package sklearn

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package sklearn
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type tag = [
  1. | `Kernel
]
type t = [ `Kernel | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val clone_with_theta : theta:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> Py.Object.t

Returns a clone of self with given hyperparameters theta.

Parameters ---------- theta : ndarray of shape (n_dims,) The hyperparameters

val diag : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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-like of shape (n_samples,) Left argument of the returned kernel k(X, Y)

Returns ------- K_diag : ndarray of shape (n_samples_X,) Diagonal of kernel k(X, X)

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val is_stationary : [> tag ] Obj.t -> Py.Object.t

Returns whether the kernel is stationary.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.