Class for testing various Adaptively sampling methods using geometric, statisctical, and topological information of the surrogate. More...

Inheritance diagram for NonDAdaptiveSampling:

Public Member Functions
	NonDAdaptiveSampling (ProblemDescDB &problem_db, Model &model)
	standard constructor More...

	~NonDAdaptiveSampling ()
	alternate constructor for sample generation and evaluation "on the fly" has not been implemented More...

bool	resize ()
	reinitializes iterator based on new variable size

Public Member Functions inherited from NonDSampling
	NonDSampling (Model &model, const RealMatrix &sample_matrix)
	alternate constructor for evaluating and computing statistics for the provided set of samples More...

	~NonDSampling ()
	destructor

void	compute_statistics (const RealMatrix &vars_samples, const IntResponseMap &resp_samples)
	For the input sample set, computes mean, standard deviation, and probability/reliability/response levels (aleatory uncertainties) or intervals (epsitemic or mixed uncertainties)

void	compute_intervals (RealRealPairArray &extreme_fns)
	called by compute_statistics() to calculate min/max intervals using allResponses

void	compute_intervals (const IntResponseMap &samples)
	called by compute_statistics() to calculate extremeValues from samples

void	compute_intervals (RealRealPairArray &extreme_fns, const IntResponseMap &samples)
	called by compute_statistics() to calculate min/max intervals using samples

void	compute_moments (const RealVectorArray &fn_samples)
	calculates sample moments from a matrix of observations for a set of QoI

void	compute_moments (const IntResponseMap &samples)
	calculate sample moments and confidence intervals from a map of response observations

void	compute_moments (const IntResponseMap &samples, RealMatrix &moment_stats, RealMatrix &moment_grads, RealMatrix &moment_conf_ints, short moments_type, const StringArray &labels)
	convert IntResponseMap to RealVectorArray and invoke helpers

void	compute_moment_gradients (const RealVectorArray &fn_samples, const RealMatrixArray &grad_samples, const RealMatrix &moment_stats, RealMatrix &moment_grads, short moments_type)
	compute moment_grads from function and gradient samples

void	compute_moment_confidence_intervals (const RealMatrix &moment_stats, RealMatrix &moment_conf_ints, const SizetArray &sample_counts, short moments_type)
	compute moment confidence intervals from moment values

void	archive_moments (size_t inc_id=0)
	archive moment statistics in results DB

void	archive_moment_confidence_intervals (size_t inc_id=0)
	archive moment confidence intervals in results DB

void	archive_std_regress_coeffs ()
	archive standardized regression coefficients in results DB

void	archive_extreme_responses (size_t inc_id=0)
	archive extreme values (epistemic result) in results DB

void	compute_level_mappings (const IntResponseMap &samples)
	called by compute_statistics() to calculate CDF/CCDF mappings of z to p/beta and of p/beta to z as well as PDFs More...

void	print_statistics (std::ostream &s) const
	prints the statistics computed in compute_statistics()

void	print_intervals (std::ostream &s) const
	prints the intervals computed in compute_intervals() with default qoi_type and moment_labels

void	print_intervals (std::ostream &s, String qoi_type, const StringArray &interval_labels) const
	prints the intervals computed in compute_intervals()

void	print_moments (std::ostream &s) const
	prints the moments computed in compute_moments() with default qoi_type and moment_labels

void	print_moments (std::ostream &s, String qoi_type, const StringArray &moment_labels) const
	prints the moments computed in compute_moments()

void	print_wilks_stastics (std::ostream &s) const
	prints the Wilks stastics

void	print_tolerance_intervals_statistics (std::ostream &s) const
	prints the tolerance intervals stastics

void	archive_tolerance_intervals (size_t inc_id=0)
	archive the tolerance intervals statistics in results DB

void	update_final_statistics ()
	update finalStatistics from minValues/maxValues, momentStats, and computedProbLevels/computedRelLevels/computedRespLevels

void	transform_samples (Model &src_model, Model &tgt_model, bool x_to_u=true)
	transform allSamples using configuration data from the source and target models

void	transform_samples (Pecos::ProbabilityTransformation &nataf, bool x_to_u=true)
	alternate version to transform allSamples. This is needed since random variable distribution parameters are not updated until run time and an imported sample_matrix is typically in x-space. More...

void	transform_samples (Pecos::ProbabilityTransformation &nataf, RealMatrix &sample_matrix, bool x_to_u=true)
	transform the specified samples matrix from x to u or u to x, assuming identical view and ids

void	transform_samples (Pecos::ProbabilityTransformation &nataf, RealMatrix &sample_matrix, SizetMultiArrayConstView src_cv_ids, SizetMultiArrayConstView tgt_cv_ids, bool x_to_u=true)
	transform the specified samples matrix from x to u or u to x

unsigned short	sampling_scheme () const
	return sampleType

const String &	random_number_generator () const
	return rngName

Public Member Functions inherited from NonD
void	requested_levels (const RealVectorArray &req_resp_levels, const RealVectorArray &req_prob_levels, const RealVectorArray &req_rel_levels, const RealVectorArray &req_gen_rel_levels, short resp_lev_tgt, short resp_lev_tgt_reduce, bool cdf_flag, bool pdf_output)
	set requestedRespLevels, requestedProbLevels, requestedRelLevels, requestedGenRelLevels, respLevelTarget, cdfFlag, and pdfOutput (used in combination with alternate ctors)

void	print_level_mappings (std::ostream &s) const
	prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels for default qoi_type and qoi_labels

void	print_level_mappings (std::ostream &s, String qoi_type, const StringArray &qoi_labels) const
	prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels More...

void	print_level_mappings (std::ostream &s, const RealVector &level_maps, bool moment_offset, const String &prepend="")
	print level mapping statistics using optional pre-pend More...

bool	pdf_output () const
	get pdfOutput

void	pdf_output (bool output)
	set pdfOutput

short	final_moments_type () const
	get finalMomentsType

void	final_moments_type (short type)
	set finalMomentsType

Public Member Functions inherited from Analyzer
const VariablesArray &	all_variables ()
	return the complete set of evaluated variables

const RealMatrix &	all_samples ()
	return the complete set of evaluated samples

const IntResponseMap &	all_responses () const
	return the complete set of computed responses

Public Member Functions inherited from Iterator
	Iterator (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	default constructor More...

	Iterator (ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	standard envelope constructor, which constructs its own model(s) More...

	Iterator (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor which uses the ProblemDescDB but accepts a model from a higher level (meta-iterator) context, instead of constructing its own More...

