A class implementing the BayesOpt algorithm as seen in: https://browse.arxiv.org/pdf/1807.02811.pdf. More...

#include <Bayes_Opt.h>

Public Member Functions
	Bayes_Opt (double(f_pointer)(Matrix_real, void ), void *meta_data_in)
	Constructor of the class. More...

double	Start_Optimization (Matrix_real &x, int max_iterations_in)

	~Bayes_Opt ()
	Destructor of the class. More...

Public Attributes
Matrix_real	covariance
	covariance matrix More...

double	mu_0
	constant for the mean function More...

Protected Member Functions
void	calculate_conditional_distribution (Matrix_real x, Matrix_real cov_x, double &mu_n, double &sigma2_n)

void	calculate_conditional_distribution_combined (Matrix_real x, Matrix_real cov_x, Matrix_real cov_x_grad, Matrix_real cov_self_grad, double &mu_n, double &sigma2_n, Matrix_real &grad_mu, Matrix_real &grad_sigma)

double	cdf (double mu, double sigma)

double	expected_improvement (double mu_n, double sigma_n)

void	expected_improvement_combined (double mu_n, double sigma_n, Matrix_real &grad_mu, Matrix_real &grad_sigma, double *f, Matrix_real &grad)

double	grad_pdf (double mu, double sigma, double grad_mu, double grad_sigma)

double	kernel (Matrix_real x0, Matrix_real x1)

void	kernel_combined (Matrix_real x0, Matrix_real x, double &f, Matrix_real &grad, int grad_var, bool self)

double	pdf (double mu, double sigma)

void	update_covariance (Matrix_real cov_new)

Static Protected Member Functions
static double	optimization_problem (Matrix_real x_Powell, void *void_instance)

static void	optimization_problem_combined (Matrix_real x, void void_instance, double f0, Matrix_real &grad)

Protected Attributes
double	alpha0
	amplitude of the kernel More...

double(*	costfnc )(Matrix_real x, void *params)
	function pointer to evaluate the cost function and its gradient vector More...

double	current_maximum

std::vector< double >	f_prev

std::mt19937	gen

int	initial_samples

long	maximal_iterations
	maximal count of iterations during the optimization More...

void *	meta_data
	additional data needed to evaluate the cost function More...

double	num_precision
	numerical precision used in the calculations More...

int	variable_num
	number of independent variables in the problem More...

std::vector< Matrix_real >	x_prev
	previous parameters More...

Detailed Description

A class implementing the BayesOpt algorithm as seen in: https://browse.arxiv.org/pdf/1807.02811.pdf.

Definition at line 59 of file Bayes_Opt.h.

Constructor & Destructor Documentation

◆ Bayes_Opt()

Bayes_Opt::Bayes_Opt	(	double()(Matrix_real, void )	f_pointer,
		void *	meta_data_in
	)

Constructor of the class.

Parameters

f_pointer	A function pointer (x, meta_data, f, grad) to evaluate the cost function and its gradients. The cost function and the gradient vector are returned via reference by the two last arguments.
meta_data	void pointer to additional meta data needed to evaluate the cost function.

Returns: An instance of the class

Definition at line 76 of file common/Bayes_Opt.cpp.

◆ ~Bayes_Opt()

Bayes_Opt::~Bayes_Opt ( )

Destructor of the class.

Definition at line 433 of file common/Bayes_Opt.cpp.

Member Function Documentation

◆ calculate_conditional_distribution()

void Bayes_Opt::calculate_conditional_distribution	(	Matrix_real	x,
		Matrix_real	cov_x,
		double &	mu_n,
		double &	sigma2_n
	)

protected

Definition at line 220 of file common/Bayes_Opt.cpp.

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◆ calculate_conditional_distribution_combined()

void Bayes_Opt::calculate_conditional_distribution_combined	(	Matrix_real	x,
		Matrix_real	cov_x,
		Matrix_real	cov_x_grad,
		Matrix_real	cov_self_grad,
		double &	mu_n,
		double &	sigma2_n,
		Matrix_real &	grad_mu,
		Matrix_real &	grad_sigma
	)

protected

Definition at line 326 of file common/Bayes_Opt.cpp.

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◆ cdf()

double Bayes_Opt::cdf	(	double	mu,
		double	sigma
	)

protected

Definition at line 271 of file common/Bayes_Opt.cpp.

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◆ expected_improvement()

double Bayes_Opt::expected_improvement	(	double	mu_n,
		double	sigma_n
	)

protected

Definition at line 211 of file common/Bayes_Opt.cpp.

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◆ expected_improvement_combined()

void Bayes_Opt::expected_improvement_combined	(	double	mu_n,
		double	sigma_n,
		Matrix_real &	grad_mu,
		Matrix_real &	grad_sigma,
		double *	f,
		Matrix_real &	grad
	)

protected

Definition at line 382 of file common/Bayes_Opt.cpp.

