#include <ExponentialCovariance.h>

Inheritance diagram for ExponentialCovariance:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	ExponentialCovariance (const InputParameters &parameters)

void	computeCovarianceMatrix (RealEigenMatrix &K, const RealEigenMatrix &x, const RealEigenMatrix &xp, const bool is_self_covariance) const override
	Generates the Covariance Matrix given two points in the parameter space. More...

bool	computedKdhyper (RealEigenMatrix &dKdhp, const RealEigenMatrix &x, const std::string &hyper_param_name, unsigned int ind) const override
	Redirect dK/dhp for hyperparameter "hp". More...

void	loadHyperParamMap (const std::unordered_map< std::string, Real > &map, const std::unordered_map< std::string, std::vector< Real >> &vec_map)
	Load some hyperparameters into the local maps contained in this object. More...

void	buildHyperParamMap (std::unordered_map< std::string, Real > &map, std::unordered_map< std::string, std::vector< Real >> &vec_map) const
	Populates the input maps with the owned hyperparameters. More...

virtual bool	getTuningData (const std::string &name, unsigned int &size, Real &min, Real &max) const
	Get the default minimum and maximum and size of a hyperparameter. More...

void	dependentCovarianceTypes (std::map< UserObjectName, std::string > &name_type_map) const
	Populate a map with the names and types of the dependent covariance functions. More...

const std::vector< UserObjectName > &	dependentCovarianceNames () const
	Get the names of the dependent covariances. More...

virtual bool	isTunable (const std::string &name) const
	Check if a given parameter is tunable. More...

unsigned int	numOutputs () const
	Return the number of outputs assumed for this covariance function. More...

std::unordered_map< std::string, Real > &	hyperParamMapReal ()
	Get the map of scalar parameters. More...

std::unordered_map< std::string, std::vector< Real > > &	hyperParamMapVectorReal ()
	Get the map of vector parameters. More...

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

std::shared_ptr< const MooseObject >	getSharedPtr () const

MooseApp &	getMooseApp () const

const std::string &	type () const

virtual const std::string &	name () const

std::string	typeAndName () const

std::string	errorPrefix (const std::string &error_type) const

void	callMooseError (std::string msg, const bool with_prefix) const

MooseObjectParameterName	uniqueParameterName (const std::string &parameter_name) const

const InputParameters &	parameters () const

MooseObjectName	uniqueName () const

const T &	getParam (const std::string &name) const

std::vector< std::pair< T1, T2 > >	getParam (const std::string &param1, const std::string &param2) const

const T *	queryParam (const std::string &name) const

const T &	getRenamedParam (const std::string &old_name, const std::string &new_name) const

T	getCheckedPointerParam (const std::string &name, const std::string &error_string="") const

bool	isParamValid (const std::string &name) const

bool	isParamSetByUser (const std::string &nm) const

void	paramError (const std::string &param, Args... args) const

void	paramWarning (const std::string &param, Args... args) const

void	paramInfo (const std::string &param, Args... args) const

void	connectControllableParams (const std::string &parameter, const std::string &object_type, const std::string &object_name, const std::string &object_parameter) const

void	mooseError (Args &&... args) const

void	mooseErrorNonPrefixed (Args &&... args) const

void	mooseDocumentedError (const std::string &repo_name, const unsigned int issue_num, Args &&... args) const

void	mooseWarning (Args &&... args) const

void	mooseWarningNonPrefixed (Args &&... args) const

void	mooseDeprecated (Args &&... args) const

void	mooseInfo (Args &&... args) const

std::string	getDataFileName (const std::string &param) const

std::string	getDataFileNameByName (const std::string &relative_path) const

std::string	getDataFilePath (const std::string &relative_path) const

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

Static Public Member Functions
static InputParameters	validParams ()

static void	ExponentialFunction (RealEigenMatrix &K, const RealEigenMatrix &x, const RealEigenMatrix &xp, const std::vector< Real > &length_factor, const Real sigma_f_squared, const Real sigma_n_squared, const Real gamma, const bool is_self_covariance)

static void	computedKdlf (RealEigenMatrix &K, const RealEigenMatrix &x, const std::vector< Real > &length_factor, const Real sigma_f_squared, const Real gamma, const int ind)
	Computes dK/dlf for individual length factors. More...

