GaussianSampleCorrection#

class skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting.GaussianSampleCorrection(cov_estimator=None)[source]#

Gaussian correction with sample covariance.

Correction assuming that the process is Gaussian and using as the kernel the sample covariance.

Parameters:

cov_estimator (CovarianceEstimator[FDataGrid] | None) – Covariance estimator to use.

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()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

conditional_mean(X, selected_index)[source]#

Mean of the process conditioned to the value observed.

Parameters:
  • X (FDataGrid) – Functions in the current iteration of the algorithm.

  • selected_index (int or tuple of int) – Index of the selected point in the data_matrix.

Return type:

FDataGrid

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.

Parameters:
Return type:

Correction

correct(X, selected_index)[source]#

Correct the trajectories.

This method subtracts the influence of the selected point from the other points in the function.

Parameters:
  • X (FDataGrid) – Functions in the current iteration of the algorithm.

  • selected_index (Tuple[int, ...]) – Index of the selected point in the data_matrix.

Return type:

FDataGrid

fit(X, y)[source]#

Initialize the correction for a run.

The initial parameters of Recursive Maxima Hunting can be used there.

Parameters:
Return type:

None

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.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

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