kernelfuncs.h

This is SvmFu, a package for training and testing support vector machines (SVMs). It s written in C

// Copyright (C) 2000 Ryan M. Rifkin <[e-mail]>
//  
// This program 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.
//  
// This program 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.

/* Kernel function headers.  These will be include in both the training
 * and testing clients.
 *
 * We have two kinds of sparse representations:
 *    sparse01 has integers in the nonzero dimensions, all nonzero 
 *              values are 1
 *    sparseN has pairs, where the first value is the number of the dimensions,
 *              the second number is the value.
 * i.e., to represent a vector that had ones in the 5th and 8th position,
 * and zeros everywhere else in a dataset with at most four nonzeros per
 * data point (i.e., you'd feed a dimensionality of 4 to a "linear" specialized
 * machine):
 *   sparse01:  5 8 -1 -1
 *   sparseN: 5 1 8 1 -1 -1 
 */

//! Provides all the kernel functions for SvmFu client programs.
template<class KernVal, class DataElt> class KernelFuncs
{
 public:
  KernelFuncs<KernVal, DataElt> (KernType k_, MachType m_);
  
  // dense functions
  static const inline KernVal linearProduct (const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline void addToW(double *&w, const DataPoint<DataElt> &p, double amt); 
  static const inline double multW(double *w, const DataPoint<DataElt> &p);
  static const inline KernVal polynomialProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline KernVal gaussianProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  
  // sparse01 functions
  static const inline KernVal sparse01InternalProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline KernVal sparse01BinaryProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline void addToWsparse01(double *&w, const DataPoint<DataElt> &p, double amt); 
  static const inline double multWsparse01(double *w, const DataPoint<DataElt> &p);
  static const inline KernVal sparse01PolynomialProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline KernVal sparse01GaussianProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  
  // sparseN functions
  static const inline KernVal sparseNInternalProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline KernVal sparseNLinearProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline void addToWsparseN(double *&w, const DataPoint<DataElt> &p, double amt); 
  static const inline double multWsparseN(double *w, const DataPoint<DataElt> &p);
  static const inline KernVal sparseNPolynomialProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  static const inline KernVal sparseNGaussianProduct(const DataPoint<DataElt> &p1, const DataPoint<DataElt> &p2);
  
  const KernVal (*pfunc) (const DataPoint<DataElt> &, const DataPoint<DataElt> &);
  const void (*afunc) (double *&w, const DataPoint<DataElt> &, double amt);
  const double (*mfunc) (double *w, const DataPoint <DataElt> &);
};

// Global kernel variables
int degree = 2; // for polynomial kernels
double offset = 0; // for polynomial kernels
double sigma = 1; // for Gaussian kernels
double normalizer = 1; // amount to divide kernel products by