📄 tablelookupdistribution.h
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// 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.
// 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 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
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