dynamicstrategy_d.h

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#ifndef DYNAMICSTRATEGY_D_H
#define DYNAMICSTRATEGY_D_H

#include <aims/classification/dynamicstrategy.h>
#include <aims/classification/classifstrategy.h>
#include <aims/classification/iterativeclassification.h>
#include <aims/classification/distance_d.h>
#include <aims/classification/individuals_d.h>
#include <vector>
#include <list>
#include <stdlib.h>


template <class T>
aims::DynamicStrategy<T>::DynamicStrategy( const DynamicStrategy<T>& dynamicStrat ) :
  aims::KmeansStrategy<T>( dynamicStrat )
{
  this->myMeanVector = dynamicStrat.myMeanVector ;
  this->myVarianceVector = dynamicStrat.myVarianceVector ;
  this->myDistance = dynamicStrat.myDistance ;
  this->myBeginIndex = dynamicStrat.myBeginIndex ;
  this->myEndIndex = dynamicStrat.myEndIndex ;
}


template <class T>
aims::DynamicStrategy<T>::DynamicStrategy( int nbIterations, DistanceType distanceType, 
                                           int beginIndex , int endIndex ,
                                           const std::vector< aims::Individuals<T> >& codeVector ) :
  aims::KmeansStrategy<T>( nbIterations, distanceType, beginIndex, endIndex, codeVector )
{
}


template <class T>
aims::DynamicStrategy<T>::~DynamicStrategy()
{
}


template <class T>
aims::ClassifStrategy<T> * aims::DynamicStrategy<T>::clone() const
{
  return new aims::DynamicStrategy<T>( *this ) ;
}


template <class T>
double aims::DynamicStrategy<T>::iterate( int& nbOfIterations, 
                                           std::vector< std::list< Individuals<T> > >& classes )
{
  std::cout << "Dynamic clustering - Iteration n° " << nbOfIterations << std::endl ; 
  int nbOfClasses = classes.size() ;
  int indMin = 0 ;
  unsigned int classCardNew, classCardOld ;
  int nbChanges = 0 ;

/*   std::cout << "nb d'individus ds les classes en entrant ds iterate:" ; */
/*   for( int c = 0 ; c < nbOfClasses ; ++c ) */
/*     std::cout << " " << classes[c].size() ; */
/*   std::cout << std::endl ; */

  std::vector< std::list< aims::Individuals<T> > >* newClasses ;
  newClasses = &classes ;  

  // QUAND LES INDIVIDUS SONT DS LA CLASSE 0, FAIRE 1 ITERATION DE KMEANS
  if( !nbOfIterations && ( classes[0].size() != 0 ) ){
    // dispatcher les individus de la classe 0 dans les classes 1 à c
    typename std::list< aims::Individuals<T> >::iterator iter( classes[0].begin() ), 
      last( classes[0].end() ) ;
    while( iter != last ){
      int indMin = aggregate( *iter ) ;
      (*newClasses)[indMin].push_back( *iter ) ;
      ++iter ;
    }
    (*newClasses)[0].clear() ;
    // faire l'analyse des classes
    analyse( *newClasses ) ;
  }
  else{    
    for( int c = 0 ; c < nbOfClasses ; ++c ){
      typename std::list< aims::Individuals<T> >::iterator iter( classes[c].begin() ), 
        last( classes[c].end() ), iterToMove ;
      while( iter != last ){
        iterToMove = iter ;
        indMin = aggregate( *iter ) ;
        ++iter ;
        // si l'individu change de classe, faire les modifs des classes et le calcul des centres de ces classes
        if( c != indMin ){
          ++nbChanges ;
          classCardNew = (*newClasses)[indMin].size() ;
          classCardOld = (*newClasses)[c].size() ;

          centerComputation( indMin, c, classCardNew, classCardOld, *iterToMove ) ;

          (*newClasses)[indMin].push_back( *iterToMove ) ;
          (*newClasses)[c].erase( iterToMove ) ;
        }
      }
    }
  }
  
  classes = *newClasses ;

  std::cout << "nb d'individus - iterate end :" ;
  for( int c = 0 ; c < nbOfClasses ; ++c )
    std::cout << " " << classes[c].size() ;
  std::cout << std::endl ;

  double new_inertia = globInertia( classes ) ; // calcul de l'inertie globale */
  
  ++nbOfIterations ;                                           // compteur nb d'iterations

  return new_inertia ;
}


template <class T>
void aims::DynamicStrategy<T>::centerComputation( int cNew, int cOld, unsigned int cardNew, 
                                                  unsigned int cardOld, const Individuals<T>& ind )
{
  // récupérer le vecteur code moyen de la classe
  Individuals<T> meanIndivNew = this->getMeanValue( cNew ) ;
  Individuals<T> meanIndivOld = this->getMeanValue( cOld ) ;

  Point3df meanIndivPosNew = meanIndivNew.position() ;
  Point3df meanIndivPosOld = meanIndivOld.position() ;
  Point3df indPos = ind.position() ;

  for ( int i = 0 ; i < 3 ; ++i ){ 
    meanIndivPosNew[i] = ( ( meanIndivPosNew[i] * cardNew ) + indPos[i] ) / ( cardNew + 1 ) ;
    meanIndivPosOld[i] = ( ( meanIndivPosOld[i] * cardOld ) - indPos[i] ) / ( cardOld - 1 ) ;
  }

  std::vector<T> indVal = ind.value() ;
  unsigned int valIndSize = indVal.size() ;

  std::vector<T> meanIndivValNew = meanIndivNew.value() ;
  std::vector<T> meanIndivValOld = meanIndivOld.value() ;
  std::vector<T> meanIndivValNewClass = meanIndivValNew ;
  std::vector<T> meanIndivValOldClass = meanIndivValOld ;

  for( unsigned int k = 0 ; k < valIndSize ; ++k ){
    meanIndivValNewClass[k] = ( ( meanIndivValNew[k] * cardNew ) + indVal[k] ) / ( cardNew + 1 ) ;
    meanIndivValOldClass[k] = ( ( meanIndivValOld[k] * cardOld ) - indVal[k] ) / ( cardOld - 1 ) ;
  }

  Individuals<T> newMeanIndiv( meanIndivPosNew, meanIndivValNewClass ) ;
  this->myMeanVector[cNew] = newMeanIndiv ;
  Individuals<T> oldMeanIndiv( meanIndivPosOld, meanIndivValOldClass ) ;
  this->myMeanVector[cOld] = oldMeanIndiv ;
}


#endif