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Dakota
Version 6.13
Explore and Predict with Confidence
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Performs Multilevel Monte Carlo sampling for uncertainty quantification. More...
Public Member Functions | |
NonDMultilevelSampling (ProblemDescDB &problem_db, Model &model) | |
standard constructor More... | |
~NonDMultilevelSampling () | |
destructor | |
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 | derived_free_communicators (ParLevLIter pl_iter) |
derived class contributions to freeing the communicators associated with this Iterator instance | |
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 | 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 () |
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 | 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 | derived_set_communicators (ParLevLIter pl_iter) |
derived class contributions to setting the communicators associated with this Iterator instance | |
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 | 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 void | derived_init_communicators (ParLevLIter pl_iter) |
derived class contributions to initializing the communicators associated with this Iterator instance | |
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 | multilevel_mc_Ysum (unsigned short model_form) |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using discrepancy accumulators (sum_Y) More... | |
void | multilevel_mc_Qsum (unsigned short model_form) |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using QoI accumulators (sum_Q) More... | |
void | control_variate_mc (const UShortArray &active_key) |
Perform control variate Monte Carlo across two model forms. More... | |
void | multilevel_control_variate_mc_Ycorr (unsigned short lf_model_form, unsigned short hf_model_form) |
Perform multilevel Monte Carlo across levels in combination with control variate Monte Carlo across model forms at each level; CV computes correlations for Y (LH correlations for level discrepancies) More... | |
void | multilevel_control_variate_mc_Qcorr (unsigned short lf_model_form, unsigned short hf_model_form) |
Perform multilevel Monte Carlo across levels in combination with control variate Monte Carlo across model forms at each level; CV computes correlations for Q (LH correlations for QoI) More... | |
void | shared_increment (size_t iter, size_t lev) |
perform a shared increment of LF and HF samples for purposes of computing/updating the evaluation ratio and the MSE ratio | |
bool | lf_increment (Real avg_eval_ratio, const SizetArray &N_lf, const SizetArray &N_hf, size_t iter, size_t lev) |
perform final LF sample increment as indicated by the evaluation ratio | |
void | aggregated_models_mode () |
synchronize iteratedModel and activeSet on AGGREGATED_MODELS mode | |
void | bypass_surrogate_mode () |
synchronize iteratedModel and activeSet on BYPASS_SURROGATE mode | |
void | uncorrected_surrogate_mode () |
synchronize iteratedModel and activeSet on UNCORRECTED_SURROGATE mode | |
Real | level_cost (const RealVector &cost, unsigned short step) |
return (aggregate) level cost | |
void | assign_specification_sequence (size_t index) |
advance any sequence specifications | |
int | random_seed (size_t index) const |
extract current random seed from randomSeedSeqSpec More... | |
void | configure_indices (unsigned short group, unsigned short form, unsigned short lev, unsigned short s_index) |
manage response mode and active model key from {group,form,lev} triplet. s_index is the sequence index that defines the active dimension for a model sequence. | |
void | initialize_ml_Ysums (IntRealMatrixMap &sum_Y, size_t num_lev) |
initialize the ML accumulators for computing means, variances, and covariances across fidelity levels | |
void | initialize_ml_Qsums (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, size_t num_lev) |
initialize the ML accumulators for computing means, variances, and covariances across fidelity levels | |
void | initialize_cv_sums (IntRealVectorMap &sum_L_shared, IntRealVectorMap &sum_L_refined, IntRealVectorMap &sum_H, IntRealVectorMap &sum_LL, IntRealVectorMap &sum_LH) |
initialize the CV accumulators for computing means, variances, and covariances across fidelity levels | |
