sklearn 0.22-0.2.0 · OCaml Package

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?gamma:float ->
  ?gamma_bounds:Py.Object.t ->
  ?metric:[ `S of string | `Callable of Py.Object.t ] ->
  ?pairwise_kernels_kwargs:Dict.t ->
  unit ->
  t

Wrapper for kernels in sklearn.metrics.pairwise.

A thin wrapper around the functionality of the kernels in sklearn.metrics.pairwise.

Note: Evaluation of eval_gradient is not analytic but numeric and all kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel parameters are set directly at initialization and are kept fixed.

.. versionadded:: 0.18

Parameters ---------- gamma : float >= 0, default: 1.0 Parameter gamma of the pairwise kernel specified by metric

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

metric : string, or callable, default: "linear" The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is "precomputed", X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.

pairwise_kernels_kwargs : dict, default: None All entries of this dict (if any) are passed as keyword arguments to the pairwise kernel function.

val clone_with_theta : theta:Arr.t -> t -> Py.Object.t

Returns a clone of self with given hyperparameters theta.

Parameters ---------- theta : array, shape (n_dims,) The hyperparameters

val diag : x:Arr.t -> t -> Arr.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, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y)

Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X)

val get_params : ?deep:bool -> 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 : t -> Py.Object.t

Returns whether the kernel is stationary.

val set_params : ?params:(string * Py.Object.t) list -> 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