Public Attributes
const ConsoleStream	_console

Protected Member Functions
const Real &	addRealHyperParameter (const std::string &name, const Real value, const bool is_tunable)
	Register a scalar hyperparameter to this covariance function. More...

const std::vector< Real > &	addVectorRealHyperParameter (const std::string &name, const std::vector< Real > value, const bool is_tunable)
	Register a vector hyperparameter to this covariance function. More...

CovarianceFunctionBase *	getCovarianceFunctionByName (const UserObjectName &name) const
	Lookup a CovarianceFunction object by name and return pointer. More...

Protected Attributes
std::unordered_map< std::string, Real >	_hp_map_real
	Map of real-valued hyperparameters. More...

std::unordered_map< std::string, std::vector< Real > >	_hp_map_vector_real
	Map of vector-valued hyperparameters. More...

std::unordered_set< std::string >	_tunable_hp
	list of tunable hyper-parameters More...

const unsigned int	_num_outputs
	The number of outputs this covariance function is used to describe. More...

const std::vector< UserObjectName >	_dependent_covariance_names
	The names of the dependent covariance functions. More...

std::vector< std::string >	_dependent_covariance_types
	The types of the dependent covariance functions. More...

std::vector< CovarianceFunctionBase * >	_covariance_functions
	Vector of pointers to the dependent covariance functions. More...

const bool &	_enabled

MooseApp &	_app

const std::string	_type

const std::string	_name

const InputParameters &	_pars

Factory &	_factory

ActionFactory &	_action_factory

const Parallel::Communicator &	_communicator

Private Attributes
const std::vector< Real > &	_length_factor
	lengh factor () for the kernel, in vector form for multiple parameters More...

const Real &	_sigma_f_squared
	signal variance (^2) More...

const Real &	_sigma_n_squared
	noise variance (^2) More...

const Real &	_gamma
	gamma exponential factor for use in kernel More...

Detailed Description

Definition at line 14 of file ExponentialCovariance.h.

Constructor & Destructor Documentation

◆ ExponentialCovariance()

ExponentialCovariance::ExponentialCovariance ( const InputParameters & parameters )

Definition at line 30 of file ExponentialCovariance.C.

   : CovarianceFunctionBase(parameters),
     _length_factor(addVectorRealHyperParameter(
         "length_factor", getParam<std::vector<Real>>("length_factor"), true)),
     _sigma_f_squared(
         addRealHyperParameter("signal_variance", getParam<Real>("signal_variance"), true)),
     _sigma_n_squared(
         addRealHyperParameter("noise_variance", getParam<Real>("noise_variance"), true)),
     _gamma(addRealHyperParameter("gamma", getParam<Real>("gamma"), false))
 {
 }

Member Function Documentation

◆ addRealHyperParameter()

const Real & CovarianceFunctionBase::addRealHyperParameter	(	const std::string &	name,
		const Real	value,
		const bool	is_tunable
	)

protectedinherited

Register a scalar hyperparameter to this covariance function.

Parameters

name	The name of the parameter
value	The initial value of the parameter
is_tunable	If the parameter is tunable during optimization

Definition at line 52 of file CovarianceFunctionBase.C.

 {
   const auto prefixed_name = _name + ":" + name;
   if (is_tunable)
     _tunable_hp.insert(prefixed_name);
   return _hp_map_real.emplace(prefixed_name, value).first->second;
 }

◆ addVectorRealHyperParameter()

const std::vector< Real > & CovarianceFunctionBase::addVectorRealHyperParameter	(	const std::string &	name,
		const std::vector< Real >	value,
		const bool	is_tunable
	)

protectedinherited

Register a vector hyperparameter to this covariance function.

