sklearn sk0.23-0.3.1 · OCaml Package

type tag = [

| `ExpSineSquared

]

type t =
  [ `ExpSineSquared
  | `NormalizedKernelMixin
  | `Object
  | `StationaryKernelMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

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

val as_normalized_kernel : t -> [ `NormalizedKernelMixin ] Obj.t

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

val create : 
  ?length_scale:float ->
  ?periodicity:float ->
  ?length_scale_bounds:[ `Tuple of float * float | `Fixed ] ->
  ?periodicity_bounds:[ `Tuple of float * float | `Fixed ] ->
  unit ->
  t

Exp-Sine-Squared kernel (aka periodic kernel).

The ExpSineSquared kernel allows one to model functions which repeat themselves exactly. It is parameterized by a length scale parameter :math:`l>0` and a periodicity parameter :math:`p>0`. Only the isotropic variant where :math:`l` is a scalar is supported at the moment. The kernel is given by:

.. math:: k(x_i, x_j) = \textxp\left(- \frac 2\sin^2(\pi d(x_i, x_j)/p) l^ 2 \right)

where :math:`l` is the length scale of the kernel, :math:`p` the periodicity of the kernel and :math:`d(\\cdot,\\cdot)` is the Euclidean distance.

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

.. versionadded:: 0.18

Parameters ----------

length_scale : float > 0, default=1.0 The length scale of the kernel.

periodicity : float > 0, default=1.0 The periodicity of the kernel.

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

periodicity_bounds : pair of floats >= 0 or 'fixed', default=(1e-5, 1e5) The lower and upper bound on 'periodicity'. If set to 'fixed', 'periodicity' 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 ExpSineSquared >>> X, y = make_friedman2(n_samples=50, noise=0, random_state=0) >>> kernel = ExpSineSquared(length_scale=1, periodicity=1) >>> gpr = GaussianProcessRegressor(kernel=kernel, alpha=5, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 0.0144... >>> gpr.predict(X:2,:, return_std=True) (array(425.6..., 457.5...), array(0.3894..., 0.3467...))

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:[> `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 : ndarray of shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y)

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