landmark_elastic_registration#
- skfda.preprocessing.registration.landmark_elastic_registration(fd, landmarks, *, location=None, grid_points=None)[source]#
Perform landmark registration of the curves.
Let \(t_{ij}\) the time where the sample \(i\) has the feature \(j\) and \(t^*_j\) the new time for the feature. The registered samples will have their features aligned, i.e., \(x^*_i(t^*_j)=x_i(t_{ij})\).
See [1] for a detailed explanation.
- Parameters:
fd (FData) – Functional data object.
landmarks (ArrayLike) – List containing landmarks for each samples.
location (ArrayLike | None) – Defines where the landmarks will be alligned. By default it will be used as location the mean of the landmarks.
grid_points (Union[ArrayLike, Sequence[ArrayLike]] | None) – Grid of points where the functions are evaluated to obtain a discrete representation of the object. In case of objects with multidimensional domain a list axis with points of evaluation for each dimension.
- Returns:
FDataGrid with the functional data object registered.
- Return type:
References
Examples
>>> from skfda.datasets import make_multimodal_landmarks >>> from skfda.datasets import make_multimodal_samples >>> from skfda.preprocessing.registration import ( ... landmark_elastic_registration, ... ) >>> from skfda.representation.basis import BSplineBasis
We will create a data with landmarks as example
>>> fd = make_multimodal_samples(n_samples=3, n_modes=2, ... random_state=9) >>> landmarks = make_multimodal_landmarks(n_samples=3, n_modes=2, ... random_state=9) >>> landmarks = landmarks.squeeze()
The function will return the registered curves
>>> landmark_elastic_registration(fd, landmarks) FDataGrid(...)
This method will work for FDataBasis as for FDataGrids
>>> fd = fd.to_basis(BSplineBasis(n_basis=12)) >>> landmark_elastic_registration(fd, landmarks) FDataGrid(...)