Parameters

name	The name of the parameter
value	The initial value of the parameter
is_tunable	If the parameter is tunable during optimization

Definition at line 63 of file CovarianceFunctionBase.C.

Referenced by LMC::LMC().

 {
   const auto prefixed_name = _name + ":" + name;
   if (is_tunable)
     _tunable_hp.insert(prefixed_name);
   return _hp_map_vector_real.emplace(prefixed_name, value).first->second;
 }

◆ buildHyperParamMap()

void CovarianceFunctionBase::buildHyperParamMap	(	std::unordered_map< std::string, Real > &	map,
		std::unordered_map< std::string, std::vector< Real >> &	vec_map
	)		const

inherited

Populates the input maps with the owned hyperparameters.

Parameters

map	Map of scalar hyperparameters that should be populated
vec_map	Map of vector hyperparameters that should be populated

Definition at line 115 of file CovarianceFunctionBase.C.

Referenced by StochasticTools::GaussianProcess::setupCovarianceMatrix(), and StochasticTools::GaussianProcess::tuneHyperParamsAdam().

 {
   // First, add the hyperparameters of the dependent covariance functions
   for (const auto dependent_covar : _covariance_functions)
     dependent_covar->buildHyperParamMap(map, vec_map);
 
   // At the end we just append the hyperparameters this object owns
   for (const auto & iter : _hp_map_real)
     map[iter.first] = iter.second;
   for (const auto & iter : _hp_map_vector_real)
     vec_map[iter.first] = iter.second;
 }

◆ computeCovarianceMatrix()

void ExponentialCovariance::computeCovarianceMatrix	(	RealEigenMatrix &	K,
		const RealEigenMatrix &	x,
		const RealEigenMatrix &	xp,
		const bool	is_self_covariance
	)		const

overridevirtual

Generates the Covariance Matrix given two points in the parameter space.

Implements CovarianceFunctionBase.

Definition at line 43 of file ExponentialCovariance.C.

 {
   if ((unsigned)x.cols() != _length_factor.size())
     mooseError("length_factor size does not match dimension of trainer input.");
 
   ExponentialFunction(
       K, x, xp, _length_factor, _sigma_f_squared, _sigma_n_squared, _gamma, is_self_covariance);
 }

◆ computedKdhyper()

bool ExponentialCovariance::computedKdhyper	(	RealEigenMatrix &	dKdhp,
		const RealEigenMatrix &	x,
		const std::string &	hyper_param_name,
		unsigned int	ind
	)		const

overridevirtual

Redirect dK/dhp for hyperparameter "hp".

Reimplemented from CovarianceFunctionBase.

Definition at line 89 of file ExponentialCovariance.C.

 {
   if (name().length() + 1 > hyper_param_name.length())
     return false;
 
   const std::string name_without_prefix = hyper_param_name.substr(name().length() + 1);
 
   if (name_without_prefix == "noise_variance")
   {
     ExponentialFunction(dKdhp, x, x, _length_factor, 0, 1, _gamma, true);
     return true;
   }
 
   if (name_without_prefix == "signal_variance")
   {
     ExponentialFunction(dKdhp, x, x, _length_factor, 1, 0, _gamma, false);
     return true;
   }
 
   if (name_without_prefix == "length_factor")
   {
     computedKdlf(dKdhp, x, _length_factor, _sigma_f_squared, _gamma, ind);
     return true;
   }
 
   return false;
 }

◆ computedKdlf()

void ExponentialCovariance::computedKdlf	(	RealEigenMatrix &	K,
		const RealEigenMatrix &	x,
		const std::vector< Real > &	length_factor,
		const Real	sigma_f_squared,
		const Real	gamma,
		const int	ind
	)

static

Computes dK/dlf for individual length factors.

Definition at line 121 of file ExponentialCovariance.C.

Referenced by computedKdhyper().

