KiTrack::SubsetHopfieldNN< T > Class Template Reference

A class to get the best subset with help of a Hopfield Neural Network. More...

#include <SubsetHopfieldNN.h>

Inheritance diagram for KiTrack::SubsetHopfieldNN< T >:

Public Member Functions
template<class GetQI , class AreCompatible >
void	calculateBestSet (AreCompatible areCompatible, GetQI getQI)
	Calculates the best set using a Hopfield Neural Network (see this for more info) . More...
void	setTStart (double tStart)
void	setTInf (double tInf)
void	setOmega (double omega)
void	setLimitForStable (double limitForStable)
void	setInitStateMin (double initStateMin)
void	setInitStateMax (double initStateMax)
void	setActivationThreshold (double activationThreshold)
double	getTStart ()
double	getTInf ()
double	getOmega ()
double	getLimitForStable ()
double	getInitStateMin ()
double	getInitStateMax ()
double	getActivationThreshold ()
Public Member Functions inherited from KiTrack::Subset< T >
void	add (T newElement)
	Adds an element.
void	add (std::vector< T > newElements)
	Adds a vector of elements.
std::vector< T >	getAccepted ()
std::vector< T >	getRejected ()

Protected Attributes
double	_TStart
double	_TInf
double	_omega
double	_limitForStable
double	_initStateMin
double	_initStateMax
double	_activationThreshold
Protected Attributes inherited from KiTrack::Subset< T >
std::vector< T >	_elements
std::vector< T >	_acceptedElements
std::vector< T >	_rejectedElements

Detailed Description

template<class T>
class KiTrack::SubsetHopfieldNN< T >

A class to get the best subset with help of a Hopfield Neural Network.

Member Function Documentation

template<class T >

template<class GetQI , class AreCompatible >

void KiTrack::SubsetHopfieldNN< T >::calculateBestSet	(	AreCompatible	areCompatible,
		GetQI	getQI
	)

Calculates the best set using a Hopfield Neural Network (see this for more info) .

After this the results can be accessed with the getAccepted() and getRejected() methods (provided by the baseclass Subset).

     This is a templated class to allow the reuse with objects from different.
     The goal is this: if you have a set of things that are somehow compatible or incompatible 
     with each other and you want a subset that only contains compatible ones and has a high quality, to find
     this subset.

     For example: you plan a concert where a lot of different artists shall perform together. You have 20 that
     you are interested in asking. BUT: not all of them get along together very well. So artist 1 might be incompatible
     with artist 2 and 7 for some arbitrary reasons. And artist 2 is incompatible with artist 1,3 and 13 and so on.
     In order not to completely mess up the concert you can only invite artists who are entirely compatible with each other.
     AND: not all artists are equal: some are really famous and some are pretty mediocre.
     So you want to find a set of artists with the highest possible quality which is completely compatible.

     This is done by this class. The algorithm used for it is a Hopfield Neural Network (HNN).

     In order to work it needs to know two things: how to calculate the quality of an element and how to determine
     if two elements are compatible. These are of course things the user has to provide.
     For both a functor object is needed.

     Here is how it could look like in the artist example:

class AristQI{
  
   public:
 
   // returns the quality of an artist (a value between 0 and 1, 0 being bad and 1 being fantastic)  
   inline double operator()( Artist artist ){
   
      return artist.numberOfFans()/nPeopleOnEarth;    // a number between 0 and 1
      
  }

};

class ArtistCompatibility{

public:

   // returns whether two artists are compatible
   inline bool operator()( Artist artistA, Artist artistB ){
   
      if( artistA.hates( artistB ) ) return false;
      else return true; 
     
   }

};


//Somewhere within the program:

ArtistCompatibility comp;
ArtistQI qi;

SubsetHopfieldNN< Artist > subset;
subset.add( vecOfArtists );                                 
subset.calculateBestSet( comp, qi );

std::vector< Artist > artistsToInvite = subset.getAccepted();
std::vector< Artist > artistsToStayAtHome = subset.getRejected();

     @param areCompatible a functor of type bool( T, T ) that should tell whether two elements are compatible
     or not.

     @param getQI a functor of type double( T ) that returns the quality of an element and should range between 0 and 1.

References KiTrack::HopfieldNeuralNet::doIteration(), KiTrack::HopfieldNeuralNet::getStates(), KiTrack::HopfieldNeuralNet::setLimitForStable(), KiTrack::HopfieldNeuralNet::setT(), and KiTrack::HopfieldNeuralNet::setTInf().

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The documentation for this class was generated from the following file:

• SubsetHopfieldNN.h