mojolearn.WhiteKernel

class mojolearn.WhiteKernel(noise_level=1.0, noise_level_bounds=(1e-05, 100000.0))

White noise kernel: adds noise_level to the training diagonal.

Follows scikit-learn’s WhiteKernel: the noise appears only on the diagonal of the training covariance, and contributes zero to the covariance between training and query points. Add it to another kernel to model observation noise.

Parameters:

• noise_level (float, default 1.0) – The noise variance.

• noise_level_bounds (pair of float or “fixed”, default (1e-5, 1e5)) – Bounds for the optimizer, or "fixed" to keep the value fixed. Both bounds must be finite and positive with low <= high; otherwise ValueError is raised.

Examples

>>> import mojolearn
>>> kernel = mojolearn.RBF(1.0) + mojolearn.WhiteKernel(0.1)

See also

User guide: Gaussian processes

Choosing an estimator

Attributes

bounds	Natural log of each free hyperparameter's (low, high) bounds, as a list of [low, high] pairs.
n_dims	Number of free hyperparameters (the length of theta).
theta	Natural log of the free hyperparameters, as a list of floats.

property bounds

Natural log of each free hyperparameter’s (low, high) bounds, as a list of [low, high] pairs.

property n_dims

Number of free hyperparameters (the length of theta).

property theta

Natural log of the free hyperparameters, as a list of floats.

On a fitted kernel_ this is the optimizer’s own theta.