EMMixedEffectsConverter#

class skfda.representation.conversion.EMMixedEffectsConverter(basis)[source]#

Mixed effects to-basis-converter using the EM algorithm.

Minimizes the profile loglikelihood of the mixed effects model with the EM algorithm as proposed by Nan Laird and Stram[1].

Methods

`fit`(X[, y, initial_params, maxiter, ...])	Fit the model using the EM algorithm.
`fit_transform`(X[, y])	Fit to data, then transform it.
`set_output`(*[, transform])	Set output container.
`transform`(X)	Transform X to FDataBasis using the fitted converter.

fit(X, y=None, *, initial_params=None, maxiter=700, convergence_criterion=None, rtol=0.001)[source]#

Fit the model using the EM algorithm.

Parameters:

X (FDataIrregular) – irregular data to fit.
y (object) – ignored.
initial_params (Params | ndarray[tuple[int, ...], dtype[floating[Any]]] | None) – initial params of the model.
maxiter (int) – maximum number of iterations.
convergence_criterion (Literal['params', 'squared-error', 'loglikelihood'] | None) –
convergence criterion to use when fitting.
- ”params”:
  to use relative differences between parameters (the default).
- ”squared-error”:
  to use relative changes in the squared error of the estimated values with respect to the original data.
- ”loglikelihood”:
  to use relative changes in the loglikelihood.
rtol (float) – relative tolerance for convergence.

Returns:

The converter after fitting.

Return type:

Self

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

transform(X)[source]#

Transform X to FDataBasis using the fitted converter.

Examples using `skfda.representation.conversion.EMMixedEffectsConverter`#