Collaboration diagram for bayesopt::Parameters:

Public Member Functions
	Parameters (bopt_params c_params)

bopt_params	generate_bopt_params ()

void	set_learning (std::string name)

std::string	get_learning ()

void	set_score (std::string name)

std::string	get_score ()

Public Attributes
size_t	n_iterations
	Maximum BayesOpt evaluations (budget)

size_t	n_inner_iterations
	Maximum inner optimizer evaluations.

size_t	n_init_samples
	Number of samples before optimization.

size_t	n_iter_relearn
	Number of samples before relearn kernel.

size_t	init_method
	Sampling method for initial set 1-LHS, 2-Sobol (if available), other value-uniformly distributed.

int	random_seed
	>=0 -> Fixed seed, <0 -> Time based (variable). More...

int	verbose_level
	Neg-Error,0-Warning,1-Info,2-Debug -> stdout 3-Error,4-Warning,5-Info,>5-Debug -> logfile.

std::string	log_filename
	Log file path (if applicable)

size_t	load_save_flag
	1-Load data,2-Save data, 3-Load and save data. More...

std::string	load_filename
	Init data file path (if applicable)

std::string	save_filename
	Sava data file path (if applicable)

std::string	surr_name
	Name of the surrogate function.

double	sigma_s
	Signal variance (if known). More...

double	noise
	Variance of observation noise (and nugget)

double	alpha
	Inverse Gamma prior for signal var. More...

double	beta
	Inverse Gamma prior for signal var. More...

score_type	sc_type
	Score type for kernel hyperparameters (ML,MAP,etc)

learning_type	l_type
	Type of learning for the kernel params.

bool	l_all
	Learn all hyperparameters or only kernel.

double	epsilon
	For epsilon-greedy exploration.

size_t	force_jump
	If >0, and the difference between two consecutive observations is pure noise, for n consecutive steps, force a random jump. More...

KernelParameters	kernel
	Kernel parameters.

MeanParameters	mean
	Mean (parametric function) parameters.

std::string	crit_name
	Name of the criterion.

vectord	crit_params
	Criterion hyperparameters (if needed)