sklearn.gaussian_process.kernels.Kernel

class sklearn.gaussian_process.kernels.Kernel

Base class for all kernels.

New in version 0.18.

Methods

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__()

x.__init__(…) initializes x; see help(type(x)) for signature

__call__(X, Y=None, eval_gradient=False)

Evaluate the kernel.

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)

Returns a clone of self with given hyperparameters theta.

diag(X)

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)

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()

Returns whether the kernel is stationary.

n_dims

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

set_params(**params)

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