make_gaussian#

skfda.datasets.make_gaussian(n_samples=100, *, grid_points, domain_range=None, mean=0, cov=None, noise=0, random_state=None)[source]#

Generate Gaussian random fields.

Parameters:

n_samples (int) – The total number of trajectories.
grid_points (Union[ArrayLike, Sequence[ArrayLike]]) – Sample points for the evaluation grid of the Gaussian field.
domain_range (Tuple[Tuple[float, float], ...] | Sequence[float] | Sequence[Sequence[float]] | None) – The domain range of the returned functional observations.
mean (float | ndarray[tuple[int, ...], dtype[floating[Any]]] | Callable[[ndarray], ndarray]) – The mean function of the random field. Can be a callable accepting a vector with the locations, or a vector with appropriate size.
cov (float | ndarray[tuple[int, ...], dtype[floating[Any]]] | Callable[[ndarray[tuple[int, ...], dtype[floating[Any]]], ndarray[tuple[int, ...], dtype[floating[Any]]]], ndarray[tuple[int, ...], dtype[floating[Any]]]] | None) – The covariance function of the process. Can be a callable accepting two vectors with the locations, or a matrix with appropriate size. By default, the Brownian covariance function is used.
noise (float) – Standard deviation of Gaussian noise added to the data.
random_state (int | RandomState | Generator | None) – Random state.

Returns: