GaussianConditionedCorrection#

class skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting.GaussianConditionedCorrection(conditioning_points, *, mean=0, cov=1)[source]#

Correction for a conditioned Gaussian process.

Correction assuming that the underlying process is Gaussian, with several values conditioned to 0.

The conditional mean is inherited from GaussianCorrection, with the conditioned mean and covariance.

The corrections after this is applied are of type GaussianConditionedCorrection, adding additional points.

Parameters:

  • conditioning_points (NDArrayFloat) – Points where the process is conditioned to have the value 0.

  • mean (Union[float, Callable[[NDArrayFloat], NDArrayFloat]]) – Mean function of the (unconditioned) Gaussian process.

  • cov (CovarianceLike) – Covariance function of the (unconditioned) Gaussian process.

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