Helper methods for calculating Kullback-Leibler divergences. More...

Public Member Functions
	KLDivergence ()

	~KLDivergence ()

double	distanceGaussian (const std::vector< double > &means1, const std::vector< double > &variances1, const std::vector< double > &means2, const std::vector< double > &variances2)
	Calculate a symmetrised Kullback-Leibler divergence of Gaussian models based on mean and variance vectors. More...

double	distanceDistribution (const std::vector< double > &d1, const std::vector< double > &d2, bool symmetrised)
	Calculate a Kullback-Leibler divergence of two probability distributions. More...

Detailed Description

Helper methods for calculating Kullback-Leibler divergences.

Definition at line 23 of file KLDivergence.h.

Constructor & Destructor Documentation

KLDivergence::KLDivergence ( )

inline

Definition at line 26 of file KLDivergence.h.

KLDivergence::~KLDivergence ( )

inline

Definition at line 27 of file KLDivergence.h.

double KLDivergence::distanceGaussian	(	const std::vector< double > &	means1,
		const std::vector< double > &	variances1,
		const std::vector< double > &	means2,
		const std::vector< double > &	variances2
	)

Calculate a symmetrised Kullback-Leibler divergence of Gaussian models based on mean and variance vectors.

All input vectors must be of equal size.

Definition at line 22 of file KLDivergence.cpp.

double KLDivergence::distanceDistribution	(	const std::vector< double > &	d1,
		const std::vector< double > &	d2,
		bool	symmetrised
	)

Calculate a Kullback-Leibler divergence of two probability distributions.

Input vectors must be of equal size. If symmetrised is true, the result will be the symmetrised distance (equal to KL(d1, d2) + KL(d2, d1)).

Definition at line 47 of file KLDivergence.cpp.

The documentation for this class was generated from the following files: