Dakota
Version 6.20
Explore and Predict with Confidence
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Body class for model specification data. More...
Public Member Functions | |
~DataModelRep () | |
destructor | |
Public Attributes | |
String | idModel |
string identifier for the model specification data set (from the id_model specification in ModelIndControl) | |
String | modelType |
model type selection: single, surrogate, or nested (from the model type specification in ModelIndControl) | |
String | variablesPointer |
string pointer to the variables specification to be used by this model (from the variables_pointer specification in ModelIndControl) | |
String | interfacePointer |
string pointer to the interface specification to be used by this model (from the interface_pointer specification in ModelSingle and the optional_interface_pointer specification in ModelNested) | |
String | responsesPointer |
string pointer to the responses specification to be used by this model (from the responses_pointer specification in ModelIndControl) | |
bool | hierarchicalTags |
whether this model and its children will add hierarchy-based tags to eval ids | |
String | subMethodPointer |
pointer to a sub-iterator used for global approximations (from the dace_method_pointer specification in ModelSurrG) or by nested models (from the sub_method_pointer specification in ModelNested) | |
String | solutionLevelControl |
(state) variable identifier that defines a set or range of solution level controls (space/time discretization levels, iterative convergence tolerances, etc.) for defining a secondary hierarchy of fidelity within the scope of a single model form (from solution_level_control specification; see also ordered_model_fidelities ) | |
RealVector | solutionLevelCost |
array of relative simulation costs corresponding to each of the solution levels (from solution_level_cost specification; see also solution_level_control ); a scalar input is interpreted as a constant cost multiplier to be applied recursively | |
String | costRecoveryMetadata |
identifier for response metadata that returns the incurred cost of a simulation execution. This online recovery option (typically averaged over a pilot sample) can replace the need for a priori specification of solutionLevelCost. | |
SizetSet | surrogateFnIndices |
array specifying the response function set that is approximated | |
String | surrogateType |
the selected surrogate type: local_taylor, multipoint_tana, global_(neural_network,mars,orthogonal_polynomial,gaussian, polynomial,kriging), or hierarchical | |
String | truthModelPointer |
pointer to the model specification for constructing the truth model used in constructing surrogates | |
StringArray | ensembleModelPointers |
an ordered (low to high) or unordered (peer) set of model pointers corresponding to a ensemble of modeling fidelities (from the ordered_model_fidelities specification in ModelSurrH or the unordered_model_fidelities specification in ModelSurrNonH) | |
int | pointsTotal |
user-specified lower bound on total points with which to build the model (if reuse_points < pointsTotal, new samples will make up the difference) | |
short | pointsManagement |
points management configuration for DataFitSurrModel: DEFAULT_POINTS, MINIMUM_POINTS, or RECOMMENDED_POINTS | |
String | approxPointReuse |
sample reuse selection for building global approximations: none, all, region, or file (from the reuse_samples specification in ModelSurrG) | |
String | importBuildPtsFile |
the file name from the import_build_points_file specification in ModelSurrG | |
unsigned short | importBuildFormat |
tabular format for the build point import file | |
bool | importUseVariableLabels |
whether to parse/validate variable labels from header | |
bool | importBuildActive |
whether to import active variables only | |
String | exportApproxPtsFile |
the file name from the export_approx_points_file specification in ModelSurrG | |
unsigned short | exportApproxFormat |
tabular format for the approx point export file | |
String | exportApproxVarianceFile |
filename for surrogate variance evaluation export | |
unsigned short | exportApproxVarianceFormat |
tabular format for the approx variance export file | |
bool | exportSurrogate |
Option to turn on surrogate model export (export_model) | |
String | modelExportPrefix |
the filename prefix for export_model | |
unsigned short | modelExportFormat |
Format selection for export_model. | |
bool | importSurrogate |
Option to turn on surrogate model import (import_model) | |
String | modelImportPrefix |
the filename prefix for import_model | |
unsigned short | modelImportFormat |
Format selection for import_model. | |
short | approxCorrectionType |
correction type for global and hierarchical approximations: NO_CORRECTION, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, or COMBINED_CORRECTION (from the correction specification in ModelSurrG and ModelSurrH) | |
short | approxCorrectionOrder |
correction order for global and hierarchical approximations: 0, 1, or 2 (from the correction specification in ModelSurrG and ModelSurrH) | |
bool | modelUseDerivsFlag |
flags the use of derivatives in building global approximations (from the use_derivatives specification in ModelSurrG) | |
bool | respScalingFlag |
