Dakota
Version 6.19
Explore and Predict with Confidence
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Performs LHS and Monte Carlo sampling for uncertainty quantification. More...
Public Member Functions | |
NonDLHSSampling (ProblemDescDB &problem_db, Model &model) | |
standard constructor More... | |
NonDLHSSampling (Model &model, unsigned short sample_type, int samples, int seed, const String &rng, bool vary_pattern=true, short sampling_vars_mode=ACTIVE) | |
alternate constructor for sample generation and evaluation "on the fly" More... | |
NonDLHSSampling (unsigned short sample_type, int samples, int seed, const String &rng, const RealVector &lower_bnds, const RealVector &upper_bnds) | |
alternate constructor for uniform sample generation "on the fly" More... | |
NonDLHSSampling (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... | |
~NonDLHSSampling () | |
destructor | |
Public Member Functions inherited from NonDSampling | |
NonDSampling (Model &model, const RealMatrix &sample_matrix) | |
alternate constructor for evaluating and computing statistics for the provided set of samples More... | |
~NonDSampling () | |
destructor | |
void | compute_statistics (const RealMatrix &vars_samples, const IntResponseMap &resp_samples) |
For the input sample set, computes mean, standard deviation, and probability/reliability/response levels (aleatory uncertainties) or intervals (epsitemic or mixed uncertainties) | |
void | compute_intervals (RealRealPairArray &extreme_fns) |
called by compute_statistics() to calculate min/max intervals using allResponses | |
void | compute_intervals (const IntResponseMap &samples) |
called by compute_statistics() to calculate extremeValues from samples | |
void | compute_intervals (RealRealPairArray &extreme_fns, const IntResponseMap &samples) |
called by compute_statistics() to calculate min/max intervals using samples | |
void | compute_moments (const RealVectorArray &fn_samples) |
calculates sample moments from a matrix of observations for a set of QoI | |
void | compute_moments (const IntResponseMap &samples) |
calculate sample moments and confidence intervals from a map of response observations | |
void | compute_moments (const IntResponseMap &samples, RealMatrix &moment_stats, RealMatrix &moment_grads, RealMatrix &moment_conf_ints, short moments_type, const StringArray &labels) |
convert IntResponseMap to RealVectorArray and invoke helpers | |
void | compute_moment_gradients (const RealVectorArray &fn_samples, const RealMatrixArray &grad_samples, const RealMatrix &moment_stats, RealMatrix &moment_grads, short moments_type) |
compute moment_grads from function and gradient samples | |
void | compute_moment_confidence_intervals (const RealMatrix &moment_stats, RealMatrix &moment_conf_ints, const SizetArray &sample_counts, short moments_type) |
compute moment confidence intervals from moment values | |
void | archive_moments (size_t inc_id=0) |
archive moment statistics in results DB | |
void | archive_moment_confidence_intervals (size_t inc_id=0) |
archive moment confidence intervals in results DB | |
void | archive_std_regress_coeffs () |
archive standardized regression coefficients in results DB | |
void | archive_extreme_responses (size_t inc_id=0) |
archive extreme values (epistemic result) in results DB | |
void | compute_level_mappings (const IntResponseMap &samples) |
called by compute_statistics() to calculate CDF/CCDF mappings of z to p/beta and of p/beta to z as well as PDFs More... | |
void | print_statistics (std::ostream &s) const |
prints the statistics computed in compute_statistics() | |
void | print_intervals (std::ostream &s) const |
prints the intervals computed in compute_intervals() with default qoi_type and moment_labels | |
void | print_intervals (std::ostream &s, String qoi_type, const StringArray &interval_labels) const |
prints the intervals computed in compute_intervals() | |
void | print_moments (std::ostream &s) const |
prints the moments computed in compute_moments() with default qoi_type and moment_labels | |
void | print_moments (std::ostream &s, String qoi_type, const StringArray &moment_labels) const |
prints the moments computed in compute_moments() | |
void | print_wilks_stastics (std::ostream &s) const |
prints the Wilks stastics | |
void | print_tolerance_intervals_statistics (std::ostream &s) const |
prints the tolerance intervals stastics | |
void | archive_tolerance_intervals (size_t inc_id=0) |
archive the tolerance intervals statistics in results DB | |
void | update_final_statistics () |
update finalStatistics from minValues/maxValues, momentStats, and computedProbLevels/computedRelLevels/computedRespLevels | |
void | transform_samples (Model &src_model, Model &tgt_model, bool x_to_u=true) |
transform allSamples using configuration data from the source and target models | |
void | transform_samples (Pecos::ProbabilityTransformation &nataf, bool x_to_u=true) |
alternate version to transform allSamples. This is needed since random variable distribution parameters are not updated until run time and an imported sample_matrix is typically in x-space. More... | |
void | transform_samples (Pecos::ProbabilityTransformation &nataf, RealMatrix &sample_matrix, bool x_to_u=true) |
transform the specified samples matrix from x to u or u to x, assuming identical view and ids | |
void | transform_samples (Pecos::ProbabilityTransformation &nataf, RealMatrix &sample_matrix, SizetMultiArrayConstView src_cv_ids, SizetMultiArrayConstView tgt_cv_ids, bool x_to_u=true) |
transform the specified samples matrix from x to u or u to x | |
unsigned short | sampling_scheme () const |
return sampleType | |
const String & | random_number_generator () const |
return rngName | |
Public Member Functions inherited from NonD | |
void | requested_levels (const RealVectorArray &req_resp_levels, const RealVectorArray &req_prob_levels, const RealVectorArray &req_rel_levels, const RealVectorArray &req_gen_rel_levels, short resp_lev_tgt, short resp_lev_tgt_reduce, bool cdf_flag, bool pdf_output) |
set requestedRespLevels, requestedProbLevels, requestedRelLevels, requestedGenRelLevels, respLevelTarget, cdfFlag, and pdfOutput (used in combination with alternate ctors) | |
void | print_level_mappings (std::ostream &s) const |
prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels for default qoi_type and qoi_labels | |
void | print_level_mappings (std::ostream &s, String qoi_type, const StringArray &qoi_labels) const |
prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels More... | |
void | print_level_mappings (std::ostream &s, const RealVector &level_maps, bool moment_offset, const String &prepend="") |
print level mapping statistics using optional pre-pend More... | |