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◆ grad_pdf()

double Bayes_Opt::grad_pdf	(	double	mu,
		double	sigma,
		double	grad_mu,
		double	grad_sigma
	)

protected

Definition at line 275 of file common/Bayes_Opt.cpp.

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◆ kernel()

double Bayes_Opt::kernel	(	Matrix_real	x0,
		Matrix_real	x1
	)

protected

Definition at line 253 of file common/Bayes_Opt.cpp.

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◆ kernel_combined()

void Bayes_Opt::kernel_combined	(	Matrix_real	x0,
		Matrix_real	x,
		double &	f,
		Matrix_real &	grad,
		int	grad_var,
		bool	self
	)

protected

Definition at line 302 of file common/Bayes_Opt.cpp.

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◆ optimization_problem()

double Bayes_Opt::optimization_problem	(	Matrix_real	x_Powell,
		void *	void_instance
	)

staticprotected

Definition at line 195 of file common/Bayes_Opt.cpp.

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◆ optimization_problem_combined()

void Bayes_Opt::optimization_problem_combined	(	Matrix_real	x,
		void *	void_instance,
		double *	f0,
		Matrix_real &	grad
	)

staticprotected

Definition at line 399 of file common/Bayes_Opt.cpp.

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◆ pdf()

double Bayes_Opt::pdf	(	double	mu,
		double	sigma
	)

protected

Definition at line 267 of file common/Bayes_Opt.cpp.

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◆ Start_Optimization()

double Bayes_Opt::Start_Optimization	(	Matrix_real &	x,
		int	max_iterations_in
	)

Definition at line 104 of file common/Bayes_Opt.cpp.

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◆ update_covariance()

void Bayes_Opt::update_covariance ( Matrix_real cov_new )

protected

covariance matrix

Definition at line 279 of file common/Bayes_Opt.cpp.

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Member Data Documentation

◆ alpha0

double Bayes_Opt::alpha0

protected

amplitude of the kernel

Definition at line 77 of file Bayes_Opt.h.

◆ costfnc

double(* Bayes_Opt::costfnc) (Matrix_real x, void *params)

protected

function pointer to evaluate the cost function and its gradient vector

Definition at line 80 of file Bayes_Opt.h.

◆ covariance

Matrix_real Bayes_Opt::covariance

covariance matrix

Definition at line 65 of file Bayes_Opt.h.

◆ current_maximum

double Bayes_Opt::current_maximum

protected

Definition at line 94 of file Bayes_Opt.h.

◆ f_prev

std::vector<double> Bayes_Opt::f_prev

protected

Definition at line 90 of file Bayes_Opt.h.

◆ gen

std::mt19937 Bayes_Opt::gen

protected

Definition at line 99 of file Bayes_Opt.h.

◆ initial_samples

int Bayes_Opt::initial_samples

protected

Definition at line 97 of file Bayes_Opt.h.

◆ maximal_iterations

long Bayes_Opt::maximal_iterations

protected

maximal count of iterations during the optimization

Definition at line 71 of file Bayes_Opt.h.

◆ meta_data

void* Bayes_Opt::meta_data

protected

additional data needed to evaluate the cost function

Definition at line 83 of file Bayes_Opt.h.

◆ mu_0

double Bayes_Opt::mu_0

constant for the mean function

Definition at line 62 of file Bayes_Opt.h.

◆ num_precision

double Bayes_Opt::num_precision

protected

numerical precision used in the calculations

Definition at line 74 of file Bayes_Opt.h.

◆ variable_num

int Bayes_Opt::variable_num

protected

number of independent variables in the problem

Definition at line 68 of file Bayes_Opt.h.

◆ x_prev

std::vector<Matrix_real> Bayes_Opt::x_prev

protected

previous parameters

Definition at line 86 of file Bayes_Opt.h.

The documentation for this class was generated from the following files:

Public Member Functions

Public Attributes

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ Bayes_Opt()

◆ ~Bayes_Opt()

Member Function Documentation

◆ calculate_conditional_distribution()

◆ calculate_conditional_distribution_combined()

◆ cdf()

◆ expected_improvement()

◆ expected_improvement_combined()

◆ grad_pdf()

◆ kernel()

◆ kernel_combined()

◆ optimization_problem()

◆ optimization_problem_combined()

◆ pdf()

◆ Start_Optimization()

◆ update_covariance()

Member Data Documentation

◆ alpha0

◆ costfnc

◆ covariance

◆ current_maximum

◆ f_prev

◆ gen

◆ initial_samples

◆ maximal_iterations

◆ meta_data

◆ mu_0

◆ num_precision

◆ variable_num

◆ x_prev