 {
   unsigned int num_samples_x = x.rows();
   unsigned int num_params_x = x.cols();
 
   mooseAssert(ind < x.cols(), "Incorrect length factor index");
 
   for (unsigned int ii = 0; ii < num_samples_x; ++ii)
   {
     for (unsigned int jj = 0; jj < num_samples_x; ++jj)
     {
       // Compute distance per parameter, scaled by length factor
       Real r_scaled = 0;
       for (unsigned int kk = 0; kk < num_params_x; ++kk)
         r_scaled += pow((x(ii, kk) - x(jj, kk)) / length_factor[kk], 2);
       r_scaled = sqrt(r_scaled);
       if (r_scaled != 0)
       {
         K(ii, jj) = gamma * std::pow(r_scaled, gamma - 2) * sigma_f_squared *
                     std::exp(-pow(r_scaled, gamma));
         K(ii, jj) =
             std::pow(x(ii, ind) - x(jj, ind), 2) / std::pow(length_factor[ind], 3) * K(ii, jj);
       }
       else // avoid div by 0. 0/0=0 scenario.
         K(ii, jj) = 0;
     }
   }
 }

◆ dependentCovarianceNames()

const std::vector<UserObjectName>& CovarianceFunctionBase::dependentCovarianceNames ( ) const

inlineinherited

Get the names of the dependent covariances.

Definition at line 62 of file CovarianceFunctionBase.h.

Referenced by StochasticTools::GaussianProcess::linkCovarianceFunction().

   {
     return _dependent_covariance_names;
   }

◆ dependentCovarianceTypes()

void CovarianceFunctionBase::dependentCovarianceTypes ( std::map< UserObjectName, std::string > & name_type_map ) const

inherited

Populate a map with the names and types of the dependent covariance functions.

Parameters

name_type_map Reference to the map which should be populated

Definition at line 163 of file CovarianceFunctionBase.C.

Referenced by StochasticTools::GaussianProcess::linkCovarianceFunction().

 {
   for (const auto dependent_covar : _covariance_functions)
   {
     dependent_covar->dependentCovarianceTypes(name_type_map);
     name_type_map.insert(std::make_pair(dependent_covar->name(), dependent_covar->type()));
   }
 }

◆ ExponentialFunction()

void ExponentialCovariance::ExponentialFunction	(	RealEigenMatrix &	K,
		const RealEigenMatrix &	x,
		const RealEigenMatrix &	xp,
		const std::vector< Real > &	length_factor,
		const Real	sigma_f_squared,
		const Real	sigma_n_squared,
		const Real	gamma,
		const bool	is_self_covariance
	)

static

Definition at line 56 of file ExponentialCovariance.C.

Referenced by computeCovarianceMatrix(), and computedKdhyper().

 {
   unsigned int num_samples_x = x.rows();
   unsigned int num_samples_xp = xp.rows();
   unsigned int num_params_x = x.cols();
 
   mooseAssert(num_params_x == xp.cols(),
               "Number of parameters do not match in covariance kernel calculation");
 
   for (unsigned int ii = 0; ii < num_samples_x; ++ii)
   {
     for (unsigned int jj = 0; jj < num_samples_xp; ++jj)
     {
       // Compute distance per parameter, scaled by length factor
       Real r_scaled = 0;
       for (unsigned int kk = 0; kk < num_params_x; ++kk)
         r_scaled += pow((x(ii, kk) - xp(jj, kk)) / length_factor[kk], 2);
       r_scaled = sqrt(r_scaled);
       K(ii, jj) = sigma_f_squared * std::exp(-pow(r_scaled, gamma));
     }
     if (is_self_covariance)
       K(ii, ii) += sigma_n_squared;
   }
 }

◆ getCovarianceFunctionByName()

CovarianceFunctionBase * CovarianceInterface::getCovarianceFunctionByName ( const UserObjectName & name ) const

protectedinherited

Lookup a CovarianceFunction object by name and return pointer.

