source: trunk/c++_tools/classifier/GaussianKernelFunction.cc @ 675

// $Id$

/*
  Copyright (C) The authors contributing to this file.

  This file is part of the yat library, http://lev.thep.lu.se/trac/yat

  The yat library 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.

  The yat 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
  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.
*/

#include "yat/classifier/GaussianKernelFunction.h"

#include "yat/classifier/KernelFunction.h"
#include "yat/classifier/DataLookup1D.h"
#include "yat/classifier/DataLookupWeighted1D.h"

#include <math.h>

namespace theplu {
namespace classifier {  

  GaussianKernelFunction::GaussianKernelFunction(double sigma) 
    : KernelFunction(), sigma2_(sigma*sigma)
  {
  }

  double GaussianKernelFunction::operator()(const DataLookup1D& x,
                                            const DataLookup1D& y) const
  {
    assert(x.size()==y.size());
    double d2 = 0;
    for (size_t i=0; i<x.size(); i++){
      double d = x(i)-y(i);
      d2 += d*d;
    }
    return exp(-d2/sigma2_); 
  }


  double GaussianKernelFunction::operator()(const DataLookup1D& x,
                                            const DataLookupWeighted1D& y) const
  {
    assert(x.size()==y.size());
    double d2 = 0;
    double normalization_factor = 0;
    for (size_t i=0; i<x.size(); i++) {
      // ignoring Nan with accompanied weight zero
      if (y.weight(i)){
        d2 += y.weight(i) * (x(i)-y.data(i)) * (x(i)-y.data(i));
        normalization_factor += y.weight(i);
      }
    }
    // to make it coherent with no weight case
    normalization_factor /= x.size(); 
    return exp(d2/normalization_factor/sigma2_);
  }


  double GaussianKernelFunction::operator()(const DataLookupWeighted1D& x,
                                            const DataLookupWeighted1D& y) const
  {
    assert(x.size()==y.size());
    double d2 = 0;
    double normalization_factor = 0;
    for (size_t i=0; i<x.size(); i++) {
      // ignoring Nan with accompanied weight zero
      if (x.weight(i) && y.weight(i)){
        d2 += x.weight(i) * y.weight(i) * (x.data(i)-y.data(i)) * 
          (x.data(i)-y.data(i));
        normalization_factor += x.weight(i) * y.weight(i);
      }
    }
    // to make it coherent with no weight case
    normalization_factor /= x.size(); 
    return exp(d2/normalization_factor/sigma2_);
  }


}} // of namespace svn and namespace theplu