void | initialize_mlcv_sums (IntRealMatrixMap &sum_L_shared, IntRealMatrixMap &sum_L_refined, IntRealMatrixMap &sum_H, IntRealMatrixMap &sum_LL, IntRealMatrixMap &sum_LH, IntRealMatrixMap &sum_HH, size_t num_ml_lev, size_t num_cv_lev) |
initialize the MLCV accumulators for computing means, variances, and covariances across fidelity levels | |
void | initialize_mlcv_sums (IntRealMatrixMap &sum_Ll, IntRealMatrixMap &sum_Llm1, IntRealMatrixMap &sum_Ll_refined, IntRealMatrixMap &sum_Llm1_refined, IntRealMatrixMap &sum_Hl, IntRealMatrixMap &sum_Hlm1, IntRealMatrixMap &sum_Ll_Ll, IntRealMatrixMap &sum_Ll_Llm1, IntRealMatrixMap &sum_Llm1_Llm1, IntRealMatrixMap &sum_Hl_Ll, IntRealMatrixMap &sum_Hl_Llm1, IntRealMatrixMap &sum_Hlm1_Ll, IntRealMatrixMap &sum_Hlm1_Llm1, IntRealMatrixMap &sum_Hl_Hl, IntRealMatrixMap &sum_Hl_Hlm1, IntRealMatrixMap &sum_Hlm1_Hlm1, size_t num_ml_lev, size_t num_cv_lev) |
initialize the MLCV accumulators for computing means, variances, and covariances across fidelity levels | |
void | accumulate_offsets (RealVector &mu) |
accumulate initial approximation to mean vector, for use as offsets in subsequent accumulations | |
void | accumulate_ml_Qsums (IntRealMatrixMap &sum_Q, size_t lev, const RealVector &offset, SizetArray &num_Q) |
update running QoI sums for one model (sum_Q) using set of model evaluations within allResponses; used for level 0 from other accumulators | |
void | accumulate_ml_Ysums (IntRealMatrixMap &sum_Y, RealMatrix &sum_YY, size_t lev, const RealVector &offset, SizetArray &num_Y) |
update accumulators for multilevel telescoping running sums using set of model evaluations within allResponses | |
void | accumulate_ml_Qsums (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, size_t lev, const RealVector &offset, SizetArray &num_Q) |
update running QoI sums for two models (sum_Ql, sum_Qlm1) using set of model evaluations within allResponses | |
void | accumulate_cv_sums (IntRealVectorMap &sum_L, const RealVector &offset, SizetArray &num_L) |
update running sums for one model (sum_L) using set of model evaluations within allResponses | |
void | accumulate_cv_sums (IntRealVectorMap &sum_L_shared, IntRealVectorMap &sum_L_refined, IntRealVectorMap &sum_H, IntRealVectorMap &sum_LL, IntRealVectorMap &sum_LH, RealVector &sum_HH, const RealVector &offset, SizetArray &num_L, SizetArray &num_H) |
update running sums for two models (sum_L, sum_H, and sum_LH) from set of low/high fidelity model evaluations within allResponses | |
void | accumulate_mlcv_Qsums (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, size_t lev, const RealVector &offset, SizetArray &num_Q) |
update running QoI sums for one model at two levels (sum_Ql, sum_Qlm1) using set of model evaluations within allResponses | |
void | accumulate_mlcv_Ysums (IntRealMatrixMap &sum_Y, size_t lev, const RealVector &offset, SizetArray &num_Y) |
update running discrepancy sums for one model (sum_Y) using set of model evaluations within allResponses | |
void | accumulate_mlcv_Qsums (const IntResponseMap &lf_resp_map, const IntResponseMap &hf_resp_map, IntRealMatrixMap &sum_L_shared, IntRealMatrixMap &sum_L_refined, IntRealMatrixMap &sum_H, IntRealMatrixMap &sum_LL, IntRealMatrixMap &sum_LH, IntRealMatrixMap &sum_HH, size_t lev, const RealVector &lf_offset, const RealVector &hf_offset, SizetArray &num_L, SizetArray &num_H) |
update running QoI sums for two models (sum_L, sum_H, sum_LL, sum_LH, and sum_HH) from set of low/high fidelity model evaluations within {lf,hf}_resp_map; used for level 0 from other accumulators | |
void | accumulate_mlcv_Ysums (const IntResponseMap &lf_resp_map, const IntResponseMap &hf_resp_map, IntRealMatrixMap &sum_L_shared, IntRealMatrixMap &sum_L_refined, IntRealMatrixMap &sum_H, IntRealMatrixMap &sum_LL, IntRealMatrixMap &sum_LH, IntRealMatrixMap &sum_HH, size_t lev, const RealVector &lf_offset, const RealVector &hf_offset, SizetArray &num_L, SizetArray &num_H) |
update running two-level discrepancy sums for two models (sum_L, sum_H, sum_LL, sum_LH, and sum_HH) from set of low/high fidelity model evaluations within {lf,hf}resp_map | |
void | accumulate_mlcv_Qsums (const IntResponseMap &lf_resp_map, const IntResponseMap &hf_resp_map, IntRealMatrixMap &sum_Ll, IntRealMatrixMap &sum_Llm1, IntRealMatrixMap &sum_Ll_refined, IntRealMatrixMap &sum_Llm1_refined, IntRealMatrixMap &sum_Hl, IntRealMatrixMap &sum_Hlm1, IntRealMatrixMap &sum_Ll_Ll, IntRealMatrixMap &sum_Ll_Llm1, IntRealMatrixMap &sum_Llm1_Llm1, IntRealMatrixMap &sum_Hl_Ll, IntRealMatrixMap &sum_Hl_Llm1, IntRealMatrixMap &sum_Hlm1_Ll, IntRealMatrixMap &sum_Hlm1_Llm1, IntRealMatrixMap &sum_Hl_Hl, IntRealMatrixMap &sum_Hl_Hlm1, IntRealMatrixMap &sum_Hlm1_Hlm1, size_t lev, const RealVector &lf_offset, const RealVector &hf_offset, SizetArray &num_L, SizetArray &num_H) |
update running QoI sums for two models and two levels from set of low/high fidelity model evaluations within {lf,hf}_resp_map | |
Real | eval_ratio (const RealVector &sum_L_shared, const RealVector &sum_H, const RealVector &sum_LL, const RealVector &sum_LH, const RealVector &sum_HH, Real cost_ratio, const SizetArray &N_shared, RealVector &var_H, RealVector &rho2_LH) |
compute the LF/HF evaluation ratio, averaged over the QoI | |
Real | eval_ratio (RealMatrix &sum_L_shared, RealMatrix &sum_H, RealMatrix &sum_LL, RealMatrix &sum_LH, RealMatrix &sum_HH, Real cost_ratio, size_t lev, const SizetArray &N_shared, RealMatrix &var_H, RealMatrix &rho2_LH) |
compute the LF/HF evaluation ratio, averaged over the QoI | |
Real | eval_ratio (RealMatrix &sum_Ll, RealMatrix &sum_Llm1, RealMatrix &sum_Hl, RealMatrix &sum_Hlm1, RealMatrix &sum_Ll_Ll, RealMatrix &sum_Ll_Llm1, RealMatrix &sum_Llm1_Llm1, RealMatrix &sum_Hl_Ll, RealMatrix &sum_Hl_Llm1, RealMatrix &sum_Hlm1_Ll, RealMatrix &sum_Hlm1_Llm1, RealMatrix &sum_Hl_Hl, RealMatrix &sum_Hl_Hlm1, RealMatrix &sum_Hlm1_Hlm1, Real cost_ratio, size_t lev, const SizetArray &N_shared, RealMatrix &var_YHl, RealMatrix &rho_dot2_LH) |
compute the LF/HF evaluation ratio, averaged over the QoI | |
Real | MSE_ratio (Real avg_eval_ratio, const RealVector &var_H, const RealVector &rho2_LH, size_t iter, const SizetArray &N_hf) |
compute ratio of MC and CVMC mean squared errors, averaged over the QoI | |
void | cv_raw_moments (IntRealVectorMap &sum_L_shared, IntRealVectorMap &sum_H, IntRealVectorMap &sum_LL, IntRealVectorMap &sum_LH, const SizetArray &N_shared, IntRealVectorMap &sum_L_refined, const SizetArray &N_refined, const RealVector &rho2_LH, RealMatrix &H_raw_mom) |
compute control variate parameters for CVMC and estimate raw moments | |
void | cv_raw_moments (IntRealMatrixMap &sum_L_shared, IntRealMatrixMap &sum_H, IntRealMatrixMap &sum_LL, IntRealMatrixMap &sum_LH, const SizetArray &N_shared, IntRealMatrixMap &sum_L_refined, const SizetArray &N_refined, const RealMatrix &rho2_LH, size_t lev, RealMatrix &H_raw_mom) |
apply control variate parameters for MLCVMC to estimate raw moment contributions | |
void | cv_raw_moments (IntRealMatrixMap &sum_Ll, IntRealMatrixMap &sum_Llm1, IntRealMatrixMap &sum_Hl, IntRealMatrixMap &sum_Hlm1, IntRealMatrixMap &sum_Ll_Ll, IntRealMatrixMap &sum_Ll_Llm1, IntRealMatrixMap &sum_Llm1_Llm1, IntRealMatrixMap &sum_Hl_Ll, IntRealMatrixMap &sum_Hl_Llm1, IntRealMatrixMap &sum_Hlm1_Ll, IntRealMatrixMap &sum_Hlm1_Llm1, IntRealMatrixMap &sum_Hl_Hl, IntRealMatrixMap &sum_Hl_Hlm1, IntRealMatrixMap &sum_Hlm1_Hlm1, const SizetArray &N_shared, IntRealMatrixMap &sum_Ll_refined, IntRealMatrixMap &sum_Llm1_refined, const SizetArray &N_refined, const RealMatrix &rho_dot2_LH, size_t lev, RealMatrix &H_raw_mom) |
apply control variate parameters for MLCVMC to estimate raw moment contributions | |
void | compute_control (Real sum_L, Real sum_H, Real sum_LL, Real sum_LH, size_t N_shared, Real &beta) |
compute scalar control variate parameters | |
void | compute_control (Real sum_L, Real sum_H, Real sum_LL, Real sum_LH, Real sum_HH, size_t N_shared, Real &var_H, Real &rho2_LH) |
compute scalar variance and correlation parameters for control variates | |
void | compute_control (Real sum_Ll, Real sum_Llm1, Real sum_Hl, Real sum_Hlm1, Real sum_Ll_Ll, Real sum_Ll_Llm1, Real sum_Llm1_Llm1, Real sum_Hl_Ll, Real sum_Hl_Llm1, Real sum_Hlm1_Ll, Real sum_Hlm1_Llm1, Real sum_Hl_Hl, Real sum_Hl_Hlm1, Real sum_Hlm1_Hlm1, size_t N_shared, Real &var_YH, Real &rho_dot2_LH, Real &beta_dot, Real &gamma) |
compute scalar control variate parameters | |
void | compute_control (const RealVector &sum_L, const RealVector &sum_H, const RealVector &sum_LL, const RealVector &sum_LH, const SizetArray &N_shared, RealVector &beta) |
