Extracts statistically independent components from data. Only affects numerical features. See fastICA::fastICA for details.
R6Class object inheriting from
PipeOpICA$new(id = "ica", param_vals = list())
Identifier of resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
Input and output channels are inherited from
The output is the input
Task with all affected numeric parameters replaced by independent components.
$state is a named
list with the
$state elements inherited from
PipeOpTaskPreproc, as well as the elements of the function
with the exception of the
$S slots. These are in particular:
Matrix that projects data onto the first
n.comp principal components.
Estimated un-mixing matrix. See
Estimated mixing matrix. See
The mean of each numeric feature during training.
The parameters are the parameters inherited from
PipeOpTaskPreproc, as well as the following parameters
Number of components to extract. Default is
NULL, which sets it
to the number of available numeric columns.
Algorithm type. One of "parallel" (default) or "deflation".
One of "logcosh" (default) or "exp".
[1, 2], Used for negentropy calculation when
fun is "logcosh".
Default is 1.0.
Internal calculation method. "C" (default) or "R". See
Logical value indicating whether rows should be standardized beforehand. Default is
Maximum number of iterations. Default is 200.
Tolerance for convergence, default is
Logical value indicating the level of output during the run of the algorithm. Default is
Initial un-mixing matrix. See
Only methods inherited from
library("mlr3") task = tsk("iris") pop = po("ica") task$data() pop$train(list(task))[]$data() pop$state
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