bool | resize () |
reinitializes iterator based on new variable size | |
bool | pdf_output () const |
get pdfOutput | |
void | pdf_output (bool output) |
set pdfOutput | |
short | final_moments_type () const |
get finalMomentsType | |
void | final_moments_type (short type) |
set finalMomentsType | |
Public Member Functions inherited from Analyzer | |
const VariablesArray & | all_variables () |
return the complete set of evaluated variables | |
const RealMatrix & | all_samples () |
return the complete set of evaluated samples | |
const IntResponseMap & | all_responses () const |
return the complete set of computed responses | |
Public Member Functions inherited from Iterator | |
Iterator (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
default constructor More... | |
Iterator (ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
standard envelope constructor, which constructs its own model(s) More... | |
Iterator (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate envelope constructor which uses the ProblemDescDB but accepts a model from a higher level (meta-iterator) context, instead of constructing its own More... | |
Iterator (const String &method_string, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate envelope constructor for instantiations by name without the ProblemDescDB More... | |
Iterator (const Iterator &iterator) | |
copy constructor More... | |
virtual | ~Iterator () |
destructor | |
Iterator | operator= (const Iterator &iterator) |
assignment operator | |
virtual void | derived_free_communicators (ParLevLIter pl_iter) |
derived class contributions to freeing the communicators associated with this Iterator instance | |
virtual void | reset () |
restore initial state for repeated sub-iterator executions | |
virtual void | nested_variable_mappings (const SizetArray &c_index1, const SizetArray &di_index1, const SizetArray &ds_index1, const SizetArray &dr_index1, const ShortArray &c_target2, const ShortArray &di_target2, const ShortArray &ds_target2, const ShortArray &dr_target2) |
set primaryA{CV,DIV,DRV}MapIndices, secondaryA{CV,DIV,DRV}MapTargets within derived Iterators; supports computation of higher-level sensitivities in nested contexts (e.g., derivatives of statistics w.r.t. inserted design variables) | |
virtual void | nested_response_mappings (const RealMatrix &primary_coeffs, const RealMatrix &secondary_coeffs) |
set primaryResponseCoefficients, secondaryResponseCoefficients within derived Iterators; Necessary for scalarization case in MLMC NonDMultilevelSampling to map scalarization in nested context | |
virtual void | initialize_iterator (int job_index) |
used by IteratorScheduler to set the starting data for a run | |
virtual void | pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index) |
used by IteratorScheduler to pack starting data for an iterator run | |
virtual void | unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer, int job_index) |
used by IteratorScheduler to unpack starting data for an iterator run | |
virtual void | unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer, int job_index) |
used by IteratorScheduler to unpack starting data and initialize an iterator run | |
virtual void | pack_results_buffer (MPIPackBuffer &send_buffer, int job_index) |
used by IteratorScheduler to pack results data from an iterator run | |
virtual void | unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index) |
used by IteratorScheduler to unpack results data from an iterator run | |
virtual void | update_local_results (int job_index) |
used by IteratorScheduler to update local results arrays | |
virtual bool | accepts_multiple_points () const |
indicates if this iterator accepts multiple initial points. Default return is false. Override to return true if appropriate. | |
virtual void | initial_point (const Variables &pt) |
sets the initial point for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | initial_point (const RealVector &pt) |
sets the initial point (active continuous variables) for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | initial_points (const VariablesArray &pts) |
sets the multiple initial points for this iterator. This should only be used if accepts_multiple_points() returns true. | |
virtual void | update_callback_data (const RealVector &cv_initial, const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt, const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt) |
assign variable values and bounds and constraint coefficients and bounds for this iterator (user-functions mode for which iteratedModel is null) | |
virtual const RealMatrix & | callback_linear_ineq_coefficients () const |
return linear constraint coefficients for this iterator (user-functions mode for which iteratedModel is null) | |
virtual const RealVector & | callback_linear_ineq_lower_bounds () const |
return linear constraint lower bounds for this iterator (user-functions mode for which iteratedModel is null) | |
virtual const RealVector & | callback_linear_ineq_upper_bounds () const |
return linear constraint upper bounds for this iterator (user-functions mode for which iteratedModel is null) | |
virtual void | initialize_graphics (int iterator_server_id=1) |
initialize the 2D graphics window and the tabular graphics data More... | |
virtual void | check_sub_iterator_conflict () |
detect any conflicts due to recursive use of the same Fortran solver More... | |
virtual unsigned short | uses_method () const |
return name of any enabling iterator used by this iterator | |
virtual void | method_recourse (unsigned short method_name) |
perform a method switch, if possible, due to a detected conflict with the simultaneous use of method_name at an higher-level | |
virtual IntIntPair | estimate_partition_bounds () |
estimate the minimum and maximum partition sizes that can be utilized by this Iterator | |
virtual void | declare_sources () |
Declare sources to the evaluations database. | |
void | init_communicators (ParLevLIter pl_iter) |
initialize the communicators associated with this Iterator instance | |
void | set_communicators (ParLevLIter pl_iter) |
set the communicators associated with this Iterator instance | |
void | free_communicators (ParLevLIter pl_iter) |
free the communicators associated with this Iterator instance | |
void | resize_communicators (ParLevLIter pl_iter, bool reinit_comms) |
Resize the communicators. This is called from the letter's resize() | |
void | parallel_configuration_iterator (ParConfigLIter pc_iter) |
set methodPCIter | |
ParConfigLIter | parallel_configuration_iterator () const |
return methodPCIter | |
void | parallel_configuration_iterator_map (std::map< size_t, ParConfigLIter > pci_map) |