compute vector control variate parameters | |
void | compute_control (const RealVector &sum_L, const RealVector &sum_H, const RealVector &sum_LL, const RealVector &sum_LH, const RealVector &sum_HH, const SizetArray &N_shared, RealVector &var_H, RealVector &rho2_LH) |
compute vector variance and correlation parameters for control variates | |
void | compute_control (const RealMatrix &sum_L, const RealMatrix &sum_H, const RealMatrix &sum_LL, const RealMatrix &sum_LH, const SizetArray &N_shared, size_t lev, RealVector &beta) |
compute matrix control variate parameters | |
void | compute_control (const RealMatrix &sum_Ll, const RealMatrix &sum_Llm1, const RealMatrix &sum_Hl, const RealMatrix &sum_Hlm1, const RealMatrix &sum_Ll_Ll, const RealMatrix &sum_Ll_Llm1, const RealMatrix &sum_Llm1_Llm1, const RealMatrix &sum_Hl_Ll, const RealMatrix &sum_Hl_Llm1, const RealMatrix &sum_Hlm1_Ll, const RealMatrix &sum_Hlm1_Llm1, const RealMatrix &sum_Hl_Hl, const RealMatrix &sum_Hl_Hlm1, const RealMatrix &sum_Hlm1_Hlm1, const SizetArray &N_shared, size_t lev, RealVector &beta_dot, RealVector &gamma) |
compute matrix control variate parameters | |
void | apply_control (Real sum_H, Real sum_L_shared, size_t N_shared, Real sum_L_refined, size_t N_refined, Real beta, Real &H_raw_mom) |
apply scalar control variate parameter (beta) to approximate HF moment | |
void | apply_control (Real sum_Hl, Real sum_Hlm1, Real sum_Ll, Real sum_Llm1, size_t N_shared, Real sum_Ll_refined, Real sum_Llm1_refined, size_t N_refined, Real beta_dot, Real gamma, Real &H_raw_mom) |
apply scalar control variate parameter (beta) to approximate HF moment | |
void | apply_control (const RealVector &sum_H, const RealVector &sum_L_shared, const SizetArray &N_shared, const RealVector &sum_L_refined, const SizetArray &N_refined, const RealVector &beta, RealVector &H_raw_mom) |
apply vector control variate parameter (beta) to approximate HF moment | |
void | apply_control (const RealMatrix &sum_H, const RealMatrix &sum_L_shared, const SizetArray &N_shared, const RealMatrix &sum_L_refined, const SizetArray &N_refined, size_t lev, const RealVector &beta, RealVector &H_raw_mom) |
apply matrix control variate parameter (beta) to approximate HF moment | |
void | apply_control (const RealMatrix &sum_Hl, const RealMatrix &sum_Hlm1, const RealMatrix &sum_Ll, const RealMatrix &sum_Llm1, const SizetArray &N_shared, const RealMatrix &sum_Ll_refined, const RealMatrix &sum_Llm1_refined, const SizetArray &N_refined, size_t lev, const RealVector &beta_dot, const RealVector &gamma, RealVector &H_raw_mom) |
apply matrix control variate parameter (beta) to approximate HF moment | |
void | export_all_samples (String root_prepend, const Model &model, size_t iter, size_t lev) |
export allSamples to tagged tabular file | |
void | convert_moments (const RealMatrix &raw_mom, RealMatrix &final_mom) |
convert uncentered raw moments (multilevel expectations) to standardized moments | |
void | compute_error_estimates (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, Sizet2DArray &num_Q) |
populate finalStatErrors for MLMC based on Q sums | |
Real | variance_Ysum (Real sum_Y, Real sum_YY, size_t Nlq) |
compute variance from sum accumulators | |
Real | variance_Qsum (Real sum_Ql, Real sum_Qlm1, Real sum_QlQl, Real sum_QlQlm1, Real sum_Qlm1Qlm1, size_t Nlq) |
compute variance from sum accumulators | |
Real | aggregate_variance_Ysum (const Real *sum_Y, const Real *sum_YY, const SizetArray &N_l) |
sum up variances across QoI (using sum_YY with means from sum_Y) | |
Real | aggregate_variance_Qsum (const Real *sum_Ql, const Real *sum_Qlm1, const Real *sum_QlQl, const Real *sum_QlQlm1, const Real *sum_Qlm1Qlm1, const SizetArray &N_l, const size_t &lev) |
sum up variances across QoI (using sum_YY with means from sum_Y) | |
Real | aggregate_variance_Qsum (const Real *sum_Ql, const Real *sum_Qlm1, const Real *sum_QlQl, const Real *sum_QlQlm1, const Real *sum_Qlm1Qlm1, const SizetArray &N_l, const size_t &lev, const size_t &qoi) |
Real | aggregate_mse_Yvar (const Real *var_Y, const SizetArray &N_l) |
sum up Monte Carlo estimates for mean squared error (MSE) across QoI using discrepancy variances | |
Real | aggregate_mse_Ysum (const Real *sum_Y, const Real *sum_YY, const SizetArray &N_l) |
