/usr/include/torch/MAPDiagonalGMM.h is in libtorch3-dev 3.1-2.1build1.
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// and Samy Bengio (bengio@idiap.ch)
//
// This file is part of Torch 3.1.
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#ifndef MAP_DIAGONAL_GMM_INC
#define MAP_DIAGONAL_GMM_INC
#include "DiagonalGMM.h"
namespace Torch {
/** This class is a special case of a DiagonalGMM that implements the
MAP algorithm instead of the EM algorithm. This means that the
mean parameters will be changed according to the Maximum A Posteriori
algorithm, given a prior value of the means (through a prior DiagonalGMM
given in the constructor). Moreover, the variances and weights are
not changed, as experimental results tend to show that there is no
effects when they are changed.
@author Samy Bengio (bengio@idiap.ch)
@author Johnny Mariethoz (Johnny.Mariethoz@idiap.ch)
*/
class MAPDiagonalGMM : public DiagonalGMM
{
public:
/// The prior distribution used in MAP
DiagonalGMM* prior_distribution;
/// The weight to give to the prior parameters during update
real weight_on_prior;
/// update Gaussian's weights
bool learn_weights;
/// update Gaussian's variances
bool learn_variances;
/// update Gaussian's means
bool learn_means;
///
MAPDiagonalGMM(DiagonalGMM* prior_distribution_);
/// The backward step of Viterbi for a frame
virtual void frameViterbiAccPosteriors(int t, real* inputs, real log_posterior);
/// The backward step of EM for a frame
virtual void frameEMAccPosteriors(int t, real *inputs, real log_posterior);
/// The update after each iteration for EM
virtual void eMUpdate();
/**
Copy the parameters of the prior distribution
*/
virtual void setDataSet(DataSet* data_);
virtual ~MAPDiagonalGMM();
};
}
#endif
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