set methodPCIterMap | |
std::map< size_t, ParConfigLIter > | parallel_configuration_iterator_map () const |
return methodPCIterMap | |
void | run (ParLevLIter pl_iter) |
invoke set_communicators(pl_iter) prior to run() | |
void | run () |
orchestrate initialize/pre/core/post/finalize phases More... | |
void | assign_rep (std::shared_ptr< Iterator > iterator_rep) |
replaces existing letter with a new one More... | |
void | iterated_model (const Model &model) |
set the iteratedModel (iterators and meta-iterators using a single model instance) | |
Model & | iterated_model () |
return the iteratedModel (iterators & meta-iterators using a single model instance) | |
ProblemDescDB & | problem_description_db () const |
return the problem description database (probDescDB) | |
ParallelLibrary & | parallel_library () const |
return the parallel library (parallelLib) | |
void | method_name (unsigned short m_name) |
set the method name to an enumeration value | |
unsigned short | method_name () const |
return the method name via its native enumeration value | |
void | method_string (const String &m_str) |
set the method name by string | |
String | method_string () const |
return the method name by string | |
String | method_enum_to_string (unsigned short method_enum) const |
convert a method name enumeration value to a string | |
unsigned short | method_string_to_enum (const String &method_str) const |
convert a method name string to an enumeration value | |
String | submethod_enum_to_string (unsigned short submethod_enum) const |
convert a sub-method name enumeration value to a string | |
const String & | method_id () const |
return the method identifier (methodId) | |
int | maximum_evaluation_concurrency () const |
return the maximum evaluation concurrency supported by the iterator | |
void | maximum_evaluation_concurrency (int max_conc) |
set the maximum evaluation concurrency supported by the iterator | |
size_t | maximum_iterations () const |
return the maximum iterations for this iterator | |
void | maximum_iterations (size_t max_iter) |
set the maximum iterations for this iterator | |
void | convergence_tolerance (Real conv_tol) |
set the method convergence tolerance (convergenceTol) | |
Real | convergence_tolerance () const |
return the method convergence tolerance (convergenceTol) | |
void | output_level (short out_lev) |
set the method output level (outputLevel) | |
short | output_level () const |
return the method output level (outputLevel) | |
void | summary_output (bool summary_output_flag) |
Set summary output control; true enables evaluation/results summary. | |
size_t | num_final_solutions () const |
return the number of solutions to retain in best variables/response arrays | |
void | num_final_solutions (size_t num_final) |
set the number of solutions to retain in best variables/response arrays | |
void | active_set (const ActiveSet &set) |
set the default active set (for use with iterators that employ evaluate_parameter_sets()) | |
const ActiveSet & | active_set () const |
return the default active set (used by iterators that employ evaluate_parameter_sets()) | |
void | active_set_request_vector (const ShortArray &asv) |
return the default active set request vector (used by iterators that employ evaluate_parameter_sets()) | |
const ShortArray & | active_set_request_vector () const |
return the default active set request vector (used by iterators that employ evaluate_parameter_sets()) | |
void | active_set_request_values (short asv_val) |
return the default active set request vector (used by iterators that employ evaluate_parameter_sets()) | |
void | sub_iterator_flag (bool si_flag) |
set subIteratorFlag (and update summaryOutputFlag if needed) | |
bool | is_null () const |
function to check iteratorRep (does this envelope contain a letter?) | |
std::shared_ptr< Iterator > | iterator_rep () const |
returns iteratorRep for access to derived class member functions that are not mapped to the top Iterator level | |
virtual void | eval_tag_prefix (const String &eval_id_str) |
set the hierarchical eval ID tag prefix More... | |
std::shared_ptr< TraitsBase > | traits () const |
returns methodTraits for access to derived class member functions that are not mapped to the top TraitsBase level | |
bool | top_level () |
Return whether the iterator is the top level iterator. | |
void | top_level (bool tflag) |
Set the iterator's top level flag. | |
Protected Member Functions | |
void | sampling_increment () |
increment to next in sequence of refinement samples | |
void | pre_run () |
generate LHS samples in non-VBD cases | |
void | core_run () |
perform the evaluate parameter sets portion of run More... | |
void | post_run (std::ostream &s) |
generate statistics for LHS runs in non-VBD cases | |
void | post_input () |
read tabular data for post-run mode | |
void | update_final_statistics () |
update finalStatistics and (if MC sampling) finalStatErrors | |
void | compute_pca (std::ostream &s) |
compute a principal components analysis on the sample set | |
void | print_results (std::ostream &s, short results_state=FINAL_RESULTS) |
print the final statistics | |
void | d_optimal_parameter_set (int previous_samples, int new_samples, RealMatrix &full_samples) |
generate a d-optimal parameter set, leaving the first previous_samples columns intact and adding new_samples new columns following them More... | |
void | initial_increm_lhs_set (int new_samples, RealMatrix &full_samples, IntMatrix &full_ranks) |
Populate the first new_samples columns of allSamples with an LHS design and update the stored ranks. | |
void | increm_lhs_parameter_set (int previous_samples, int new_samples, RealMatrix &full_samples, IntMatrix &all_ranks) |
generate a new batch that is Latin w.r.t. the previous samples | |
void | store_ranks (const RealMatrix &sample_values, IntMatrix &sample_ranks) |
store the ranks of the last generated sample for continuous (based on sampleRanks) and calculate/store discrete ranks | |
void | store_ranks (IntMatrix &full_ranks) |
store the combined ranks from sampleRanks to leading submatrix local cached ranks matrix | |
void | combine_discrete_ranks (const RealMatrix &initial_values, const RealMatrix &increm_values) |
merge the discrete ranks into a submatrix of sampleRanks | |
void | print_header_and_statistics (std::ostream &s, const int &num_samples) |
Print a header and summary statistics. | |
void | archive_results (int num_samples, size_t ind_inc=0) |
Archive all results. | |
void | store_evaluations () |
Store samples in a matrix for bootstrapping. | |
Real | bootstrap_covariance (const size_t qoi) |
Protected Member Functions inherited from NonDSampling | |
NonDSampling (ProblemDescDB &problem_db, Model &model) | |
