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// Copyright (C) 2002 Ronan Collobert (collober@iro.umontreal.ca)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch 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.
//
// Torch 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 Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef QC_TRAINER_INC
#define QC_TRAINER_INC
#include "Trainer.h"
#include "QCMachine.h"
#include "QCCache.h"
namespace Torch {
/** Train a #QCMachine#.
With the conventions of QCMachine.h,
Q is given by the class QCCache (in #cache#)
Options:
\begin{tabular}{lcll}
"unshrink" & bool & unshrink or not unshrink & [false] \\
"max unshrink" & int & maximal number of unshrinking & [1] \\
"iter shrink" & int & minimal number of iterations to shrink& [100] \\
"eps shrink" & real & shrinking accuracy & [1E-4 (f) 1E-9 (d)] \\
"end accuracy" & real & end accuracy & [0.01] \\
"iter message" & int & number of iterations between messages & [1000]
\end{tabular}
Note: "iter shrink" must be carefully chosen.
Read http://www.ai.mit.edu/projects/jmlr/papers/volume1/collobert01a/collobert01a.ps.gz
for more details.
@author Ronan Collobert (collober@iro.umontreal.ca)
@see QCCache
@see QCMachine
*/
class QCTrainer : public Trainer
{
public:
// ohhh boy, c'est la zone
QCMachine *qcmachine;
QCCache *cache;
int n_unshrink;
int n_max_unshrink;
real *k_xi;
real *k_xj;
real old_alpha_xi;
real old_alpha_xj;
real current_error;
int *active_var_new;
int n_active_var_new;
int l; // le nb de alphas
bool deja_shrink;
bool unshrink_mode;
real *y;
real *alpha;
real *grad;
real eps_shrink;
real eps_fin;
real eps_bornes;
int n_active_var;
int *active_var;
int *not_at_bound_at_iter;
int iter;
int n_iter_min_to_shrink;
int n_iter_message;
char *status_alpha;
real *Cup;
real *Cdown;
//-----
///
QCTrainer(QCMachine *qcmachine_, DataSet *data_, QCCache *cache_);
/** Train it...
Before calling this function, #grad# in #qcmachine#
must contain the gradient of QP(alpha) with respect
to alpha = 0.
( = $beta$, with the conventions of QCMachine.h)
Moreover #alpha# in #qcmachine# has to be zero.
*/
void train(List *measurers);
//-----
void prepareToLaunch();
void atomiseAll();
bool bCompute();
bool selectVariables(int *i, int *j);
int checkShrinking(real bmin, real bmax);
void shrink();
void unShrink();
void analyticSolve(int xi, int xj);
void updateStatus(int i);
inline bool isNotUp(int i) { return(status_alpha[i] != 2); };
inline bool isNotDown(int i) { return(status_alpha[i] != 1); };
virtual ~QCTrainer();
};
}
#endif
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