Class for the Adaptive Importance Sampling methods within DAKOTA. More...

Inheritance diagram for NonDAdaptImpSampling:

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

	NonDAdaptImpSampling (Model &model, unsigned short sample_type, int samples, int seed, const String &rng, bool vary_pattern, unsigned short is_type, bool cdf_flag, bool x_space_model, bool use_model_bounds, bool track_extreme)
	alternate constructor for on-the-fly instantiations More...

	~NonDAdaptImpSampling ()
	destructor

bool	resize ()
	reinitializes iterator based on new variable size

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	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)

void	core_run ()
	performs adaptive importance sampling and computes probability of failure

void	print_results (std::ostream &s, short results_state=FINAL_RESULTS)
	print the final statistics

unsigned short	sampling_scheme () const
	return importanceSamplingType

int	refinement_samples () const
	return refineSamples

void	initialize (const RealVectorArray &full_points, bool x_space_data, size_t resp_index, Real initial_prob, Real failure_threshold)
	initializes data needed for importance sampling: an initial set of points around which to sample, a failure threshold, an initial probability to refine, and flags to control transformations More...

void	initialize (const RealMatrix &full_points, bool x_space_data, size_t resp_index, Real initial_prob, Real failure_threshold)
	initializes data needed for importance sampling: an initial set of points around which to sample, a failure threshold, an initial probability to refine, and flags to control transformations More...

void	initialize (const RealVector &full_point, bool x_space_data, size_t resp_index, Real initial_prob, Real failure_threshold)
	initializes data needed for importance sampling: an initial point around which to sample, a failure threshold, an initial probability to refine, and flags to control transformations More...

Real	final_probability ()
	returns the final probability calculated by the importance sampling

const RealRealPairArray &	extreme_values () const
	return extremeValues

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

void	transform_samples (Pecos::ProbabilityTransformation &nataf, bool x_to_u=true)
	transform allSamples imported by alternate constructor. 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, int num_samples=0, 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()))
	alternate envelope constructor that assigns a representation pointer 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_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	variable_bounds (const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds)
	assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)

virtual void	linear_constraints (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)
	assign linear inequality and linear equality constraints for this iterator (user-functions mode for which Model updating is not used)

virtual void	nonlinear_constraints (const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt)
	assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)

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

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

virtual void	method_recourse ()
	perform a method switch, if possible, due to a detected conflict

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	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 (const bool &tflag)
	Set the iterator's top level flag.

Private Member Functions
void	select_rep_points (const RealVectorArray &var_samples_u, const RealVector &fn_samples)
	select representative points from a set of samples

void	converge_statistics (bool cov_flag)
	iteratively generate samples and select representative points until probability and (optionally) coefficient of variation converge

void	generate_samples (RealVectorArray &var_samples_u)
	generate a set of samples based on multimodal sampling density

void	evaluate_samples (const RealVectorArray &var_samples_u, RealVector &fn_samples)
	evaluate the model at the sample points and store the responses

void	calculate_statistics (const RealVectorArray &var_samples_u, const RealVector &fn_samples, size_t total_samples, Real &sum_prob, Real &prob, bool compute_cov, Real &sum_var, Real &cov)
	calculate the probability of exceeding the failure threshold and the coefficent of variation (if requested)

Real	distance (const RealVector &a, const RealVector &b)
	compute Euclidean distance between points a and b

Real	recentered_density (const RealVector &sample_point)
	compute density between a representative point and a sample point, assuming standard normal

Private Attributes
Model	uSpaceModel
	importance sampling is performed in standardized probability space. This u-space model is either passed in (alternate constructor for helper AIS) or constructed using ProbabilityTransformModel (standard constructor for stand-alone AIS)

unsigned short	importanceSamplingType
	integration type (is, ais, mmais) provided by input specification

bool	initLHS
	flag to identify if initial points are generated from an LHS sample

bool	useModelBounds
	flag to control if the sampler should respect the model bounds

bool	invertProb
	flag for inversion of probability values using 1.-p

bool	trackExtremeValues
	flag for tracking min/max values encountered when evaluating samples

int	refineSamples
	size of sample batch within each refinement iteration

size_t	respFnIndex
	the active response function index in the model to be sampled

RealVector	designPoint
	design subset for which uncertain subset is being sampled

RealVectorArray	initPointsU
	the original set of u-space samples passed in initialize()

RealVectorArray	repPointsU
	the set of representative points in u-space around which to sample

RealVector	repWeights
	the weight associated with each representative point

Real	probEstimate
	the probability estimate that is iteratively refined by importance sampling

Real	failThresh
	the failure threshold (z-bar) for the problem.

