make_gaussian

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

Generate Gaussian random fields.

Parameters:

• n_samples (int) – The total number of trajectories.

• grid_points (GridPointsLike) – Sample points for the evaluation grid of the Gaussian field.

• domain_range (DomainRangeLike | None) – The domain range of the returned functional observations.

• mean (MeanLike) – The mean function of the random field. Can be a callable accepting a vector with the locations, or a vector with appropriate size.

• cov (CovarianceLike | 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 (RandomStateLike) – Random state.

Returns:

FDataGrid object comprising all the trajectories.

Return type:

FDataGrid

See also

make_gaussian_process(): Simpler function for generating Gaussian processes.

Examples using skfda.datasets.make_gaussian

Getting the data

Getting the data