GaussianKernel.h

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// -*- C++ -*-

// PLearn (A C++ Machine Learning Library)
// Copyright (C) 1998 Pascal Vincent
// Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
// Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
// Copyright (C) 2002 Xiangdong Wang, Christian Dorion

// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 
//  1. Redistributions of source code must retain the above copyright
//     notice, this list of conditions and the following disclaimer.
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//  2. Redistributions in binary form must reproduce the above copyright
//     notice, this list of conditions and the following disclaimer in the
//     documentation and/or other materials provided with the distribution.
// 
//  3. The name of the authors may not be used to endorse or promote
//     products derived from this software without specific prior written
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// THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
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/* *******************************************************      
   * $Id: GaussianKernel.h,v 1.8 2004/06/16 18:25:04 tihocan Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#ifndef GaussianKernel_INC
#define GaussianKernel_INC

#include "Kernel.h"

namespace PLearn {
using namespace std;



class GaussianKernel: public Kernel
{

private:

  typedef Kernel inherited;
                
public:

  bool scale_by_sigma;
  real sigma;

protected:

  real minus_one_over_sigmasquare;  
  real sigmasquare_over_two;        

  Vec squarednorms; 
         
 public:

  GaussianKernel();
  
  GaussianKernel(real the_sigma);

  PLEARN_DECLARE_OBJECT(GaussianKernel);

  virtual void build();

  virtual void makeDeepCopyFromShallowCopy(map<const void*, void*>& copies);

  inline real evaluateFromSquaredNormOfDifference(real sqnorm_of_diff) const;

  inline real evaluateFromDotAndSquaredNorm(real sqnorm_x1, real dot_x1_x2, real sqnorm_x2) const
  { return evaluateFromSquaredNormOfDifference((sqnorm_x1+sqnorm_x2)-(dot_x1_x2+dot_x1_x2)); }

  virtual void setDataForKernelMatrix(VMat the_data);
  
  virtual void addDataForKernelMatrix(const Vec& newRow);

  virtual real evaluate(const Vec& x1, const Vec& x2) const; 
  virtual real evaluate_i_j(int i, int j) const; 
  virtual real evaluate_i_x(int i, const Vec& x, real squared_norm_of_x=-1) const; 
  virtual real evaluate_x_i(const Vec& x, int i, real squared_norm_of_x=-1) const; 

  virtual void setParameters(Vec paramvec);

protected:
  static void declareOptions(OptionList& ol);  

private:
  void build_();
};

DECLARE_OBJECT_PTR(GaussianKernel);

} // end of namespace PLearn

#endif

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