Definition at line 25 of file CovarianceInterface.C.

Referenced by ActiveLearningGaussianProcess::ActiveLearningGaussianProcess(), CovarianceFunctionBase::CovarianceFunctionBase(), GaussianProcessTrainer::GaussianProcessTrainer(), and GaussianProcessSurrogate::setupCovariance().

 {
   std::vector<CovarianceFunctionBase *> models;
   _covar_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("CovarianceFunction")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a CovarianceFunction object with the name '" + name + "'");
   return models[0];
 }

◆ getTuningData()

bool CovarianceFunctionBase::getTuningData	(	const std::string &	name,
		unsigned int &	size,
		Real &	min,
		Real &	max
	)		const

virtualinherited

Get the default minimum and maximum and size of a hyperparameter.

Returns false is the parameter has not been found in this covariance object.

Parameters

name	The name of the hyperparameter
size	Reference to an unsigned int that will contain the size of the hyperparameter (will be populated with 1 if it is scalar)
min	Reference to a number which will be populated by the maximum allowed value of the hyperparameter
max	Reference to a number which will be populated by the minimum allowed value of the hyperparameter

Definition at line 131 of file CovarianceFunctionBase.C.

Referenced by StochasticTools::GaussianProcess::generateTuningMap().

 {
   // First, check the dependent covariances
   for (const auto dependent_covar : _covariance_functions)
     if (dependent_covar->getTuningData(name, size, min, max))
       return true;
 
   min = 1e-9;
   max = 1e9;
 
   if (_hp_map_real.find(name) != _hp_map_real.end())
   {
     size = 1;
     return true;
   }
   else if (_hp_map_vector_real.find(name) != _hp_map_vector_real.end())
   {
     const auto & vector_value = _hp_map_vector_real.find(name);
     size = vector_value->second.size();
     return true;
   }
   else
   {
     size = 0;
     return false;
   }
 }

◆ hyperParamMapReal()

std::unordered_map<std::string, Real>& CovarianceFunctionBase::hyperParamMapReal ( )

inlineinherited

Get the map of scalar parameters.

Definition at line 88 of file CovarianceFunctionBase.h.

88 { return _hp_map_real; }

CovarianceFunctionBase::_hp_map_real

std::unordered_map< std::string, Real > _hp_map_real

Map of real-valued hyperparameters.

Definition: CovarianceFunctionBase.h:113

◆ hyperParamMapVectorReal()

std::unordered_map<std::string, std::vector<Real> >& CovarianceFunctionBase::hyperParamMapVectorReal ( )

inlineinherited

Get the map of vector parameters.

Definition at line 91 of file CovarianceFunctionBase.h.

   {
     return _hp_map_vector_real;
   }

◆ isTunable()

bool CovarianceFunctionBase::isTunable ( const std::string & name ) const

virtualinherited

Check if a given parameter is tunable.

Parameters

The	name of the hyperparameter

Definition at line 74 of file CovarianceFunctionBase.C.

Referenced by StochasticTools::GaussianProcess::generateTuningMap().

 {
   // First, we check if the dependent covariances have the parameter
   for (const auto dependent_covar : _covariance_functions)
     if (dependent_covar->isTunable(name))
       return true;
 
   if (_tunable_hp.find(name) != _tunable_hp.end())
     return true;
   else if (_hp_map_real.find(name) != _hp_map_real.end() ||
            _hp_map_vector_real.find(name) != _hp_map_vector_real.end())
     mooseError("We found hyperparameter ", name, " but it was not declared tunable!");
 
   return false;
 }

◆ loadHyperParamMap()

void CovarianceFunctionBase::loadHyperParamMap	(	const std::unordered_map< std::string, Real > &	map,
		const std::unordered_map< std::string, std::vector< Real >> &	vec_map
	)

inherited

Load some hyperparameters into the local maps contained in this object.

