sklearn sk0.23-0.3.1 · OCaml Package

type tag = [

| `Product

]

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

val of_pyobject : Py.Object.t -> t

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

val create : k1:Py.Object.t -> k2:Py.Object.t -> unit -> t

The `Product` kernel takes two kernels :math:`k_1` and :math:`k_2` and combines them via

.. math:: k_prod(X, Y) = k_1(X, Y) * k_2(X, Y)

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

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

.. versionadded:: 0.18

Parameters ---------- k1 : Kernel The first base-kernel of the product-kernel

k2 : Kernel The second base-kernel of the product-kernel

Examples -------- >>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import (RBF, Product, ... ConstantKernel) >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = Product(ConstantKernel(2), RBF()) >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 1.0 >>> kernel 1.41**2 * RBF(length_scale=1)

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