rbf.h

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// -*-c++-*-
 
/*
 *Copyright:
 
 Copyright (C) Hidehisa AKIYAMA
 
 This code is free software; you can redistribute it and/or
 modify it under the terms of the GNU Lesser General Public
 License as published by the Free Software Foundation; either
 version 3 of the License, or (at your option) any later version.
 
 This library 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
 Lesser General Public License for more details.
 
 You should have received a copy of the GNU Lesser General Public
 License along with this library; if not, write to the Free Software
 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 
 *EndCopyright:
 */
 
 
#ifndef RCSC_ANN_RBF_H
#define RCSC_ANN_RBF_H
 
#include <boost/array.hpp>
 
#include <vector>
#include <iostream>
#include <cmath>
 
namespace rcsc {
 
 
class RBFNetwork {
public:
 
    typedef std::vector< double > input_vector;
    typedef std::vector< double > output_vector;
 
    struct Unit {
        input_vector center_; 
 
        output_vector weights_; 
        output_vector delta_weights_; 
 
        double sigma_; 
        double delta_sigma_; 
 
    private:
        // not used
        Unit() = delete;
 
    public:
        Unit( std::size_t input_dim,
              std::size_t output_dim );
 
        void randomize( const double & min_weight,
                        const double & max_weight,
                        const double & initial_sigma );
 
        double dist2( const input_vector & input ) const
          {
              const std::size_t INPUT = input.size();
              if ( INPUT != center_.size() )
              {
                  return 0.0;
              }
 
              double d2 = 0.0;
              for ( std::size_t i = 0; i < INPUT; ++i )
              {
                  d2 += std::pow( center_[i] - input[i], 2 );
              }
              return d2;
          }
 
        double dist( const input_vector & input ) const
          {
              return std::sqrt( dist2( input ) );
          }
 
        double calc( const input_vector & input ) const
          {
              return std::exp( - dist2( input ) / ( 2.0 * sigma_ * sigma_ ) );
          }
    };
 
private:
 
    const std::size_t M_input_dim; 
    const std::size_t M_output_dim; 
 
    double M_eta; 
    double M_alpha; 
    double M_min_weight; 
    double M_max_weight; 
    double M_initial_sigma; 
 
    std::vector< Unit > M_units; 
 
    // not used
    RBFNetwork() = delete;
 
public:
 
    RBFNetwork( const std::size_t input_dim,
                const std::size_t output_dim );
 
    void setLearningRate( const double & eta,
                          const double & alpha )
      {
          M_eta = eta;
          M_alpha = alpha;
      }
 
    void setWeightRange( const double & min_weight,
                         const double & max_weight )
      {
          M_min_weight = min_weight;
          M_max_weight = max_weight;
      }
 
    void setInitialSigma( const double & initial_sigma )
      {
          M_initial_sigma = initial_sigma;
      }
 
    const
    std::vector< Unit > & units() const
      {
          return M_units;
      }
 
    void addCenter( const input_vector & center );
 
    void propagate( const input_vector & input,
                    output_vector & output ) const;
 
    double train( const input_vector & input,
                  const output_vector & teacher );
 
    bool read( std::istream & is );
 
    std::ostream & print( std::ostream & os ) const;
 
    std::ostream & printUnits( std::ostream & os ) const;
 
};
 
}
 
#endif