flag to indicate bounds-based scaling of current response data set prior to surrogate build; important for data fits of decaying discrepancy data using regression with absolute tolerances | |
short | polynomialOrder |
scalar integer indicating the order of the polynomial approximation (1=linear, 2=quadratic, 3=cubic; from the polynomial specification in ModelSurrG) | |
RealVector | krigingCorrelations |
vector of correlations used in building a kriging approximation (from the correlations specification in ModelSurrG) | |
String | krigingOptMethod |
optimization method to use in finding optimal correlation parameters: none, sampling, local, global | |
short | krigingMaxTrials |
maximum number of trials in optimization of kriging correlations | |
RealVector | krigingMaxCorrelations |
upper bound on kriging correlation vector | |
RealVector | krigingMinCorrelations |
lower bound on kriging correlation vector | |
Real | krigingNugget |
nugget value for kriging | |
short | krigingFindNugget |
option to have Kriging find the best nugget value to use | |
short | mlsWeightFunction |
weight function for moving least squares approximation | |
short | rbfBases |
bases for radial basis function approximation | |
short | rbfMaxPts |
maximum number of points for radial basis function approximation | |
short | rbfMaxSubsets |
maximum number of subsets for radial basis function approximation | |
short | rbfMinPartition |
minimum partition for radial basis function approximation | |
short | marsMaxBases |
maximum number of bases for MARS approximation | |
String | marsInterpolation |
interpolation type for MARS approximation | |
short | annRandomWeight |
random weight for artificial neural network approximation | |
short | annNodes |
number of nodes for artificial neural network approximation | |
Real | annRange |
range for artificial neural network approximation | |
int | numRestarts |
number of restarts for gradient-based optimization in GP | |
bool | domainDecomp |
whether domain decomposition is enabled | |
String | decompCellType |
type of local cell of domain decomp | |
int | decompSupportLayers |
number of support layers for each local basis function | |
bool | decompDiscontDetect |
whether discontinuity detection is enabled | |
Real | discontJumpThresh |
function value (jump) threshold for discontinuity detection in domain decomp | |
Real | discontGradThresh |
gradient threshold for discontinuity detection in domain decomp | |
String | trendOrder |
scalar integer indicating the order of the Gaussian process mean (0= constant, 1=linear, 2=quadratic, 3=cubic); from the gaussian_process specification in ModelSurrG) | |
bool | pointSelection |
flag indicating the use of point selection in the Gaussian process | |
StringArray | diagMetrics |
List of diagnostic metrics the user requests to assess the goodness of fit for a surrogate model. | |
bool | crossValidateFlag |
flag indicating the use of cross validation on the metrics specified | |
int | numFolds |
number of folds to perform in cross validation | |
Real | percentFold |
percentage of data to withhold for cross validation process | |
bool | pressFlag |
flag indicating the use of PRESS on the metrics specified | |
String | importChallengePtsFile |
the file name from the challenge_points_file specification in ModelSurrG | |
unsigned short | importChallengeFormat |
tabular format of the challenge data file | |
bool | importChalUseVariableLabels |
whether to parse/validate variable labels from header | |
bool | importChallengeActive |
whether to import active variables only | |
String | advancedOptionsFilename |
file containing advanced surrogate option overrides | |
String | moduleAndClassName |
file containing python module methods used by python surrogates | |
String | optionalInterfRespPointer |
string pointer to the responses specification used by the optional interface in nested models (from the optional_interface_responses_pointer specification in ModelNested) | |
StringArray | primaryVarMaps |
the primary variable mappings used in nested models for identifying the lower level variable targets for inserting top level variable values (from the primary_variable_mapping specification in ModelNested) | |
StringArray | secondaryVarMaps |
the secondary variable mappings used in nested models for identifying the (distribution) parameter targets within the lower level variables for inserting top level variable values (from the secondary_variable_mapping specification in ModelNested) | |
RealVector | primaryRespCoeffs |
the primary response mapping matrix used in nested models for weighting contributions from the sub-iterator responses in the top level (objective) functions (from the primary_response_mapping specification in ModelNested) | |
RealVector | secondaryRespCoeffs |
the secondary response mapping matrix used in nested models for weighting contributions from the sub-iterator responses in the top level (constraint) functions (from the secondary_response_mapping specification in ModelNested) | |
bool | identityRespMap |
whether an identity response map is requested in lieu of explicit maps | |
int | subMethodServers |
number of servers for concurrent sub-iterator parallelism | |
int | subMethodProcs |
number of processors for each concurrent sub-iterator partition | |
short | subMethodScheduling |
scheduling approach for concurrent sub-iterator parallelism: {DEFAULT,MASTER,PEER}_SCHEDULING | |
int | initialSamples |
