normanalysis.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 NormAnalysis
                  
  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 NormAnalysis_SOURCE
#define NormAnalysis_SOURCE

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



// necessary includes
#include "NormAnalysis.hpp"

// data


// implementation
template < class ANALYSIS >
NormAnalysis< ANALYSIS >::NormAnalysis
  (
  OnePassAlgorithm<ANALYSIS>* ap_alg
  )
  : mp_succ_alg(ap_alg)
  
  , m_elem_funcs_called(false)
  , m_emulated_elem_wise(false)
  {
  
  }
template < class ANALYSIS >
NormAnalysis< ANALYSIS >::~NormAnalysis
  (
  )
  {
  FUNCLOG("~NormAnalysis");
  
  delete mp_succ_alg;
  }
template < class ANALYSIS >
void
NormAnalysis< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis
  )
  {
  FUNCLOG("NormAnalysis");
  if (!m_elem_funcs_called)
    {
    m_emulated_elem_wise = true;
    operator()(a_analysis,a_analysis.option());
    for (
        typename ANALYSIS::data_iter i_data(a_analysis.data().begin());
        i_data != a_analysis.data().end();
        ++i_data
        )
      { operator()(a_analysis,*i_data); }
    for (
        typename ANALYSIS::prot_iter i_prot(a_analysis.prototypes().begin());
        i_prot != a_analysis.prototypes().end();
        ++i_prot
        )
      { operator()(a_analysis,*i_prot); }
    for (
        typename ANALYSIS::link_iter i_link(a_analysis.links().begin());
        i_link != a_analysis.links().end();
        ++i_link
        )
      { operator()(a_analysis,*i_link); }
    }
  (*mp_succ_alg)(a_analysis);
  m_elem_funcs_called=false;
  m_emulated_elem_wise=false;
  }
template < class ANALYSIS >
void
NormAnalysis< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis,
  NormAnalysis< ANALYSIS >::opt_type& a_option
  )
  {
  FUNCLOG("NormAnalysis::operator(opt)");
  if (!m_elem_funcs_called) reset(a_analysis);
  int dim(a_option.data_scale());
  if (dim<0) dim=-dim;
  dim=min(dim,a_option.data_dimension());

  m_displace.adjust(dim);
  m_scaling.adjust(dim);

  /* // adjust local bboxes
  for (axis=0;axis<last;++axis)
    {
    if (a_analysis.option().bbox_local().rows()>=axis)
      {
      a_analysis.option().bbox_local().lowerBound(axis)=0;
      a_analysis.option().bbox_local().upperBound(axis)=1;
      }
    if (a_analysis.option().bbox_global().rows()>=axis)
      {
      a_analysis.option().bbox_global().lowerBound(axis)=0;
      a_analysis.option().bbox_global().upperBound(axis)=1;
      }
    }
  */

  real_type max_length(0);
  for (int axis=0;axis<dim;++axis)
    {
    max_length = max(max_length,a_option.bbox().length(axis));
    m_scaling[axis] = 
      (a_option.bbox().length(axis)>0)?1.0/a_option.bbox().length(axis):1.0;
    m_displace[axis] = a_option.bbox().minimum(axis);
    }
  if (a_option.data_scale()<0) // same scaling for all axes
    for (int axis=0;axis<dim;++axis)
      m_scaling[axis] = 1.0/max_length;

  (*mp_succ_alg)(a_analysis,a_option);
  }
template < class ANALYSIS >
void
NormAnalysis< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis,
  NormAnalysis< ANALYSIS >::data_type& a_datum
  )
  {
  FUNCLOG("NormAnalysis::operator(data)");
  if (!m_elem_funcs_called) reset(a_analysis);
  for (int axis=0;axis<m_displace.rows();++axis)
    {
    a_datum.datum()[axis] -= m_displace[axis];
    a_datum.datum()[axis] *= m_scaling[axis];
    }
  (*mp_succ_alg)(a_analysis,a_datum);
  }
template < class ANALYSIS >
void
NormAnalysis< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis,
  NormAnalysis< ANALYSIS >::prot_type& a_prototype
  )
  {
  FUNCLOG("NormAnalysis::operator(prot)");
  if (!m_elem_funcs_called) reset(a_analysis);
  (*mp_succ_alg)(a_analysis,a_prototype);
  }
template < class ANALYSIS >
void
NormAnalysis< ANALYSIS >::operator()
  (
  ANALYSIS& a_analysis,
  NormAnalysis< ANALYSIS >::link_type& a_link
  )
  {
  FUNCLOG("NormAnalysis::operator(link)");
  if (!m_elem_funcs_called) reset(a_analysis);
  (*mp_succ_alg)(a_analysis,a_link);
  }
template < class ANALYSIS >
void
NormAnalysis< ANALYSIS >::reset
  (
  ANALYSIS& a_analysis
  )
  {
  FUNCLOG("NormAnalysis::reset");
  m_displace.adjust(0);
  m_scaling.adjust(0);
  m_elem_funcs_called=true;
  }

// template instantiation



#endif // NormAnalysis_SOURCE