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Module Kernels.PairwiseKernelSource

Sourcetype tag = [
  1. | `PairwiseKernel
]
Sourcetype t = [ `Object | `PairwiseKernel ] Obj.t
Sourceval of_pyobject : Py.Object.t -> t
Sourceval to_pyobject : [> tag ] Obj.t -> Py.Object.t
Sourceval create : ?gamma:float -> ?gamma_bounds:[ `Tuple of float * float | `Fixed ] -> ?metric: [ `Sigmoid | `Rbf | `Poly | `Cosine | `Callable of Py.Object.t | `Polynomial | `Chi2 | `Additive_chi2 | `Laplacian | `Linear ] -> ?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, default=1.0 Parameter gamma of the pairwise kernel specified by metric. It should be positive.

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

metric : 'linear', 'additive_chi2', 'chi2', 'poly', 'polynomial', 'rbf', 'laplacian', 'sigmoid', 'cosine' 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.

Sourceval 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

Sourceval 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)

Sourceval 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.

Sourceval is_stationary : [> tag ] Obj.t -> Py.Object.t

Returns whether the kernel is stationary.

Sourceval 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

Sourceval to_string : t -> string

Print the object to a human-readable representation.

Sourceval show : t -> string

Print the object to a human-readable representation.

Sourceval pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.