Optimization with the GNU Scientific library (GSL)
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 -> 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:Spec.Hyper.t array ->
inputs:Eval.Spec.Inputs.t ->
targets:Lacaml.D.vec ->
unit ->
Eval.Trained.ttrain ?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.
the trained model obtained by evidence maximization (= type II maximum likelihood).
default = 1e-1
default = 1e-1
default = 1e-1
default = ignored
default = ignored
default = default kernel computed from specification
default = target variance
default = randomly selected subset of inputs
default = 10% of inputs, at most 1000
default = true
default = all hyper parameters