hops::MultivariateGaussian Class Reference

#include <MultivariateGaussian.hpp>

Inheritance diagram for hops::MultivariateGaussian:

Collaboration diagram for hops::MultivariateGaussian:

Public Member Functions
	MultivariateGaussian (VectorType mean, MatrixType covariance)
MatrixType::Scalar	computeNegativeLogLikelihood (const VectorType &x) const override
	Evaluates the negative log likelihood for input x. More...
std::optional< VectorType >	computeLogLikelihoodGradient (const VectorType &x) const override
std::optional< MatrixType >	computeExpectedFisherInformation (const VectorType &) const override
const VectorType &	getMean () const
const MatrixType &	getCovariance () const
Public Member Functions inherited from hops::Model
virtual	~Model ()=default
virtual std::optional< std::vector< std::string > >	getParameterNames () const

Constructor & Destructor Documentation

MultivariateGaussian()

hops::MultivariateGaussian::MultivariateGaussian	(	VectorType	mean,
		MatrixType	covariance
	)

Member Function Documentation

computeExpectedFisherInformation()

std::optional< MatrixType > hops::MultivariateGaussian::computeExpectedFisherInformation ( const VectorType &  ) const

overridevirtual

Reimplemented from hops::Model.

computeLogLikelihoodGradient()

std::optional< VectorType > hops::MultivariateGaussian::computeLogLikelihoodGradient ( const VectorType &  x ) const

overridevirtual

Reimplemented from hops::Model.

computeNegativeLogLikelihood()

MatrixType::Scalar hops::MultivariateGaussian::computeNegativeLogLikelihood ( const VectorType &  x ) const

overridevirtual

Evaluates the negative log likelihood for input x.

Parameters

x

Returns

Implements hops::Model.

getCovariance()

const MatrixType & hops::MultivariateGaussian::getCovariance

(

)

const

getMean()

const VectorType & hops::MultivariateGaussian::getMean

(

)

const

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

• MultivariateGaussian.hpp