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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 MULTINOMIAL_INC
#define MULTINOMIAL_INC
#include "Distribution.h"
namespace Torch {
/** This class can be used to model Multinomial Distributions.
They can be trained using either EM (with EMTrainer) or gradient descent
(with GMTrainer).
@author Samy Bengio (bengio@idiap.ch)
*/
class Multinomial : public Distribution
{
public:
/// the number of different values that can take this discrete distribution
int n_values;
/// the prior weight given to each value. kind of smoother
real prior_weights;
/// optional initialization parameters
List* initial_params;
/// optional initialization file
char* initial_file;
/// the pointers to the parameters
real* log_weights;
/// the pointers to the d_parameters
real* dlog_weights;
/// accumulators for EM
real* weights_acc;
Multinomial(int n_values_,real prior_weights_);
virtual void reset();
virtual int numberOfParams();
virtual void allocateMemory();
virtual void freeMemory();
virtual void eMIterInitialize();
virtual void iterInitialize();
virtual real frameLogProbability(real *observations, real *inputs, int t);
virtual void sequenceInitialize(List* inputs);
virtual void eMSequenceInitialize(List* inputs);
virtual void frameEMAccPosteriors(real *observations, real log_posterior, real *inputs, int t);
virtual void eMUpdate();
virtual void frameBackward(real *observations, real *alpha, real *inputs, int t);
virtual void frameExpectation(real *observations, real *inputs, int t);
virtual ~Multinomial();
};
}
#endif
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