# sklearn.gaussian_process.kernels.CompoundKernel¶

class sklearn.gaussian_process.kernels.CompoundKernel(kernels)[source]

Kernel which is composed of a set of other kernels.

New in version 0.18.

Parameters
kernelslist of Kernels

The other kernels

Attributes
bounds

Returns the log-transformed bounds on the theta.

hyperparameters

Returns a list of all hyperparameter specifications.

n_dims

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

requires_vector_input

Returns whether the kernel is defined on discrete structures.

theta

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

Examples

>>> from sklearn.gaussian_process.kernels import WhiteKernel
>>> from sklearn.gaussian_process.kernels import RBF
>>> from sklearn.gaussian_process.kernels import CompoundKernel
>>> kernel = CompoundKernel(
...     [WhiteKernel(noise_level=3.0), RBF(length_scale=2.0)])
>>> print(kernel.bounds)
[[-11.51292546  11.51292546]
[-11.51292546  11.51292546]]
>>> print(kernel.n_dims)
2
>>> print(kernel.theta)
[1.09861229 0.69314718]


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. Returns the diagonal of the kernel k(X, X). get_params([deep]) Get parameters of this kernel. Returns whether the kernel is stationary. set_params(**params) Set the parameters of this kernel.

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

Note that this compound kernel returns the results of all simple kernel stacked along an additional axis.

Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object, default=None

Left argument of the returned kernel k(X, Y)

Yarray-like of shape (n_samples_X, n_features) or list of object, default=None

Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead.

Determines whether the gradient with respect to the log of the kernel hyperparameter is computed.

Returns
Kndarray of shape (n_samples_X, n_samples_Y, n_kernels)

Kernel k(X, Y)

K_gradientndarray of shape (n_samples_X, n_samples_X, n_dims, n_kernels), optional

The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True.

property bounds

Returns the log-transformed bounds on the theta.

Returns
boundsarray of 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
thetandarray of 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
Xarray-like of shape (n_samples_X, n_features) or list of object

Argument to the kernel.

Returns
K_diagndarray of shape (n_samples_X, n_kernels)

Diagonal of kernel k(X, X)

get_params(deep=True)[source]

Get parameters of this kernel.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

property hyperparameters

Returns a list of all hyperparameter specifications.

is_stationary()[source]

Returns whether the kernel is stationary.

property n_dims

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

property requires_vector_input

Returns whether the kernel is defined on discrete structures.

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
property 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
thetandarray of shape (n_dims,)

The non-fixed, log-transformed hyperparameters of the kernel