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// Copyright (C) 2002 Samy Bengio (bengio@idiap.ch)
//
//
// 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 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 int numberOfParams();
virtual real frameLogProbability(real *observations, real *inputs, int t);
virtual ~TableLookupDistribution();
};
}
#endif
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