landmark_shift_registration

skfda.preprocessing.registration.landmark_shift_registration(fd, landmarks, location=None, *, restrict_domain=False, extrapolation=None, grid_points=None)

Perform a shift of the curves to align the landmarks.

Let \(t^*\) the time where the landmarks of the curves will be aligned, \(t_i\) the location of the landmarks for each curve and \(\delta_i= t_i - t^*\).

The registered samples will have their feature aligned.

\[x_i^*(t^*)=x_i(t^* + \delta_i)=x_i(t_i)\]

Parameters:

• fd (FData) – Functional data object.

• landmarks (ArrayLike) – List with the landmarks of the samples.

• location (float | Sequence[float] | Callable[[ndarray], float | Sequence[float]] | None) – Defines where the landmarks will be alligned. If a numeric value is passed the landmarks will be alligned to it. In case of a callable is passed the location will be the result of the the call, the function should be accept as an unique parameter a numpy array with the list of landmarks. By default it will be used as location \(\frac{1}{2}(max( \text{landmarks})+ min(\text{landmarks}))\) wich minimizes the max shift.

• restrict_domain (bool) – If True restricts the domain to avoid evaluate points outside the domain using extrapolation. Defaults uses extrapolation.

• extrapolation (Evaluator | typing_extensions.Literal[bounds, exception, nan, none, periodic, zeros] | None) – Controls the extrapolation mode for elements outside the domain range. By default uses the method defined in fd. See extrapolation to more information.

• grid_points (Union[ArrayLike, Sequence[ArrayLike]] | None) – Grid of points where the functions are evaluated in shift().

Returns:

Functional data object with the registered samples.

Return type:

FDataGrid

Examples

>>> from skfda.datasets import make_multimodal_landmarks
>>> from skfda.datasets import make_multimodal_samples
>>> from skfda.preprocessing.registration import (
...     landmark_shift_registration,
... )

We will create a data with landmarks as example

>>> fd = make_multimodal_samples(n_samples=3, random_state=1)
>>> landmarks = make_multimodal_landmarks(n_samples=3, random_state=1)
>>> landmarks = landmarks.squeeze()

The function will return the sample registered

>>> landmark_shift_registration(fd, landmarks)
FDataGrid(...)

Examples using skfda.preprocessing.registration.landmark_shift_registration

Landmark shift

Landmark shift