Linearly Transform the Domain of Functional Data.

Linearly transform the domain of functional data so they are between lower and upper. The formula for this is \(x' = offset + x * scale\), where \(scale\) is \((upper - lower) / (max(x) - min(x))\) and \(offset\) is \(-min(x) * scale + lower\). The same transformation is applied during training and prediction.

Parameters

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

• lower :: numeric(1)
  Target value of smallest item of input data. Initialized to 0.

• uppper :: numeric(1)
  Target value of greatest item of input data. Initialized to 1.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> PipeOpFDAScaleRange

Methods

Public methods

• PipeOpFDAScaleRange$new()

• PipeOpFDAScaleRange$clone()

Inherited methods

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

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Method new()

Initializes a new instance of this Class.

Usage

PipeOpFDAScaleRange$new(id = "fda.scalerange", param_vals = list())

Arguments

id

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

param_vals

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

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Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpFDAScaleRange$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("fuel")
po_scale = po("fda.scalerange", lower = -1, upper = 1)
task_scale = po_scale$train(list(task))[[1L]]
task_scale$data()
#>       heatan    h20                                                        NIR
#>        <num>  <num>                                                  <tfd_reg>
#>   1: 26.7810 2.3000  [1]: (-1.00,   0.23);(-0.99,   0.29);(-0.98,   0.30); ...
#>   2: 27.4720 3.0000  [2]: (-1.00,   0.24);(-0.99,   0.32);(-0.98,   0.16); ...
#>   3: 23.8400 2.0002  [3]: (-1.00, -0.052);(-0.99,  0.045);(-0.98, -0.078); ...
#>   4: 18.1680 1.8500  [4]: (-1.00, -0.082);(-0.99, -0.082);(-0.98,  0.059); ...
#>   5: 17.5170 2.3898  [5]: (-1.00, -0.228);(-0.99, -0.116);(-0.98, -0.042); ...
#>  ---                                                                          
#> 125: 23.8340 2.1100  [6]: (-1.00, -0.037);(-0.99, -0.023);(-0.98, -0.062); ...
#> 126: 11.8050 1.6200  [7]: (-1.00,  -0.63);(-0.99,  -0.57);(-0.98,  -0.65); ...
#> 127:  8.8315 1.4200  [8]: (-1.00,  -0.73);(-0.99,  -0.82);(-0.98,  -0.80); ...
#> 128: 11.3450 1.4800  [9]: (-1.00,-0.0581);(-0.99,-0.0457);(-0.98,-0.0053); ...
#> 129: 28.9940 2.5000 [10]: (-1.00,   0.10);(-0.99,   0.12);(-0.98,   0.18); ...
#>                                                        UVVIS
#>                                                    <tfd_reg>
#>   1:  [1]: (-1.00,  0.87);(-0.98,  0.75);(-0.97,  0.77); ...
#>   2:  [2]: (-1.00, -0.86);(-0.98, -1.29);(-0.97, -0.83); ...
#>   3:  [3]: (-1.00,-0.085);(-0.98,-0.294);(-0.97,-0.202); ...
#>   4:  [4]: (-1.00, -0.58);(-0.98, -0.49);(-0.97, -0.33); ...
#>   5:  [5]: (-1.00, -0.64);(-0.98, -1.12);(-0.97, -0.66); ...
#>  ---                                                        
#> 125:  [6]: (-1.00, -0.54);(-0.98, -0.67);(-0.97, -0.59); ...
#> 126:  [7]: (-1.00, -0.96);(-0.98, -0.82);(-0.97, -1.08); ...
#> 127:  [8]: (-1.00, -0.89);(-0.98, -0.63);(-0.97, -0.99); ...
#> 128:  [9]: (-1.00,  0.52);(-0.98,  0.57);(-0.97,  0.61); ...
#> 129: [10]: (-1.00, -0.54);(-0.98, -1.30);(-0.97, -0.79); ...