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

Inheritance diagram for bayesopt::EmpiricalBayes:

Collaboration diagram for bayesopt::EmpiricalBayes:

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

virtual	~EmpiricalBayes ()
	Default destructor.

void	updateHyperParameters ()

void	fitSurrogateModel ()

void	updateSurrogateModel ()

double	evaluateCriteria (const vectord &query)

void	updateCriteria (const vectord &query)

bool	criteriaRequiresComparison ()

void	setFirstCriterium ()

bool	setNextCriterium (const vectord &prevResult)

std::string	getBestCriteria (vectord &best)

ProbabilityDistribution *	getPrediction (const vectord &query)

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

virtual	~PosteriorModel ()
	Default destructor.

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 ()

Private Member Functions
void	setSurrogateModel (randEngine &eng)

void	setCriteria (randEngine &eng)

Private Attributes
boost::scoped_ptr< NonParametricProcess >	mGP
	Pointer to surrogate model.

boost::scoped_ptr< Criteria >	mCrit
	Metacriteria model.

boost::scoped_ptr< NLOPT_Optimization >	kOptimizer

Additional Inherited Members
Static Public Member Functions inherited from bayesopt::PosteriorModel
static PosteriorModel *	create (size_t dim, Parameters params, randEngine &eng)

Protected Attributes inherited from bayesopt::PosteriorModel
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 45 of file posterior_empirical.hpp.

Constructor & Destructor Documentation

bayesopt::EmpiricalBayes::EmpiricalBayes	(	size_t	dim,
		Parameters	params,
		randEngine &	eng
	)

Constructor.

Parameters

params set of parameters (see parameters.hpp)

Definition at line 31 of file posterior_empirical.cpp.

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