Nothing

```
#' @title Normalize Data Row-wise
#'
#' @usage NULL
#' @name mlr_pipeops_spatialsign
#' @format [`R6Class`] object inheriting from [`PipeOpTaskPreprocSimple`]/[`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @description
#' Normalizes the data row-wise. This is a natural generalization of the "sign" function to higher dimensions.
#'
#' @section Construction:
#' ```
#' PipeOpSpatialSign$new(id = "spatialsign", param_vals = list())
#' ```
#'
#' * `id` :: `character(1)`\cr
#' Identifier of resulting object, default `"spatialsign"`.
#' * `param_vals` :: named `list`\cr
#' List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default `list()`.
#'
#' @section Input and Output Channels:
#' Input and output channels are inherited from [`PipeOpTaskPreproc`].
#'
#' The output is the input [`Task`][mlr3::Task] with all affected numeric features replaced by their normalized versions.
#'
#'
#' @section State:
#' The `$state` is a named `list` with the `$state` elements inherited from [`PipeOpTaskPreproc`].
#'
#' @section Parameters:
#' The parameters are the parameters inherited from [`PipeOpTaskPreproc`], as well as:
#' * `length` :: `numeric(1)` \cr
#' Length to scale rows to. Default is 1.
#' * `norm` :: `numeric(1)` \cr
#' Norm to use. Rows are scaled to `sum(x^norm)^(1/norm) == length` for finite `norm`, or to `max(abs(x)) == length`
#' if `norm` is `Inf`. Default is 2.
#'
#' @section Methods:
#' Only methods inherited from [`PipeOpTaskPreprocSimple`]/[`PipeOpTaskPreproc`]/[`PipeOp`].
#'
#' @examples
#' library("mlr3")
#'
#' task = tsk("iris")
#'
#' task$data()
#'
#' pop = po("spatialsign")
#'
#' pop$train(list(task))[[1]]$data()
#' @family PipeOps
#' @seealso https://mlr3book.mlr-org.com/list-pipeops.html
#' @include PipeOpTaskPreproc.R
#' @export
PipeOpSpatialSign = R6Class("PipeOpSpatialSign",
inherit = PipeOpTaskPreprocSimple,
public = list(
initialize = function(id = "spatialsign", param_vals = list()) {
ps = ParamSet$new(params = list(
ParamDbl$new("length", tags = c("train", "predict"), lower = 0),
ParamDbl$new("norm", tags = c("train", "predict"), lower = 0)
))
ps$values = list(norm = 2, length = 1)
super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"))
}
),
private = list(
.transform_dt = function(dt, levels) {
if (!nrow(dt)) {
# if dt has no rows then we still have to convert columns to numeric.
return(dt[, lapply(.SD, as.numeric)])
}
norm = self$param_set$values$norm
t(apply(dt, 1, function(x) {
if (is.finite(norm)) {
len = sum(abs(x) ^ norm) ^ (1 / norm)
} else {
len = max(abs(x))
}
if (len != 0) {
x = x / len * self$param_set$values$length
}
x
}))
}
)
)
mlr_pipeops$add("spatialsign", PipeOpSpatialSign)
```

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