sklearn.gaussian_process.kernels.WhiteKernel

class sklearn.gaussian_process.kernels.WhiteKernel(noise_level=1.0, noise_level_bounds=(1e-05, 100000.0)) [source]

White kernel.

The main use-case of this kernel is as part of a sum-kernel where it explains the noise-component of the signal. Tuning its parameter corresponds to estimating the noise-level.

k(x_1, x_2) = noise_level if x_1 == x_2 else 0

New in version 0.18.

Parameters:	noise_level : float, default: 1.0 Parameter controlling the noise level noise_level_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on noise_level
Attributes:	bounds Returns the log-transformed bounds on the theta. hyperparameter_noise_level hyperparameters Returns a list of all hyperparameter specifications. n_dims Returns the number of non-fixed hyperparameters of the kernel. theta Returns the (flattened, log-transformed) non-fixed hyperparameters.

Methods

__call__(X[, Y, eval_gradient])	Return the kernel k(X, Y) and optionally its gradient.
clone_with_theta(theta)	Returns a clone of self with given hyperparameters theta.
diag(X)	Returns the diagonal of the kernel k(X, X).
get_params([deep])	Get parameters of this kernel.
is_stationary()	Returns whether the kernel is stationary.
set_params(**params)	Set the parameters of this kernel.

__init__(noise_level=1.0, noise_level_bounds=(1e-05, 100000.0)) [source]

__call__(X, Y=None, eval_gradient=False) [source]

Return the kernel k(X, Y) and optionally its gradient.

Parameters:

X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Y : array, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None.

Returns:

K : array, shape (n_samples_X, n_samples_Y) Kernel k(X, Y) K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True.

bounds

Returns the log-transformed bounds on the theta.

Returns:	bounds : array, shape (n_dims, 2) The log-transformed bounds on the kernel’s hyperparameters theta

clone_with_theta(theta) [source]

Returns a clone of self with given hyperparameters theta.

Parameters:	theta : array, shape (n_dims,) The hyperparameters

diag(X) [source]

Returns the diagonal of the kernel k(X, X).

The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated.

Parameters:	X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y)
Returns:	K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X)

get_params(deep=True) [source]

Get parameters of this kernel.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

hyperparameters

Returns a list of all hyperparameter specifications.

is_stationary() [source]

Returns whether the kernel is stationary.

n_dims

Returns the number of non-fixed hyperparameters of the kernel.

set_params(**params) [source]

Set the parameters of this kernel.

The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self

theta

Returns the (flattened, log-transformed) non-fixed hyperparameters.

Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale.

Returns:	theta : array, shape (n_dims,) The non-fixed, log-transformed hyperparameters of the kernel

Examples using sklearn.gaussian_process.kernels.WhiteKernel