Extra utils: math functions, ublas helpers, etc. More...

Classes
class	BoundingBox
	Defines a bounding box or axis-alligned bound constraints. More...

class	CUnique
	Simple class to generate sequences of unique numbers. More...

class	DisplayProblem1D

class	DisplayProblem2D

class	FileParser

class	ParamLoader

Enumerations
enum	RunningStatus { RUN, STEP, STOP, NOT_READY }

Functions
void	deepenGrid (size_t comp, const vectori ndivs, vectord &x, vecOfvec &result)

void	buildGrid (const vectori &dims, vecOfvec &result)

std::vector< int >	return_index_vector (size_t n)
	Generates a vector of indexes (1..n) More...

std::vector< int >	return_index_vector (int a, size_t n)
	Generates a vector of indexes starting at A and size_t n. More...

void	modify_index_vector (std::vector< int > &arr)
	Modify a vector of indexes (0..n) More...

template<class M >
void	samplePoints (M &xPoints, int method, randEngine &mtRandom)
	Selects the sampling method. More...

template<class M >
void	lhs (M &Result, randEngine &mtRandom)
	Latin hypercube sampling It is used to generate a uniform Latin hypercube.

template<class M >
void	uniformSampling (M &Result, randEngine &mtRandom)
	Hypercube sampling based on Sobol sequences It uses the external Sobol library. More...

template<class D >
void	randomPerms (D &arr, randEngine &mtRandom)
	Modify an array using ramdom permutations. More...

void	parseExpresion (std::string input, std::string &parent, std::string &child1, std::string &child2)
	Parse expresions of the form Parent(Child1, Child2). More...

void	parseExpresion (std::string input, std::string &parent, std::vector< std::string > &childs)
	Parse expresions of the form Parent(Child1, ... More...

void	split (std::string &input, char delim, std::vector< std::string > &elems)
	Splits the input string with a delimiter to extract elements.

template<class MATRIX , class TRIA >
size_t	cholesky_decompose (const MATRIX &A, TRIA &L)
	decompose the symmetric positive definit matrix A into product L L^T. More...

template<class MATRIX >
size_t	cholesky_decompose (MATRIX &A)
	decompose the symmetric positive definit matrix A into product L L^T. More...

template<class MATRIX >
size_t	incomplete_cholesky_decompose (MATRIX &A)
	decompose the symmetric positive definit matrix A into product L L^T. More...

template<class TRIA , class MATRIX >
void	cholesky_solve (const TRIA &L, MATRIX &x, ublas::lower)
	solve system L L^T x = b inplace More...

template<class Min , class Mout >
size_t	inverse_cholesky (const Min &M, Mout &inverse)
	Computes the inverse matrix of a symmetric positive definite matrix. More...

template<class TRIA , class VECTOR >
void	cholesky_add_row (TRIA &L, const VECTOR &v)
	decompose the symmetric positive definit matrix A into product L L^T. More...

template<class v1 , class v2 >
v1	ublas_elementwise_prod (const v1 &a, const v2 &b)
	Computes the elementwise product of two vectors or matrices. More...

template<class v1 , class v2 >
v1	ublas_elementwise_div (const v1 &a, const v2 &b)
	Computes the elementwise division of two vectors or matrices. More...

boost::numeric::ublas::vector< double >	array2vector (const double array[], const size_t n)

template<class V , class D >
void	append (V &vect, D element)

template<class V , class I >
void	erase (V &vect, I begin)

template<class M >
void	erase_column (M &mat, size_t pos)

template<class M , class V >
void	add_to_diagonal (M &mat, const V &vec)

template<class E >
E::value_type	trace (const E &A)

template<class E >
E::value_type	log_trace (const E &A)

template<class E1 , class E2 >
E1::value_type	trace_prod (const E1 &A, const E2 &B)

Detailed Description

Extra utils: math functions, ublas helpers, etc.

