val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create : k1:Py.Object.t -> k2:Py.Object.t -> unit -> tSum-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)
.. versionadded:: 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
val clone_with_theta : theta:Ndarray.t -> t -> Py.Object.tReturns a clone of self with given hyperparameters theta.
Parameters ---------- theta : array, shape (n_dims,) The hyperparameters
val diag : x:Py.Object.t -> t -> Ndarray.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 : sequence of length n_samples_X Argument to the kernel. Could either be array-like with shape = (n_samples_X, n_features) or a list of objects.
Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X)
val get_params : ?deep:bool -> t -> Py.Object.tGet 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.
val is_stationary : t -> Py.Object.tReturns whether the kernel is stationary.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.