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hops::Mixture Class Reference

#include <Mixture.hpp>

Inheritance diagram for hops::Mixture:
Inheritance graph
Collaboration diagram for hops::Mixture:
Collaboration graph

Public Member Functions

 Mixture (const std::vector< std::shared_ptr< Model >> &models)
 
 Mixture (std::vector< std::shared_ptr< Model >> models, std::vector< double > weights)
 
MatrixType::Scalar computeNegativeLogLikelihood (const VectorType &x) const override
 Evaluates the negative log likelihood for input x. More...
 
std::optional< VectorTypecomputeLogLikelihoodGradient (const VectorType &x) const override
 
std::optional< MatrixTypecomputeExpectedFisherInformation (const VectorType &x) const override
 
- Public Member Functions inherited from hops::Model
virtual ~Model ()=default
 
virtual std::optional< std::vector< std::string > > getParameterNames () const
 

Constructor & Destructor Documentation

◆ Mixture() [1/2]

hops::Mixture::Mixture ( const std::vector< std::shared_ptr< Model >> &  models)
inlineexplicit

◆ Mixture() [2/2]

hops::Mixture::Mixture ( std::vector< std::shared_ptr< Model >>  models,
std::vector< double >  weights 
)
inline

Member Function Documentation

◆ computeExpectedFisherInformation()

std::optional<MatrixType> hops::Mixture::computeExpectedFisherInformation ( const VectorType x) const
inlineoverridevirtual

Reimplemented from hops::Model.

◆ computeLogLikelihoodGradient()

std::optional<VectorType> hops::Mixture::computeLogLikelihoodGradient ( const VectorType x) const
inlineoverridevirtual

@ Brief Implementation derived from using chain rule on log(sum(...)).

Parameters
x
Returns

Reimplemented from hops::Model.

◆ computeNegativeLogLikelihood()

MatrixType::Scalar hops::Mixture::computeNegativeLogLikelihood ( const VectorType x) const
inlineoverridevirtual

Evaluates the negative log likelihood for input x.

Parameters
x
Returns

Implements hops::Model.


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