sum up Monte Carlo estimates for mean squared error (MSE) across QoI using discrepancy sums | |
Real | aggregate_mse_Qsum (const Real *sum_Ql, const Real *sum_Qlm1, const Real *sum_QlQl, const Real *sum_QlQlm1, const Real *sum_Qlm1Qlm1, const SizetArray &N_l, const size_t &lev) |
sum up Monte Carlo estimates for mean squared error (MSE) across QoI using discrepancy sums | |
Real | aggregate_mse_Qsum (const Real *sum_Ql, const Real *sum_Qlm1, const Real *sum_QlQl, const Real *sum_QlQlm1, const Real *sum_Qlm1Qlm1, const SizetArray &N_l, const size_t &lev, const size_t &qoi) |
Real | sum (const Real *vec, size_t vec_len) const |
compute sum of a set of observations | |
Real | average (const Real *vec, size_t vec_len) const |
compute average of a set of observations | |
Real | average (const RealVector &vec) const |
compute average of a set of observations | |
Real | average (const SizetArray &sa) const |
compute average of a set of observations | |
void | assign_static_member (Real &conv_tol, size_t &qoi, RealVector &level_cost_vec, IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, RealVector &pilot_samples) const |
void | assign_static_member_problem18 (Real &var_L_exact, Real &var_H_exact, Real &mu_four_L_exact, Real &mu_four_H_exact, Real &Ax, RealVector &level_cost_vec) const |
Static Private Member Functions | |
static void | uncentered_to_centered (Real rm1, Real rm2, Real rm3, Real rm4, Real &cm1, Real &cm2, Real &cm3, Real &cm4, size_t Nlq) |
convert uncentered (raw) moments to centered moments; biased estimators More... | |
static void | uncentered_to_centered (Real rm1, Real rm2, Real rm3, Real rm4, Real &cm1, Real &cm2, Real &cm3, Real &cm4) |
convert uncentered (raw) moments to centered moments; unbiased estimators More... | |
static void | centered_to_standard (Real cm1, Real cm2, Real cm3, Real cm4, Real &sm1, Real &sm2, Real &sm3, Real &sm4) |
convert centered moments to standardized moments | |
static void | check_negative (Real &cm) |
detect, warn, and repair a negative central moment (for even orders) | |
static Real | unbiased_mean_product_pair (const Real &sumQ1, const Real &sumQ2, const Real &sumQ1Q2, const size_t &Nlq) |
compute the unbiased product of two sampling means | |
static Real | unbiased_mean_product_triplet (const Real &sumQ1, const Real &sumQ2, const Real &sumQ3, const Real &sumQ1Q2, const Real &sumQ1Q3, const Real &sumQ2Q3, const Real &sumQ1Q2Q3, const size_t &Nlq) |
compute the unbiased product of three sampling means | |
static Real | unbiased_mean_product_pairpair (const Real &sumQ1, const Real &sumQ2, const Real &sumQ1Q2, const Real &sumQ1sq, const Real &sumQ2sq, const Real &sumQ1sqQ2, const Real &sumQ1Q2sq, const Real &sumQ1sqQ2sq, const size_t &Nlq) |
compute the unbiased product of two pairs of products of sampling means | |
static Real | var_of_var_ml_l0 (IntRealMatrixMap sum_Ql, IntRealMatrixMap sum_Qlm1, IntIntPairRealMatrixMap sum_QlQlm1, const size_t &Nlq_pilot, const Real &Nlq, const size_t &qoi, const bool &compute_gradient, Real &grad_g) |
static Real | var_of_var_ml_lmax (IntRealMatrixMap sum_Ql, IntRealMatrixMap sum_Qlm1, IntIntPairRealMatrixMap sum_QlQlm1, const size_t &Nlq_pilot, const Real &Nlq, const size_t &qoi, const bool &compute_gradient, Real &grad_g) |
static Real | var_of_var_ml_l (IntRealMatrixMap sum_Ql, IntRealMatrixMap sum_Qlm1, IntIntPairRealMatrixMap sum_QlQlm1, const size_t &Nlq_pilot, const Real &Nlq, const size_t &qoi, const size_t &lev, const bool &compute_gradient, Real &grad_g) |
static void | target_var_objective_eval_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
OPTPP definition. | |
static void | target_var_constraint_eval_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_var_constraint_eval_logscale_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_var_objective_eval_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
NPSOL definition (Wrapper using OPTPP implementation above under the hood) | |
static void | target_var_constraint_eval_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_var_constraint_eval_logscale_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
Private Attributes | |
Sizet3DArray | NLev |
total number of successful sample evaluations (excluding faults) for each model form, discretization level, and QoI | |
SizetArray | pilotSamples |
