Structure containing the optimization options for hyperparameter-tuning. More...

#include <GaussianProcess.h>

Public Member Functions
	GPOptimizerOptions ()
	Default constructor. More...

	GPOptimizerOptions (const bool show_every_nth_iteration=1, const unsigned int num_iter=1000, const unsigned int batch_size=0, const Real learning_rate=1e-3, const Real b1=0.9, const Real b2=0.999, const Real eps=1e-7, const Real lambda=0.0)
	Construct a new GPOptimizerOptions object using input parameters that will control the optimization. More...

Public Attributes
const unsigned int	show_every_nth_iteration = false
	Switch to enable verbose output for parameter tuning at every n-th iteration. More...

const unsigned int	num_iter = 1000
	The number of iterations for Adam optimizer. More...

const unsigned int	batch_size = 0
	The batch isize for Adam optimizer. More...

const Real	learning_rate = 1e-3
	The learning rate for Adam optimizer. More...

const Real	b1 = 0.9
	Tuning parameter from the paper. More...

const Real	b2 = 0.999
	Tuning parameter from the paper. More...

const Real	eps = 1e-7
	Tuning parameter from the paper. More...

const Real	lambda = 0.0
	Tuning parameter from the paper. More...

Detailed Description

Structure containing the optimization options for hyperparameter-tuning.

Definition at line 47 of file GaussianProcess.h.

Constructor & Destructor Documentation

◆ GPOptimizerOptions() [1/2]

StochasticTools::GaussianProcess::GPOptimizerOptions::GPOptimizerOptions ( )

Default constructor.

◆ GPOptimizerOptions() [2/2]

StochasticTools::GaussianProcess::GPOptimizerOptions::GPOptimizerOptions	(	const bool	show_every_nth_iteration = `1`,
		const unsigned int	num_iter = `1000`,
		const unsigned int	batch_size = `0`,
		const Real	learning_rate = `1e-3`,
		const Real	b1 = `0.9`,
		const Real	b2 = `0.999`,
		const Real	eps = `1e-7`,
		const Real	lambda = `0.0`
	)

Construct a new GPOptimizerOptions object using input parameters that will control the optimization.

Parameters

show_every_nth_iteration	To show the loss value at every n-th iteration, if set to 0, nothing is displayed
num_iter	The number of iterations we want in the optimization of the GP
batch_size	The number of samples in each batch
learning_rate	The learning rate for parameter updates
b1	Tuning constant for the Adam algorithm
b2	Tuning constant for the Adam algorithm
eps	Tuning constant for the Adam algorithm
lambda	Tuning constant for the Adam algorithm

Definition at line 27 of file GaussianProcess.C.

   : show_every_nth_iteration(show_every_nth_iteration),
     num_iter(num_iter),
     batch_size(batch_size),
     learning_rate(learning_rate),
     b1(b1),
     b2(b2),
     eps(eps),
     lambda(lambda)
 {
 }