initial samples to build the subspace model | |
unsigned short | subspaceSampleType |
sampling method for building the subspace model | |
IntVector | refineSamples |
refinement samples to add in each batch | |
size_t | maxIterations |
maximum number of subspace build iterations | |
Real | convergenceTolerance |
convergence tolerance on build process | |
bool | subspaceIdBingLi |
Flag to use Bing Li method to identify active subspace dimension. | |
bool | subspaceIdConstantine |
Flag to use Constantine method to identify active subspace dimension. | |
bool | subspaceIdEnergy |
Flag to use eigenvalue energy method to identify active subspace dimension. | |
bool | subspaceBuildSurrogate |
Flag to build surrogate over active subspace. | |
int | subspaceDimension |
Size of subspace. | |
unsigned short | subspaceNormalization |
Normalization to use when forming a subspace with multiple response functions. | |
int | numReplicates |
Number of bootstrap samples for subspace identification. | |
bool | subspaceIdCV |
Flag to use cross validation to identify active subspace dimension. | |
Real | relTolerance |
relative tolerance used by cross validation subspace dimension id method | |
Real | decreaseTolerance |
decrease tolerance used by cross validation subspace dimension id method | |
int | subspaceCVMaxRank |
maximum rank considered by cross validation subspace dimension id method | |
bool | subspaceCVIncremental |
flag to use incremental dimension estimation in the cross validation metric | |
unsigned short | subspaceIdCVMethod |
Contains which cutoff method to use in the cross validation metric. | |
short | regressionType |
type of (regularized) regression: FT_LS or FT_RLS2 | |
Real | regressionL2Penalty |
penalty parameter for regularized regression (FT_RLS2) | |
size_t | maxSolverIterations |
max iterations for optimization solver used in FT regression | |
int | maxCrossIterations |
maximum number of cross iterations | |
Real | solverTol |
optimization tolerance for FT regression | |
Real | solverRoundingTol |
Rounding tolerance for FT regression. | |
Real | statsRoundingTol |
arithmetic (rounding) tolerance for FT sums and products | |
bool | tensorGridFlag |
sub-sample a tensor grid for generating regression data | |
unsigned short | startOrder |
starting polynomial order | |
unsigned short | kickOrder |
polynomial order increment when adapting | |
unsigned short | maxOrder |
maximum order of basis polynomials | |
bool | adaptOrder |
whether or not to adapt order by cross validation | |
size_t | startRank |
starting rank | |
size_t | kickRank |
rank increase increment | |
size_t | maxRank |
maximum rank | |
bool | adaptRank |
whether or not to adapt rank | |
size_t | maxCVRankCandidates |
maximum number of cross-validation candidates for adaptRank | |
unsigned short | maxCVOrderCandidates |
maximum number of cross-validation candidates for adaptOrder | |
short | c3AdvanceType |
quantity to increment (start rank, start order, max rank, max order, max rank + max order) for FT (uniform) p-refinement | |
size_t | collocationPoints |
number of data points used in FT construction by regression | |
Real | collocationRatio |
ratio of number of points to nuber of unknowns | |
bool | autoRefine |
whether automatic surrogate refinement is enabled | |
size_t | maxFunctionEvals |
maximum evals in refinement | |
String | refineCVMetric |
metric to use in cross-validation guided refinement | |
int | softConvergenceLimit |
max number of iterations in refinement without improvement | |
int | refineCVFolds |
number of cross-validation folds in guided refinement | |
unsigned short | adaptedBasisSparseGridLev |
sparse grid level for low-order PCE used to compute rotation matrix within adapted basis approach to dimension reduction | |
unsigned short | adaptedBasisExpOrder |
expansion order for low-order PCE used to compute rotation matrix within adapted basis approach to dimension reduction | |
Real | adaptedBasisCollocRatio |
collocation ratio for low-order PCE used to compute rotation matrix within adapted basis approach to dimension reduction | |
short | method_rotation |
Real | adaptedBasisTruncationTolerance |
unsigned short | randomFieldIdForm |
Contains which type of random field model. | |
unsigned short | analyticCovIdForm |
Contains which type of analytic covariance function. | |
Real | truncationTolerance |
truncation tolerance on build process: percent variance explained | |
String | propagationModelPointer |
pointer to the model through which to propagate the random field | |
String | rfDataFileName |
File from which to build the random field. | |
Private Member Functions | |
DataModelRep () | |
constructor | |
void | write (std::ostream &s) const |
write a DataModelRep object to an std::ostream | |
void | read (MPIUnpackBuffer &s) |
read a DataModelRep object from a packed MPI buffer | |
void | write (MPIPackBuffer &s) const |
write a DataModelRep object to a packed MPI buffer | |
Friends | |
class | DataModel |
the handle class can access attributes of the body class directly | |
Body class for model specification data.
The DataModelRep class is used to contain the data from a model keyword specification. Default values are managed in the DataModelRep constructor. Data is public to avoid maintaining set/get functions, but is still encapsulated within ProblemDescDB since ProblemDescDB::dataModelList is private.