Virtual class for hierarchical Gaussian processes. More...

#include <gaussian_process_hierarchical.hpp>

Inheritance diagram for bayesopt::HierarchicalGaussianProcess:

Collaboration diagram for bayesopt::HierarchicalGaussianProcess:

Public Member Functions
	HierarchicalGaussianProcess (size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng)

Public Member Functions inherited from bayesopt::ConditionalBayesProcess
	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
double	negativeTotalLogLikelihood ()
	Computes the negative log likelihood of the data for all the parameters. More...

Protected Member Functions inherited from bayesopt::ConditionalBayesProcess
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.

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