	Iterator (const String &method_string, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor for instantiations by name without the ProblemDescDB More...

	Iterator (const Iterator &iterator)
	copy constructor More...

virtual	~Iterator ()
	destructor

Iterator	operator= (const Iterator &iterator)
	assignment operator

virtual void	post_input ()
	read tabular data for post-run mode

virtual void	reset ()
	restore initial state for repeated sub-iterator executions

virtual void	nested_variable_mappings (const SizetArray &c_index1, const SizetArray &di_index1, const SizetArray &ds_index1, const SizetArray &dr_index1, const ShortArray &c_target2, const ShortArray &di_target2, const ShortArray &ds_target2, const ShortArray &dr_target2)
	set primaryA{CV,DIV,DRV}MapIndices, secondaryA{CV,DIV,DRV}MapTargets within derived Iterators; supports computation of higher-level sensitivities in nested contexts (e.g., derivatives of statistics w.r.t. inserted design variables)

virtual void	nested_response_mappings (const RealMatrix &primary_coeffs, const RealMatrix &secondary_coeffs)
	set primaryResponseCoefficients, secondaryResponseCoefficients within derived Iterators; Necessary for scalarization case in MLMC NonDMultilevelSampling to map scalarization in nested context

virtual void	initialize_iterator (int job_index)
	used by IteratorScheduler to set the starting data for a run

virtual void	pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index)
	used by IteratorScheduler to pack starting data for an iterator run

virtual void	unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack starting data for an iterator run

virtual void	unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack starting data and initialize an iterator run

virtual void	pack_results_buffer (MPIPackBuffer &send_buffer, int job_index)
	used by IteratorScheduler to pack results data from an iterator run

virtual void	unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack results data from an iterator run

virtual void	update_local_results (int job_index)
	used by IteratorScheduler to update local results arrays

virtual bool	accepts_multiple_points () const
	indicates if this iterator accepts multiple initial points. Default return is false. Override to return true if appropriate.

virtual void	initial_point (const Variables &pt)
	sets the initial point for this iterator (user-functions mode for which Model updating is not used)

virtual void	initial_point (const RealVector &pt)
	sets the initial point (active continuous variables) for this iterator (user-functions mode for which Model updating is not used)

virtual void	initial_points (const VariablesArray &pts)
	sets the multiple initial points for this iterator. This should only be used if accepts_multiple_points() returns true.

virtual void	update_callback_data (const RealVector &cv_initial, const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt, const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt)
	assign variable values and bounds and constraint coefficients and bounds for this iterator (user-functions mode for which iteratedModel is null)

virtual const RealMatrix &	callback_linear_ineq_coefficients () const
	return linear constraint coefficients for this iterator (user-functions mode for which iteratedModel is null)

virtual const RealVector &	callback_linear_ineq_lower_bounds () const
	return linear constraint lower bounds for this iterator (user-functions mode for which iteratedModel is null)

virtual const RealVector &	callback_linear_ineq_upper_bounds () const
	return linear constraint upper bounds for this iterator (user-functions mode for which iteratedModel is null)

virtual void	initialize_graphics (int iterator_server_id=1)
	initialize the 2D graphics window and the tabular graphics data More...

virtual void	check_sub_iterator_conflict ()
	detect any conflicts due to recursive use of the same Fortran solver More...

virtual unsigned short	uses_method () const
	return name of any enabling iterator used by this iterator

virtual void	method_recourse (unsigned short method_name)
	perform a method switch, if possible, due to a detected conflict with the simultaneous use of method_name at an higher-level

virtual void	sampling_increment ()
	increment to next in sequence of refinement samples

virtual IntIntPair	estimate_partition_bounds ()
	estimate the minimum and maximum partition sizes that can be utilized by this Iterator

virtual void	declare_sources ()
	Declare sources to the evaluations database.

void	init_communicators (ParLevLIter pl_iter)
	initialize the communicators associated with this Iterator instance

void	set_communicators (ParLevLIter pl_iter)
	set the communicators associated with this Iterator instance

void	free_communicators (ParLevLIter pl_iter)
	free the communicators associated with this Iterator instance

void	resize_communicators (ParLevLIter pl_iter, bool reinit_comms)
	Resize the communicators. This is called from the letter's resize()

void	parallel_configuration_iterator (ParConfigLIter pc_iter)
	set methodPCIter

ParConfigLIter	parallel_configuration_iterator () const
	return methodPCIter

void	parallel_configuration_iterator_map (std::map< size_t, ParConfigLIter > pci_map)
	set methodPCIterMap

std::map< size_t, ParConfigLIter >	parallel_configuration_iterator_map () const
	return methodPCIterMap

void	run (ParLevLIter pl_iter)
	invoke set_communicators(pl_iter) prior to run()

void	run ()
	orchestrate initialize/pre/core/post/finalize phases More...

void	assign_rep (std::shared_ptr< Iterator > iterator_rep)
	replaces existing letter with a new one More...

void	iterated_model (const Model &model)
	set the iteratedModel (iterators and meta-iterators using a single model instance)

Model &	iterated_model ()
	return the iteratedModel (iterators & meta-iterators using a single model instance)

ProblemDescDB &	problem_description_db () const
	return the problem description database (probDescDB)

ParallelLibrary &	parallel_library () const
	return the parallel library (parallelLib)

void	method_name (unsigned short m_name)
	set the method name to an enumeration value

unsigned short	method_name () const
	return the method name via its native enumeration value

void	method_string (const String &m_str)
	set the method name by string

String	method_string () const
	return the method name by string

String	method_enum_to_string (unsigned short method_enum) const
	convert a method name enumeration value to a string

unsigned short	method_string_to_enum (const String &method_str) const
	convert a method name string to an enumeration value

String	submethod_enum_to_string (unsigned short submethod_enum) const
	convert a sub-method name enumeration value to a string

const String &	method_id () const
	return the method identifier (methodId)

int	maximum_evaluation_concurrency () const
	return the maximum evaluation concurrency supported by the iterator

void	maximum_evaluation_concurrency (int max_conc)
	set the maximum evaluation concurrency supported by the iterator

size_t	maximum_iterations () const
	return the maximum iterations for this iterator

void	maximum_iterations (size_t max_iter)
	set the maximum iterations for this iterator

void	convergence_tolerance (Real conv_tol)
	set the method convergence tolerance (convergenceTol)

