Zoom In/Out on Functional Columns

Zoom in or out on functional features by restricting their domain to a specified window. This operation extracts a subset of each function by defining new lower and upper boundaries, effectively cropping the functional data to focus on a specific region of interest. Calls tf::tf_zoom() from package tf.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreprocSimple, as well as the following parameters:

• begin :: numeric()
  The lower limit of the domain. Can be a single value applied to all functional columns, or a numeric of length equal to the number of observations. The window includes all values where argument >= begin. If not specified, defaults to the lower limit of each function's domain.

• end :: numeric()
  The upper limit of the domain.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFDAZoom

Methods

Public methods

• PipeOpFDAZoom$new()

• PipeOpFDAZoom$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

---

Method new()

Initializes a new instance of this Class.

Usage

PipeOpFDAZoom$new(id = "fda.zoom", param_vals = list())

Arguments

id

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

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

PipeOpFDAZoom$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("fuel")
pop = po("fda.zoom", begin = 50, end = 100)
task_zoom = pop$train(list(task))[[1L]]
task_zoom$data()
#>       heatan    h20                        NIR                      UVVIS
#>        <num>  <num>                  <tfd_reg>                  <tfd_reg>
#>   1: 26.7810 2.3000 ██████████████████████████ ▇▇▇▇▇▇▇▇▇▇▇███████████████
#>   2: 27.4720 3.0000 ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇████ ▁▂▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃
#>   3: 23.8400 2.0002 ▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅ ▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄
#>   4: 18.1680 1.8500 ▅▄▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅ ▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃
#>   5: 17.5170 2.3898 ▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅ ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▂▃▃
#>  ---                                                                     
#> 125: 23.8340 2.1100 ▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅ ▃▃▃▃▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃
#> 126: 11.8050 1.6200 ▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂
#> 127:  8.8315 1.4200 ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂▂▂▂▂ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
#> 128: 11.3450 1.4800 ▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▆▅▆▆▆▆▆▆▆▆ ▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆
#> 129: 28.9940 2.5000 ▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆▆ ▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃▃