ConditionalMeanCorrection#
- class skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting.ConditionalMeanCorrection[source]#
Base class for applying a correction based on the conditional expectation.
The functions are assumed to be realizations of a particular stochastic process. The information subtracted in each iteration would be the mean of the process conditioned to the value observed at the selected point.
Methods
conditional_mean
(X, selected_index)Mean of the process conditioned to the value observed.
conditioned
(*, X, T, t_0)Return a correction object conditioned to the value of a point.
correct
(X, selected_index)Correct the trajectories.
fit
(X, y)Initialize the correction for a run.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- abstract conditional_mean(X, selected_index)[source]#
Mean of the process conditioned to the value observed.
- conditioned(*, X, T, t_0)[source]#
Return a correction object conditioned to the value of a point.
This method is necessary because after the RMH correction step, the functions follow a different model.
- correct(X, selected_index)[source]#
Correct the trajectories.
This method subtracts the influence of the selected point from the other points in the function.
- fit(X, y)[source]#
Initialize the correction for a run.
The initial parameters of Recursive Maxima Hunting can be used there.
- get_metadata_routing()#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- get_params(deep=True)#
Get parameters for this estimator.
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance