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// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//
// This file is part of Torch 3.1.
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
// IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
// THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef TRAINER_INC
#define TRAINER_INC
#include "Object.h"
#include "Machine.h"
#include "DataSet.h"
#include "List.h"
#include "Measurer.h"
namespace Torch {
DEFINE_NEW_LIST(MeasurerList, Measurer);
/** Trainer.
A trainer takes a #Machine# and is able to train this machine on a given dataset
with the #train()# method.
For each machine, it should exist a trainer which knows how to train this machine.
Testing the machine is possible with the #test()# method.
@author Ronan Collobert (collober@idiap.ch)
*/
class Trainer : public Object
{
public:
Machine *machine;
//-----
///
Trainer(Machine *machine_);
//-----
/** Train the machine.
The Trainer has to call the measurers
when it want.
*/
virtual void train(DataSet *data_, MeasurerList *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(MeasurerList *measurers);
/** 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 (*meas)[i].
\item Returns the number of measureurs associated to (*datas)[i] in (*n_meas)[i].
\item Returns in *n_datas the number of datasets in *datas.
\end{itemize}
Returns an allocator to all the memory allocated by the function.
You have to delete this allocator by yourself.
*/
static Allocator *extractMeasurers(MeasurerList *measurers, DataSet *train, DataSet ***datas, Measurer ****meas, int **n_meas, int *n_datas);
/// By default, just load the machine
virtual void loadXFile(XFile *file);
/// By default, just save the machine
virtual void saveXFile(XFile *file);
//-----
virtual ~Trainer();
};
}
#endif
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