| dict_scalors_domcount | R Documentation |
Scalor that returns a the number of (weakly, epsilon-) dominated or dominating individuals for each individuum.
output :: character(1)
What to count: individuals that are being dominated by the point under consideration("count_dominated"),
or individuals that do not dominate the point under consideration ("count_not_dominating").
In both cases, a larger output means the individual is "better", in some way, according to the fitness values.
Initialized with "count_not_dominating".
epsilon :: numeric
Epsilon-value for non-dominance, as used by rank_nondominated. Initialized to 0.
jitter :: logical(1)
Whether to add random jitter to points, with magnitude sqrt(.Machine$double.eps) relative to fitness values.
This is used to effectively break ties.
scale_output :: logical(1)
Whether to scale output by the total numberof individuals, giving output between 0 and 1 (inclusive) when TRUE
or integer outputs ranging from 0 and nrow(fitnesses) (inclusive) when FALSE. Initialized to TRUE.
Supported Domain classes are: p_lgl ('ParamLgl'), p_int ('ParamInt'), p_dbl ('ParamDbl'), p_fct ('ParamFct')
This Scalor can be created with the short access form scl()
(scls() to get a list), or through the the dictionary
dict_scalors in the following way:
# preferred:
scl("domcount")
scls("domcount") # takes vector IDs, returns list of Scalors
# long form:
dict_scalors$get("domcount")
miesmuschel::MiesOperator -> miesmuschel::Scalor -> ScalorDomcount
new()Initialize the ScalorNondom object.
ScalorDomcount$new()
clone()The objects of this class are cloneable with this method.
ScalorDomcount$clone(deep = FALSE)
deepWhether to make a deep clone.
Other scalors:
Scalor,
dict_scalors_aggregate,
dict_scalors_fixedprojection,
dict_scalors_hypervolume,
dict_scalors_nondom,
dict_scalors_one,
dict_scalors_proxy,
dict_scalors_single
p = ps(x = p_dbl(-5, 5))
data = data.frame(x = rep(0, 5))
sd = scl("domcount")
sd$prime(p)
(fitnesses = matrix(c(1, 5, 2, 3, 0, 3, 1, 0, 10, 8), ncol = 2))
# to see the fitness matrix, use:
## plot(fitnesses, pch = as.character(1:5))
# note that for both 2 and 4, all points do not dominate them
# their value is therefore 1
sd$operate(data, fitnesses)
sd$param_set$values$scale_output = FALSE
sd$operate(data, fitnesses)
sd$param_set$values$output = "count_dominated"
# point 4 dominates three other points, point 2 only one other point.
sd$operate(data, fitnesses)
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