Function Documentation

◆ cholesky_add_row()

template<class TRIA , class VECTOR >

void bayesopt::utils::cholesky_add_row	(	TRIA &	L,
		const VECTOR &	v
	)

decompose the symmetric positive definit matrix A into product L L^T.

Parameters

MATRIX	type of input matrix
TRIA	type of lower triangular output matrix
A	square symmetric positive definite input matrix (only the lower triangle is accessed)
L	lower triangular output matrix

Definition at line 264 of file ublas_cholesky.hpp.

Referenced by bayesopt::KernelRegressor::addNewPointToCholesky(), and bayesopt::KernelRegressor::updateSurrogateModel().

◆ cholesky_decompose() [1/2]

template<class MATRIX , class TRIA >

size_t bayesopt::utils::cholesky_decompose	(	const MATRIX &	A,
		TRIA &	L
	)

decompose the symmetric positive definit matrix A into product L L^T.

Parameters

MATRIX	type of input matrix
TRIA	type of lower triangular output matrix
A	square symmetric positive definite input matrix (only the lower triangle is accessed)
L	lower triangular output matrix

Returns: nonzero if decompositon fails (the value ist 1 + the numer of the failing row)

Definition at line 57 of file ublas_cholesky.hpp.

Referenced by bayesopt::KernelRegressor::computeCholeskyCorrelation(), inverse_cholesky(), bayesopt::GaussianProcess::negativeLogLikelihood(), bayesopt::HierarchicalGaussianProcess::negativeTotalLogLikelihood(), bayesopt::StudentTProcessJeffreys::precomputePrediction(), and bayesopt::GaussianProcessML::precomputePrediction().

◆ cholesky_decompose() [2/2]

template<class MATRIX >

size_t bayesopt::utils::cholesky_decompose ( MATRIX & A )

decompose the symmetric positive definit matrix A into product L L^T.

Parameters

MATRIX	type of matrix A
A	input: square symmetric positive definite matrix (only the lower triangle is accessed)
A	output: the lower triangle of A is replaced by the cholesky factor

Returns: nonzero if decompositon fails (the value ist 1 + the numer of the failing row)

Definition at line 99 of file ublas_cholesky.hpp.

◆ cholesky_solve()

template<class TRIA , class MATRIX >

void bayesopt::utils::cholesky_solve	(	const TRIA &	L,
		MATRIX &	x,
		ublas::lower
	)

solve system L L^T x = b inplace

Parameters

L	a triangular matrix
x	input: right hand side b; output: solution x

Definition at line 222 of file ublas_cholesky.hpp.

Referenced by inverse_cholesky(), bayesopt::HierarchicalGaussianProcess::negativeTotalLogLikelihood(), bayesopt::StudentTProcessJeffreys::precomputePrediction(), and bayesopt::GaussianProcessML::precomputePrediction().

◆ incomplete_cholesky_decompose()

template<class MATRIX >

size_t bayesopt::utils::incomplete_cholesky_decompose ( MATRIX & A )

decompose the symmetric positive definit matrix A into product L L^T.

Parameters

MATRIX	type of matrix A
A	input: square symmetric positive definite matrix (only the lower triangle is accessed)
A	output: the lower triangle of A is replaced by the cholesky factor

Returns: nonzero if decompositon fails (the value ist 1 + the numer of the failing row)

Definition at line 175 of file ublas_cholesky.hpp.

◆ inverse_cholesky()

template<class Min , class Mout >

size_t bayesopt::utils::inverse_cholesky	(	const Min &	M,
		Mout &	inverse
	)

Computes the inverse matrix of a symmetric positive definite matrix.

Parameters

M	original matrix
inverse	inverse of M

Returns: nonzero if decompositon fails (the value ist 1 + the numer of the failing row)

Definition at line 240 of file ublas_cholesky.hpp.

References cholesky_decompose(), and cholesky_solve().

