Dakota
Version 6.20
Explore and Predict with Confidence
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Derived approximation class for Surrogates approximation classes. More...
Public Member Functions | |
SurrogatesGPApprox () | |
default constructor | |
SurrogatesGPApprox (const ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label) | |
standard constructor: | |
SurrogatesGPApprox (const SharedApproxData &shared_data) | |
alternate constructor More... | |
~SurrogatesGPApprox () | |
destructor | |
Public Member Functions inherited from SurrogatesBaseApprox | |
SurrogatesBaseApprox () | |
default constructor | |
SurrogatesBaseApprox (const ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label) | |
standard constructor: | |
SurrogatesBaseApprox (const SharedApproxData &shared_data) | |
alternate constructor | |
~SurrogatesBaseApprox () | |
destructor | |
bool | diagnostics_available () override |
check if diagnostics are available for this approximation type | |
Real | diagnostic (const String &metric_type) override |
retrieve a single diagnostic metric for the diagnostic type specified | |
RealArray | cv_diagnostic (const StringArray &metric_types, unsigned num_folds) override |
retrieve diagnostic metrics for the diagnostic types specified, applying | |
void | primary_diagnostics (size_t fn_index) override |
compute and print all requested diagnostics and cross-validation | |
void | challenge_diagnostics (size_t fn_index, const RealMatrix &challenge_points, const RealVector &challenge_responses) override |
compute and print all requested diagnostics for user provided challenge pts | |
dakota::ParameterList & | getSurrogateOpts () |
Public Member Functions inherited from Approximation | |
Approximation () | |
default constructor More... | |
Approximation (ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label) | |
standard constructor for envelope More... | |
Approximation (const SharedApproxData &shared_data) | |
alternate constructor More... | |
Approximation (const Approximation &approx) | |
copy constructor More... | |
virtual | ~Approximation () |
destructor | |
Approximation | operator= (const Approximation &approx) |
assignment operator | |
virtual void | active_model_key (const Pecos::ActiveKey &sd_key) |
activate an approximation state based on its multi-index key | |
virtual void | clear_model_keys () |
reset initial state by removing all model keys for an approximation | |
virtual void | rebuild () |
rebuilds the approximation incrementally | |
virtual void | replace (const IntResponsePair &response_pr, size_t fn_index) |
replace the response data | |
virtual void | pop_coefficients (bool save_data) |
removes entries from end of SurrogateData::{vars,resp}Data (last points appended, or as specified in args) | |
virtual void | push_coefficients () |
restores state prior to previous pop() | |
virtual void | finalize_coefficients () |
finalize approximation by applying all remaining trial sets | |
virtual void | clear_current_active_data () |
clear current build data in preparation for next build More... | |
virtual void | combine_coefficients () |
combine all level approximations into a single aggregate approximation | |
virtual void | combined_to_active_coefficients (bool clear_combined=true) |
promote combined approximation into active approximation | |
virtual void | clear_inactive_coefficients () |
prune inactive coefficients following combination and promotion to active | |
virtual const RealSymMatrix & | hessian (const Variables &vars) |
retrieve the approximate function Hessian for a given parameter vector | |
virtual const RealSymMatrix & | hessian (const RealVector &c_vars) |
retrieve the approximate function Hessian for a given parameter vector | |
virtual Real | mean () |
return the mean of the expansion, where all active vars are random | |
virtual Real | mean (const RealVector &x) |
return the mean of the expansion for a given parameter vector, where a subset of the active variables are random | |
virtual Real | combined_mean () |
return the mean of the combined expansion, where all active vars are random | |
virtual Real | combined_mean (const RealVector &x) |
return the mean of the combined expansion for a given parameter vector, where a subset of the active variables are random | |
virtual const RealVector & | mean_gradient () |
return the gradient of the expansion mean | |
virtual const RealVector & | mean_gradient (const RealVector &x, const SizetArray &dvv) |
return the gradient of the expansion mean | |
virtual Real | variance () |
return the variance of the expansion, where all active vars are random | |
virtual Real | variance (const RealVector &x) |
return the variance of the expansion for a given parameter vector, where a subset of the active variables are random | |
virtual const RealVector & | variance_gradient () |
virtual const RealVector & | variance_gradient (const RealVector &x, const SizetArray &dvv) |
virtual Real | covariance (Approximation &approx_2) |
return the covariance between two response expansions, treating all variables as random | |
virtual Real | covariance (const RealVector &x, Approximation &approx_2) |
return the covariance between two response expansions, treating a subset of the variables as random | |
virtual Real | combined_covariance (Approximation &approx_2) |
return the covariance between two combined response expansions, where all active variables are random | |
virtual Real | combined_covariance (const RealVector &x, Approximation &approx_2) |
return the covariance between two combined response expansions, where a subset of the active variables are random | |
virtual void | compute_moments (bool full_stats=true, bool combined_stats=false) |
virtual void | compute_moments (const RealVector &x, bool full_stats=true, bool combined_stats=false) |
virtual const RealVector & | moments () const |
virtual const RealVector & | expansion_moments () const |
virtual const RealVector & | numerical_integration_moments () const |
virtual const RealVector & | combined_moments () const |
virtual Real | moment (size_t i) const |
virtual void | moment (Real mom, size_t i) |
virtual Real | combined_moment (size_t i) const |
virtual void | combined_moment (Real mom, size_t i) |
virtual void | clear_component_effects () |
virtual void | compute_component_effects () |
virtual void | compute_total_effects () |
virtual const RealVector & | sobol_indices () const |
virtual const RealVector & | total_sobol_indices () const |
virtual ULongULongMap | sparse_sobol_index_map () const |
virtual bool | advancement_available () |
check if resolution advancement (e.g., order, rank) is available for this approximation instance | |
virtual RealArray | challenge_diagnostic (const StringArray &metric_types, const RealMatrix &challenge_points, const RealVector &challenge_responses) |
compute requested diagnostics for user provided challenge pts | |
virtual RealVector | approximation_coefficients (bool normalized) const |
return the coefficient array computed by build()/rebuild() | |
virtual void | approximation_coefficients (const RealVector &approx_coeffs, bool normalized) |
set the coefficient array from external sources, rather than computing with build()/rebuild() | |
virtual void | coefficient_labels (std::vector< std::string > &coeff_labels) const |
print the coefficient array computed in build()/rebuild() | |
virtual void | print_coefficients (std::ostream &s, bool normalized) |
print the coefficient array computed in build()/rebuild() | |
virtual int | recommended_coefficients () const |
return the recommended number of samples (unknowns) required to build the derived class approximation type in numVars dimensions | |
virtual int | num_constraints () const |
return the number of constraints to be enforced via an anchor point | |
virtual void | expansion_coefficient_flag (bool) |
virtual bool | expansion_coefficient_flag () const |
virtual void | expansion_gradient_flag (bool) |
virtual bool | expansion_gradient_flag () const |
virtual void | clear_computed_bits () |
clear tracking of computed moments, due to (expansion) change that invalidates previous results | |
int | min_points (bool constraint_flag) const |
return the minimum number of points required to build the approximation type in numVars dimensions. Uses *_coefficients() and num_constraints(). | |
int | recommended_points (bool constraint_flag) const |
return the recommended number of samples to build the approximation type in numVars dimensions (default same as min_points) | |
void | pop_data (bool save_data) |
removes entries from end of SurrogateData::{vars,resp}Data (last points appended, or as specified in args) | |
void | push_data () |
restores SurrogateData state prior to previous pop() | |
void | finalize_data () |
finalize SurrogateData by applying all remaining trial sets | |
const Pecos::SurrogateData & | surrogate_data () const |
return approxData | |
Pecos::SurrogateData & | surrogate_data () |
return approxData | |
void | add (const Variables &vars, bool v_copy, const Response &response, size_t fn_index, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS) |
create SurrogateData{Vars,Resp} and append to SurrogateData:: {varsData,respData,dataIdentifiers} | |
void | add (const Real *c_vars, bool v_copy, const Response &response, size_t fn_index, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS) |
create SurrogateData{Vars,Resp} and append to SurrogateData:: {varsData,respData,dataIdentifiers} | |
void | add (const Pecos::SurrogateDataVars &sdv, bool v_copy, const Response &response, size_t fn_index, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS) |
create a SurrogateDataResp and append to SurrogateData:: {varsData,respData,dataIdentifiers} | |
void | add (const Pecos::SurrogateDataVars &sdv, bool v_copy, const Pecos::SurrogateDataResp &sdr, bool r_copy, bool anchor_flag, int eval_id, size_t key_index=_NPOS) |
append to SurrogateData::{varsData,respData,dataIdentifiers} | |
void | add_array (const RealMatrix &sample_vars, bool v_copy, const RealVector &sample_resp, bool r_copy, size_t key_index=_NPOS) |
add surrogate data from the provided sample and response data, assuming continuous variables and function values only More... | |
void | pop_count (size_t count, size_t key_index) |
appends to SurrogateData::popCountStack (number of entries to pop from end of SurrogateData::{vars,resp}Data, based on size of last data append) | |
void | clear_data () |
clear SurrogateData::{vars,resp}Data for activeKey + embedded keys More... | |
void | clear_active_data () |
clear active approximation data | |
void | clear_inactive_data () |
clear inactive approximation data | |
void | clear_active_popped () |
clear SurrogateData::popped{Vars,Resp}Trials,popCountStack for activeKey | |
void | clear_popped () |
clear SurrogateData::popped{Vars,Resp}Trials,popCountStack for all keys | |
void | set_bounds (const RealVector &c_l_bnds, const RealVector &c_u_bnds, const IntVector &di_l_bnds, const IntVector &di_u_bnds, const RealVector &dr_l_bnds, const RealVector &dr_u_bnds) |
set approximation lower and upper bounds (currently only used by graphics) | |
std::shared_ptr< Approximation > | approx_rep () const |
returns approxRep for access to derived class member functions that are not mapped to the top Approximation level | |
Protected Member Functions | |
int | min_coefficients () const override |
return the minimum number of samples (unknowns) required to build the derived class approximation type in numVars dimensions | |
void | build () override |
Do the build. | |
Real | prediction_variance (const Variables &vars) override |
retrieve the variance of the predicted value for a given parameter vector | |
Real | prediction_variance (const RealVector &c_vars) override |
retrieve the variance of the predicted value for a given parameter vector | |
Protected Member Functions inherited from SurrogatesBaseApprox | |
void | convert_surrogate_data (dakota::MatrixXd &vars, dakota::MatrixXd &resp) |
convert Pecos surrogate data to reshaped Eigen vars/resp matrices | |
Real | value (const Variables &vars) override |
retrieve the approximate function value for a given parameter vector | |
const RealVector & | gradient (const Variables &vars) override |
retrieve the approximate function gradient for a given parameter vector | |
Real | value (const RealVector &c_vars) override |
retrieve the approximate function value for a given parameter vector | |
const RealVector & | gradient (const RealVector &c_vars) override |
retrieve the approximate function gradient for a given parameter vector | |
void | set_verbosity () |
set the surrogate's verbosity level according to Dakota's verbosity | |
void | import_model (const ProblemDescDB &problem_db) |
construct-time only import of serialized surrogate | |
void | map_variable_labels (const Variables &vars) |
validate imported labels and initialize map if needed | |
RealVector | map_eval_vars (const Variables &vars) |
extract active or all view as vector, mapping if needed for import | |
void | export_model (const StringArray &var_labels, const String &fn_label, const String &export_prefix, const unsigned short export_format) override |
export the model to disk | |
void | export_model (const Variables &vars, const String &fn_label, const String &export_prefix, const unsigned short export_format) override |
approximation export that generates labels from the passed Variables, since only the derived classes know how the variables are ordered w.r.t. the surrogate build; if export_format > NO_MODEL_FORMAT, uses all 3 parameters, otherwise extracts these from the Approximation's sharedDataRep to build a filename | |
Protected Member Functions inherited from Approximation | |
Approximation (BaseConstructor, const ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label) | |
constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More... | |
Approximation (NoDBBaseConstructor, const SharedApproxData &shared_data) | |
constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More... | |
Pecos::SurrogateDataVars | variables_to_sdv (const Real *sample_c_vars) |
create a SurrogateDataVars instance from a Real* | |
Pecos::SurrogateDataVars | variables_to_sdv (const Variables &vars) |
create a SurrogateDataVars instance by extracting data from a Variables object | |
Pecos::SurrogateDataResp | response_to_sdr (const Response &response, size_t fn_index) |
create a SurrogateDataResp instance by extracting data for a particular QoI from a Response object | |
void | add (const Pecos::SurrogateDataVars &sdv, bool v_copy, const Pecos::SurrogateDataResp &sdr, bool r_copy, bool anchor_flag) |
tracks a new data point by appending to SurrogateData::{vars,Resp}Data | |
void | add (int eval_id) |
tracks a new data point by appending to SurrogateData::dataIdentifiers | |
void | check_points (size_t num_build_pts) |
Check number of build points against minimum required. | |
void | assign_key_index (size_t key_index) |
extract and assign i-th embedded active key | |
Additional Inherited Members | |
Protected Attributes inherited from SurrogatesBaseApprox | |
dakota::ParameterList | surrogateOpts |
Key/value config options for underlying surrogate. | |
std::shared_ptr< dakota::surrogates::Surrogate > | model |
The native surrogate model. | |
String | advanced_options_file |
Advanced configurations options filename. | |
bool | modelIsImported |
whether model serialized in from disk | |
Protected Attributes inherited from Approximation | |
Pecos::SurrogateData | approxData |
contains the variables/response data for constructing a single approximation model (one response function). There is only one SurrogateData instance per Approximation, although it may contain keys for different model forms/resolutions and aggregations (e.g., discrepancies) among forms/resolutions. | |
RealVector | approxGradient |
gradient of the approximation returned by gradient() | |
RealSymMatrix | approxHessian |
Hessian of the approximation returned by hessian() | |
String | approxLabel |
label for approximation, if applicable | |
std::shared_ptr< SharedApproxData > | sharedDataRep |
contains the approximation data that is shared among the response set | |
Derived approximation class for Surrogates approximation classes.
This class interfaces Dakota to the Dakota Surrogates Gaussian Process Module.
SurrogatesGPApprox | ( | const SharedApproxData & | shared_data | ) |