mlr_acqfunctions_sd {mlr3mbo} | R Documentation |
Acquisition Function Standard Deviation
Description
Posterior Standard Deviation.
Dictionary
This AcqFunction can be instantiated via the dictionary
mlr_acqfunctions or with the associated sugar function acqf()
:
mlr_acqfunctions$get("sd") acqf("sd")
Super classes
bbotk::Objective
-> mlr3mbo::AcqFunction
-> AcqFunctionSD
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
AcqFunctionSD$new(surrogate = NULL)
Arguments
surrogate
(
NULL
| SurrogateLearner).
Method clone()
The objects of this class are cloneable with this method.
Usage
AcqFunctionSD$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
Other Acquisition Function:
AcqFunction
,
mlr_acqfunctions
,
mlr_acqfunctions_aei
,
mlr_acqfunctions_cb
,
mlr_acqfunctions_ehvi
,
mlr_acqfunctions_ehvigh
,
mlr_acqfunctions_ei
,
mlr_acqfunctions_eips
,
mlr_acqfunctions_mean
,
mlr_acqfunctions_pi
,
mlr_acqfunctions_smsego
Examples
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud")) {
library(bbotk)
library(paradox)
library(mlr3learners)
library(data.table)
fun = function(xs) {
list(y = xs$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)))
}
[Package mlr3mbo version 0.2.4 Index]