gaussnet.h

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#ifndef NEUR_GAUSS_GAUSSNET_H
#define NEUR_GAUSS_GAUSSNET_H
 
 
#include <neur/gauss/gaussian.h>
#include <vector>
 

class GaussNet
{
public:

  typedef double (GaussNet::*LearnFunc)( const std::vector<double> & vec, 
                                         double d, 
                                         double etaW, double etaC, 
                                         double etaS);
  typedef void (GaussNet::*RandInitFunc)( double cmax, double smin, 
                                          double smax, double wmax );


  static unsigned       CyclePeriod;


  GaussNet();
  GaussNet( const GaussNet & gn );
  virtual ~GaussNet();


  GaussNet & operator = ( const GaussNet & gn );


  virtual void clear();
  virtual void init( unsigned ninp, unsigned ngauss, bool samesigma = true );
  RandInitFunc      randInit;
  void randInitAll( double cmax = 1, double smin = 0.3, 
                    double smax = 5, double wmax = 1 );
  void randInitWeights( double cmax = 0, double smin = 0.3, 
                        double smax = 5, double wmax = 1 );
  void randInitCenters( double cmax = 0, double smin = 0.3, 
                        double smax = 5, double wmax = 1 );
  void randInitSigma( double cmax = 1, double smin = 0.5, 
                      double smax = 5, double wmax = 1 );
  void randInitWtSig( double cmax = 1, double smin = 0.5, 
                      double smax = 5, double wmax = 1 );

  unsigned nInputs() const { return( _ninputs ); }
  unsigned nGauss() const;
  const Gaussian* gauss( unsigned n ) const;
  Gaussian* gauss( unsigned n );
  double weight( unsigned n ) const;
  void setWeight( unsigned n, double w );
  template<class InputIterator> 
  void setCenter( unsigned n, const InputIterator & start );
  Gaussian* addGaussian();
  void removeGaussian( unsigned num );
  template<class InputIterator> 
  double prop( const InputIterator & start ) const;
  LearnFunc    learn;
  double learnAll( const std::vector<double> & vec, double d, double etaW, 
                   double etaC = 0, double etaS = 0 );
  double learnWeights( const std::vector<double> & vec, double d, double etaW, 
                       double etaC = 0, double etaS = 0 );
  double learnCenters( const std::vector<double> & vec, double d, double etaW, 
                       double etaC = 0, double etaS = 0 );
  double learnSigma( const std::vector<double> & vec, double d, double etaW, 
                     double etaC = 0, double etaS = 0 );
  double learnWtSig( const std::vector<double> & vec, double d, double etaW, 
                     double etaC = 0, double etaS = 0 );
  double learnMulSig( const std::vector<double> & vec, double d, double etaW, 
                      double etaC = 0, double etaS = 0 );
  double learnCycle( const std::vector<double> & vec, double d, double etaW, 
                     double etaC = 0, double etaS = 0 );
 
protected:
 
private:
  struct Private;
  Private       *d;
  unsigned      _ninputs;
};
 
 
//      inline
 
 
template<class InputIterator>
inline double GaussNet::prop( const InputIterator & start ) const
{
  unsigned      i, n = nGauss();
  double        sum = 0;
 
  for( i=0; i<n; ++i )
    sum += gauss( i )->value( start ) * weight( i );
 
  return( sum );
}
 
 
template<class InputIterator>
inline void GaussNet::setCenter( unsigned n, const InputIterator & start )
{
  gauss(n)->setCenter( start );
}
 
 
#endif