| mlr_acqfunctions_sd | R Documentation | 
Posterior Standard Deviation.
This AcqFunction can be instantiated via the dictionary
mlr_acqfunctions or with the associated sugar function acqf():
mlr_acqfunctions$get("sd")
acqf("sd")
bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionSD
new()Creates a new instance of this R6 class.
AcqFunctionSD$new(surrogate = NULL)
surrogate(NULL | SurrogateLearner).
clone()The objects of this class are cloneable with this method.
AcqFunctionSD$clone(deep = FALSE)
deepWhether to make a deep clone.
Other Acquisition Function: 
AcqFunction,
mlr_acqfunctions,
mlr_acqfunctions_aei,
mlr_acqfunctions_cb,
mlr_acqfunctions_ehvi,
mlr_acqfunctions_ehvigh,
mlr_acqfunctions_ei,
mlr_acqfunctions_ei_log,
mlr_acqfunctions_eips,
mlr_acqfunctions_mean,
mlr_acqfunctions_multi,
mlr_acqfunctions_pi,
mlr_acqfunctions_smsego,
mlr_acqfunctions_stochastic_cb,
mlr_acqfunctions_stochastic_ei
if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {
  library(bbotk)
  library(paradox)
  library(mlr3learners)
  library(data.table)
  fun = function(xs) {
    list(y = xs$x ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)
  instance = OptimInstanceBatchSingleCrit$new(
    objective = objective,
    terminator = trm("evals", n_evals = 5))
  instance$eval_batch(data.table(x = c(-6, -5, 3, 9)))
  learner = default_gp()
  surrogate = srlrn(learner, archive = instance$archive)
  acq_function = acqf("sd", surrogate = surrogate)
  acq_function$surrogate$update()
  acq_function$update()
  acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}
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