sklearn sk0.23-0.3.1 (latest)

type tag = [

| `WhiteKernel

]

type t =
  [ `GenericKernelMixin | `Object | `StationaryKernelMixin | `WhiteKernel ]
    Obj.t

val of_pyobject : Py.Object.t -> t

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

val as_stationary_kernel : t -> [ `StationaryKernelMixin ] Obj.t

val as_generic_kernel : t -> [ `GenericKernelMixin ] Obj.t

val create : 
  ?noise_level:float ->
  ?noise_level_bounds:[ `Tuple of float * float | `Fixed ] ->
  unit ->
  t

White kernel.

The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-distributed. The parameter noise_level equals the variance of this noise.

.. math:: k(x_1, x_2) = noise\_level \text if x_i == x_j \text else 0

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

.. versionadded:: 0.18

Parameters ---------- noise_level : float, default=1.0 Parameter controlling the noise level (variance)

noise_level_bounds : pair of floats >= 0 or 'fixed', default=(1e-5, 1e5) The lower and upper bound on 'noise_level'. If set to 'fixed', 'noise_level' cannot be changed during hyperparameter tuning.

Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = DotProduct() + WhiteKernel(noise_level=0.5) >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.3680... >>> gpr.predict(X:2,:, return_std=True) (array(653.0..., 592.1... ), array(316.6..., 316.6...))

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