constructor More... | |
NonDSampling (unsigned short method_name, Model &model, unsigned short sample_type, size_t samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode) | |
alternate constructor for sample generation and evaluation "on the fly" More... | |
NonDSampling (unsigned short sample_type, size_t samples, int seed, const String &rng, const RealVector &lower_bnds, const RealVector &upper_bnds) | |
alternate constructor for sample generation "on the fly" More... | |
NonDSampling (unsigned short sample_type, size_t samples, int seed, const String &rng, const RealVector &means, const RealVector &std_devs, const RealVector &lower_bnds, const RealVector &upper_bnds, RealSymMatrix &correl) | |
alternate constructor for sample generation of correlated normals "on the fly" More... | |
void | pre_run () |
pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More... | |
void | core_run () |
size_t | num_samples () const |
void | sampling_reset (size_t min_samples, bool all_data_flag, bool stats_flag) |
resets number of samples and sampling flags More... | |
void | sampling_reference (size_t samples_ref) |
set reference number of samples, which is a lower bound during reset | |
void | random_seed (int seed) |
assign randomSeed | |
void | vary_pattern (bool pattern_flag) |
set varyPattern | |
void | get_parameter_sets (Model &model) |
Uses lhsDriver to generate a set of samples from the distributions/bounds defined in the incoming model. More... | |
void | get_parameter_sets (Model &model, const size_t num_samples, RealMatrix &design_matrix) |
Uses 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 size_t num_samples, RealMatrix &design_matrix, bool write_msg) |
core of get_parameter_sets that accepts message print control | |
void | get_parameter_sets (const RealVector &lower_bnds, const RealVector &upper_bnds) |
Uses 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 RealSymMatrix & | response_error_estimates () const |
return error estimates associated with each of the finalStatistics | |
virtual bool | seed_updated () |
detect whether the seed has been updated since the most recent sample set generation | |
virtual void | active_set_mapping () |
in the case of sub-iteration, map from finalStatistics.active_set() requests to activeSet used in evaluate_parameter_sets() More... | |
void | initialize_sample_driver (bool write_message, size_t num_samples) |
increments numLHSRuns, sets random seed, and initializes lhsDriver | |
void | mode_counts (const Variables &vars, size_t &cv_start, size_t &num_cv, size_t &div_start, size_t &num_div, size_t &dsv_start, size_t &num_dsv, size_t &drv_start, size_t &num_drv) const |
compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More... | |
void | mode_bits (const Variables &vars, BitArray &active_vars, BitArray &active_corr) const |
define subset views for sampling modes | |
Protected Member Functions inherited from NonD | |
NonD (ProblemDescDB &problem_db, Model &model) | |
constructor | |
NonD (unsigned short method_name, Model &model) | |
alternate constructor for sample generation and evaluation "on the fly" | |
NonD (unsigned short method_name, Model &model, const ShortShortPair &approx_view) | |
alternate constructor for sample generation and evaluation "on the fly" | |
NonD (unsigned short method_name, const RealVector &lower_bnds, const RealVector &upper_bnds) | |
alternate constructor for sample generation "on the fly" | |
~NonD () | |
destructor | |
void | 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... | |
virtual bool | discrepancy_sample_counts () const |
flag identifying whether sample counts correspond to level discrepancies | |
void | pull_level_mappings (RealVector &level_maps, size_t offset) |
concatenate computed{Resp,Prob,Rel,GenRel}Levels into level_maps | |
void | push_level_mappings (const RealVector &level_maps, size_t offset) |
update computed{Resp,Prob,Rel,GenRel}Levels from level_maps | |
void | configure_sequence (size_t &num_steps, size_t &secondary_index, short &seq_type) |
configure fidelity/level counts from model hierarchy More... | |
void | configure_cost (unsigned short num_steps, bool multilevel, RealVector &cost) |
extract cost estimates from model hierarchy (forms or resolutions) | |
bool | query_cost (unsigned short num_steps, bool multilevel, RealVector &cost) |
extract cost estimates from model hierarchy, if available | |
bool | query_cost (unsigned short num_steps, Model &model, RealVector &cost) |
extract cost estimates from model hierarchy, if available | |
bool | valid_cost_values (const RealVector &cost) |
test cost for valid values > 0 | |
void | load_pilot_sample (const SizetArray &pilot_spec, size_t num_steps, SizetArray &delta_N_l) |
distribute pilot sample specification across model forms or levels | |
void | load_pilot_sample (const SizetArray &pilot_spec, short seq_type, const Sizet3DArray &N_l, Sizet2DArray &delta_N_l) |
distribute pilot sample specification across model forms and levels | |
template<typename ArrayType > | |
void | inflate_approx_samples (const ArrayType &N_l, bool multilev, size_t secondary_index, std::vector< ArrayType > &N_l_vec) |
update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile | |
template<typename ArrayType > | |
void | inflate_sequence_samples (const ArrayType &N_l, bool multilev, size_t secondary_index, std::vector< ArrayType > &N_l_vec) |
update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile | |
void | resize_final_statistics_gradients () |
resizes finalStatistics::functionGradients based on finalStatistics ASV | |
void | update_aleatory_final_statistics () |
update finalStatistics::functionValues from momentStats and computed{Prob,Rel,GenRel,Resp}Levels | |
void | update_system_final_statistics () |
update system metrics from component metrics within finalStatistics | |
void | update_system_final_statistics_gradients () |
update finalStatistics::functionGradients | |
void | initialize_level_mappings () |
size computed{Resp,Prob,Rel,GenRel}Levels | |
void | compute_densities (const RealRealPairArray &min_max_fns, bool prob_refinement=false, bool all_levels_computed=false) |
compute the PDF bins from the CDF/CCDF values and store in computedPDF{Abscissas,Ordinates} More... | |
void | print_densities (std::ostream &s) const |
output the PDFs reflected in computedPDF{Abscissas,Ordinates} using default qoi_type and pdf_labels | |
void | print_densities (std::ostream &s, String qoi_type, const StringArray &pdf_labels) const |
output the PDFs reflected in computedPDF{Abscissas,Ordinates} | |
void | print_system_mappings (std::ostream &s) const |
print system series/parallel mappings for response levels | |
