Parameters

The following classes of the twinlab Parameter function define parameters that can be use to further refine functionality for the respective twinLab function.

Parameter classes

DesignParams([sampling_method, seed])	Parameter configuration to setup an initial experimental or simulations design structure.
EstimatorParams([detrend, covar_module, ...])	Parameter configuration for the emulator.
ModelSelectionParams([seed, ...])	Parameter configuration for the Bayesian model selection process.
TrainParams([estimator, estimator_params, ...])	Parameter configuration for training an emulator.
ScoreParams([metric, combined_score])	Parameter configuration for scoring a trained emulator.
BenchmarkParams([type])	Parameter configuration for benchmarking a trained emulator.
PredictParams([observation_noise, fidelity])	Parameter configuration for making predictions using a trained emulator.
SampleParams([seed, observation_noise, fidelity])	Parameter configuration for sampling from a trained emulator.
RecommendParams([weights, num_restarts, ...])	Parameter configuration for recommending new points to sample using the Bayesian-optimisation routine.
CalibrateParams([y_std_model, ...])	Parameter configuration for inverting a trained emulator to estimate the input parameters that generated a given output.
MaximizeParams([opt_weights])	Parameter configuration for finding the location of the maximum output of your emulator.