# sklearn.gaussian_process.kernels.Sum¶

class sklearn.gaussian_process.kernels.Sum(k1, k2)

Sum-kernel k1 + k2 of two kernels k1 and k2.

The resulting kernel is defined as k_sum(X, Y) = k1(X, Y) + k2(X, Y)

New in version 0.18.

Parameters: k1 : Kernel object The first base-kernel of the sum-kernel k2 : Kernel object The second base-kernel of the sum-kernel

Attributes

 bounds Returns the log-transformed bounds on the theta. hyperparameters Returns a list of all hyperparameter. 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__(k1, k2)

Initialize self. See help(type(self)) for accurate signature.

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

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. 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)

Returns a clone of self with given hyperparameters theta.

Parameters: theta : array, shape (n_dims,) The hyperparameters
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) 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. params : mapping of string to any Parameter names mapped to their values.
hyperparameters

Returns a list of all hyperparameter.

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