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 (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[Any, dtype[float64]] | 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[Any, dtype[float64]] | Callable[[_SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]], ndarray[Any, dtype[float64]]] | 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:

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