Real	convergence_tolerance () const
	return the method convergence tolerance (convergenceTol)

void	output_level (short out_lev)
	set the method output level (outputLevel)

short	output_level () const
	return the method output level (outputLevel)

void	summary_output (bool summary_output_flag)
	Set summary output control; true enables evaluation/results summary.

size_t	num_final_solutions () const
	return the number of solutions to retain in best variables/response arrays

void	num_final_solutions (size_t num_final)
	set the number of solutions to retain in best variables/response arrays

void	active_set (const ActiveSet &set)
	set the default active set (for use with iterators that employ evaluate_parameter_sets())

const ActiveSet &	active_set () const
	return the default active set (used by iterators that employ evaluate_parameter_sets())

void	active_set_request_vector (const ShortArray &asv)
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())

const ShortArray &	active_set_request_vector () const
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())

void	active_set_request_values (short asv_val)
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())

void	sub_iterator_flag (bool si_flag)
	set subIteratorFlag (and update summaryOutputFlag if needed)

bool	is_null () const
	function to check iteratorRep (does this envelope contain a letter?)

std::shared_ptr< Iterator >	iterator_rep () const
	returns iteratorRep for access to derived class member functions that are not mapped to the top Iterator level

virtual void	eval_tag_prefix (const String &eval_id_str)
	set the hierarchical eval ID tag prefix More...

std::shared_ptr< TraitsBase >	traits () const
	returns methodTraits for access to derived class member functions that are not mapped to the top TraitsBase level

bool	top_level ()
	Return whether the iterator is the top level iterator.

void	top_level (bool tflag)
	Set the iterator's top level flag.

Protected Member Functions
void	derived_init_communicators (ParLevLIter pl_iter)
	derived class contributions to initializing the communicators associated with this Iterator instance

void	derived_set_communicators (ParLevLIter pl_iter)
	derived class contributions to setting the communicators associated with this Iterator instance

void	derived_free_communicators (ParLevLIter pl_iter)
	derived class contributions to freeing the communicators associated with this Iterator instance

void	core_run ()
	core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post More...

Real	final_probability ()

void	print_results (std::ostream &s, short results_state=FINAL_RESULTS)
	print the final iterator results More...

Protected Member Functions inherited from NonDSampling
	NonDSampling (ProblemDescDB &problem_db, Model &model)
	constructor More...

	NonDSampling (unsigned short method_name, Model &model, unsigned short sample_type, size_t samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode)
	alternate constructor for sample generation and evaluation "on the fly" More...

	NonDSampling (unsigned short sample_type, size_t samples, int seed, const String &rng, const RealVector &lower_bnds, const RealVector &upper_bnds)
	alternate constructor for sample generation "on the fly" More...

	NonDSampling (unsigned short sample_type, size_t samples, int seed, const String &rng, const RealVector &means, const RealVector &std_devs, const RealVector &lower_bnds, const RealVector &upper_bnds, RealSymMatrix &correl)
	alternate constructor for sample generation of correlated normals "on the fly" More...

void	pre_run ()
	pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...

void	core_run ()

size_t	num_samples () const

void	sampling_reset (size_t min_samples, bool all_data_flag, bool stats_flag)
	resets number of samples and sampling flags More...

void	sampling_reference (size_t samples_ref)
	set reference number of samples, which is a lower bound during reset

void	random_seed (int seed)
	assign randomSeed

void	vary_pattern (bool pattern_flag)
	set varyPattern

void	get_parameter_sets (Model &model)
	Uses samplerDriver to generate a set of samples from the distributions/bounds defined in the incoming model. More...

void	get_parameter_sets (Model &model, const size_t num_samples, RealMatrix &design_matrix)
	Uses samplerDriver to generate a set of samples from the distributions/bounds defined in the incoming model and populates the specified design matrix. More...

void	get_parameter_sets (Model &model, const size_t num_samples, RealMatrix &design_matrix, bool write_msg)
	core of get_parameter_sets that accepts message print control

void	get_parameter_sets (const RealVector &lower_bnds, const RealVector &upper_bnds)
	Uses samplerDriver to generate a set of uniform samples over lower_bnds/upper_bnds. More...

void	get_parameter_sets (const RealVector &means, const RealVector &std_devs, const RealVector &lower_bnds, const RealVector &upper_bnds, RealSymMatrix &correl)
	Uses samplerDriver to generate a set of normal samples. More...

void	update_model_from_sample (Model &model, const Real *sample_vars)
	Override default update of continuous vars only.

void	sample_to_variables (const Real *sample_vars, Variables &vars)
	override default mapping of continuous variables only

void	variables_to_sample (const Variables &vars, Real *sample_vars)
	override default mapping of continuous variables only

const RealSymMatrix &	response_error_estimates () const
	return error estimates associated with each of the finalStatistics

virtual bool	seed_updated ()
	detect whether the seed has been updated since the most recent sample set generation

virtual void	active_set_mapping ()
	in the case of sub-iteration, map from finalStatistics.active_set() requests to activeSet used in evaluate_parameter_sets() More...

void	initialize_sample_driver (bool write_message, size_t num_samples)
	increments numLHSRuns, sets random seed, and initializes samplerDriver

void	mode_counts (const Variables &vars, size_t &cv_start, size_t &num_cv, size_t &div_start, size_t &num_div, size_t &dsv_start, size_t &num_dsv, size_t &drv_start, size_t &num_drv) const
	compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More...

void	mode_bits (const Variables &vars, BitArray &active_vars, BitArray &active_corr) const
	define subset views for sampling modes

Protected Member Functions inherited from NonD
	NonD (ProblemDescDB &problem_db, Model &model)
	constructor

	NonD (unsigned short method_name, Model &model)
	alternate constructor for sample generation and evaluation "on the fly"

	NonD (unsigned short method_name, Model &model, const ShortShortPair &approx_view)
	alternate constructor for sample generation and evaluation "on the fly"

	NonD (unsigned short method_name, const RealVector &lower_bnds, const RealVector &upper_bnds)
	alternate constructor for sample generation "on the fly"

	~NonD ()
	destructor

void	initialize_run ()
	utility function to perform common operations prior to pre_run(); typically memory initialization; setting of instance pointers More...

void	finalize_run ()
	utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...

const Response &	response_results () const
	return the final statistics from the nondeterministic iteration

void	response_results_active_set (const ActiveSet &set)
	set the active set within finalStatistics

virtual void	initialize_response_covariance ()
	initializes respCovariance

virtual void	initialize_final_statistics ()
	initializes finalStatistics for storing NonD final results More...