store the pilot_samples input specification, prior to run-time invocation of load_pilot_sample() | |
SizetArray | randomSeedSeqSpec |
user specification for seed_sequence | |
size_t | mlmfIter |
major iteration counter | |
short | allocationTarget |
store the allocation_target input specification, prior to run-time Options right now: More... | |
bool | useTargetVarianceOptimizationFlag |
option to switch on numerical optimization for solution of sample alloation of allocationTarget Variance | |
short | qoiAggregation |
store the qoi_aggregation_norm input specification, prior to run-time Options right now: More... | |
RealVector | mcMSEIter0 |
mean squared error of mean estimator from pilot sample MC on HF model | |
Real | equivHFEvals |
equivalent number of high fidelity evaluations accumulated using samples across multiple model forms and/or discretization levels | |
bool | finalCVRefinement |
if defined, complete the final CV refinement when terminating MLCV based on maxIterations (the total number of refinements beyond the pilot sample will be one more for CV than for ML). This approach is consistent with normal termination based on l1_norm(delta_N_hf) = 0. | |
bool | exportSampleSets |
if defined, export each of the sample increments in ML, CV, MLCV using tagged tabular files | |
unsigned short | exportSamplesFormat |
format for exporting sample increments using tagged tabular files | |
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 | |
Performs Multilevel Monte Carlo sampling for uncertainty quantification.
Multilevel Monte Carlo (MLMC) is a variance-reduction technique that utilitizes lower fidelity simulations that have response QoI that are correlated with the high-fidelity response QoI.
NonDMultilevelSampling | ( | 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(), NonDMultilevelSampling::aggregated_models_mode(), Iterator::iteratedModel, Iterator::maxEvalConcurrency, NonDMultilevelSampling::NLev, NonDMultilevelSampling::pilotSamples, NonDMultilevelSampling::random_seed(), NonDSampling::randomSeed, NonDSampling::sampleType, NonDSampling::seedSpec, Model::subordinate_models(), and Model::surrogate_type().
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protectedvirtual |
pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori
pre-run phase, which a derived iterator may optionally reimplement; when not present, pre-run is likely integrated into the derived run function. This is a virtual function; when re-implementing, a derived class must call its nearest parent's pre_run(), if implemented, typically before performing its own implementation steps.
Reimplemented from Analyzer.
References NonDMultilevelSampling::NLev, Analyzer::numFunctions, and NonDSampling::pre_run().
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protectedvirtual |
The primary run function manages the general case: a hierarchy of model forms (from the ordered model fidelities within a HierarchSurrModel), each of which may contain multiple discretization levels.
Reimplemented from Iterator.
References NonDMultilevelSampling::control_variate_mc(), NonDMultilevelSampling::multilevel_control_variate_mc_Qcorr(), NonDMultilevelSampling::multilevel_control_variate_mc_Ycorr(), NonDMultilevelSampling::multilevel_mc_Qsum(), NonDMultilevelSampling::multilevel_mc_Ysum(), and NonDMultilevelSampling::NLev.
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protectedvirtual |
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
Post-run phase, which a derived iterator may optionally reimplement; when not present, post-run is likely integrated into run. This is a virtual function; when re-implementing, a derived class must call its nearest parent's post_run(), typically after performing its own implementation steps.
Reimplemented from Analyzer.
References Analyzer::post_run(), and NonDSampling::update_final_statistics().
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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::archive_equiv_hf_evals(), NonDSampling::archive_moments(), NonDMultilevelSampling::equivHFEvals, Iterator::iteratedModel, NonDMultilevelSampling::NLev, NonDSampling::print_moments(), NonD::print_multilevel_evaluation_summary(), Model::response_labels(), NonDSampling::statsFlag, and Model::truth_model().
