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Convert regular functional features (e.g. all individuals are observed at the same time-points) to new columns, one for each input value to the function.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreprocSimple.

Naming

The new names generally append a _1, ..., to the corresponding column name. However this can lead to name clashes with existing columns. This is solved as follows: If a column was called "x" and the feature is "mean", the corresponding new column will be called "x_mean". In case of duplicates, unique names are obtained using make.unique() and a warning is given.

Methods

Inherited methods


Method new()

Initializes a new instance of this Class.

Usage

PipeOpFDAFlatten$new(id = "fda.flatten", param_vals = list())

Arguments

id

(character(1))
Identifier of resulting object, default "fda.flatten".

param_vals

(named list)
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().


Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpFDAFlatten$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3pipelines)
task = tsk("fuel")
pop = po("fda.flatten")
task_flat = pop$train(list(task))