Derived approximation class for QMEA Quadratic Multipoint Exponential Approximation (a multipoint approximation). More...

Inheritance diagram for QMEApproximation:

Public Member Functions
	QMEApproximation ()
	default constructor

	QMEApproximation (ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label)
	standard constructor

	QMEApproximation (const SharedApproxData &shared_data)
	alternate constructor

	~QMEApproximation ()
	destructor

Public Member Functions inherited from Approximation
	Approximation ()
	default constructor More...

	Approximation (ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label)
	standard constructor for envelope More...

	Approximation (const SharedApproxData &shared_data)
	alternate constructor More...

	Approximation (const Approximation &approx)
	copy constructor More...

virtual	~Approximation ()
	destructor

Approximation	operator= (const Approximation &approx)
	assignment operator

virtual void	active_model_key (const Pecos::ActiveKey &sd_key)
	activate an approximation state based on its multi-index key

virtual void	clear_model_keys ()
	reset initial state by removing all model keys for an approximation

virtual void	export_model (const StringArray &var_labels=StringArray(), const String &fn_label="", const String &export_prefix="", const unsigned short export_format=NO_MODEL_FORMAT)
	exports the approximation; if export_format > NO_MODEL_FORMAT, uses all 3 parameters, otherwise extracts these from the Approximation's sharedDataRep to build a filename

virtual void	export_model (const Variables &vars, const String &fn_label="", const String &export_prefix="", const unsigned short export_format=NO_MODEL_FORMAT)
	approximation export that generates labels from the passed Variables, since only the derived classes know how the variables are ordered w.r.t. the surrogate build; if export_format > NO_MODEL_FORMAT, uses all 3 parameters, otherwise extracts these from the Approximation's sharedDataRep to build a filename

virtual void	rebuild ()
	rebuilds the approximation incrementally

virtual void	replace (const IntResponsePair &response_pr, size_t fn_index)
	replace the response data

virtual void	pop_coefficients (bool save_data)
	removes entries from end of SurrogateData::{vars,resp}Data (last points appended, or as specified in args)

virtual void	push_coefficients ()
	restores state prior to previous pop()

virtual void	finalize_coefficients ()
	finalize approximation by applying all remaining trial sets

virtual void	combine_coefficients ()
	combine all level approximations into a single aggregate approximation

virtual void	combined_to_active_coefficients (bool clear_combined=true)
	promote combined approximation into active approximation

virtual void	clear_inactive_coefficients ()
	prune inactive coefficients following combination and promotion to active

virtual const RealSymMatrix &	hessian (const Variables &vars)
	retrieve the approximate function Hessian for a given parameter vector

virtual Real	prediction_variance (const Variables &vars)
	retrieve the variance of the predicted value for a given parameter vector

virtual Real	value (const RealVector &c_vars)
	retrieve the approximate function value for a given parameter vector

virtual const RealVector &	gradient (const RealVector &c_vars)
	retrieve the approximate function gradient for a given parameter vector

virtual const RealSymMatrix &	hessian (const RealVector &c_vars)
	retrieve the approximate function Hessian for a given parameter vector

virtual Real	prediction_variance (const RealVector &c_vars)
	retrieve the variance of the predicted value for a given parameter vector

virtual Real	mean ()
	return the mean of the expansion, where all active vars are random

virtual Real	mean (const RealVector &x)
	return the mean of the expansion for a given parameter vector, where a subset of the active variables are random

virtual Real	combined_mean ()
	return the mean of the combined expansion, where all active vars are random

virtual Real	combined_mean (const RealVector &x)
	return the mean of the combined expansion for a given parameter vector, where a subset of the active variables are random

virtual const RealVector &	mean_gradient ()
	return the gradient of the expansion mean

virtual const RealVector &	mean_gradient (const RealVector &x, const SizetArray &dvv)
	return the gradient of the expansion mean

virtual Real	variance ()
	return the variance of the expansion, where all active vars are random

virtual Real	variance (const RealVector &x)
	return the variance of the expansion for a given parameter vector, where a subset of the active variables are random

virtual const RealVector &	variance_gradient ()

virtual const RealVector &	variance_gradient (const RealVector &x, const SizetArray &dvv)

virtual Real	covariance (Approximation &approx_2)
	return the covariance between two response expansions, treating all variables as random

virtual Real	covariance (const RealVector &x, Approximation &approx_2)
	return the covariance between two response expansions, treating a subset of the variables as random

virtual Real	combined_covariance (Approximation &approx_2)
	return the covariance between two combined response expansions, where all active variables are random

virtual Real	combined_covariance (const RealVector &x, Approximation &approx_2)
	return the covariance between two combined response expansions, where a subset of the active variables are random

virtual void	compute_moments (bool full_stats=true, bool combined_stats=false)

virtual void	compute_moments (const RealVector &x, bool full_stats=true, bool combined_stats=false)