virtual bool	discrepancy_sample_counts () const
	flag identifying whether sample counts correspond to level discrepancies

void	pull_level_mappings (RealVector &level_maps, size_t offset)
	concatenate computed{Resp,Prob,Rel,GenRel}Levels into level_maps

void	push_level_mappings (const RealVector &level_maps, size_t offset)
	update computed{Resp,Prob,Rel,GenRel}Levels from level_maps

void	configure_1d_sequence (size_t &num_steps, size_t &secondary_index, short &seq_type)
	configure a one-dimensional hierarchical sequence (ML or MF) More...

void	configure_2d_sequence (size_t &num_steps, size_t &secondary_index, short &seq_type)
	configure a two-dimensional hierarchical sequence (MLMF) More...

void	configure_enumeration (size_t &num_combinations, short &seq_type)
	configure the total number of model form/resolution level options More...

short	configure_cost (size_t num_steps, short seq_type, RealVector &cost)
	extract cost estimates from model ensemble, enforcing requirements (case without metadata support)

short	configure_cost (size_t num_steps, short seq_type, RealVector &cost, SizetSizetPairArray &cost_md_indices)
	extract cost estimates from model ensemble, enforcing requirements (case with metadata support)

short	query_cost (size_t num_steps, short seq_type, RealVector &cost)
	optionally extract cost estimates from model ensemble, if available (case without metadata support)

short	query_cost (size_t num_steps, short seq_type, RealVector &cost, BitArray &model_cost_spec, const SizetSizetPairArray &cost_md_indices)
	optionally extract cost estimates from model ensemble, if available (case with metadata support)

void	test_cost (short seq_type, const BitArray &model_cost_spec, SizetSizetPairArray &cost_md_indices)
	check cost specification and metadata indices for each active model

bool	test_cost (bool cost_spec, SizetSizetPair &cost_md_indices, const String &model_id)
	check cost specification and metadata indices for a given model

bool	valid_cost (Real cost) const
	test cost for value > 0

bool	valid_costs (const RealVector &costs) const
	test costs for valid values > 0

void	load_pilot_sample (const SizetArray &pilot_spec, size_t num_steps, SizetArray &delta_N_l)
	distribute pilot sample specification across model forms or levels

void	load_pilot_sample (const SizetArray &pilot_spec, short seq_type, const Sizet3DArray &N_l, Sizet2DArray &delta_N_l)
	distribute pilot sample specification across model forms and levels

template<typename ArrayType >
void	inflate_approx_samples (const ArrayType &N_l, bool multilev, size_t secondary_index, std::vector< ArrayType > &N_l_vec)
	update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile

template<typename ArrayType >
void	inflate_sequence_samples (const ArrayType &N_l, bool multilev, size_t secondary_index, std::vector< ArrayType > &N_l_vec)
	update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile

void	resize_final_statistics_gradients ()
	resizes finalStatistics::functionGradients based on finalStatistics ASV

void	update_aleatory_final_statistics ()
	update finalStatistics::functionValues from momentStats and computed{Prob,Rel,GenRel,Resp}Levels

void	update_system_final_statistics ()
	update system metrics from component metrics within finalStatistics

void	update_system_final_statistics_gradients ()
	update finalStatistics::functionGradients

void	initialize_level_mappings ()
	size computed{Resp,Prob,Rel,GenRel}Levels

void	compute_densities (const RealRealPairArray &min_max_fns, bool prob_refinement=false, bool all_levels_computed=false)
	compute the PDF bins from the CDF/CCDF values and store in computedPDF{Abscissas,Ordinates} More...

void	print_densities (std::ostream &s) const
	output the PDFs reflected in computedPDF{Abscissas,Ordinates} using default qoi_type and pdf_labels

void	print_densities (std::ostream &s, String qoi_type, const StringArray &pdf_labels) const
	output the PDFs reflected in computedPDF{Abscissas,Ordinates}

void	print_system_mappings (std::ostream &s) const
	print system series/parallel mappings for response levels

void	print_multilevel_evaluation_summary (std::ostream &s, const SizetArray &N_m)
	print evaluation summary for multilevel sampling across 1D level profile

void	print_multilevel_evaluation_summary (std::ostream &s, const Sizet2DArray &N_m)
	print evaluation summary for multilevel sampling across 2D level+QoI profile

void	print_multilevel_discrepancy_summary (std::ostream &s, const SizetArray &N_m)
	print evaluation summary for multilevel sampling across 1D level profile for discrepancy across levels

void	print_multilevel_discrepancy_summary (std::ostream &s, const SizetArray &N_m, const SizetArray &N_mp1)
	print evaluation summary for multilevel sampling across 1D level profile for discrepancy across model forms

void	print_multilevel_discrepancy_summary (std::ostream &s, const Sizet2DArray &N_m)
	print evaluation summary for multilevel sampling across 2D level+QoI profile for discrepancy across levels

void	print_multilevel_discrepancy_summary (std::ostream &s, const Sizet2DArray &N_m, const Sizet2DArray &N_mp1)
	print evaluation summary for multilevel sampling across 2D level+QoI profile for discrepancy across model forms

template<typename ArrayType >
void	print_multilevel_model_summary (std::ostream &s, const std::vector< ArrayType > &N_samp, String type, short seq_type, bool discrep_flag)
	print evaluation summary for multilevel sampling across 2D model+level profile (allocations) or 3D model+level+QoI profile (actual)

void	construct_lhs (Iterator &u_space_sampler, Model &u_model, unsigned short sample_type, int num_samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode=ACTIVE)
	assign a NonDLHSSampling instance within u_space_sampler

unsigned short	sub_optimizer_select (unsigned short requested_sub_method, unsigned short default_sub_method=SUBMETHOD_NPSOL)
	utility for vetting sub-method request against optimizers within the package configuration

size_t	one_sided_relax_round (Real diff, Real relax_factor=1.)
	compute a one-sided sample increment for multilevel methods to move current sampling level to a new target

size_t	one_sided_delta (Real current, Real target, Real relax_factor=1.)
	compute a one-sided sample increment for multilevel methods to move current sampling level to a new target

size_t	one_sided_delta (const SizetArray &current, const RealVector &targets, Real relax_factor=1., size_t power=1)
	compute a one-sided sample increment for multilevel methods to move current sampling level to a new target

size_t	one_sided_delta (const SizetArray &current, Real target, Real relax_factor=1., size_t power=1)
	compute a one-sided sample increment for multilevel methods to move current sampling level to a new target

