package sklearn

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sklearn
- Sklearn

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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:(float * float) ->
  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.

k(x_1, x_2) = noise_level if x_1 == x_2 else 0

.. versionadded:: 0.18

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

noise_level_bounds : pair of floats >= 0, default: (1e-5, 1e5) The lower and upper bound on noise_level

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 : array, shape (n_dims,) The hyperparameters

val diag : x:Py.Object.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 : 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 -> [> tag ] Obj.t -> Dict.t

Get 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 : [> 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.