void | print_multilevel_evaluation_summary (std::ostream &s, const SizetArray &N_m) |
print evaluation summary for multilevel sampling across 1D level profile | |
void | print_multilevel_evaluation_summary (std::ostream &s, const Sizet2DArray &N_m) |
print evaluation summary for multilevel sampling across 2D level+QoI profile | |
void | print_multilevel_discrepancy_summary (std::ostream &s, const SizetArray &N_m) |
print evaluation summary for multilevel sampling across 1D level profile for discrepancy across levels | |
void | print_multilevel_discrepancy_summary (std::ostream &s, const SizetArray &N_m, const SizetArray &N_mp1) |
print evaluation summary for multilevel sampling across 1D level profile for discrepancy across model forms | |
void | print_multilevel_discrepancy_summary (std::ostream &s, const Sizet2DArray &N_m) |
print evaluation summary for multilevel sampling across 2D level+QoI profile for discrepancy across levels | |
void | print_multilevel_discrepancy_summary (std::ostream &s, const Sizet2DArray &N_m, const Sizet2DArray &N_mp1) |
print evaluation summary for multilevel sampling across 2D level+QoI profile for discrepancy across model forms | |
template<typename ArrayType > | |
void | print_multilevel_model_summary (std::ostream &s, const std::vector< ArrayType > &N_samp, String type, short seq_type, bool discrep_flag) |
print evaluation summary for multilevel sampling across 2D model+level profile (allocations) or 3D model+level+QoI profile (actual) | |
void | construct_lhs (Iterator &u_space_sampler, Model &u_model, unsigned short sample_type, int num_samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode=ACTIVE) |
assign a NonDLHSSampling instance within u_space_sampler | |
unsigned short | sub_optimizer_select (unsigned short requested_sub_method, unsigned short default_sub_method=SUBMETHOD_NPSOL) |
utility for vetting sub-method request against optimizers within the package configuration | |
size_t | one_sided_delta (Real current, Real target) |
compute a one-sided sample increment for multilevel methods to move current sampling level to a new target | |
size_t | one_sided_delta (const SizetArray ¤t, const RealVector &targets, size_t power) |
compute a one-sided sample increment for multilevel methods to move current sampling level to a new target | |
size_t | one_sided_delta (const SizetArray ¤t, Real target, size_t power) |
compute a one-sided sample increment for multilevel methods to move current sampling level to a new target | |
bool | differ (size_t N_alloc_ij, const SizetArray &N_actual_ij) const |
return true if fine-grained reporting differs from coarse-grained | |
bool | differ (const SizetArray &N_alloc_i, const Sizet2DArray &N_actual_i) const |
return true if fine-grained reporting differs from coarse-grained | |
bool | differ (const Sizet2DArray &N_alloc, const Sizet3DArray &N_actual) const |
return true if fine-grained reporting differs from coarse-grained | |
void | archive_allocate_mappings () |
allocate results array storage for distribution mappings | |
void | archive_from_resp (size_t fn_index, size_t inc_id=0) |
archive the mappings from specified response levels for specified fn | |
void | archive_to_resp (size_t fn_index, size_t inc_id=0) |
archive the mappings to computed response levels for specified fn and (optional) increment id. | |
void | archive_allocate_pdf () |
allocate results array storage for pdf histograms | |
void | archive_pdf (size_t fn_index, size_t inc_id=0) |
archive a single pdf histogram for specified function | |
void | archive_equiv_hf_evals (const Real equiv_hf_evals) |
archive the equivalent number of HF evals (used by ML/MF methods) | |
Protected Member Functions inherited from Analyzer | |
Analyzer () | |
default constructor | |
Analyzer (ProblemDescDB &problem_db, Model &model) | |
standard constructor | |
Analyzer (unsigned short method_name, Model &model) | |
alternate constructor for instantiations "on the fly" with a Model | |
Analyzer (unsigned short method_name, Model &model, const ShortShortPair &view_override) | |
alternate constructor for instantiations "on the fly" with a Model | |
Analyzer (unsigned short method_name) | |
alternate constructor for instantiations "on the fly" without a Model | |
~Analyzer () | |
destructor | |
virtual void | update_model_from_variables (Model &model, const Variables &vars) |
update model's current variables with data from vars | |
void | update_from_model (const Model &model) |
set inherited data attributes based on extractions from incoming model | |
void | 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, size_t num_samples) |
generate replicate parameter sets for use in variance-based decomposition More... | |
virtual void | archive_model_variables (const Model &, size_t idx) const |
archive model evaluation points | |
virtual void | archive_model_response (const Response &, size_t idx) const |
archive model evaluation responses | |
void | read_variables_responses (int num_evals, size_t num_vars) |
convenience function for reading variables/responses (used in derived classes post_input) More... | |
void | samples_to_variables_array (const RealMatrix &sample_matrix, VariablesArray &vars_array) |
convert samples array to variables array; e.g., allSamples to allVariables | |
void | variables_array_to_samples (const VariablesArray &vars_array, RealMatrix &sample_matrix) |
convert variables array to samples array; e.g., allVariables to allSamples | |
Protected Member Functions inherited from Iterator | |
Iterator (BaseConstructor, ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More... | |
Iterator (NoDBBaseConstructor, unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate constructor for base iterator classes constructed on the fly More... | |
Iterator (NoDBBaseConstructor, unsigned short method_name, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate constructor for base iterator classes constructed on the fly More... | |
Iterator (NoDBBaseConstructor, Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate envelope constructor for instantiations without ProblemDescDB More... | |
virtual 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 | initialize_model_graphics (Model &model, int iterator_server_id) |
helper function that encapsulates initialization operations, modular on incoming Model instance More... | |
void | export_final_surrogates (Model &data_fit_surr_model) |
export final surrogates generated, e.g., GP in EGO and friends More... | |
Static Protected Member Functions | |
static bool | rank_sort (const int &x, const int &y) |
sort algorithm to compute ranks for rank correlations | |
Static Protected Member Functions inherited from Iterator | |
