A class used to generate a LinearSum function of observing model predictions. More...

#include <LinearSum.h>

Inheritance diagram for LinearSum:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	LinearSum (const InputParameters &parameters)

virtual Real	function (const std::vector< Real > &x) const override
	Return the probability density or mass function at vector x. More...

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

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

bool	isKokkosObject () const

MooseApp &	getMooseApp () const

const std::string &	type () const

const std::string &	name () const

std::string	typeAndName () const

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

MooseObjectName	uniqueName () const

const InputParameters &	parameters () const

const hit::Node *	getHitNode () const

bool	hasBase () const

const std::string &	getBase () 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	haveParameter (const std::string &name) const

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

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

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

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

void	paramWarning (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

std::string	messagePrefix (const bool hit_prefix=true) const

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

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

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

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

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

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

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

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

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

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

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

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

void	callMooseError (std::string msg, const bool with_prefix, const hit::Node *node=nullptr, const bool show_trace=true) 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 Real	function (const std::vector< Real > &exp, const std::vector< Real > &model, const Real &noise)
	Return the probability density function. More...

static Real	function (const std::vector< Real > &exp, const std::vector< Real > &model, const Real &noise, const bool &log_likelihood)
	Return the probability density function. More...

static void	callMooseError (MooseApp const app, const InputParameters &params, std::string msg, const bool with_prefix, const hit::Node node, const bool show_trace=true)

Public Attributes
	usingCombinedWarningSolutionWarnings

const ConsoleStream	_console

Static Public Attributes
static const std::string	type_param

static const std::string	name_param

static const std::string	unique_name_param

static const std::string	app_param

static const std::string	moose_base_param

static const std::string	kokkos_object_param

Protected Member Functions
void	flagInvalidSolutionInternal (const InvalidSolutionID invalid_solution_id) const

InvalidSolutionID	registerInvalidSolutionInternal (const std::string &message, const bool warning) const

const ReporterContextBase &	getReporterContextBaseByName (const ReporterName &reporter_name) const

const ReporterName &	getReporterName (const std::string &param_name) const

virtual void	addReporterDependencyHelper (const ReporterName &)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

Protected Attributes
const bool	_log_likelihood
	return log-likelihood or likelihood More...

const Real &	_noise
	Noise value. More...

std::vector< Real >	_exp_values
	Experimental data values. More...

const bool &	_enabled

MooseApp &	_app

Factory &	_factory

ActionFactory &	_action_factory

const std::string &	_type

const std::string &	_name

const InputParameters &	_pars

const Parallel::Communicator &	_communicator

Detailed Description

A class used to generate a LinearSum function of observing model predictions.

Definition at line 17 of file LinearSum.h.

Constructor & Destructor Documentation

◆ LinearSum()

LinearSum::LinearSum ( const InputParameters & parameters )

Definition at line 23 of file LinearSum.C.

23 : Gaussian(parameters) {}

Gaussian::Gaussian

Gaussian(const InputParameters &parameters)

Definition: Gaussian.C:33

MooseObject::parameters

const InputParameters & parameters() const

Member Function Documentation

◆ function() [1/3]

Real LinearSum::function ( const std::vector< Real > & x ) const

overridevirtual

Return the probability density or mass function at vector x.

Parameters

x	The input vector x

Reimplemented from Gaussian.

Definition at line 42 of file LinearSum.C.

 {
   return function(_exp_values, x, _noise);
 }

◆ function() [2/3]

Real LinearSum::function	(	const std::vector< Real > &	exp,
		const std::vector< Real > &	model,
		const Real &	noise
	)

static

Return the probability density function.

Parameters

exp	The experimental measurement
model	The model prediction
noise	The standard deviation

Definition at line 26 of file LinearSum.C.

 {
   mooseAssert(exp.size() == model.size(), "LinearSum: Vectors must be the same size.");
   mooseAssert(noise != 0.0, "LinearSum: Noise must not be zero.");
   Real result = 0.0;
   for (unsigned int i = 0; i < exp.size(); ++i)
   {
     const Real diff = exp[i] - model[i];
     result += diff * diff;
   }
   return result / (noise * noise);
 }

◆ function() [3/3]

Real Gaussian::function	(	const std::vector< Real > &	exp,
		const std::vector< Real > &	model,
		const Real &	noise,
		const bool &	log_likelihood
	)

staticinherited

Return the probability density function.

Parameters

exp	The experimental measurement
model	The model prediction
noise	The standard deviation
log_likelihood	Bool to return the log likelihood value

Definition at line 58 of file Gaussian.C.

 {
   Real result = 0.0;
   Real val1;
   for (unsigned i = 0; i < exp.size(); ++i)
   {
     val1 = Normal::pdf(exp[i], model[i], noise);
     val1 = std::log(val1);
     result += val1;
   }
   if (!log_likelihood)
     result = std::exp(result);
   return result;
 }

◆ validParams()

InputParameters LinearSum::validParams ( )

static

Definition at line 15 of file LinearSum.C.

 {
   InputParameters params = LikelihoodFunctionBase::validParams();
   params.addClassDescription(
       "LinearSum function evaluating the model goodness against experiments.");
   return params;
 }

Member Data Documentation

◆ _exp_values

std::vector<Real> Gaussian::_exp_values

protectedinherited

Experimental data values.

Definition at line 47 of file Gaussian.h.

Referenced by ExtremeValue::function(), TruncatedGaussian::function(), function(), Gaussian::function(), and Gaussian::Gaussian().

◆ _log_likelihood

const bool Gaussian::_log_likelihood

protectedinherited

return log-likelihood or likelihood

Definition at line 41 of file Gaussian.h.

Referenced by ExtremeValue::function(), TruncatedGaussian::function(), and Gaussian::function().

◆ _noise

const Real& Gaussian::_noise

protectedinherited

Noise value.

Definition at line 44 of file Gaussian.h.

Referenced by ExtremeValue::function(), TruncatedGaussian::function(), function(), and Gaussian::function().

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

stochastic_tools/include/likelihoods/LinearSum.h
stochastic_tools/src/likelihoods/LinearSum.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ LinearSum()

Member Function Documentation

◆ function() [1/3]

◆ function() [2/3]

◆ function() [3/3]

◆ validParams()

Member Data Documentation

◆ _exp_values

◆ _log_likelihood

◆ _noise