◆ modify_index_vector()

void bayesopt::utils::modify_index_vector ( std::vector< int > & arr )

inline

Modify a vector of indexes (0..n)

Parameters

arr vector

Definition at line 77 of file indexvector.hpp.

◆ parseExpresion() [1/2]

void bayesopt::utils::parseExpresion	(	std::string	input,
		std::string &	parent,
		std::string &	child1,
		std::string &	child2
	)

Parse expresions of the form Parent(Child1, Child2).

The "childs" can also be expressions of the same type.

Definition at line 35 of file parser.cpp.

Referenced by bayesopt::KernelFactory::create(), bayesopt::CriteriaFactory::create(), and bayesopt::MeanFactory::create().

◆ parseExpresion() [2/2]

void bayesopt::utils::parseExpresion	(	std::string	input,
		std::string &	parent,
		std::vector< std::string > &	childs
	)

Parse expresions of the form Parent(Child1, ...

,ChildN). The "childs" can also be expressions of the same type.

Definition at line 70 of file parser.cpp.

◆ randomPerms()

template<class D >

void bayesopt::utils::randomPerms	(	D &	arr,
		randEngine &	mtRandom
	)

Modify an array using ramdom permutations.

It is used to generate a uniform Latin hypercube. Equivalent to std::random_shuffle but using boost::random

Definition at line 80 of file lhs.hpp.

Referenced by bayesopt::DiscreteModel::generateInitialPoints().

◆ return_index_vector() [1/2]

std::vector<int> bayesopt::utils::return_index_vector ( size_t n )

inline

Generates a vector of indexes (1..n)

Parameters

n	vector size

Returns: index vector

Definition at line 50 of file indexvector.hpp.

◆ return_index_vector() [2/2]

std::vector<int> bayesopt::utils::return_index_vector	(	int	a,
		size_t	n
	)

inline

Generates a vector of indexes starting at A and size_t n.

Parameters

a	starting point
n	vector size

Returns: index vector

Definition at line 64 of file indexvector.hpp.

◆ samplePoints()

template<class M >

void bayesopt::utils::samplePoints	(	M &	xPoints,
		int	method,
		randEngine &	mtRandom
	)

Selects the sampling method.

Definition at line 141 of file lhs.hpp.

Referenced by bayesopt::ContinuousModel::generateInitialPoints().

◆ ublas_elementwise_div()

template<class v1 , class v2 >

v1 bayesopt::utils::ublas_elementwise_div	(	const v1 &	a,
		const v2 &	b
	)

Computes the elementwise division of two vectors or matrices.

c_i = a_i / b_i

Definition at line 53 of file ublas_elementwise.hpp.

◆ ublas_elementwise_prod()

template<class v1 , class v2 >

v1 bayesopt::utils::ublas_elementwise_prod	(	const v1 &	a,
		const v2 &	b
	)

Computes the elementwise product of two vectors or matrices.

c_i = a_i * b_i

Definition at line 40 of file ublas_elementwise.hpp.

◆ uniformSampling()

template<class M >

void bayesopt::utils::uniformSampling	(	M &	Result,
		randEngine &	mtRandom
	)

Hypercube sampling based on Sobol sequences It uses the external Sobol library.

Thus it do not depend on boost random.Uniform hypercube sampling It is used to generate a set of uniformly distributed samples in hypercube

Definition at line 126 of file lhs.hpp.

Classes

Enumerations

Functions

Detailed Description

Function Documentation

◆ cholesky_add_row()

◆ cholesky_decompose() [1/2]

◆ cholesky_decompose() [2/2]

◆ cholesky_solve()

◆ incomplete_cholesky_decompose()

◆ inverse_cholesky()

◆ modify_index_vector()

◆ parseExpresion() [1/2]

◆ parseExpresion() [2/2]

◆ randomPerms()

◆ return_index_vector() [1/2]

◆ return_index_vector() [2/2]

◆ samplePoints()

◆ ublas_elementwise_div()

◆ ublas_elementwise_prod()

◆ uniformSampling()