sklearn sk0.23-0.3.1 · OCaml Package

type tag = [

| `ConstantKernel

]

type t =
  [ `ConstantKernel | `GenericKernelMixin | `Object | `StationaryKernelMixin ]
    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 : 
  ?constant_value:float ->
  ?constant_value_bounds:[ `Tuple of float * float | `Fixed ] ->
  unit ->
  t

Constant kernel.

Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, where it modifies the mean of the Gaussian process.

.. math:: k(x_1, x_2) = constant\_value \;\forall\; x_1, x_2

Adding a constant kernel is equivalent to adding a constant::

kernel = RBF() + ConstantKernel(constant_value=2)

is the same as::

kernel = RBF() + 2

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

.. versionadded:: 0.18

Parameters ---------- constant_value : float, default=1.0 The constant value which defines the covariance: k(x_1, x_2) = constant_value

constant_value_bounds : pair of floats >= 0 or 'fixed', default=(1e-5, 1e5) The lower and upper bound on `constant_value`. If set to 'fixed', `constant_value` 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 RBF, ConstantKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = RBF() + ConstantKernel(constant_value=2) >>> gpr = GaussianProcessRegressor(kernel=kernel, alpha=5, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.3696... >>> gpr.predict(X:1,:, return_std=True) (array(606.1...), array(0.24...))

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

Pretty-print the object to a formatter.

package sklearn