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private |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using discrepancy accumulators (sum_Y)
This function performs "geometrical" MLMC on a single model form with multiple discretization levels.
References NonDMultilevelSampling::accumulate_ml_Ysums(), Model::active_model_key(), NonDMultilevelSampling::aggregate_mse_Ysum(), NonDMultilevelSampling::aggregate_variance_Ysum(), NonDMultilevelSampling::assign_specification_sequence(), NonDMultilevelSampling::average(), NonDMultilevelSampling::configure_indices(), Iterator::convergenceTol, NonDMultilevelSampling::convert_moments(), NonDMultilevelSampling::equivHFEvals, Analyzer::evaluate_parameter_sets(), NonDMultilevelSampling::export_all_samples(), NonDMultilevelSampling::exportSampleSets, NonDSampling::get_parameter_sets(), NonDMultilevelSampling::initialize_ml_Ysums(), Iterator::iteratedModel, NonDMultilevelSampling::level_cost(), NonD::load_pilot_sample(), Iterator::maxIterations, NonDMultilevelSampling::mlmfIter, NonD::momentStats, NonDMultilevelSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonD::one_sided_delta(), Iterator::outputLevel, NonDMultilevelSampling::pilotSamples, Model::solution_level_costs(), Model::solution_levels(), and Model::truth_model().
Referenced by NonDMultilevelSampling::core_run().
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private |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using QoI accumulators (sum_Q)
This function performs "geometrical" MLMC on a single model form with multiple discretization levels.
References Dakota::abort_handler(), NonDMultilevelSampling::accumulate_ml_Qsums(), Model::active_model_key(), NonDMultilevelSampling::aggregate_mse_Qsum(), NonDMultilevelSampling::aggregate_variance_Qsum(), NonDMultilevelSampling::allocationTarget, NonDMultilevelSampling::assign_specification_sequence(), NonDMultilevelSampling::average(), NonDMultilevelSampling::centered_to_standard(), NonDMultilevelSampling::check_negative(), NonDMultilevelSampling::compute_error_estimates(), NonDMultilevelSampling::configure_indices(), Iterator::convergenceTol, NonDMultilevelSampling::equivHFEvals, Analyzer::evaluate_parameter_sets(), NonDMultilevelSampling::export_all_samples(), NonDMultilevelSampling::exportSampleSets, NonD::finalMomentsType, NonDSampling::get_parameter_sets(), NonDMultilevelSampling::initialize_ml_Qsums(), Iterator::iteratedModel, NonDMultilevelSampling::level_cost(), NonD::load_pilot_sample(), Iterator::maxIterations, NonDMultilevelSampling::mlmfIter, NonD::momentStats, NonDMultilevelSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonD::one_sided_delta(), Iterator::outputLevel, NonDMultilevelSampling::pilotSamples, NonDMultilevelSampling::qoiAggregation, Model::solution_level_costs(), Model::solution_levels(), NonDMultilevelSampling::target_var_objective_eval_npsol(), NonDMultilevelSampling::target_var_objective_eval_optpp(), Model::truth_model(), NonDMultilevelSampling::uncentered_to_centered(), and NonDMultilevelSampling::useTargetVarianceOptimizationFlag.
Referenced by NonDMultilevelSampling::core_run().
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Perform control variate Monte Carlo across two model forms.
This function performs control variate MC across two combinations of model form and discretization level.
References NonDMultilevelSampling::accumulate_cv_sums(), Model::active_model_key(), NonDMultilevelSampling::aggregated_models_mode(), Iterator::convergenceTol, NonDMultilevelSampling::convert_moments(), NonDMultilevelSampling::cv_raw_moments(), NonDMultilevelSampling::equivHFEvals, NonDMultilevelSampling::eval_ratio(), NonDMultilevelSampling::initialize_cv_sums(), Iterator::iteratedModel, NonDMultilevelSampling::lf_increment(), NonD::load_pilot_sample(), Iterator::maxIterations, NonDMultilevelSampling::mlmfIter, NonD::momentStats, NonDMultilevelSampling::MSE_ratio(), NonDMultilevelSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonDMultilevelSampling::pilotSamples, NonDMultilevelSampling::shared_increment(), Model::solution_level_cost(), Model::surrogate_model(), Model::truth_model(), and NonDMultilevelSampling::uncorrected_surrogate_mode().
Referenced by NonDMultilevelSampling::core_run().
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Perform multilevel Monte Carlo across levels in combination with control variate Monte Carlo across model forms at each level; CV computes correlations for Y (LH correlations for level discrepancies)
This function performs "geometrical" MLMC across discretization levels for the high fidelity model form where CVMC si employed across two model forms to exploit correlation in the discrepancies at each level (Y_l).