virtual const RealVector &	moments () const

virtual const RealVector &	expansion_moments () const

virtual const RealVector &	numerical_integration_moments () const

virtual const RealVector &	combined_moments () const

virtual Real	moment (size_t i) const

virtual void	moment (Real mom, size_t i)

virtual Real	combined_moment (size_t i) const

virtual void	combined_moment (Real mom, size_t i)

virtual void	clear_component_effects ()

virtual void	compute_component_effects ()

virtual void	compute_total_effects ()

virtual const RealVector &	sobol_indices () const

virtual const RealVector &	total_sobol_indices () const

virtual ULongULongMap	sparse_sobol_index_map () const

virtual bool	advancement_available ()
	check if resolution advancement (e.g., order, rank) is available for this approximation instance

virtual bool	diagnostics_available ()
	check if diagnostics are available for this approximation type

virtual Real	diagnostic (const String &metric_type)
	retrieve a single diagnostic metric for the diagnostic type specified

virtual RealArray	cv_diagnostic (const StringArray &metric_types, unsigned num_folds)
	retrieve diagnostic metrics for the diagnostic types specified, applying

virtual void	primary_diagnostics (size_t fn_index)
	compute and print all requested diagnostics and cross-validation

virtual RealArray	challenge_diagnostic (const StringArray &metric_types, const RealMatrix &challenge_points, const RealVector &challenge_responses)
	compute requested diagnostics for user provided challenge pts

virtual void	challenge_diagnostics (size_t fn_index, const RealMatrix &challenge_points, const RealVector &challenge_responses)
	compute and print all requested diagnostics for user provided challenge pts

virtual RealVector	approximation_coefficients (bool normalized) const
	return the coefficient array computed by build()/rebuild()

virtual void	approximation_coefficients (const RealVector &approx_coeffs, bool normalized)
	set the coefficient array from external sources, rather than computing with build()/rebuild()

virtual void	coefficient_labels (std::vector< std::string > &coeff_labels) const
	print the coefficient array computed in build()/rebuild()

virtual void	print_coefficients (std::ostream &s, bool normalized)
	print the coefficient array computed in build()/rebuild()

virtual int	recommended_coefficients () const
	return the recommended number of samples (unknowns) required to build the derived class approximation type in numVars dimensions

virtual int	num_constraints () const
	return the number of constraints to be enforced via an anchor point

virtual void	expansion_coefficient_flag (bool)

virtual bool	expansion_coefficient_flag () const

virtual void	expansion_gradient_flag (bool)

virtual bool	expansion_gradient_flag () const

virtual void	clear_computed_bits ()
	clear tracking of computed moments, due to (expansion) change that invalidates previous results

virtual void	map_variable_labels (const Variables &dfsm_vars)
	if needed, map passed all variable labels to approximation's labels

int	min_points (bool constraint_flag) const
	return the minimum number of points required to build the approximation type in numVars dimensions. Uses *_coefficients() and num_constraints().

int	recommended_points (bool constraint_flag) const
	return the recommended number of samples to build the approximation type in numVars dimensions (default same as min_points)

void	pop_data (bool save_data)
	removes entries from end of SurrogateData::{vars,resp}Data (last points appended, or as specified in args)

void	push_data ()
	restores SurrogateData state prior to previous pop()

void	finalize_data ()
	finalize SurrogateData by applying all remaining trial sets

const Pecos::SurrogateData &	surrogate_data () const
	return approxData

Pecos::SurrogateData &	surrogate_data ()
	return approxData

void	add (const Variables &vars, bool v_copy, const Response &response, size_t fn_index, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS)
	create SurrogateData{Vars,Resp} and append to SurrogateData:: {varsData,respData,dataIdentifiers}

void	add (const Real *c_vars, bool v_copy, const Response &response, size_t fn_index, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS)
	create SurrogateData{Vars,Resp} and append to SurrogateData:: {varsData,respData,dataIdentifiers}

void	add (const Pecos::SurrogateDataVars &sdv, bool v_copy, const Response &response, size_t fn_index, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS)
	create a SurrogateDataResp and append to SurrogateData:: {varsData,respData,dataIdentifiers}

void	add (const Pecos::SurrogateDataVars &sdv, bool v_copy, const Pecos::SurrogateDataResp &sdr, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS)
	append to SurrogateData::{varsData,respData,dataIdentifiers}

void	add_array (const RealMatrix &sample_vars, bool v_copy, const RealVector &sample_resp, bool r_copy, size_t key_index=_NPOS)
	add surrogate data from the provided sample and response data, assuming continuous variables and function values only More...

void	pop_count (size_t count, size_t key_index)
	appends to SurrogateData::popCountStack (number of entries to pop from end of SurrogateData::{vars,resp}Data, based on size of last data append)

void	clear_data ()
	clear SurrogateData::{vars,resp}Data for activeKey + embedded keys More...

void	clear_active_data ()
	clear active approximation data

void	clear_inactive_data ()
	clear inactive approximation data

void	clear_active_popped ()
	clear SurrogateData::popped{Vars,Resp}Trials,popCountStack for activeKey

void	clear_popped ()
	clear SurrogateData::popped{Vars,Resp}Trials,popCountStack for all keys

void	set_bounds (const RealVector &c_l_bnds, const RealVector &c_u_bnds, const IntVector &di_l_bnds, const IntVector &di_u_bnds, const RealVector &dr_l_bnds, const RealVector &dr_u_bnds)
	set approximation lower and upper bounds (currently only used by graphics)

std::shared_ptr< Approximation >	approx_rep () const
	returns approxRep for access to derived class member functions that are not mapped to the top Approximation level

Protected Member Functions
int	min_coefficients () const
	return the minimum number of samples (unknowns) required to build the derived class approximation type in numVars dimensions

void	build ()
	builds the approximation from scratch More...

