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NonDGPImpSampling Class Reference

Class for the Gaussian Process-based Importance Sampling method. More...

Inheritance diagram for NonDGPImpSampling:
NonDSampling NonD Analyzer Iterator

Public Member Functions

 NonDGPImpSampling (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 ~NonDGPImpSampling ()
 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 core_run ()
 perform the GP importance sampling and return probability of failure More...
 
void print_results (std::ostream &s)
 print the final statistics
 
Real final_probability ()
 returns the probability calculated by the importance sampling
 
- 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...
 
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 IntResponseMap &samples)
 called by compute_statistics() to calculate sample moments and confidence intervals
 
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 update_final_statistics ()
 update finalStatistics from minValues/maxValues, momentStats, and computedProbLevels/computedRelLevels/computedRespLevels
 
void compute_moments (const RealMatrix &samples)
 calculates sample moments for an array of observations for a set of QoI
 
void transform_samples (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.
 
void transform_samples (RealMatrix &sample_matrix, bool x_to_u, int num_samples=0)
 transform the specified samples matrix from x to u or u to x
 
- Public Member Functions inherited from NonD
void initialize_random_variables (short u_space_type)
 initialize natafTransform based on distribution data from iteratedModel More...
 
void initialize_random_variables (const Pecos::ProbabilityTransformation &transform, bool deep_copy=false)
 alternate form: initialize natafTransform based on incoming data More...
 
void initialize_random_variable_transformation ()
 instantiate natafTransform
 
void initialize_random_variable_types ()
 initializes ranVarTypesX within natafTransform (u-space not needed) More...
 
void initialize_random_variable_types (short u_space_type)
 initializes ranVarTypesX and ranVarTypesU within natafTransform More...
 
void initialize_random_variable_parameters ()
 initializes ranVarMeansX, ranVarStdDevsX, ranVarLowerBndsX, ranVarUpperBndsX, and ranVarAddtlParamsX within natafTransform More...
 
void initialize_random_variable_correlations ()
 propagate iteratedModel correlations to natafTransform
 
void verify_correlation_support (short u_space_type)
 verify that correlation warping is supported by Nataf for given variable types
 
void transform_correlations ()
 perform correlation warping for variable types supported by Nataf
 
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 distribution_parameter_derivatives (bool dist_param_derivs)
 set distParamDerivs
 
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...
 
bool pdf_output () const
 get pdfOutput
 
void pdf_output (bool output)
 set pdfOutput
 
Pecos::ProbabilityTransformation & variable_transformation ()
 return natafTransform
 
- 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 ()
 default constructor More...
 
 Iterator (ProblemDescDB &problem_db)
 standard envelope constructor, which constructs its own model(s) More...
 
 Iterator (ProblemDescDB &problem_db, Model &model)
 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)
 alternate envelope constructor for instantiations by name without the ProblemDescDB More...
 
 Iterator (const Iterator &iterator)
 copy constructor More...
 
virtual ~Iterator ()
 destructor More...
 
Iterator operator= (const Iterator &iterator)
 assignment operator More...
 
virtual void post_input ()
 read tabular data for post-run mode
 
virtual void reset ()
 restore initial state for repeated sub-iterator executions
 
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)
 used by IteratorScheduler to unpack starting data for an iterator run
 
virtual void unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer)
 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_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 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
 
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 (Iterator *iterator_rep, bool ref_count_incr=true)
 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)
 
Modeliterated_model ()
 return the iteratedModel (iterators & meta-iterators using a single model instance)
 
ProblemDescDBproblem_description_db () const
 return the problem description database (probDescDB)
 
ParallelLibraryparallel_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_name) const
 convert a method name enumeration value to a string
 
unsigned short method_string_to_enum (const String &method_name) const
 convert a method name string to an enumeration value
 
String submethod_enum_to_string (unsigned short submethod_name) const
 convert a 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
 
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 vector (for use with iterators that employ evaluate_parameter_sets())
 
const ActiveSetactive_set () const
 return the default active set vector (used by iterators that employ evaluate_parameter_sets())
 
void sub_iterator_flag (bool si_flag)
 set subIteratorFlag (and update summaryOutputFlag if needed)
 
void active_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
 
bool is_null () const
 function to check iteratorRep (does this envelope contain a letter?)
 
Iteratoriterator_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...
 

Private Member Functions

RealVector calcExpIndicator (const int respFnCount, const Real respThresh)
 function to calculate the expected indicator probabilities
 
Real calcExpIndPoint (const int respFnCount, const Real respThresh, const RealVector this_mean, const RealVector this_var)
 function to calculate the expected indicator probabilities for one point
 
void calcRhoDraw ()
 function to update the rhoDraw data, adding x values and rho draw values
 
RealVector drawNewX (int this_k)
 function to pick the next X value to be evaluated by the Iterated model
 

Private Attributes

Iterator gpBuild
 LHS iterator for building the initial GP.
 
Iterator gpEval
 LHS iterator for sampling on the GP.
 
Model gpModel
 GP model of response, one approximation per response function.
 
Iterator sampleRhoOne
 LHS iterator for sampling from the rhoOneDistribution.
 
int numPtsAdd
 the number of points added 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
 
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 expIndicator
 Vector to hold the expected indicator values for the current GP samples.
 
RealVector rhoDraw
 Vector to hold the rhoDraw values for the current GP samples.
 
RealVector normConst
 Vector to hold the normalization constant calculated for each point added.
 
RealVector indicator
 IntVector to hold indicator for actual simulation values vs. threshold.
 
