Problems found when evaluating FDataIrregular objects in some cases are described in #616 and #617
Describe the proposed changes
1. Mixed-effects model
Implementation of the mixed effects method for converting irregularly sampled functional data to basis expansion.
This implementation includes the addition of a new module: skfda.representation.conversion, which is meant for including classes (transformers) that convert between different FData sublasses.
For this type of conversion (FDataIrregular to FDataBasis) two classes have been added: EMMixedEffectsConverter and MinimizeMixedEffectsConverter, each of which implements the conversion with a different method to fit the MLE of the mixed-effects model.
The EMMixedEffectsConverter uses the EM algorithm and the MinimizeMixedEffectsConverter uses generic optimizers from scipy.optimize.minimize to find maxima of the loglikelihood function.
2. Create an FDataIrregular object from another FData
A function: irregular_sample has been added to skfda.datasets, which creates an irregular sample by randomly selecting points from another FData object.
Additional information
All implementation added has been adapted (and tested) for multidimensional domains and codomains.
However, some problems have been found regarding this topic and are described in #616 and #617 .
Checklist before requesting a review
[x] I have performed a self-review of my code
[ ] The code conforms to the style used in this package
[x] The code is fully documented and typed (type-checked with Mypy)
[x] I have added thorough tests for the new/changed functionality
References to issues or other PRs
This branch includes the changes not yet merged into develop from https://github.com/GAA-UAM/scikit-fda/pull/610
Problems found when evaluating FDataIrregular objects in some cases are described in #616 and #617
Describe the proposed changes
1. Mixed-effects model
Implementation of the mixed effects method for converting irregularly sampled functional data to basis expansion.
This implementation includes the addition of a new module: skfda.representation.conversion, which is meant for including classes (transformers) that convert between different FData sublasses.
For this type of conversion (FDataIrregular to FDataBasis) two classes have been added: EMMixedEffectsConverter and MinimizeMixedEffectsConverter, each of which implements the conversion with a different method to fit the MLE of the mixed-effects model.
The EMMixedEffectsConverter uses the EM algorithm and the MinimizeMixedEffectsConverter uses generic optimizers from scipy.optimize.minimize to find maxima of the loglikelihood function.
2. Create an FDataIrregular object from another FData
A function:
irregular_samplehas been added to skfda.datasets, which creates an irregular sample by randomly selecting points from another FData object.Additional information
All implementation added has been adapted (and tested) for multidimensional domains and codomains.
However, some problems have been found regarding this topic and are described in #616 and #617 .
Checklist before requesting a review