void	one_sided_delta (const SizetArray &current, const RealVector &targets, SizetArray &delta_N, Real relax_factor=1.)
	compute a one-sided sample increment vector to move current sampling levels to new targets

void	one_sided_delta (const Sizet2DArray &current, const RealVector &targets, SizetArray &delta_N, Real relax_factor=1.)
	compute a one-sided sample increment vector to move current sampling levels to new targets

bool	differ (size_t N_alloc_ij, const SizetArray &N_actual_ij) const
	return true if fine-grained reporting differs from coarse-grained

bool	differ (const SizetArray &N_alloc_i, const Sizet2DArray &N_actual_i) const
	return true if fine-grained reporting differs from coarse-grained

bool	differ (const Sizet2DArray &N_alloc, const Sizet3DArray &N_actual) const
	return true if fine-grained reporting differs from coarse-grained

void	archive_allocate_mappings ()
	allocate results array storage for distribution mappings

void	archive_from_resp (size_t fn_index, size_t inc_id=0)
	archive the mappings from specified response levels for specified fn

void	archive_to_resp (size_t fn_index, size_t inc_id=0)
	archive the mappings to computed response levels for specified fn and (optional) increment id.

void	archive_allocate_pdf ()
	allocate results array storage for pdf histograms

void	archive_pdf (size_t fn_index, size_t inc_id=0)
	archive a single pdf histogram for specified function

void	archive_equiv_hf_evals (const Real equiv_hf_evals)
	archive the equivalent number of HF evals (used by ML/MF methods)

bool	zeros (const SizetArray &N_m) const
	return true if N_m is empty or only populated with zeros

bool	zeros (const Sizet2DArray &N_m) const
	return true if N_m is empty or only populated with zeros

bool	zeros (const SizetVector &N_m) const
	return true if N_m is empty or only populated with zeros

bool	homogeneous (const SizetArray &N_l) const
	return true if N_l has consistent values

Protected Member Functions inherited from Analyzer
	Analyzer ()
	default constructor

	Analyzer (ProblemDescDB &problem_db, Model &model)
	standard constructor

	Analyzer (unsigned short method_name, Model &model)
	alternate constructor for instantiations "on the fly" with a Model

	Analyzer (unsigned short method_name, Model &model, const ShortShortPair &view_override)
	alternate constructor for instantiations "on the fly" with a Model

	Analyzer (unsigned short method_name)
	alternate constructor for instantiations "on the fly" without a Model

	~Analyzer ()
	destructor

virtual void	update_model_from_variables (Model &model, const Variables &vars)
	update model's current variables with data from vars

void	update_from_model (const Model &model)
	set inherited data attributes based on extractions from incoming model

void	post_run (std::ostream &s)
	post-run portion of run (optional); verbose to print results; re-implemented by Iterators that can read all Variables/Responses and perform final analysis phase in a standalone way More...

void	pre_output ()

const Model &	algorithm_space_model () const

const Variables &	variables_results () const
	return a single final iterator solution (variables)

const VariablesArray &	variables_array_results ()
	return multiple final iterator solutions (variables). This should only be used if returns_multiple_points() returns true.

const ResponseArray &	response_array_results ()
	return multiple final iterator solutions (response). This should only be used if returns_multiple_points() returns true.

bool	compact_mode () const
	returns Analyzer::compactMode

bool	returns_multiple_points () const
	indicates if this iterator returns multiple final points. Default return is false. Override to return true if appropriate.

void	evaluate_parameter_sets (Model &model, bool log_resp_flag=true, bool log_best_flag=false)
	perform function evaluations to map parameter sets (allVariables) into response sets (allResponses) More...

void	evaluate_batch (Model &model, int batch_id, bool log_best_flag=false)
	perform function evaluations to map a keyed batch of parameter sets (allVariablesMap[key]) into a corresponding batch of response sets (allResponsesMap[key])

const IntIntResponse2DMap &	synchronize_batches (Model &model, bool log_best_flag=false)
	perform function evaluations to map a keyed batch of parameter sets (allVariablesMap[key]) into a corresponding batch of response sets (allResponsesMap[key])

void	clear_batches ()
	since synchronize returns the aggregation of all evaluated batches, we use a separate call to indicate when processing of this data is complete and bookkeeping can be cleared

void	get_vbd_parameter_sets (Model &model, size_t num_samples)
	generate replicate parameter sets for use in variance-based decomposition More...

virtual void	archive_model_variables (const Model &, size_t idx) const
	archive model evaluation points

virtual void	archive_model_response (const Response &, size_t idx) const
	archive model evaluation responses

void	read_variables_responses (int num_evals, size_t num_vars)
	convenience function for reading variables/responses (used in derived classes post_input) More...

void	samples_to_variables_array (const RealMatrix &sample_matrix, VariablesArray &vars_array)
	convert samples array to variables array; e.g., allSamples to allVariables

void	variables_array_to_samples (const VariablesArray &vars_array, RealMatrix &sample_matrix)
	convert variables array to samples array; e.g., allVariables to allSamples

Protected Member Functions inherited from Iterator
	Iterator (BaseConstructor, ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More...

	Iterator (NoDBBaseConstructor, unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for base iterator classes constructed on the fly More...

	Iterator (NoDBBaseConstructor, unsigned short method_name, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for base iterator classes constructed on the fly More...

	Iterator (NoDBBaseConstructor, Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor for instantiations without ProblemDescDB More...

virtual const VariablesArray &	initial_points () const
	gets the multiple initial points for this iterator. This will only be meaningful after a call to initial_points mutator.

StrStrSizet	run_identifier () const
	get the unique run identifier based on method name, id, and number of executions

void	initialize_model_graphics (Model &model, int iterator_server_id)
	helper function that encapsulates initialization operations, modular on incoming Model instance More...

void	export_final_surrogates (Model &data_fit_surr_model)
	export final surrogates generated, e.g., GP in EGO and friends More...

Private Member Functions
void	calc_score_alm ()
	Function to compute the ALM scores for the candidate points ALM score is the variance computed by the surrogate at the point.

void	calc_score_delta_x ()
	Function to compute the Distance scores for the candidate points Distance score is the shortest distance between the candidate and an existing training point.

void	calc_score_delta_y ()
	Function to compute the Gradient scores for the candidate points Gradient score is the function value difference between a candidate's surrogate response and its nearest evaluated true response from the training set.

void	calc_score_topo_bottleneck ()
	Function to compute the Bottleneck scores for the candidate points Bottleneck score is computed by determining the bottleneck distance between the persistence diagrams of two approximate Morse-Smale complices. The complices used include one built from only the training data, and another built from the training data and the single candidate.

void	calc_score_topo_avg_persistence (int respFnCount)
	Function to compute the Average Change in Persistence scores for the candidate points Avg_Persistence score is computed as the average change in persistence each point undergoes between two approximate Morse-Smale complices. The complices used include one built from only the training data, and another built from the training data and the single candidate.

void	calc_score_topo_highest_persistence (int respFnCount)
	Function to compute the Highest Persistence scores for the candidate points Highest Persistence score is calculated as a ranking of a set of candidates by constructing an approximate Morse-Smale complex over the entire set of candidates, using their surrogate responses, and the training data, using their true responses, and ranking points based on the most topological significance as measured by their persistence values. In the case where there are no topologically significant points, the point will be chosen randomly TODO: It may be wiser to fall back to a scheme that ranks points based on proximity to extrema, or the most significant extream?

void	calc_score_topo_alm_hybrid (int respFnCount)
	Function to comptue the Hybrid scores for the candidate points Hybrid score is computed the same as Avg_Persistence score except that instead of computing one score, three scores are computing not only a mean surface, but a mean +/- std. dev. surfaces and then averaging the three separate scores. The hope is that you strike a balance between selecting points in topologically important areas and areas of high uncertainty.

Real	calc_score_alm (int respFnCount, RealVector &test_point)
	Same as the otehr function of the same name, only this allows the user to specify the location of the candidate.

Real	calc_score_delta_x (int respFnCount, RealVector &test_point)
	Same as the otehr function of the same name, only this allows the user to specify the location of the candidate.

Real	calc_score_delta_y (int respFnCount, RealVector &test_point)
	Same as the otehr function of the same name, only this allows the user to specify the location of the candidate.

Real	calc_score_topo_bottleneck (int respFnCount, RealVector &test_point)
	Same as the otehr function of the same name, only this allows the user to specify the location of the candidate.

Real	calc_score_topo_avg_persistence (int respFnCount, RealVector &test_point)
	Same as the otehr function of the same name, only this allows the user to specify the location of the candidate.

Real	calc_score_topo_alm_hybrid (int respFnCount, RealVector &test_point)
	Same as the otehr function of the same name, only this allows the user to specify the location of the candidate.

Real	compute_rmspe ()
	Using the validationSet, compute the RMSE over the surface.

void	compare_complices (int dim, std::ostream &output)
	Using the validationSet, compute the approximate Morse-Smale complices of the true model over the validationSet as well as the surrogate model over the validationSet, and output some topological comparisons.

void	parse_options ()
	Parse misc_options specified in a user input deck.

RealVectorArray	drawNewX (int this_k, int respFnCount=0)
	function to pick the next X value to be evaluated by the Iterated model

void	output_round_data (int round, int respFnCount=0)
	Temporary function for dumping validation data to output files to be visualized in TopoAS.

void	update_amsc (int respFnCount=0)
	Update the approximate Morse-Smale complex based on the training points and selected candidates. Uses surrogate function responses.

void	construct_fsu_sampler (Iterator &u_space_sampler, Model &u_model, int num_samples, int seed, unsigned short sample_type)
	Copy of construct_lhs only it allows for the construction of FSU sample designs. This can break the fsu_cvt, so it is not used at the moment, and these designs only affect the initial sample build not the candidate sets constructed at each round.

void	output_for_optimization (int dim)
	This function will write an input deck for a multi-start global optimization run of DAKOTA by extracting all of the local minima off the approximate Morse-Smale complex created from the validation set of the surrogate model.

Real	median (const RealVector &sorted_data)
	compute the median of the sorted values passed in

void	pick_new_candidates ()
	Pick new candidates from Emulator.

void	score_new_candidates ()
	Score New candidates based on the chosen metrics.

Private Attributes
Iterator	gpBuild
	LHS iterator for building the initial GP.

Iterator	gpEval
	LHS iterator for sampling on the GP.

Iterator	gpFinalEval
	LHS iterator for sampling on the final GP.

Model	gpModel
	GP model of response, one approximation per response function.

int	numRounds
	the number of rounds of additions of size batchSize to add to the original set of LHS samples

int	numPtsTotal
	the total number of points

int	numEmulEval
	the number of points evaluated by the GP each iteration

int	numFinalEmulEval
	number of points evaluated on the final GP

int	scoringMethod
	the type of scoring metric to use for sampling

Real	finalProb
	the final calculated probability (p)

RealVectorArray	gpCvars
	Vector to hold the current values of the current sample inputs on the GP.

RealVectorArray	gpMeans
	Vector to hold the current values of the current mean estimates for the sample values on the GP.

RealVectorArray	gpVar
	Vector to hold the current values of the current variance estimates for the sample values on the GP.

RealVector	emulEvalScores
	Vector to hold the scored values for the current GP samples.

RealVector	predictionErrors
	Vector to hold the RMSE after each round of adaptively fitting the model.

RealVectorArray	validationSet
	Validation point set used to determine predictionErrors above.

RealVector	yTrue
	True function responses at the values corresponding to validationSet.

RealVector	yModel
	Surrogate function responses at the values corresponding to validationSet.

int	validationSetSize
	Number of points used in the validationSet.

int	batchSize
	Number of points to add each round, default = 1.

String	batchStrategy
	String describing the tpye of batch addition to use. Allowable values are naive, distance, topology.

String	outputDir
	Temporary string for dumping validation files used in TopoAS visualization.

String	scoringMetric
	String describing the method for scoring candidate points. Options are: alm, distance, gradient, highest_persistence, avg_persistence, bottleneck, alm_topo_hybrid Note: alm and alm_topo_hybrid will fail when used with surrogates other than global_kriging as it is based on the variance of the surrogate. At the time of implementation, global_kriging is the only surrogate capable of yielding this information.

unsigned short	sampleDesign
	enum describing the initial sample design. Options are: RANDOM_SAMPLING, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY

String	approx_type
	String describing type of surrogate is used to fit the data. Options are: global_kriging, global_mars, global_neural_network, global_polynomial, globabl_moving_least_squares, global_radial_basis.

MS_Complex *	AMSC
	The approximate Morse-Smale complex data structure.

int	numKneighbors
	The number of approximate nearest neighbors to use in computing the AMSC.

bool	outputValidationData
	Temporary variable for toggling writing of data files to be used by TopoAS.

