package sklearn
Module Kernels.CompoundKernelSource
Kernel which is composed of a set of other kernels.
.. versionadded:: 0.18
Parameters ---------- kernels : list of Kernels The other kernels
Returns a clone of self with given hyperparameters theta.
Parameters ---------- theta : ndarray of shape (n_dims,) The hyperparameters
val diag :
x:[ `List_of_object of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tReturns 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-like of shape (n_samples_X, n_features) or list of object Argument to the kernel.
Returns ------- K_diag : ndarray of shape (n_samples_X, n_kernels) Diagonal of kernel k(X, X)
Get parameters of this kernel.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : dict Parameter names mapped to their values.
Returns whether the kernel is stationary.
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
Pretty-print the object to a formatter.