![]() |
Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
|
Enable export of multilevel/multifidelity sample sequences to individual files
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Optional (Choose One) | Tabular Format (Group 1) | custom_annotated | Export sample sequences enabling file format customization | |
annotated | Export sample sequences with descriptors | |||
freeform | Export sample sequences without heading descriptors |
When this option is active, separate output files are written for each unique sample increment and are tagged by algorithm type, simulation interface, iteration count, level count, and the number of samples as described below. The data content is comprised of the input variables only, without corresponding responses, as an intended use case is to support evaluation of these sample sets offline.
Default Behavior
If not specified, the annotated
format is assumed.
Expected Output
Separate output files are generated according to the following format: {ml/cv}_{interface_id}_{iteration_number}_{level_number}_{number_of_samples}
.dat.
With respect to the algorithm type, interface id, and level counter, the following definitions are employed:
ml_
is pre-pended for all sample increments and sample sets are tagged with the interface id from the HF model. cv_
is prepended for all sample increments, shared sample sets are tagged with the HF interface id, and LF-only refinements are tagged by the LF interface id. ml_
is pre-pended for all HF sample increments and sample sets are tagged with the interface id from the HF model; cv_
is prepended for all LF increments and sample sets are tagged with the LF interface id. Note that the LF model shares the same ml_
sample sets, but a redundant file is not created for this data. cv_
is prepended for all sample increments and sample sets are tagged with the corresponding model interface id. The level number corresponds to the index of the approximation model for LF increments and to the id of the truth model (number of LF approximations + 1) for shared increments.With respect to iteration count, pilot samples are tagged with iteration 0.
The following method block
method, model_pointer = 'HIERARCH' multilevel_sampling pilot_samples = 20 seed = 1237 convergence_tolerance = .01 output silent export_sample_sequence
results in enabling the sample output of sample increments for each level to individual files using the default annotated
format.
The following variables block
variables, id_variables = 'LF_VARS' uniform_uncertain = 7 lower_bounds = 7*-1. upper_bounds = 7* 1. descriptors 'u1' 'u2' 'u3' 'u4' 'u5' 'u6' 'u7' discrete_state_set integer = 2 num_set_values = 4 1 set_values = 5 15 30 60 # number of spatial coords 3 # number of Fourier solution modes initial_state = 5 3 descriptors 'N_x' 'N_mod'
illustrates how to define descriptors for the variables. For this case, the descriptors u1
through u7
, N_x
, and N_mod
are reported in the sample files to help annotate the data.