Additional Inherited Members
Static Public Member Functions inherited from NonDSampling
static void	compute_moments (const RealVectorArray &fn_samples, SizetArray &sample_counts, RealMatrix &moment_stats, short moments_type, const StringArray &labels)
	core compute_moments() implementation with all data as inputs

static void	compute_moments (const RealVectorArray &fn_samples, RealMatrix &moment_stats, short moments_type)
	core compute_moments() implementation with all data as inputs

static void	compute_moments (const RealMatrix &fn_samples, RealMatrix &moment_stats, short moments_type)
	alternate RealMatrix samples API for use by external clients

static void	print_moments (std::ostream &s, const RealMatrix &moment_stats, const RealMatrix moment_cis, String qoi_type, short moments_type, const StringArray &moment_labels, bool print_cis)
	core print moments that can be called without object

static int	compute_wilks_sample_size (unsigned short order, Real alpha, Real beta, bool twosided=false)
	calculates the number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH

static Real	compute_wilks_residual (unsigned short order, int nsamples, Real alpha, Real beta, bool twosided)
	Helper function - calculates the Wilks residual.

static Real	compute_wilks_alpha (unsigned short order, int nsamples, Real beta, bool twosided=false)
	calculates the alpha paramter given number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH

static Real	compute_wilks_beta (unsigned short order, int nsamples, Real alpha, bool twosided=false)
	calculates the beta parameter given number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH

static Real	get_wilks_alpha_min ()
	Get the lower and upper bounds supported by Wilks bisection solves.

static Real	get_wilks_alpha_max ()

static Real	get_wilks_beta_min ()

static Real	get_wilks_beta_max ()

Static Protected Member Functions inherited from Iterator
static void	gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set)
	conversion of request vector values for the Gauss-Newton Hessian approximation More...

Protected Attributes inherited from NonDSampling
int	seedSpec
	the user seed specification (default is 0)

int	randomSeed
	the current seed

const int	samplesSpec
	initial specification of number of samples

size_t	samplesRef
	reference number of samples updated for refinement

size_t	numSamples
	the current number of samples to evaluate

String	rngName
	name of the random number generator

unsigned short	sampleType
	the sample type: default, random, lhs, incremental random, or incremental lhs

bool	wilksFlag
	flags use of Wilks formula to calculate num samples

unsigned short	wilksOrder

Real	wilksAlpha

Real	wilksBeta

short	wilksSidedness

RealMatrix	momentGrads
	gradients of standardized or central moments of response functions, as determined by finalMomentsType. Calculated in compute_moments() and indexed as (var,moment) when moment id runs from 1:2*numFunctions.

RealSymMatrix	finalStatErrors
	standard errors (estimator std deviation) for each of the finalStatistics

int	samplesIncrement
	current increment in a sequence of samples

std::unique_ptr< SamplerDriver >	samplerDriver

size_t	numLHSRuns
	counter for number of sample set generations

bool	stdRegressionCoeffs
	flags computation/output of standardized regression coefficients

bool	toleranceIntervalsFlag
	flags of double sided tolerance interval equivalent normal

Real	tiCoverage
	coverage to be used in the calculation of the double sided tolerance interval equivaluent normal

Real	tiConfidenceLevel
	confidence interval to be used in the calculation of the double sided tolerance interval equivalent normal

size_t	tiNumValidSamples

RealVector	tiDstienMus

Real	tiDeltaMultiplicativeFactor

RealVector	tiSampleSigmas

RealVector	tiDstienSigmas

bool	statsFlag
	flags computation/output of statistics

bool	allDataFlag
	flags update of allResponses (allVariables or allSamples already defined)

short	samplingVarsMode
	the sampling mode: ALEATORY_UNCERTAIN{,_UNIFORM}, EPISTEMIC_UNCERTAIN{,_UNIFORM}, UNCERTAIN{,_UNIFORM}, ACTIVE{,_UNIFORM}, or ALL{,_UNIFORM}. This is a secondary control on top of the variables view that allows sampling over subsets of variables that may differ from the view.

short	sampleRanksMode
	mode for input/output of LHS sample ranks: IGNORE_RANKS, GET_RANKS, SET_RANKS, or SET_GET_RANKS

bool	varyPattern
	flag for generating a sequence of seed values within multiple get_parameter_sets() calls so that these executions (e.g., for SBO/SBNLS) are not repeated, but are still repeatable

RealMatrix	sampleRanks
	data structure to hold the sample ranks

SensAnalysisGlobal	nonDSampCorr
	initialize statistical post processing

bool	backfillDuplicates
	flags whether to use backfill to enforce uniqueness of discrete LHS samples

RealRealPairArray	extremeValues
	Minimum and maximum values of response functions for epistemic calculations (calculated in compute_intervals()),.

bool	functionMomentsComputed
	Function moments have been computed; used to determine whether to archive the moments.

Protected Attributes inherited from NonD
NonD *	prevNondInstance
	pointer containing previous value of nondInstance

size_t	startCAUV
	starting index of continuous aleatory uncertain variables within active continuous variables (convenience for managing offsets)

size_t	numCAUV
	number of active continuous aleatory uncertain variables

bool	epistemicStats
	flag for computing interval-type metrics instead of integrated metrics If any epistemic vars are active in a metric evaluation, then flag is set.

RealMatrix	momentStats
	standardized or central resp moments, as determined by finalMomentsType. Calculated in compute_moments()) and indexed as (moment,fn).

RealVectorArray	requestedRespLevels
	requested response levels for all response functions

RealVectorArray	computedProbLevels
	output probability levels for all response functions resulting from requestedRespLevels

RealVectorArray	computedRelLevels
	output reliability levels for all response functions resulting from requestedRespLevels

RealVectorArray	computedGenRelLevels
	output generalized reliability levels for all response functions resulting from requestedRespLevels

short	respLevelTarget
	indicates mapping of z->p (PROBABILITIES), z->beta (RELIABILITIES), or z->beta* (GEN_RELIABILITIES)

short	respLevelTargetReduce
	indicates component or system series/parallel failure metrics

RealVectorArray	requestedProbLevels
	requested probability levels for all response functions

RealVectorArray	requestedRelLevels
	requested reliability levels for all response functions

RealVectorArray	requestedGenRelLevels
	requested generalized reliability levels for all response functions

