Gaussian process with normal prior on the parameters. More...

#include <gaussian_process_normal.hpp>

Inheritance diagram for bayesopt::GaussianProcessNormal:

Collaboration diagram for bayesopt::GaussianProcessNormal:

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

ProbabilityDistribution *	prediction (const vectord &query)
	Function that returns the prediction of the GP for a query point in the hypercube [0,1]. More...

Public Member Functions inherited from bayesopt::HierarchicalGaussianProcess
	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)

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 ()

Private Member Functions
double	negativeLogLikelihood ()
	Computes the negative log likelihood and its gradient of the data. More...

void	precomputePrediction ()
	Precompute some values of the prediction that do not depends on the query.

Private Attributes
vectord	mWMap
	GP posterior parameters.

vectord	mW0

vectord	mInvVarW

vectord	mVf
	Precomputed GP prediction operations.

matrixd	mKF

matrixd	mD

GaussianDistribution *	d_
	Predictive distributions.

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

Protected Member Functions inherited from bayesopt::KernelRegressor
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.

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