mlr_acqfunctions_mean | R Documentation |
Posterior Mean.
This AcqFunction can be instantiated via the dictionary
mlr_acqfunctions or with the associated sugar function acqf()
:
mlr_acqfunctions$get("mean") acqf("mean")
bbotk::Objective
-> mlr3mbo::AcqFunction
-> AcqFunctionMean
new()
Creates a new instance of this R6 class.
AcqFunctionMean$new(surrogate = NULL)
surrogate
(NULL
| SurrogateLearner).
clone()
The objects of this class are cloneable with this method.
AcqFunctionMean$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other Acquisition Function:
AcqFunction
,
mlr_acqfunctions
,
mlr_acqfunctions_aei
,
mlr_acqfunctions_cb
,
mlr_acqfunctions_ehvi
,
mlr_acqfunctions_ehvigh
,
mlr_acqfunctions_ei
,
mlr_acqfunctions_eips
,
mlr_acqfunctions_multi
,
mlr_acqfunctions_pi
,
mlr_acqfunctions_sd
,
mlr_acqfunctions_smsego
if (requireNamespace("mlr3learners") &
requireNamespace("DiceKriging") &
requireNamespace("rgenoud")) {
library(bbotk)
library(paradox)
library(mlr3learners)
library(data.table)
fun = function(xs) {
list(y = xs$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("mean", surrogate = surrogate)
acq_function$surrogate$update()
acq_function$update()
acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}
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