/******************************************************************************

File        : SmoTutor.h

Date        : Wednesday 13th September 2000

Author      : Dr Gavin C. Cawley

Description : Implementation of the sequential minimal optimisation (SMO)
              training algorithm for Vapnik's support vector machine (SVM)
              [1], due to Platt [2].

References  : [1] V. N. Vapnik, "The Nature of Statistical Learning Theory",
                  Springer-Verlag, New York, ISBN 0-387-94559-8, 1995.

              [2] J. C. Platt, "Fast Training of Support Vector Machines using
                  Sequential Minimal Optimization".  In B. Scholkopf, C. J. C.
                  Burges and A. J. Smola, editors, "Advances in Kernel Methods
                  - Support Vector Learning", pp 185-208, MIT Press, 1998.

History     : 07/07/2000 - v1.00
              13/09/2000 - v1.01 minor improvements to comments

Copyright   : (c) Dr Gavin C. Cawley, September 2000.

   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

******************************************************************************/

class SmoTutor
{

private:

protected:

   Cache *cache;

   int ntp;

   int minimum, maximum;

   double *C;

   double tolerance;

   double epsilon;

   double *y;

   double *alpha;

   double bias;

   double *error;

   mxArray *x, *kernel, *zeta;

public:

   double fwd(int n);

   int nonBoundLagrangeMultipliers();

   int nonZeroLagrangeMultipliers();

   int takeStep(int i1, int i2, double e2);

   int examineNonBound(int i2, double e2);

   int examineBound(int i2, double e2);

   int examineFirstChoice(int i2, double e2);

   int examineExample(int i2);

   void sequentialMinimalOptimisation();

   mxArray *train();

   SmoTutor(mxArray *x, 
            mxArray *y, 
            mxArray *C,
            mxArray *kernel,
            mxArray *zeta, 
            mxArray *alpha, 
            mxArray *bias, 
            Cache   *cache);
};

/***************************** That's all Folks! *****************************/