const std::vector< unsigned int > & _sorted_indices
The selected sample indices to evaluate the subApp.
const std::vector< std::vector< Real > > & getSampleTries() const
Return random samples for the GP to try in the reporter class.
const InputParameters & parameters() const
virtual void executeSetUp() override
Gather all the samples.
const std::vector< Real > & _initial_values
Initial values of the input params to get the MCMC scheme started.
GenericActiveLearningSampler(const InputParameters ¶meters)
DIE A HORRIBLE DEATH HERE typedef LIBMESH_DEFAULT_SCALAR_TYPE Real
const unsigned int _num_parallel_proposals
Number of parallel proposals to be made and subApps to be executed.
A generic sampler to support parallel active learning.
static InputParameters validParams()
dof_id_type getNumParallelProposals() const
Return the number of parallel proposals.
std::vector< std::vector< Real > > _new_samples
Vectors of new proposed samples.
std::vector< std::vector< Real > > _inputs_all
Storage for all the proposed samples.
virtual Real computeSample(dof_id_type row_index, dof_id_type col_index) override
Return the sample for the given row and column.
const unsigned int _num_tries
Number of samples to propose in each iteration (not all are sent for subApp evals) ...
std::vector< Distribution const * > _distributions
Storage for distribution objects to be utilized.