Parameters

map	Input map of scalar hyperparameters
vec_map	Input map of vector hyperparameters

Definition at line 91 of file CovarianceFunctionBase.C.

Referenced by LoadCovarianceDataAction::load(), and StochasticTools::GaussianProcess::tuneHyperParamsAdam().

 {
   // First, load the hyperparameters of the dependent covariance functions
   for (const auto dependent_covar : _covariance_functions)
     dependent_covar->loadHyperParamMap(map, vec_map);
 
   // Then we load the hyperparameters of this object
   for (auto & iter : _hp_map_real)
   {
     const auto & map_iter = map.find(iter.first);
     if (map_iter != map.end())
       iter.second = map_iter->second;
   }
   for (auto & iter : _hp_map_vector_real)
   {
     const auto & map_iter = vec_map.find(iter.first);
     if (map_iter != vec_map.end())
       iter.second = map_iter->second;
   }
 }

◆ numOutputs()

unsigned int CovarianceFunctionBase::numOutputs ( ) const

inlineinherited

Return the number of outputs assumed for this covariance function.

Definition at line 85 of file CovarianceFunctionBase.h.

Referenced by GaussianProcessSurrogate::evaluate(), GaussianProcessTrainer::GaussianProcessTrainer(), and StochasticTools::GaussianProcess::linkCovarianceFunction().

85 { return _num_outputs; }

CovarianceFunctionBase::_num_outputs

const unsigned int _num_outputs

The number of outputs this covariance function is used to describe.

Definition: CovarianceFunctionBase.h:122

◆ validParams()

InputParameters ExponentialCovariance::validParams ( )

static

Definition at line 16 of file ExponentialCovariance.C.

 {
   InputParameters params = CovarianceFunctionBase::validParams();
   params.addClassDescription("Exponential covariance function.");
   params.addRequiredParam<std::vector<Real>>("length_factor",
                                              "Length factors to use for Covariance Kernel");
   params.addRequiredParam<Real>("signal_variance",
                                 "Signal Variance ($\\sigma_f^2$) to use for kernel calculation.");
   params.addParam<Real>(
       "noise_variance", 0.0, "Noise Variance ($\\sigma_n^2$) to use for kernel calculation.");
   params.addRequiredParam<Real>("gamma", "Gamma to use for Exponential Covariance Kernel");
   return params;
 }

Member Data Documentation

◆ _covariance_functions

std::vector<CovarianceFunctionBase *> CovarianceFunctionBase::_covariance_functions

protectedinherited

Vector of pointers to the dependent covariance functions.

Definition at line 131 of file CovarianceFunctionBase.h.

Referenced by CovarianceFunctionBase::buildHyperParamMap(), LMC::computeCovarianceMatrix(), LMC::computedKdhyper(), CovarianceFunctionBase::CovarianceFunctionBase(), CovarianceFunctionBase::dependentCovarianceTypes(), CovarianceFunctionBase::getTuningData(), CovarianceFunctionBase::isTunable(), and CovarianceFunctionBase::loadHyperParamMap().

◆ _dependent_covariance_names

const std::vector<UserObjectName> CovarianceFunctionBase::_dependent_covariance_names

protectedinherited

The names of the dependent covariance functions.

Definition at line 125 of file CovarianceFunctionBase.h.

Referenced by CovarianceFunctionBase::CovarianceFunctionBase(), and CovarianceFunctionBase::dependentCovarianceNames().

◆ _dependent_covariance_types

std::vector<std::string> CovarianceFunctionBase::_dependent_covariance_types

protectedinherited

The types of the dependent covariance functions.

Definition at line 128 of file CovarianceFunctionBase.h.

Referenced by CovarianceFunctionBase::CovarianceFunctionBase().

◆ _gamma

const Real& ExponentialCovariance::_gamma

private

gamma exponential factor for use in kernel

Definition at line 60 of file ExponentialCovariance.h.

Referenced by computeCovarianceMatrix(), and computedKdhyper().

