bayesopt::ConditionalBayesProcess Class Reference

Empirical Bayesian NonParametric process. More...

#include <conditionalbayesprocess.hpp>

Inheritance diagram for bayesopt::ConditionalBayesProcess:

Collaboration diagram for bayesopt::ConditionalBayesProcess:

Public Member Functions
	ConditionalBayesProcess (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng)
virtual ProbabilityDistribution *	prediction (const vectord &query)=0
	Function that returns the prediction of the GP for a query point in the hypercube [0,1]. More...
double	evaluate (const vectord &x)
	Computes the score (eg:likelihood) of the kernel parameters. More...
double	evaluateKernelParams ()
	Computes the score (eg:likelihood) of the current kernel parameters. More...
Public Member Functions inherited from bayesopt::KernelRegressor
	KernelRegressor (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng)
void	fitSurrogateModel ()
	Computes the initial surrogate model and updates the kernel parameters estimation. More...
void	updateSurrogateModel ()
	Sequential update of the surrogate model by adding a new row to the Kernel matrix, more precisely, to its Cholesky decomposition. More...
double	getSignalVariance ()
size_t	nHyperParameters ()
vectord	getHyperParameters ()
void	setHyperParameters (const vectord &theta)
Public Member Functions inherited from bayesopt::NonParametricProcess
	NonParametricProcess (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng)
double	getValueAtMinimum ()
const Dataset *	getData ()
double	getSignalVariance ()

Protected Member Functions
virtual double	negativeTotalLogLikelihood ()=0
	Computes the negative log likelihood of the data for all the parameters. More...
virtual double	negativeLogLikelihood ()=0
	Computes the negative log likelihood of the data for the kernel hyperparameters. More...
Protected Member Functions inherited from bayesopt::KernelRegressor
virtual void	precomputePrediction ()=0
	Sets the kind of learning methodology for kernel hyperparameters. More...
void	computeCorrMatrix (matrixd &corrMatrix)
	Computes the Correlation (Kernel or Gram) matrix.
matrixd	computeCorrMatrix ()
	Computes the Correlation (Kernel or Gram) matrix.
matrixd	computeDerivativeCorrMatrix (int dth_index)
	Computes the derivative of the correlation matrix with respect to the dth hyperparameter.
vectord	computeCrossCorrelation (const vectord &query)
double	computeSelfCorrelation (const vectord &query)
void	computeCholeskyCorrelation ()
	Computes the Cholesky decomposition of the Correlation matrix.

Private Member Functions
double	negativeCrossValidation ()
	Computes the negative score of the data using cross validation. More...

Additional Inherited Members
Static Public Member Functions inherited from bayesopt::NonParametricProcess
static NonParametricProcess *	create (size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng)
	Factory model generator for surrogate models. More...
Protected Attributes inherited from bayesopt::KernelRegressor
matrixd	mL
	Cholesky decomposition of the Correlation matrix.
score_type	mScoreType
learning_type	mLearnType
bool	mLearnAll
KernelModel	mKernel
Protected Attributes inherited from bayesopt::NonParametricProcess
const Dataset &	mData
double	mSigma
	Signal variance.
size_t	dim_
MeanModel &	mMean

Detailed Description

Empirical Bayesian NonParametric process.

Definition at line 42 of file conditionalbayesprocess.hpp.

Member Function Documentation

evaluateKernelParams()

double bayesopt::ConditionalBayesProcess::evaluateKernelParams

(

)

Computes the score (eg:likelihood) of the current kernel parameters.

Parameters

query

set of parameters.

Returns

score

Definition at line 42 of file conditionalbayesprocess.cpp.

Referenced by evaluate().

negativeCrossValidation()

double bayesopt::ConditionalBayesProcess::negativeCrossValidation ( )

private

Computes the negative score of the data using cross validation.

Returns

negative score

Definition at line 62 of file conditionalbayesprocess.cpp.

References bayesopt::Dataset::mX.

negativeLogLikelihood()

virtual double bayesopt::ConditionalBayesProcess::negativeLogLikelihood ( )

protectedpure virtual

Computes the negative log likelihood of the data for the kernel hyperparameters.

Returns

value negative log likelihood

Implemented in bayesopt::GaussianProcess, bayesopt::GaussianProcessML, bayesopt::StudentTProcessJeffreys, bayesopt::StudentTProcessNIG, and bayesopt::GaussianProcessNormal.

negativeTotalLogLikelihood()

virtual double bayesopt::ConditionalBayesProcess::negativeTotalLogLikelihood ( )

protectedpure virtual

Computes the negative log likelihood of the data for all the parameters.

Returns

value negative log likelihood

Implemented in bayesopt::GaussianProcess, and bayesopt::HierarchicalGaussianProcess.

prediction()

virtual ProbabilityDistribution* bayesopt::ConditionalBayesProcess::prediction ( const vectord &  query )

pure virtual

Function that returns the prediction of the GP for a query point in the hypercube [0,1].

Parameters

query

in the hypercube [0,1] to evaluate the Gaussian process

Returns

pointer to the probability distribution.

Implements bayesopt::NonParametricProcess.

Implemented in bayesopt::StudentTProcessNIG, bayesopt::GaussianProcessML, bayesopt::GaussianProcessNormal, bayesopt::StudentTProcessJeffreys, and bayesopt::GaussianProcess.

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

• conditionalbayesprocess.hpp
• conditionalbayesprocess.cpp