References NonDMultilevelSampling::accumulate_ml_Ysums(), NonDMultilevelSampling::accumulate_mlcv_Ysums(), Model::active_model_key(), NonDMultilevelSampling::aggregate_mse_Ysum(), NonDMultilevelSampling::aggregate_mse_Yvar(), NonDMultilevelSampling::aggregate_variance_Ysum(), Analyzer::allResponses, NonDMultilevelSampling::assign_specification_sequence(), NonDMultilevelSampling::average(), NonDMultilevelSampling::configure_indices(), Iterator::convergenceTol, NonDMultilevelSampling::convert_moments(), NonDMultilevelSampling::cv_raw_moments(), NonDMultilevelSampling::equivHFEvals, NonDMultilevelSampling::eval_ratio(), Analyzer::evaluate_parameter_sets(), NonDMultilevelSampling::export_all_samples(), NonDMultilevelSampling::exportSampleSets, NonDSampling::get_parameter_sets(), NonDMultilevelSampling::initialize_mlcv_sums(), Iterator::iteratedModel, NonDMultilevelSampling::level_cost(), NonDMultilevelSampling::lf_increment(), NonD::load_pilot_sample(), Iterator::maxIterations, NonDMultilevelSampling::mlmfIter, NonD::momentStats, NonDMultilevelSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonD::one_sided_delta(), Iterator::outputLevel, NonDMultilevelSampling::pilotSamples, Model::solution_level_costs(), Model::solution_levels(), NonDMultilevelSampling::sum(), Model::surrogate_model(), and Model::truth_model().
Referenced by NonDMultilevelSampling::core_run().
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Perform multilevel Monte Carlo across levels in combination with control variate Monte Carlo across model forms at each level; CV computes correlations for Q (LH correlations for QoI)
This function performs "geometrical" MLMC across discretization levels for the high fidelity model form where CVMC is employed across two model forms. It generalizes the Y_l correlation case to separately target correlations for each QoI level embedded within the level discrepancies.
References NonDMultilevelSampling::accumulate_ml_Ysums(), NonDMultilevelSampling::accumulate_mlcv_Qsums(), Model::active_model_key(), NonDMultilevelSampling::aggregate_mse_Ysum(), NonDMultilevelSampling::aggregate_mse_Yvar(), NonDMultilevelSampling::aggregate_variance_Ysum(), Analyzer::allResponses, NonDMultilevelSampling::assign_specification_sequence(), NonDMultilevelSampling::average(), NonDMultilevelSampling::configure_indices(), Iterator::convergenceTol, NonDMultilevelSampling::convert_moments(), NonDMultilevelSampling::cv_raw_moments(), NonDMultilevelSampling::equivHFEvals, NonDMultilevelSampling::eval_ratio(), Analyzer::evaluate_parameter_sets(), NonDMultilevelSampling::export_all_samples(), NonDMultilevelSampling::exportSampleSets, NonDSampling::get_parameter_sets(), NonDMultilevelSampling::initialize_mlcv_sums(), Iterator::iteratedModel, NonDMultilevelSampling::level_cost(), NonDMultilevelSampling::lf_increment(), NonD::load_pilot_sample(), Iterator::maxIterations, NonDMultilevelSampling::mlmfIter, NonD::momentStats, NonDMultilevelSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonD::one_sided_delta(), Iterator::outputLevel, NonDMultilevelSampling::pilotSamples, Model::solution_level_costs(), Model::solution_levels(), NonDMultilevelSampling::sum(), Model::surrogate_model(), and Model::truth_model().
Referenced by NonDMultilevelSampling::core_run().
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inlineprivate |
extract current random seed from randomSeedSeqSpec
extract an active seed from a seed sequence
References NonDMultilevelSampling::mlmfIter, NonDMultilevelSampling::randomSeedSeqSpec, and NonDSampling::varyPattern.
Referenced by NonDMultilevelSampling::assign_specification_sequence(), and NonDMultilevelSampling::NonDMultilevelSampling().
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inlinestaticprivate |
convert uncentered (raw) moments to centered moments; biased estimators
For single-level moment calculations with a scalar Nlq.
Referenced by NonDMultilevelSampling::compute_error_estimates(), NonDMultilevelSampling::convert_moments(), and NonDMultilevelSampling::multilevel_mc_Qsum().
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inlinestaticprivate |
convert uncentered (raw) moments to centered moments; unbiased estimators
For single-level moment calculations with a scalar Nlq.
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private |
store the allocation_target input specification, prior to run-time Options right now:
Referenced by NonDMultilevelSampling::multilevel_mc_Qsum().
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store the qoi_aggregation_norm input specification, prior to run-time Options right now:
Referenced by NonDMultilevelSampling::multilevel_mc_Qsum().