◆ _hp_map_real

std::unordered_map<std::string, Real> CovarianceFunctionBase::_hp_map_real

protectedinherited

Map of real-valued hyperparameters.

Definition at line 113 of file CovarianceFunctionBase.h.

Referenced by CovarianceFunctionBase::addRealHyperParameter(), CovarianceFunctionBase::buildHyperParamMap(), CovarianceFunctionBase::getTuningData(), CovarianceFunctionBase::hyperParamMapReal(), CovarianceFunctionBase::isTunable(), and CovarianceFunctionBase::loadHyperParamMap().

◆ _hp_map_vector_real

std::unordered_map<std::string, std::vector<Real> > CovarianceFunctionBase::_hp_map_vector_real

protectedinherited

Map of vector-valued hyperparameters.

Definition at line 116 of file CovarianceFunctionBase.h.

Referenced by CovarianceFunctionBase::addVectorRealHyperParameter(), CovarianceFunctionBase::buildHyperParamMap(), CovarianceFunctionBase::getTuningData(), CovarianceFunctionBase::hyperParamMapVectorReal(), CovarianceFunctionBase::isTunable(), LMC::LMC(), and CovarianceFunctionBase::loadHyperParamMap().

◆ _length_factor

const std::vector<Real>& ExponentialCovariance::_length_factor

private

lengh factor () for the kernel, in vector form for multiple parameters

Definition at line 51 of file ExponentialCovariance.h.

Referenced by computeCovarianceMatrix(), and computedKdhyper().

◆ _num_outputs

const unsigned int CovarianceFunctionBase::_num_outputs

protectedinherited

The number of outputs this covariance function is used to describe.

Definition at line 122 of file CovarianceFunctionBase.h.

Referenced by LMC::computeAGradient(), LMC::computeBMatrix(), LMC::computeCovarianceMatrix(), LMC::computedKdhyper(), LMC::LMC(), and CovarianceFunctionBase::numOutputs().

◆ _sigma_f_squared

const Real& ExponentialCovariance::_sigma_f_squared

private

signal variance (^2)

Definition at line 54 of file ExponentialCovariance.h.

Referenced by computeCovarianceMatrix(), and computedKdhyper().

◆ _sigma_n_squared

const Real& ExponentialCovariance::_sigma_n_squared

private

noise variance (^2)

Definition at line 57 of file ExponentialCovariance.h.

Referenced by computeCovarianceMatrix().

◆ _tunable_hp

std::unordered_set<std::string> CovarianceFunctionBase::_tunable_hp

protectedinherited

list of tunable hyper-parameters

Definition at line 119 of file CovarianceFunctionBase.h.

Referenced by CovarianceFunctionBase::addRealHyperParameter(), CovarianceFunctionBase::addVectorRealHyperParameter(), LMC::computedKdhyper(), and CovarianceFunctionBase::isTunable().

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

stochastic_tools/include/covariances/ExponentialCovariance.h
stochastic_tools/src/covariances/ExponentialCovariance.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ ExponentialCovariance()

Member Function Documentation

◆ addRealHyperParameter()

◆ addVectorRealHyperParameter()

◆ buildHyperParamMap()

◆ computeCovarianceMatrix()

◆ computedKdhyper()

◆ computedKdlf()

◆ dependentCovarianceNames()

◆ dependentCovarianceTypes()

◆ ExponentialFunction()

◆ getCovarianceFunctionByName()

◆ getTuningData()

◆ hyperParamMapReal()

◆ hyperParamMapVectorReal()

◆ isTunable()

◆ loadHyperParamMap()

◆ numOutputs()

◆ validParams()

Member Data Documentation

◆ _covariance_functions

◆ _dependent_covariance_names

◆ _dependent_covariance_types

◆ _gamma

◆ _hp_map_real

◆ _hp_map_vector_real

◆ _length_factor

◆ _num_outputs

◆ _sigma_f_squared

◆ _sigma_n_squared

◆ _tunable_hp