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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 PARZEN_DISTRIBUTION_INC
#define PARZEN_DISTRIBUTION_INC
#include "Distribution.h"
namespace Torch {
/** This class can be used to model a Parzen density estimator with
a Gaussian kernel:
$ p(x) = \frac{1}{N}\sum_i \frac{1}{(2 \Pi var)^{d/2}} \exp(- \frac{||x - x_i||^2}{2 var})$
where the sum is done on the whole training set.
@author Samy Bengio (bengio@idiap.ch)
*/
class ParzenDistribution : public Distribution
{
public:
/// the variance used
real var;
/// the dataset
SeqDataSet* data;
/// the indices of the training examples
int *real_examples;
int n_real_examples;
/** in order to faster the computation, we can do some "pre-computation"
pre-computed sum_log_var + n_obs * log_2_pi
*/
real sum_log_var_plus_n_obs_log_2_pi;
/// pre-computed -0.5 / var
real minus_half_over_var;
ParzenDistribution(SeqDataSet* data_, real var_);
virtual void reset();
virtual int numberOfParams();
virtual void allocateMemory();
virtual void freeMemory();
virtual void setVar(real var_);
virtual real frameLogProbability(real *observations, real *inputs, int t);
virtual real frameLogProbabilityOneFrame(real *observations, real *mean);
virtual void eMSequenceInitialize(List* inputs);
virtual void sequenceInitialize(List* inputs);
virtual void frameExpectation(real *observations, real *inputs, int t);
virtual ~ParzenDistribution();
};
}
#endif
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