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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 TRAINER_INC
#define TRAINER_INC
#include "Object.h"
#include "Machine.h"
#include "DataSet.h"
#include "List.h"
namespace Torch {
// Methodes utiles
/** Make a table of measurers from a #List#.
Given a #List# of #measurers#,
and, if you want, a #train# #DataSet# (else NULL)
\begin{itemize}
\item Returns all datasets associated to the measurers in #datas#.
For i != j, (*datas)[i] != (*datas)[j].
Moreover, if #train# != NULL, (*datas)[0] = #train#.
\item Returns the list of measurers associated to (*datas)[i] in (*mes)[i].
\item Returns the number of measureurs associated to (*datas)[i] in (*n_mes)[i].
\item Returns in *n_datas the number of datasets in *datas.
\end{itemize}
(Memory allocations are made by the fonction: use
#deleteExtractedMeasurers()# to free memory)
@see deleteExtractedMeasurers
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
void extractMeasurers(List *measurers, DataSet *train, DataSet ***datas, Measurer ****mes, int **n_mes, int *n_datas);
/** Free memory allocations did by #extractMeasurers()#.
@see extractMeasurers
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
void deleteExtractedMeasurers(DataSet **datas, Measurer ***mes, int *n_mes, int n_datas);
/** Trainer.
A trainer takes a #Machine# and a #DataSet#,
and is able to train this machine on this dataset.
*/
class Trainer : public Object
{
public:
DataSet *data;
Machine *machine;
//-----
///
Trainer(Machine *machine_, DataSet *data_);
//-----
/** Train the machine.
The Trainer has to call the measurers
when it want.
*/
virtual void train(List *measurers) = 0;
/** Test the machine.
This method call all the measurers,
for all the examples of their associated
dataset.
It's already written...
*/
virtual void test(List *measurers);
/** Test on one example.
It supposes that all the measurers
have the same dataset, and call the
measurers for the example #t# of this
dataset.
*/
virtual void testExample(List *measurers, int t);
/** K-fold cross-validation.
Do a K-fold cross-validation on #data#.
\begin{itemize}
\item #k_fold# is the number of subsets that
it will make on #data#.
\item #train_measurers# are the measurers called
during the train phase. (NULL if nothing).
\item #test_measurers# are the measurers called
during the test phase.
\item #cross_valid_measurers# are the measurers
called after each iteration of the cross-validation.
(these last measurers should'nt use #measureEx()#)
\end{itemize}
*/
virtual void crossValidate(int k_fold, List *train_measurers, List *test_measurers, List *cross_valid_measurers=NULL);
/// Load the parameters of the machine and the dataset
virtual void loadFILE(FILE *file);
/// Save the parameters of the machine and the dataset
virtual void saveFILE(FILE *file);
//-----
virtual ~Trainer();
};
}
#endif
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