i_prot).covmatrix(), 1.0/(*i_prot).total_pow_membxweight() ); 
    (*i_prot).covdet() = matrix_det( (*i_prot).covmatrix() );
    if ((*i_prot).covdet()<0)
      {
      if (fabs((*i_prot).covdet()) < COV_EPSILON) 
        { // negative due to rounding errors
        (*i_prot).covdet() = -(*i_prot).covdet();
        }
      else
        warning("determinant of covariance matrix negative",SOURCELOC);
      }
    for (int i=0;i<(*i_prot).covmatrix().rows();++i)
      {
      if (fabs((*i_prot).covmatrix()(i,i)) < COV_EPSILON)
        { (*i_prot).covmatrix()(i,i) = COV_EPSILON; }
      }
    } 
  (*mp_succ_alg)(a_analysis);
  
  }

// template instantiation



#endif // MeanCovariance_SOURCE