static void | gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set) |
conversion of request vector values for the Gauss-Newton Hessian approximation More... | |
Private Attributes | |
size_t | numResponseFunctions |
number of response functions; used to distinguish NonD from opt/NLS usage | |
IntVector | refineSamples |
list of refinement sample batch sizes | |
bool | dOptimal |
whether to generate d-optimal point sets | |
size_t | numCandidateDesigns |
number of candidate designs to generate for classical D-optimal designs | |
Real | oversampleRatio |
oversampling ratio for Leja D-optimal candidate set generation | |
unsigned short | vbdViaSamplingMethod |
sampling method for computing variance-based decomposition indices | |
int | vbdViaSamplingNumBins |
number of bins for using with the Mahadevan sampling method for computing variance-based decomposition indices | |
bool | pcaFlag |
flag to specify the calculation of principal components | |
Real | percentVarianceExplained |
Threshold to keep number of principal components that explain this much variance. | |
RealMatrix | qoiSamplesMatrix |
Datastructure to store samples which can be used for bootstrapping. | |
Static Private Attributes | |
static RealArray | rawData |
static data used by static rank_sort() fn | |
Additional Inherited Members | |
Static Public Member Functions inherited from NonDSampling | |
static void | compute_moments (const RealVectorArray &fn_samples, SizetArray &sample_counts, RealMatrix &moment_stats, short moments_type, const StringArray &labels) |
core compute_moments() implementation with all data as inputs | |
static void | compute_moments (const RealVectorArray &fn_samples, RealMatrix &moment_stats, short moments_type) |
core compute_moments() implementation with all data as inputs | |
static void | compute_moments (const RealMatrix &fn_samples, RealMatrix &moment_stats, short moments_type) |
alternate RealMatrix samples API for use by external clients | |
static void | print_moments (std::ostream &s, const RealMatrix &moment_stats, const RealMatrix moment_cis, String qoi_type, short moments_type, const StringArray &moment_labels, bool print_cis) |
core print moments that can be called without object | |
static int | compute_wilks_sample_size (unsigned short order, Real alpha, Real beta, bool twosided=false) |
calculates the number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH | |
static Real | compute_wilks_residual (unsigned short order, int nsamples, Real alpha, Real beta, bool twosided) |
Helper function - calculates the Wilks residual. | |
static Real | compute_wilks_alpha (unsigned short order, int nsamples, Real beta, bool twosided=false) |
calculates the alpha paramter given number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH | |
static Real | compute_wilks_beta (unsigned short order, int nsamples, Real alpha, bool twosided=false) |
calculates the beta parameter given number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH | |
static Real | get_wilks_alpha_min () |
Get the lower and upper bounds supported by Wilks bisection solves. | |
static Real | get_wilks_alpha_max () |
static Real | get_wilks_beta_min () |
static Real | get_wilks_beta_max () |
Protected Attributes inherited from NonDSampling | |
int | seedSpec |
the user seed specification (default is 0) | |
int | randomSeed |
the current seed | |
const int | samplesSpec |
initial specification of number of samples | |
size_t | samplesRef |
reference number of samples updated for refinement | |
size_t | numSamples |
the current number of samples to evaluate | |
String | rngName |
name of the random number generator | |
unsigned short | sampleType |
the sample type: default, random, lhs, incremental random, or incremental lhs | |
bool | wilksFlag |
flags use of Wilks formula to calculate num samples | |
unsigned short | wilksOrder |
Real | wilksAlpha |
Real | wilksBeta |
short | wilksSidedness |
RealMatrix | momentGrads |
gradients of standardized or central moments of response functions, as determined by finalMomentsType. Calculated in compute_moments() and indexed as (var,moment) when moment id runs from 1:2*numFunctions. | |
RealSymMatrix | finalStatErrors |
standard errors (estimator std deviation) for each of the finalStatistics | |
int | samplesIncrement |
current increment in a sequence of samples | |
Pecos::LHSDriver | lhsDriver |
the C++ wrapper for the F90 LHS library | |
size_t | numLHSRuns |
counter for number of sample set generations | |
bool | stdRegressionCoeffs |
flags computation/output of standardized regression coefficients | |
bool | toleranceIntervalsFlag |
flags of double sided tolerance interval equivalent normal | |
Real | tiCoverage |
coverage to be used in the calculation of the double sided tolerance interval equivaluent normal | |
Real | tiConfidenceLevel |
confidence interval to be used in the calculation of the double sided tolerance interval equivalent normal | |
size_t | tiNumValidSamples |
RealVector | tiDstienMus |
Real | tiDeltaMultiplicativeFactor |
RealVector | tiSampleSigmas |
RealVector | tiDstienSigmas |
bool | statsFlag |
flags computation/output of statistics | |
bool | allDataFlag |
flags update of allResponses (allVariables or allSamples already defined) | |
short | samplingVarsMode |
the sampling mode: ALEATORY_UNCERTAIN{,_UNIFORM}, EPISTEMIC_UNCERTAIN{,_UNIFORM}, UNCERTAIN{,_UNIFORM}, ACTIVE{,_UNIFORM}, or ALL{,_UNIFORM}. This is a secondary control on top of the variables view that allows sampling over subsets of variables that may differ from the view. | |
short | sampleRanksMode |
mode for input/output of LHS sample ranks: IGNORE_RANKS, GET_RANKS, SET_RANKS, or SET_GET_RANKS | |
bool | varyPattern |
flag for generating a sequence of seed values within multiple get_parameter_sets() calls so that these executions (e.g., for SBO/SBNLS) are not repeated, but are still repeatable | |
RealMatrix | sampleRanks |
data structure to hold the sample ranks | |
SensAnalysisGlobal | nonDSampCorr |
initialize statistical post processing | |
bool | backfillDuplicates |
flags whether to use backfill to enforce uniqueness of discrete LHS samples | |
RealRealPairArray | extremeValues |
Minimum and maximum values of response functions for epistemic calculations (calculated in compute_intervals()),. | |
bool | functionMomentsComputed |
Function moments have been computed; used to determine whether to archive the moments. | |
Protected Attributes inherited from NonD | |
NonD * | prevNondInstance |
pointer containing previous value of nondInstance | |
size_t | startCAUV |
starting index of continuous aleatory uncertain variables within active continuous variables (convenience for managing offsets) | |
size_t | numCAUV |
number of active continuous aleatory uncertain variables | |
bool | epistemicStats |
flag for computing interval-type metrics instead of integrated metrics If any epistemic vars are active in a metric evaluation, then flag is set. | |
RealMatrix | momentStats |
standardized or central resp moments, as determined by finalMomentsType. Calculated in compute_moments()) and indexed as (moment,fn). | |
RealVectorArray | requestedRespLevels |
requested response levels for all response functions | |
RealVectorArray | computedProbLevels |
output probability levels for all response functions resulting from requestedRespLevels | |
RealVectorArray | computedRelLevels |
output reliability levels for all response functions resulting from requestedRespLevels | |
RealVectorArray | computedGenRelLevels |
output generalized reliability levels for all response functions resulting from requestedRespLevels | |
short | respLevelTarget |
indicates mapping of z->p (PROBABILITIES), z->beta (RELIABILITIES), or z->beta* (GEN_RELIABILITIES) | |
short | respLevelTargetReduce |
indicates component or system series/parallel failure metrics | |
RealVectorArray | requestedProbLevels |
requested probability levels for all response functions | |
RealVectorArray | requestedRelLevels |
requested reliability levels for all response functions | |
RealVectorArray | requestedGenRelLevels |
requested generalized reliability levels for all response functions | |
RealVectorArray | computedRespLevels |
output response levels for all response functions resulting from requestedProbLevels, requestedRelLevels, or requestedGenRelLevels | |
size_t | totalLevelRequests |
total number of levels specified within requestedRespLevels, requestedProbLevels, and requestedRelLevels | |
bool | cdfFlag |
flag for type of probabilities/reliabilities used in mappings: cumulative/CDF (true) or complementary/CCDF (false) | |
bool | pdfOutput |
flag for managing output of response probability density functions (PDFs) | |
RealVectorArray | computedPDFAbscissas |
sorted response PDF intervals bounds extracted from min/max sample and requested/computedRespLevels (vector lengths = num bins + 1) | |
RealVectorArray | computedPDFOrdinates |
response PDF densities computed from bin counts divided by (unequal) bin widths (vector lengths = num bins) | |
Response | finalStatistics |
final statistics from the uncertainty propagation used in strategies: response means, standard deviations, and probabilities of failure | |
short | finalMomentsType |
type of moments logged within finalStatistics: none, central, standard | |
size_t | miPLIndex |
index for the active ParallelLevel within ParallelConfiguration::miPLIters | |
BitArray | pdfComputed |
Whether PDF was computed for function i; used to determine whether a pdf should be archived. | |
Protected Attributes inherited from Analyzer | |
size_t | numFunctions |
number of response functions | |
size_t | numContinuousVars |
number of active continuous vars | |
size_t | numDiscreteIntVars |
number of active discrete integer vars | |
size_t | numDiscreteStringVars |
number of active discrete string vars | |
size_t | numDiscreteRealVars |
number of active discrete real vars | |
bool | compactMode |
switch for allSamples (compact mode) instead of allVariables (normal mode) | |
VariablesArray | allVariables |
array of all variables to be evaluated in evaluate_parameter_sets() | |
RealMatrix | allSamples |
compact alternative to allVariables | |
IntResponseMap | allResponses |
array of all responses to be computed in evaluate_parameter_sets() | |
StringArray | allHeaders |
array of headers to insert into output while evaluating allVariables | |
size_t | numObjFns |
number of objective functions | |
size_t | numLSqTerms |
number of least squares terms | |
RealPairPRPMultiMap | bestVarsRespMap |
map which stores best set of solutions | |
bool | vbdFlag |
flag indicating the activation of variance-bsaed decomposition for computing Sobol' indices, via either PCE or sampling | |
Real | vbdDropTol |
tolerance for omitting output of small VBD indices computed via either PCE or sampling | |
Protected Attributes inherited from Iterator | |
ProblemDescDB & | probDescDB |
class member reference to the problem description database More... | |
ParallelLibrary & | parallelLib |
class member reference to the parallel library | |
ParConfigLIter | methodPCIter |
the active ParallelConfiguration used by this Iterator instance | |
Model | iteratedModel |
the model to be iterated (for iterators and meta-iterators employing a single model instance) | |
size_t | myModelLayers |
number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model | |
unsigned short | methodName |
name of the iterator (the user's method spec) | |
Real | convergenceTol |
iteration convergence tolerance | |
size_t | maxIterations |
maximum number of iterations for the method | |
size_t | maxFunctionEvals |
maximum number of fn evaluations for the method | |
int | maxEvalConcurrency |
maximum number of concurrent model evaluations More... | |
ActiveSet | activeSet |
the response data requirements on each function evaluation | |
size_t | numFinalSolutions |
number of solutions to retain in best variables/response arrays | |
VariablesArray | bestVariablesArray |
collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone) | |
ResponseArray | bestResponseArray |
collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone) | |
bool | subIteratorFlag |
flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator) | |
short | outputLevel |
output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT | |
bool | summaryOutputFlag |
flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases | |
ResultsManager & | resultsDB |
reference to the global iterator results database | |
EvaluationStore & | evaluationsDB |
reference to the global evaluation database | |
EvaluationsDBState | evaluationsDBState |
State of evaluations DB for this iterator. | |
ResultsNames | resultsNames |
valid names for iterator results | |
std::shared_ptr< TraitsBase > | methodTraits |
pointer that retains shared ownership of a TraitsBase object, or child thereof | |
bool | topLevel |
Whether this is the top level iterator. | |
bool | exportSurrogate = false |
whether to export final surrogates | |
String | surrExportPrefix |
base filename for exported surrogates | |
unsigned short | surrExportFormat = NO_MODEL_FORMAT |
(bitwise) format(s) to export | |
Static Protected Attributes inherited from NonD | |
static NonD * | nondInstance |
pointer to the active object instance used within static evaluator functions in order to avoid the need for static data | |
Performs LHS and Monte Carlo sampling for uncertainty quantification.
