/* TinySVM -- Yet Another Tiny SVM Package $Id: kernel.h,v 1.18 2002/08/20 06:31:17 taku-ku Exp $; Copyright (C) 2001-2002  Taku Kudo <taku-ku@is.aist-nara.ac.jp> All rights reserved. This library is free software; you can redistribute it and/or modify it under the terms of the GNU Library General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later verjsion. 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 Library General Public License for more details. You should have received a copy of the GNU Library 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.*/#ifndef _KERNEL_H#define _KERNEL_H#include "misc.h"#include "param.h"#include "base_example.h"#include <math.h>#include <stdio.h>#include <stdlib.h>// $Id: kernel.h,v 1.18 2002/08/20 06:31:17 taku-ku Exp $;namespace TinySVM {class Kernel{protected:  // constant param  const int    l;  const int    d;  const int    pack_d;  const int    kernel_type;  const int    feature_type;  const int    dot_kernel;  const int    degree;  const double param_g;  const double param_r;  const double param_s;  double  (Kernel::*_getKernel)(const feature_node *, const feature_node *) const;  double  (Kernel::*_getKernel2)(const double) const;  // linear  inline double _getKernel_linear(const feature_node *x1, const feature_node *x2) const  {     return dot_normal(x1,x2);  }  inline double _getKernel_linear2(const double _x) const  {     return _x;   }  // polynomial  inline double _getKernel_poly(const feature_node *x1, const feature_node *x2) const   {     return pow (param_s * dot_normal(x1,x2) + param_r, degree);   }  inline double _getKernel_poly2(const double _x) const   {     return pow (param_s * _x + param_r, degree);   }  // neural  inline double _getKernel_neural(const feature_node *x1, const feature_node *x2) const   {     return tanh (param_s * dot_normal(x1,x2) + param_r);   }  inline double _getKernel_neural2(const double _x) const  {     return tanh (param_s * _x + param_r);   }  // RBF  inline double _getKernel_rbf(const feature_node *x1, const feature_node *x2) const   {     return exp (-param_s * norm2(x1,x2));  }  inline double _getKernel_rbf2(const double _x) const  {     fprintf (stderr, "Kernel::getKernel_rbf() cannot obtain kernel value only with dot.\n");    exit (-1);    return 0.0;  }  // ANOVA  inline double _getKernel_anova(const feature_node *x1, const feature_node *x2) const   {     register double sum = 0;    register int zero = d;    while (x1->index >= 0 && x2->index >= 0) {      if (x1->index == x2->index) {	sum += exp (-param_s * (x1->value - x2->value) * (x1->value - x2->value));	++x1; ++x2;      } else if (x1->index < x2->index) {	sum += exp (-param_s * x1->value * x1->value);	++x1;      } else {	sum += exp (-param_s * x2->value * x2->value);	++x2;      }      zero--;    }    return pow (sum + (double)zero, degree);  }  inline double _getKernel_anova2(const double _x) const  {     fprintf (stderr, "Kernel::getKernel_anova() cannot obtain kernel value only with dot.\n");    exit (-1);    return 0.0;  }  inline double norm2(const feature_node *x1, const feature_node *x2) const  {    register double sum = 0;         while (x1->index >= 0 && x2->index >= 0) {      if(x1->index == x2->index) {	sum += (x1->value - x2->value) * (x1->value - x2->value);	++x1; ++x2;      } else if (x1->index < x2->index) {	sum += (x1->value * x1->value);	++x1;      } else {	sum += (x2->value * x2->value);	++x2;      }    }    while (x1->index >= 0) {      sum += (x1->value * x1->value);      ++x1;    };    while (x2->index >= 0) {      sum += (x2->value * x2->value);      ++x2;    };         return sum;  }  inline double dot_normal(const feature_node *x1, const feature_node *x2) const  {    register double sum = 0;    while (x1->index >= 0 && x2->index >= 0) {      if (x1->index == x2->index) {	sum += (x1->value * x2->value);	++x1; ++x2;      } else if (x1->index < x2->index) {	++x1;      }	else {	++x2;      }			    }    return sum;  }  inline int dot_binary(const feature_node *x1, const feature_node *x2) const  {    register int sum = 0;    while (x1->index >= 0 && x2->index >= 0) {      if (x1->index == x2->index) {	sum++; 	++x1; ++x2;      } else if (x1->index < x2->index) {	++x1;      } else {	++x2;      }			    }    return sum;  }public:  feature_node ** x;  double         *y;  Kernel(const BaseExample& example, const Param& param):    l(example.l),     d(example.d),     pack_d(example.pack_d),     kernel_type(param.kernel_type),    feature_type(example.feature_type),    dot_kernel(param.dot_kernel),    degree(param.degree),    param_g(param.param_g),    param_r(param.param_r),    param_s(param.param_s)  {    // default    switch (kernel_type) {    case LINEAR:      _getKernel  = &Kernel::_getKernel_linear;      _getKernel2 = &Kernel::_getKernel_linear2;      break;    case POLY:      _getKernel  = &Kernel::_getKernel_poly;      _getKernel2 = &Kernel::_getKernel_poly2;      break;    case NEURAL:      _getKernel  = &Kernel::_getKernel_neural;      _getKernel2 = &Kernel::_getKernel_neural2;      break;    case RBF:      _getKernel  = &Kernel::_getKernel_rbf;      _getKernel2 = &Kernel::_getKernel_rbf2;      break;    case ANOVA:      _getKernel  = &Kernel::_getKernel_anova;      _getKernel2 = &Kernel::_getKernel_anova2;      break;    default:      fprintf(stderr,"Kernel::Kernel: Unknown kernel function\n");    }  }  // wrapper for getKernel  inline double getKernel(const feature_node *x1, const feature_node *x2)  {    return (this->*_getKernel)(x1, x2);  }  inline double getKernel(const double _x)  {    return (this->*_getKernel2)(_x);  }};};#endif