RealVectorArray	computedRespLevels
	output response levels for all response functions resulting from requestedProbLevels, requestedRelLevels, or requestedGenRelLevels

size_t	totalLevelRequests
	total number of levels specified within requestedRespLevels, requestedProbLevels, and requestedRelLevels

bool	cdfFlag
	flag for type of probabilities/reliabilities used in mappings: cumulative/CDF (true) or complementary/CCDF (false)

bool	pdfOutput
	flag for managing output of response probability density functions (PDFs)

RealVectorArray	computedPDFAbscissas
	sorted response PDF intervals bounds extracted from min/max sample and requested/computedRespLevels (vector lengths = num bins + 1)

RealVectorArray	computedPDFOrdinates
	response PDF densities computed from bin counts divided by (unequal) bin widths (vector lengths = num bins)

Response	finalStatistics
	final statistics from the uncertainty propagation used in strategies: response means, standard deviations, and probabilities of failure

short	finalMomentsType
	type of moments logged within finalStatistics: none, central, standard

size_t	miPLIndex
	index for the active ParallelLevel within ParallelConfiguration::miPLIters

BitArray	pdfComputed
	Whether PDF was computed for function i; used to determine whether a pdf should be archived.

Protected Attributes inherited from Analyzer
size_t	numFunctions
	number of response functions

size_t	numContinuousVars
	number of active continuous vars

size_t	numDiscreteIntVars
	number of active discrete integer vars

size_t	numDiscreteStringVars
	number of active discrete string vars

size_t	numDiscreteRealVars
	number of active discrete real vars

bool	compactMode
	switch for allSamples (compact mode) instead of allVariables (normal mode)

VariablesArray	allVariables
	array of all variables to be evaluated in evaluate_parameter_sets()

RealMatrix	allSamples
	compact alternative to allVariables

IntResponseMap	allResponses
	array of all responses to be computed in evaluate_parameter_sets()

IntIntVariables2DMap	batchVariablesMap
	alternate container for Variables samples supporting evaluate_batch() and synchronize_batches(), a 2D map with outer batch_id and inner eval_id

IntIntRealVector2DMap	batchSamplesMap
	alternate container for RealVector samples supporting evaluate_batch() and synchronize_batches(), a 2D map with outer batch_id and inner eval_id

IntIntResponse2DMap	batchResponsesMap
	alternate container for Response samples supporting evaluate_batch() and synchronize_batches(), a 2D map with outer batch_id and inner eval_id

StringArray	allHeaders
	array of headers to insert into output while evaluating allVariables

size_t	numObjFns
	number of objective functions

size_t	numLSqTerms
	number of least squares terms

RealPairPRPMultiMap	bestVarsRespMap
	map which stores best set of solutions

bool	vbdFlag
	flag indicating the activation of variance-bsaed decomposition for computing Sobol' indices, via either PCE or sampling

Real	vbdDropTol
	tolerance for omitting output of small VBD indices computed via either PCE or sampling

Protected Attributes inherited from Iterator
ProblemDescDB &	probDescDB
	class member reference to the problem description database More...

ParallelLibrary &	parallelLib
	class member reference to the parallel library

ParConfigLIter	methodPCIter
	the active ParallelConfiguration used by this Iterator instance

Model	iteratedModel
	the model to be iterated (for iterators and meta-iterators employing a single model instance)

size_t	myModelLayers
	number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model

unsigned short	methodName
	name of the iterator (the user's method spec)

Real	convergenceTol
	iteration convergence tolerance

size_t	maxIterations
	maximum number of iterations for the method

size_t	maxFunctionEvals
	maximum number of fn evaluations for the method

int	maxEvalConcurrency
	maximum number of concurrent model evaluations More...

ActiveSet	activeSet
	the response data requirements on each function evaluation

size_t	numFinalSolutions
	number of solutions to retain in best variables/response arrays

VariablesArray	bestVariablesArray
	collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)

ResponseArray	bestResponseArray
	collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)

bool	subIteratorFlag
	flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator)

short	outputLevel
	output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT

bool	summaryOutputFlag
	flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases

ResultsManager &	resultsDB
	reference to the global iterator results database

EvaluationStore &	evaluationsDB
	reference to the global evaluation database

EvaluationsDBState	evaluationsDBState
	State of evaluations DB for this iterator.

ResultsNames	resultsNames
	valid names for iterator results

std::shared_ptr< TraitsBase >	methodTraits
	pointer that retains shared ownership of a TraitsBase object, or child thereof

bool	topLevel
	Whether this is the top level iterator.

bool	exportSurrogate = false
	whether to export final surrogates

String	surrExportPrefix
	base filename for exported surrogates

unsigned short	surrExportFormat = NO_MODEL_FORMAT
	(bitwise) format(s) to export

Static Protected Attributes inherited from NonD
static NonD *	nondInstance
	pointer to the active object instance used within static evaluator functions in order to avoid the need for static data

Detailed Description

Class for testing various Adaptively sampling methods using geometric, statisctical, and topological information of the surrogate.

NonDAdaptiveSampling implements an adaptive sampling method based on the work presented in Adaptive Sampling with Topological Scores by Dan Maljovec, Bei Wang, Ana Kupresanin, Gardar Johannesson, Valerio Pascucci, and Peer-Timo Bremer presented in IJUQ (insert issue). The method computes scores based on the topology of the known data and the topology of the surrogate model. A number of alternate adaption strategies are offered as well.

Constructor & Destructor Documentation

◆ NonDAdaptiveSampling()

NonDAdaptiveSampling	(	ProblemDescDB &	problem_db,
		Model &	model
	)

standard constructor

This constructor is called for a standard letter-envelope iterator instantiation. In this case, set_db_list_nodes has been called and probDescDB can be queried for settings from the method specification.

◆ ~NonDAdaptiveSampling()

~NonDAdaptiveSampling ( )

alternate constructor for sample generation and evaluation "on the fly" has not been implemented

destructor

Member Function Documentation

◆ core_run()

void core_run ( )

protectedvirtual

core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post

Virtual run function for the iterator class hierarchy. All derived classes need to redefine it.

Reimplemented from Iterator.

◆ print_results()

void print_results	(	std::ostream &	s,
		short	results_state = `FINAL_RESULTS`
	)

protectedvirtual

print the final iterator results

This virtual function provides additional iterator-specific final results outputs beyond the function evaluation summary printed in finalize_run().

Reimplemented from Analyzer.

References NonD::print_level_mappings(), and NonDSampling::statsFlag.

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

NonDAdaptiveSampling.hpp
NonDAdaptiveSampling.cpp

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes