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SurrogatesPolyApprox Class Reference

Derived approximation class for Surrogates Polynomial approximation classes. More...

Inheritance diagram for SurrogatesPolyApprox:
SurrogatesBaseApprox Approximation

Public Member Functions

 SurrogatesPolyApprox ()
 default constructor
 
 SurrogatesPolyApprox (const ProblemDescDB &problem_db, const SharedApproxData &shared_data, const String &approx_label)
 standard constructor:
 
 SurrogatesPolyApprox (const SharedApproxData &shared_data)
 alternate constructor More...
 
 ~SurrogatesPolyApprox ()
 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 (int fn_index) override
 compute and print all requested diagnostics and cross-validation
 
void challenge_diagnostics (int fn_index, const RealMatrix &challenge_points, const RealVector &challenge_responses) override
 compute and print all requested diagnostics for user provided challenge pts
 
dakota::ParameterListgetSurrogateOpts ()
 
- 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 UShortArray &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 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 Real prediction_variance (const Variables &vars)
 retrieve the variance of the predicted value 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 prediction_variance (const RealVector &c_vars)
 retrieve the variance of the predicted value 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 Pecos::SurrogateDataVars &sdv, bool anchor_flag, bool deep_copy, size_t key_index=_NPOS)
 append to SurrogateData::varsData
 
void add (const Variables &vars, bool anchor_flag, bool deep_copy, size_t key_index=_NPOS)
 extract the relevant vectors from Variables and invoke add(RealVector&, IntVector&, RealVector&)
 
void add (const RealVector &c_vars, const IntVector &di_vars, const RealVector &dr_vars, bool anchor_flag, bool deep_copy, size_t key_index=_NPOS)
 adds a new data point by appending to SurrogateData::varsData
 
void add (const Real *sample_c_vars, bool anchor_flag, bool deep_copy, size_t key_index=_NPOS)
 create a RealVector view and invoke add(SurrogateDataVars&)
 
void add (const Pecos::SurrogateDataResp &sdr, bool anchor_flag, bool deep_copy, size_t key_index=_NPOS)
 append to SurrogateData::respData
 
void add (const Response &response, int fn_index, bool anchor_flag, bool deep_copy, size_t key_index=_NPOS)
 adds a new data point by appending to SurrogateData::respData
 
void add_array (const RealMatrix &sample_vars, const RealVector &sample_resp, bool deep_copy=true, 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 all approxDataKeys 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 active key
 
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< Approximationapprox_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.
 
- 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 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...
 
void check_points (size_t num_build_pts)
 Check number of build points against minimum required.
 

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.
 

Detailed Description

Derived approximation class for Surrogates Polynomial approximation classes.

This class interfaces Dakota to the Dakota Surrogates Polynomial Module.

Constructor & Destructor Documentation

SurrogatesPolyApprox ( const SharedApproxData shared_data)

alternate constructor

On-the-fly constructor.


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