Module Interfaces.Sigs.Deriv.Deriv.Optim.Gsl

module Gsl: sig .. end

Optimization with the GNU Scientific library (GSL)

exception Optim_exception of exn

Optim_exception exn is raised when an internal exception occurs, e.g. because GSL fails, or because a callback raised it.

val train : ?step:float ->
       ?tol:float ->
       ?epsabs:float ->
       ?report_trained_model:(iter:int -> Interfaces.Sigs.Eval.Trained.t -> unit) ->
       ?report_gradient_norm:(iter:int -> float -> unit) ->
       ?kernel:Eval.Spec.Kernel.t ->
       ?sigma2:float ->
       ?inducing:Eval.Spec.Inducing.t ->
       ?n_rand_inducing:int ->
       ?learn_sigma2:bool ->
       ?hypers:Interfaces.Sigs.Deriv.Deriv.Spec.Hyper.t array ->
       inputs:Eval.Spec.Inputs.t ->
       targets:Lacaml.D.vec -> unit -> Interfaces.Sigs.Eval.Trained.t

train ?step ?tol ?epsabs ?report_trained_model ?report_gradient_norm ?kernel ?sigma2 ?inducing ?n_rand_inducing ?learn_sigma2 ?hypers ~inputs ~targets () takes the optional initial optimizer step size step, the optimizer line search tolerance tol, the minimum gradient norm epsabs to achieve by the optimizer, callbacks for reporting intermediate results report_trained_model and report_gradient_norm, an optional kernel, noise level sigma2, inducing inputs inducing, number of randomly chosen inducing inputs n_rand_inducing, a flag for whether the noise level should be learnt learn_sigma2, an array of optional hyper parameters hypers which should be optimized, and the inputs and targets.
Returns the trained model obtained by evidence maximization (= type II maximum likelihood).