The Latin Hypercube Sampling (LHS) package from Sandia Albuquerque's Risk and Reliability organization provides comprehensive capabilities for Monte Carlo and Latin Hypercube sampling within a broad array of user-specified probabilistic parameter distributions. It enforces user-specified rank correlations through use of a mixing routine. The NonDLHSSampling class provides a C++ wrapper for the LHS library and is used for performing forward propagations of parameter uncertainties into response statistics.
Batch generation options, including D-Optimal and incremental LHS are provided.
The incremental LHS sampling capability allows one to supplement an initial sample of size n to size 2n while maintaining the correct stratification of the 2n samples and also maintaining the specified correlation structure. The incremental version of LHS will return a sample of size n, which when combined with the original sample of size n, allows one to double the size of the sample.
NonDLHSSampling | ( | 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(), Model::current_variables(), NonDLHSSampling::dOptimal, NonD::initialize_final_statistics(), Model::num_primary_fns(), NonDLHSSampling::numCandidateDesigns, Analyzer::numDiscreteIntVars, Analyzer::numDiscreteRealVars, Analyzer::numDiscreteStringVars, Analyzer::numFunctions, NonDLHSSampling::numResponseFunctions, Iterator::outputLevel, NonDLHSSampling::oversampleRatio, Model::primary_fn_type(), NonDLHSSampling::qoiSamplesMatrix, NonDLHSSampling::refineSamples, NonDSampling::sampleType, Variables::shared_data(), Dakota::svd(), Analyzer::vbdFlag, and NonDLHSSampling::vbdViaSamplingMethod.
NonDLHSSampling | ( | Model & | model, |
unsigned short | sample_type, | ||
int | samples, | ||
int | seed, | ||
const String & | rng, | ||
bool | vary_pattern = true , |
||
short | sampling_vars_mode = ACTIVE |
||
) |
alternate constructor for sample generation and evaluation "on the fly"
This alternate constructor is used for generation and evaluation of Model-based sample sets. A set_db_list_nodes has not been performed so required data must be passed through the constructor. It's purpose is to avoid the need for a separate LHS specification within methods that use LHS sampling.
NonDLHSSampling | ( | unsigned short | sample_type, |
int | samples, | ||
int | seed, | ||
const String & | rng, | ||
const RealVector & | lower_bnds, | ||
const RealVector & | upper_bnds | ||
) |
alternate constructor for uniform sample generation "on the fly"
This alternate constructor is used by ConcurrentStrategy for generation of uniform, uncorrelated sample sets. It is not a letter-envelope instantiation and a set_db_list_nodes has not been performed. It is called with all needed data passed through the constructor and is designed to allow more flexibility in variables set definition (i.e., relax connection to a variables specification and allow sampling over parameter sets such as multiobjective weights). In this case, a Model is not used and the object must only be used for sample generation (no evaluation).
References NonDSampling::get_parameter_sets().
NonDLHSSampling | ( | 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"
This alternate constructor is used to generate correlated normal sample sets. It is not a letter-envelope instantiation and a set_db_list_nodes has not been performed. It is called with all needed data passed through the constructor. In this case, a Model is not used and the object must only be used for sample generation (no evaluation).
References NonDSampling::get_parameter_sets().
|
protectedvirtual |
perform the evaluate parameter sets portion of run
Loop over the set of samples and compute responses. Compute statistics on the set of responses if statsFlag is set.
Reimplemented from Iterator.
References NonDSampling::allDataFlag, Analyzer::evaluate_parameter_sets(), Iterator::iteratedModel, NonDLHSSampling::numResponseFunctions, and NonDSampling::statsFlag.
|
protected |
generate a d-optimal parameter set, leaving the first previous_samples columns intact and adding new_samples new columns following them
For now, when this function is called, numSamples is the number of new samples to generate.
References Model::current_variables(), Dakota::det_AtransA(), NonDSampling::get_parameter_sets(), ProbabilityTransformModel::initialize_distribution_types(), Iterator::iteratedModel, NonDSampling::mode_counts(), Model::multivariate_distribution(), NonDLHSSampling::numCandidateDesigns, Iterator::outputLevel, NonDLHSSampling::oversampleRatio, and NonDSampling::transform_samples().
Referenced by NonDLHSSampling::pre_run().