trainer.h

强化学习算法（R-Learning）难得的珍贵资料

// Copyright (C) 2003 Ronan Collobert ([e-mail])
//                
// This file is part of Torch 3.
//
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#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 exists a trainer which knows how to train this machine.
    Testing the machine is possible with the #test()# method.

    @author Ronan Collobert ([e-mail])
*/
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