Uniform Sample Mutator

"Mutates" individuals by forgetting the current value and sampling new individuals from scratch.

Since the information loss is very high, this should in most cases be combined with MutatorCmpMaybe.

Configuration Parameters

• initializer :: function
  Function that generates the initial population as a Design object, with arguments param_set and n, functioning like paradox::generate_design_random or paradox::generate_design_lhs. This is equivalent to the initializer parameter of mies_init_population(), see there for more information. Initialized to generate_design_random().

Supported Operand Types

Supported Param classes are: ParamLgl, ParamInt, ParamDbl, ParamFct

Dictionary

This Mutator can be created with the short access form mut() (muts() to get a list), or through the the dictionary dict_mutators in the following way:

# preferred:
mut("erase")
muts("erase")  # takes vector IDs, returns list of Mutators

# long form:
dict_mutators$get("erase")

See also

Other mutators: Mutator, MutatorDiscrete, MutatorNumeric, OperatorCombination, dict_mutators_cmpmaybe, dict_mutators_gauss, dict_mutators_maybe, dict_mutators_null, dict_mutators_proxy, dict_mutators_sequential, dict_mutators_unif

Super classes

miesmuschel::MiesOperator -> miesmuschel::Mutator -> MutatorErase

Methods

Public methods

• MutatorErase$new()

• MutatorErase$clone()

Inherited methods

• miesmuschel::MiesOperator$help()
• miesmuschel::MiesOperator$operate()
• miesmuschel::MiesOperator$prime()
• miesmuschel::MiesOperator$print()
• miesmuschel::MiesOperator$repr()

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Method new()

Initialize the MutatorErase object.

Usage

MutatorErase$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MutatorErase$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

set.seed(1)
mer = mut("erase")
p = ps(x = p_lgl(), y = p_fct(c("a", "b", "c")), z = p_dbl(0, 1))
data = data.frame(x = rep(TRUE, 5), y = rep("a", 5),
  z = seq(0, 1, length.out = 5),
  stringsAsFactors = FALSE)  # necessary for R <= 3.6

mer$prime(p)
mer$operate(data)
#>       x y         z
#> 1  TRUE c 0.2059746
#> 2  TRUE c 0.1765568
#> 3 FALSE b 0.6870228
#> 4 FALSE b 0.3841037
#> 5  TRUE a 0.7698414