// Copyright (C) 2003--2004 Samy Bengio (bengio@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.
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#ifndef TABLE_LOOKUP_DISTRIBUTION_INC
#define TABLE_LOOKUP_DISTRIBUTION_INC

#include "Distribution.h"

namespace Torch {

/** This class outputs one of the observations as the logProbability. It
    can eventually apply a log transformation and/or normalize by a given
    prior. It can therefore
    be used in conjunction with HMMs to implement the HMM/ANN hybrid model...

    @author Samy Bengio (bengio@idiap.ch)
*/
class TableLookupDistribution : public Distribution
{
  public:

    /** The column in the observation vector that corresponds to the
        logProbability.
    */
    int column;

    /// do we apply a log transformation
    bool apply_log;

    /// do we normalize by a given prior
    real prior;

    /** The column number corresponds to the logProbability which can
        be normalized by an eventual prior.
    */
    TableLookupDistribution(int column_ = 0, bool apply_log_ = true, real prior_ = 1.);

    virtual real frameLogProbability(int t, real *inputs);

    virtual ~TableLookupDistribution();
};


}

#endif