Real	value (const Variables &vars)
	retrieve the approximate function value for a given parameter vector

const RealVector &	gradient (const Variables &vars)
	retrieve the approximate function gradient for a given parameter vector

void	clear_current_active_data ()

Protected Member Functions inherited from Approximation
	Approximation (BaseConstructor, const ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label)
	constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More...

	Approximation (NoDBBaseConstructor, const SharedApproxData &shared_data)
	constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More...

Pecos::SurrogateDataVars	variables_to_sdv (const Real *sample_c_vars)
	create a SurrogateDataVars instance from a Real*

Pecos::SurrogateDataVars	variables_to_sdv (const Variables &vars)
	create a SurrogateDataVars instance by extracting data from a Variables object

Pecos::SurrogateDataResp	response_to_sdr (const Response &response, size_t fn_index)
	create a SurrogateDataResp instance by extracting data for a particular QoI from a Response object

void	add (const Pecos::SurrogateDataVars &sdv, bool v_copy, const Pecos::SurrogateDataResp &sdr, bool r_copy, bool anchor_flag)
	tracks a new data point by appending to SurrogateData::{vars,Resp}Data

void	add (int eval_id)
	tracks a new data point by appending to SurrogateData::dataIdentifiers

void	check_points (size_t num_build_pts)
	Check number of build points against minimum required.

void	assign_key_index (size_t key_index)
	extract and assign i-th embedded active key

Private Member Functions
void	find_scaled_coefficients ()
	compute TANA coefficients based on scaled inputs

void	offset (const RealVector &x, RealVector &s)
	based on minX, apply offset scaling to x to define s

Real	apxfn_value (const RealVector &)

Private Attributes
RealVector	pExp
	vector of exponent values

RealVector	minX
	vector of minimum parameter values used in scaling

RealVector	scX1
	vector of scaled x1 values

RealVector	scX2
	vector of scaled x2 values

Real	H
	the scalar Hessian value in the TANA-3 approximation

RealVector	beta
	vector of QMEA reduced space diagonal Hessian coefficients

RealMatrix	G_reduced_xfm
	Grahm-Schmidt orthonormal reduced subspace transformation.

size_t	numUsed
	number of previous data points used (size of reduced subspace)

size_t	currGradIndex
	index of current expansion point with gradients

size_t	prevGradIndex
	index of most recent previous point with gradients

Additional Inherited Members
Protected Attributes inherited from Approximation
Pecos::SurrogateData	approxData
	contains the variables/response data for constructing a single approximation model (one response function). There is only one SurrogateData instance per Approximation, although it may contain keys for different model forms/resolutions and aggregations (e.g., discrepancies) among forms/resolutions.

RealVector	approxGradient
	gradient of the approximation returned by gradient()

RealSymMatrix	approxHessian
	Hessian of the approximation returned by hessian()

String	approxLabel
	label for approximation, if applicable

std::shared_ptr< SharedApproxData >	sharedDataRep
	contains the approximation data that is shared among the response set

Detailed Description

Derived approximation class for QMEA Quadratic Multipoint Exponential Approximation (a multipoint approximation).

The QMEApproximation class provides a multipoint approximation based on matching value and gradient data from multiple points (typically the current and previous iterates) in parameter space.
It forms an exponential approximation in terms of intervening variables.

Member Function Documentation

◆ build()

void build ( )

protectedvirtual

builds the approximation from scratch

This is the common base class portion of the virtual fn and is insufficient on its own; derived implementations should explicitly invoke (or reimplement) this base class contribution.

Reimplemented from Approximation.

References Dakota::abort_handler(), Approximation::approxData, Approximation::build(), QMEApproximation::currGradIndex, QMEApproximation::find_scaled_coefficients(), Dakota::length(), QMEApproximation::minX, QMEApproximation::pExp, QMEApproximation::prevGradIndex, and Approximation::sharedDataRep.

◆ clear_current_active_data()

void clear_current_active_data ( )

inlineprotectedvirtual

Redefine default implementation to support history mechanism.

Reimplemented from Approximation.

References Approximation::approxData, QMEApproximation::currGradIndex, QMEApproximation::prevGradIndex, and Approximation::sharedDataRep.

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

QMEApproximation.hpp
QMEApproximation.cpp

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ build()

◆ clear_current_active_data()