// Copyright (C) 2003 Samy Bengio (bengio@idiap.ch)
//                
// This file is part of Torch 3.
//
// 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.
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// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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#ifndef EMTRAINER_INC
#define EMTRAINER_INC

#include "Trainer.h"
#include "Distribution.h"

namespace Torch {

/** This class is used to train any distribution using the EM algorithm.
    It can also train using the Viterbi training algorithm.
    
    @author Samy Bengio (bengio@idiap.ch)
*/
class EMTrainer : public Trainer
{
  public:
    /// the distribution to train
    Distribution *distribution;
    /// the stopping criterion regarding the accuracy for EM
    real end_accuracy;
    /// the stopping criterion regarding the number of iterations for EM
    int max_iter;
    /// when viterbi is true, use Viterbi training instead of EM training
    bool viterbi;

    ///
    EMTrainer(Distribution *distribution_);

    virtual void train(DataSet* data, MeasurerList *measurers);
    virtual void test(MeasurerList *measurers);

    /** this method computes the most likely path into the distribution.
        mainly used for sequential distribution such as HMMs.
    */
    virtual void decode(MeasurerList *measurers);

    virtual ~EMTrainer();
};


}

#endif