Calculates the cross-correlation between two functional vectors using tf::tf_crosscor()
.
Note that it only operates on regular data and that the cross-correlation assumes that each column
has the same domain.
To apply this PipeOp
to irregualr data, convert it to a regular grid first using PipeOpFDAInterpol
.
If you need to change the domain of the columns, use PipeOpFDAScaleRange
.
Parameters
The parameters are the parameters inherited from PipeOpTaskPreprocSimple
,
as well as the following parameters:
arg
::numeric()
Grid to use for the cross-correlation.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpTaskPreproc
-> mlr3pipelines::PipeOpTaskPreprocSimple
-> PipeOpFDACor
Methods
Method new()
Initializes a new instance of this Class.
Usage
PipeOpFDACor$new(id = "fda.cor", param_vals = list())
Arguments
id
(
character(1)
)
Identifier of resulting object, default"fda.cor"
.param_vals
(named
list
)
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist()
.
Examples
set.seed(1234L)
dt = data.table(y = 1:100, x1 = tf::tf_rgp(100L), x2 = tf::tf_rgp(100L))
task = as_task_regr(dt, target = "y")
po_cor = po("fda.cor")
task_cor = po_cor$train(list(task))[[1L]]
task_cor
#> <TaskRegr:dt> (100 x 2)
#> * Target: y
#> * Properties: -
#> * Features (1):
#> - dbl (1): x1_x2_cor