sklearn sk0.23-0.3.1 (latest)

type tag = [

| `Exponentiation

]

type t = [ `Exponentiation | `Object ] Obj.t

val of_pyobject : Py.Object.t -> t

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

val create : kernel:Py.Object.t -> exponent:float -> unit -> t

The Exponentiation kernel takes one base kernel and a scalar parameter :math:`p` and combines them via

.. math:: k_xp(X, Y) = k(X, Y) ^p

Note that the `__pow__` magic method is overridden, so `Exponentiation(RBF(), 2)` is equivalent to using the ** operator with `RBF() ** 2`.

Read more in the :ref:`User Guide <gp_kernels>`.

.. versionadded:: 0.18

Parameters ---------- kernel : Kernel The base kernel

exponent : float The exponent for the base kernel

Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import (RationalQuadratic, ... Exponentiation) >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = Exponentiation(RationalQuadratic(), exponent=2) >>> gpr = GaussianProcessRegressor(kernel=kernel, alpha=5, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.419... >>> gpr.predict(X:1,:, return_std=True) (array(635.5...), array(0.559...))

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:[ `List_of_object of Py.Object.t | `Arr of [> `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_X, n_features) or list of object Argument to the kernel.

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 : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

package sklearn