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 paramter 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 ()

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, int 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, int 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, int 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 ()

int	num_samples () const

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

void	sampling_reference (int 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 lhsDriver to generate a set of samples from the distributions/bounds defined in the incoming model. More...

void	get_parameter_sets (Model &model, const int num_samples, RealMatrix &design_matrix)
	Uses lhsDriver 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 int 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 lhsDriver 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 lhsDriver 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 RealVector &	response_error_estimates () const
	return error estimates associated with each of the finalStatistics

void	initialize_lhs (bool write_message, int num_samples)
	increments numLHSRuns, sets random seed, and initializes lhsDriver

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	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

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

int	samplesRef
	reference number of samples updated for refinement

int	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.

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

int	samplesIncrement
	current increment in a sequence of samples

Pecos::LHSDriver	lhsDriver
	the C++ wrapper for the F90 LHS library

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	backfillFlag
	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.

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 the Adaptive Importance Sampling methods within DAKOTA.

The NonDAdaptImpSampling implements the multi-modal adaptive importance sampling used for reliability calculations. (eventually we will want to broaden this). Need to add more detail to this description.

Constructor & Destructor Documentation

NonDAdaptImpSampling	(	ProblemDescDB &	problem_db,
		Model &	model
	)

standard constructor

This is the primary constructor. It accepts a Model reference. It will perform refinement for all response QOI and all probability levels.

References Dakota::abort_handler(), Model::assign_rep(), NonD::finalMomentsType, ProblemDescDB::get_iv(), Iterator::iteratedModel, NonDSampling::numSamples, Iterator::probDescDB, NonDAdaptImpSampling::refineSamples, NonDSampling::sampleType, NonDSampling::statsFlag, NonDAdaptImpSampling::useModelBounds, and NonDAdaptImpSampling::uSpaceModel.

NonDAdaptImpSampling	(	Model &	model,
		unsigned short	sample_type,
		int	refine_samples,
		int	refine_seed,
		const String &	rng,
		bool	vary_pattern,
		unsigned short	is_type,
		bool	cdf_flag,
		bool	x_space_model,
		bool	use_model_bounds,
		bool	track_extreme
	)

alternate constructor for on-the-fly instantiations

This is an alternate constructor for instantiations on the fly using a Model but no ProblemDescDB. It will perform refinement for one response QOI and one probability level (passed in initialize()).

References Model::assign_rep(), NonD::cdfFlag, NonDSampling::extremeValues, NonD::finalMomentsType, Iterator::maxEvalConcurrency, Analyzer::numFunctions, NonDAdaptImpSampling::refineSamples, NonDAdaptImpSampling::trackExtremeValues, NonDAdaptImpSampling::useModelBounds, and NonDAdaptImpSampling::uSpaceModel.

Member Function Documentation

void initialize	(	const RealVectorArray &	acv_points,
		bool	x_space_data,
		size_t	resp_index,
		Real	initial_prob,
		Real	failure_threshold
	)

initializes data needed for importance sampling: an initial set of points around which to sample, a failure threshold, an initial probability to refine, and flags to control transformations

Initializes data using a vector array of starting points.

References NonDAdaptImpSampling::designPoint, NonDAdaptImpSampling::failThresh, NonDAdaptImpSampling::initPointsU, NonDAdaptImpSampling::invertProb, NonD::numCAUV, Model::probability_transformation(), NonDAdaptImpSampling::probEstimate, NonDAdaptImpSampling::respFnIndex, NonD::startCAUV, and NonDAdaptImpSampling::uSpaceModel.

Referenced by NonDAdaptImpSampling::core_run().

void initialize	(	const RealMatrix &	acv_points,
		bool	x_space_data,
		size_t	resp_index,
		Real	initial_prob,
		Real	failure_threshold
	)

initializes data needed for importance sampling: an initial set of points around which to sample, a failure threshold, an initial probability to refine, and flags to control transformations

Initializes data using a matrix of starting points.

References NonDAdaptImpSampling::designPoint, NonDAdaptImpSampling::failThresh, NonDAdaptImpSampling::initPointsU, NonDAdaptImpSampling::invertProb, NonD::numCAUV, Analyzer::numContinuousVars, Model::probability_transformation(), NonDAdaptImpSampling::probEstimate, NonDAdaptImpSampling::respFnIndex, NonD::startCAUV, and NonDAdaptImpSampling::uSpaceModel.

void initialize	(	const RealVector &	acv_point,
		bool	x_space_data,
		size_t	resp_index,
		Real	initial_prob,
		Real	failure_threshold
	)

initializes data needed for importance sampling: an initial point around which to sample, a failure threshold, an initial probability to refine, and flags to control transformations

Initializes data using only one starting point.

References NonDAdaptImpSampling::designPoint, NonDAdaptImpSampling::failThresh, NonDAdaptImpSampling::initPointsU, NonDAdaptImpSampling::invertProb, NonD::numCAUV, Model::probability_transformation(), NonDAdaptImpSampling::probEstimate, NonDAdaptImpSampling::respFnIndex, NonD::startCAUV, and NonDAdaptImpSampling::uSpaceModel.

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

NonDAdaptImpSampling.hpp
NonDAdaptImpSampling.cpp

Public Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation