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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 DISTR_MACHINE_INC
#define DISTR_MACHINE_INC
#include "Distribution.h"
namespace Torch {
/** This class can be used to implement a conditional distribution P(y|x;theta).
It is represented as a machine followed by a distribution. The
machine, given some inputs "x", computes the parameters "theta"
of the distribution, then given these parameters, the distribution
computes its log probability.
@author Samy Bengio (bengio@idiap.ch)
*/
class DistrMachine : public Distribution
{
public:
/// the output distribution
Distribution *distribution;
/// the machine that produces the parameters of distribution
GradientMachine *machine;
/// to interface the input
List input_machine;
/// to keep the derivative of the parameters for the machine alpha
real *der_params_distribution;
/// creates a DistrMachine given a machine and and output distribution
DistrMachine(Distribution* distribution_,GradientMachine *machine_);
virtual void reset();
virtual int numberOfParams();
virtual void allocateMemory();
virtual void freeMemory();
virtual void loadFILE(FILE *file);
virtual void saveFILE(FILE *file);
virtual real frameLogProbability(real *observations, real *inputs, int t);
virtual void iterInitialize();
virtual void sequenceInitialize(List* inputs);
virtual void frameBackward(real *observations, real *alpha, real *inputs, int t);
virtual void frameExpectation(real *observations, real *inputs, int t);
virtual ~DistrMachine();
};
}
#endif
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