RealVectorArray xDrawThis
 xDrawThis, appended to locally to hold the X values of emulator points chosen
 
RealVector expIndThis
 expIndThis, appended locally to hold the expected indicator
 
RealVector rhoDrawThis
 rhoDrawThis, appended locally to hold the rhoDraw density for calculating draws
 
RealVector rhoMix
 rhoMix, mixture density
 
RealVector rhoOne
 rhoOne, original importance density
 

Additional Inherited Members

- Static Public Member Functions inherited from NonDSampling
static void print_moments (std::ostream &s, const RealMatrix &moment_stats, const RealMatrix moment_cis, String qoi_type, const StringArray &moment_labels, bool print_cis)
 core print moments that can be called without object
 
static void compute_moments (const RealMatrix &samples, RealMatrix &moment_stats)
 core compute 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 for now so I can test without instantiating a NonDSampling object - RWH
 
- 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...
 
 ~NonDSampling ()
 destructor
 
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
 
unsigned short sampling_scheme () const
 return sampleType
 
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 (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)
 
void initialize_lhs (bool write_message)
 increments numLHSRuns, sets random seed, and initializes lhsDriver
 
void view_design_counts (const Model &model, size_t &num_cdv, size_t &num_ddiv, size_t &num_ddsv, size_t &num_ddrv) const
 compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More...
 
void view_aleatory_uncertain_counts (const Model &model, size_t &num_cauv, size_t &num_dauiv, size_t &num_dausv, size_t &num_daurv) const
 compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More...
 
void view_epistemic_uncertain_counts (const Model &model, size_t &num_ceuv, size_t &num_deuiv, size_t &num_deusv, size_t &num_deurv) const
 compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More...
 
void view_uncertain_counts (const Model &model, size_t &num_cuv, size_t &num_duiv, size_t &num_dusv, size_t &num_durv) const
 compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More...
 
void view_state_counts (const Model &model, size_t &num_csv, size_t &num_dsiv, size_t &num_dssv, size_t &num_dsrv) const
 compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model
 
void mode_counts (const Model &model, 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 get_lhs_samples (const Model &model, 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...
 
- Static Protected Member Functions inherited from NonD
static void vars_u_to_x_mapping (const Variables &u_vars, Variables &x_vars)
 static function for RecastModels used for forward mapping of u-space variables from NonD Iterators to x-space variables for Model evaluations More...
 
static void vars_x_to_u_mapping (const Variables &x_vars, Variables &u_vars)
 static function for RecastModels used for inverse mapping of x-space variables from data import to u-space variables for NonD Iterators More...
 
static void set_u_to_x_mapping (const Variables &u_vars, const ActiveSet &u_set, ActiveSet &x_set)
 static function for RecastModels used to map u-space ActiveSets from NonD Iterators to x-space ActiveSets for Model evaluations More...
 
static void resp_x_to_u_mapping (const Variables &x_vars, const Variables &u_vars, const Response &x_response, Response &u_response)
 static function for RecastModels used to map x-space responses from Model evaluations to u-space responses for return to NonD Iterator.
 
- Protected Attributes inherited from NonDSampling
const 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
 
int samplesIncrement
 flags use of Wilks formula to calculate num samples More...
 
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()),.
 
- Static Protected Attributes inherited from NonD
static NonDnondInstance
 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 Gaussian Process-based Importance Sampling method.

The NonDGPImpSampling implements a method developed by Keith Dalbey that uses a Gaussian process surrogate in the calculation of the importance density. Specifically, the mean and variance of the GP prediction are used to calculate an expected value that a particular point fails, and that is used as part of the computation of the "draw distribution." The normalization constants and the mixture distribution used are defined in (need to get SAND report).

Constructor & Destructor Documentation

NonDGPImpSampling ( ProblemDescDB problem_db,
Model model 
)

Member Function Documentation

void core_run ( )
virtual

perform the GP importance sampling and return probability of failure

Calculate the failure probabilities for specified probability levels using Gaussian process based importance sampling.

Reimplemented from Iterator.

References Model::acv(), Iterator::all_responses(), Analyzer::all_samples(), Iterator::all_samples(), Model::append_approximation(), Model::approximation_data(), Model::approximation_variances(), Model::build_approximation(), NonDGPImpSampling::calcExpIndicator(), NonDGPImpSampling::calcExpIndPoint(), NonDGPImpSampling::calcRhoDraw(), NonD::cdfFlag, NonD::computedProbLevels, Model::continuous_lower_bounds(), Model::continuous_upper_bounds(), Model::continuous_variables(), Model::current_response(), Model::current_variables(), NonDGPImpSampling::drawNewX(), Model::evaluate(), Model::evaluation_id(), NonDGPImpSampling::expIndicator, NonDGPImpSampling::expIndThis, NonDGPImpSampling::finalProb, Response::function_values(), NonDGPImpSampling::gpCvars, NonDGPImpSampling::gpEval, NonDGPImpSampling::gpMeans, NonDGPImpSampling::gpModel, NonDGPImpSampling::gpVar, NonDGPImpSampling::indicator, NonD::initialize_level_mappings(), Iterator::iteratedModel, Iterator::methodPCIter, NonD::miPLIndex, NonDGPImpSampling::normConst, NonDGPImpSampling::numEmulEval, Analyzer::numFunctions, NonDGPImpSampling::numPtsAdd, NonDGPImpSampling::numPtsTotal, NonDSampling::numSamples, Iterator::outputLevel, Model::pop_approximation(), NonD::requestedRespLevels, NonDGPImpSampling::rhoDraw, NonDGPImpSampling::rhoDrawThis, NonDGPImpSampling::rhoMix, NonDGPImpSampling::rhoOne, Iterator::run(), NonDGPImpSampling::sampleRhoOne, and NonDGPImpSampling::xDrawThis.


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