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Dakota
Version 6.13
Explore and Predict with Confidence
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Class for testing various Adaptively sampling methods using geometric, statisctical, and topological information of the surrogate. More...
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 | |
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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 | |
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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 | |
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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 | |
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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_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 | 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_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 | |
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 | |
int | maximum_iterations () const |
return the maximum iterations for this iterator | |
void | maximum_iterations (int 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. | |
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... | |
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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 | |
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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, 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... | |
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 | load_pilot_sample (const SizetArray &pilot_spec, SizetArray &delta_N_l) |
distribute pilot sample specification across model levels | |
void | load_pilot_sample (const SizetArray &pilot_spec, const Sizet3DArray &N_l, SizetArray &delta_N_l) |
distribute pilot sample specification across model forms or levels | |
void | load_pilot_sample (const SizetArray &pilot_spec, const Sizet3DArray &N_l, Sizet2DArray &delta_N_l) |
distribute pilot sample specification across model forms and levels | |
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_samp) |
print evaluation summary for multilevel sampling across 1D profile | |
void | print_multilevel_evaluation_summary (std::ostream &s, const Sizet2DArray &N_samp) |
print evaluation summary for multilevel sampling across 2D profile | |
void | print_multilevel_evaluation_summary (std::ostream &s, const Sizet3DArray &N_samp) |
print evaluation summary for multilevel sampling across 3D profile | |
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 | |
size_t | one_sided_delta (Real current, Real target) |
compute a one-sided sample increment for multilevel methods to move current sampling level to a new target | |
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) | |
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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) | |
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, bool log_best_flag) |
perform function evaluations to map parameter sets (allVariables) into response sets (allResponses) More... | |
void | get_vbd_parameter_sets (Model &model, int num_samples) |
generate replicate parameter sets for use in variance-based decomposition More... | |
void | compute_vbd_stats (const int num_samples, const IntResponseMap &resp_samples) |
compute VBD-based Sobol indices More... | |
void | archive_sobol_indices () const |
archive VBD-based Sobol indices 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 | print_sobol_indices (std::ostream &s) const |
Printing of VBD results. 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 | |
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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... | |
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 | 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 | |
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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 () |
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static void | accumulate_mean (const RealVectorArray &fn_samples, size_t q, size_t &num_samp, Real &mean) |
helper to accumulate sum of finite samples | |
static void | accumulate_moments (const RealVectorArray &fn_samples, size_t q, short moments_type, Real *moments) |
helper to accumulate higher order sums of finite samples | |
static void | accumulate_moment_gradients (const RealVectorArray &fn_samples, const RealMatrixArray &grad_samples, size_t q, short moments_type, Real mean, Real mom2, Real *mean_grad, Real *mom2_grad) |
helper to accumulate gradient sums | |
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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. | |
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static NonD * | nondInstance |
pointer to the active object instance used within static evaluator functions in order to avoid the need for static data | |
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.
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.
References Dakota::abort_handler(), Response::active_set(), NonDAdaptiveSampling::AMSC, NonDAdaptiveSampling::approx_type, Iterator::assign_rep(), Model::assign_rep(), NonDAdaptiveSampling::batchSize, NonDAdaptiveSampling::batchStrategy, NonDAdaptiveSampling::construct_fsu_sampler(), NonD::construct_lhs(), Model::current_response(), ProblemDescDB::get_bool(), ProblemDescDB::get_int(), ProblemDescDB::get_iv(), ProblemDescDB::get_sa(), ProblemDescDB::get_string(), ProblemDescDB::get_ushort(), NonDAdaptiveSampling::gpBuild, NonDAdaptiveSampling::gpEval, NonDAdaptiveSampling::gpFinalEval, NonDAdaptiveSampling::gpModel, Model::gradient_type(), Model::hessian_type(), Iterator::iteratedModel, Iterator::maxIterations, NonDAdaptiveSampling::numEmulEval, NonDAdaptiveSampling::numFinalEmulEval, NonDAdaptiveSampling::numKneighbors, NonDAdaptiveSampling::numRounds, NonDSampling::numSamples, NonDAdaptiveSampling::outputDir, Iterator::outputLevel, NonDAdaptiveSampling::outputValidationData, NonDAdaptiveSampling::parse_options(), Iterator::probDescDB, NonDSampling::randomSeed, ActiveSet::request_values(), NonDSampling::rngName, NonDAdaptiveSampling::sampleDesign, NonDSampling::sampleType, NonDAdaptiveSampling::scoringMetric, NonDSampling::vary_pattern(), and NonDSampling::varyPattern.
~NonDAdaptiveSampling | ( | ) |
alternate constructor for sample generation and evaluation "on the fly" has not been implemented
destructor
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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.
References Iterator::all_responses(), Model::append_approximation(), Model::approximation_data(), NonDAdaptiveSampling::batchSize, Model::build_approximation(), NonDAdaptiveSampling::compare_complices(), NonDAdaptiveSampling::compute_rmspe(), NonD::computedProbLevels, Model::continuous_variables(), Model::current_response(), Model::current_variables(), NonDAdaptiveSampling::drawNewX(), Model::evaluate(), Model::evaluation_id(), NonDAdaptiveSampling::gpCvars, NonDAdaptiveSampling::gpFinalEval, NonDAdaptiveSampling::gpMeans, NonDAdaptiveSampling::gpModel, NonDAdaptiveSampling::gpVar, NonD::initialize_level_mappings(), Iterator::iteratedModel, Iterator::methodPCIter, NonD::miPLIndex, NonDAdaptiveSampling::numEmulEval, NonDAdaptiveSampling::numFinalEmulEval, Analyzer::numFunctions, NonDAdaptiveSampling::numPtsTotal, NonDAdaptiveSampling::numRounds, NonDSampling::numSamples, NonDAdaptiveSampling::output_round_data(), NonDAdaptiveSampling::pick_new_candidates(), NonDAdaptiveSampling::predictionErrors, NonD::requestedRespLevels, Iterator::run(), NonDAdaptiveSampling::score_new_candidates(), NonDAdaptiveSampling::scoringMetric, and NonDAdaptiveSampling::update_amsc().
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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.