Bayesian optimization using different non-parametric processes as distributions over surrogate functions. More...

Inheritance diagram for bayesopt::PosteriorModel:

Collaboration diagram for bayesopt::PosteriorModel:

Public Member Functions
	PosteriorModel (size_t dim, Parameters params, randEngine &eng)
	Constructor. More...

virtual	~PosteriorModel ()
	Default destructor.

virtual void	updateHyperParameters ()=0

virtual void	fitSurrogateModel ()=0

virtual void	updateSurrogateModel ()=0

virtual double	evaluateCriteria (const vectord &query)=0

virtual void	updateCriteria (const vectord &query)=0

virtual bool	criteriaRequiresComparison ()=0

virtual void	setFirstCriterium ()=0

virtual bool	setNextCriterium (const vectord &prevResult)=0

virtual std::string	getBestCriteria (vectord &best)=0

void	setSamples (const matrixd &x, const vectord &y)

void	setSamples (const matrixd &x)

void	setSamples (const vectord &y)

void	setSample (const vectord &x, double y)

void	addSample (const vectord &x, double y)

double	getValueAtMinimum ()

vectord	getPointAtMinimum ()

void	plotDataset (TLogLevel level)

const Dataset *	getData ()

virtual ProbabilityDistribution *	getPrediction (const vectord &query)=0

Static Public Member Functions
static PosteriorModel *	create (size_t dim, Parameters params, randEngine &eng)

Protected Attributes
Parameters	mParameters
	Configuration parameters.

size_t	mDims
	Number of dimensions.

Dataset	mData
	Dataset (x-> inputs, y-> labels/output)

MeanModel	mMean

Detailed Description

Bayesian optimization using different non-parametric processes as distributions over surrogate functions.

Definition at line 43 of file posteriormodel.hpp.

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

Public Member Functions