Classes
struct	invert

struct	squareDifference

Typedefs
typedef bclib::matrix< int >::size_type	msize_type

typedef std::vector< int >::size_type	vsize_type

Functions
void	geneticLHS (int n, int k, int pop, int gen, double pMut, const std::string &criterium, bool bVerbose, bclib::matrix< double > &result, bclib::CRandom< double > &oRandom)

void	improvedLHS (int n, int k, int dup, bclib::matrix< int > &result, bclib::CRandom< double > &oRandom)

void	maximinLHS (int n, int k, int dup, bclib::matrix< int > &result, bclib::CRandom< double > &oRandom)

void	optimumLHS (int n, int k, int maxSweeps, double eps, bclib::matrix< int > &outlhs, int optimalityRecordLength, bclib::CRandom< double > &oRandom, bool bVerbose)

void	optSeededLHS (int n, int k, int maxSweeps, double eps, bclib::matrix< double > &oldHypercube, int optimalityRecordLength, bool bVerbose)

void	randomLHS (int n, int k, bool bPreserveDraw, bclib::matrix< double > &result, bclib::CRandom< double > &oRandom)

void	randomLHS (int n, int k, bclib::matrix< int > &result, bclib::CRandom< double > &oRandom)

bool	isValidLHS (const bclib::matrix< int > &result)

bool	isValidLHS (const bclib::matrix< double > &result)

void	initializeAvailableMatrix (bclib::matrix< int > &avail)

void	runif_std (unsigned int n, std::vector< double > &output, bclib::CRandom< double > &oRandom)

template<class T >
void	rank (const std::vector< T > &toRank, std::vector< int > &ranks)

template<class T >
void	lhsPrint (const bclib::matrix< T > &A)

template<class T >
T	calculateDistanceSquared (const std::vector< T > &A, const std::vector< T > &B)

template<class T , bool ISROWWISE>
T	calculateDistanceSquared (const typename bclib::matrixConstIter< T, ISROWWISE > Abegin, const typename bclib::matrixConstIter< T, ISROWWISE > Aend, const typename bclib::matrixConstIter< T, ISROWWISE > Bbegin)

template<class T >
void	calculateDistance (const bclib::matrix< T > &mat, bclib::matrix< double > &result)

template<class T >
double	sumInvDistance (const bclib::matrix< T > &A)

template<class T >
double	sumInvDistance_deprecated (const bclib::matrix< T > &A)

template<class T >
void	copyMatrix (bclib::matrix< T > &copyTo, const bclib::matrix< T > &copyFrom)

template<class T >
double	calculateSOptimal (const bclib::matrix< T > &mat)

template<class T1 >
void	runifint (unsigned int n, T1 min, T1 max, std::vector< T1 > &output, bclib::CRandom< double > &oRandom)

template<class T1 >
void	runifint (T1 min, T1 max, T1 *output, bclib::CRandom< double > &oRandom)

Detailed Description

LHS c++ Library namespace

Typedef Documentation

◆ msize_type

typedef bclib::matrix<int>::size_type lhslib::msize_type

type of size type for use with bclib::matrix<T>

Note: the type of the matrix (i.e. int) is irrelevant for size_type

◆ vsize_type

typedef std::vector<int>::size_type lhslib::vsize_type

type of size type for use with std::vector<T>

Note: the type of the vector (i.e. int) is irrelevant for size_type

Function Documentation

◆ calculateDistance()

template<class T >

void lhslib::calculateDistance	(	const bclib::matrix< T > &	mat,
		bclib::matrix< double > &	result
	)

Calculate the distance between the rows of a matrix

Parameters

mat	the matrix to calculate distances on
result	the matrix to hold the results of the calculation

Template Parameters

T	the type of object in the matrix

◆ calculateDistanceSquared() [1/2]

template<class T >

T lhslib::calculateDistanceSquared	(	const std::vector< T > &	A,
		const std::vector< T > &	B
	)

Calculate the total squared distance between two vectors

Parameters

A	the first vector
B	the second vector

Template Parameters

T	the type of the objects in the vector

Returns: the total squared distance

◆ calculateDistanceSquared() [2/2]

template<class T , bool ISROWWISE>

T lhslib::calculateDistanceSquared	(	const typename bclib::matrixConstIter< T, ISROWWISE >	Abegin,
		const typename bclib::matrixConstIter< T, ISROWWISE >	Aend,
		const typename bclib::matrixConstIter< T, ISROWWISE >	Bbegin
	)

Calculate the distance squared between two sequence of numbers

this was primarily implemented to be able to calculate distances between rows of a matrix without having to copy those rows out

Parameters

Abegin	the beginning of the first iterator
Aend	the end of the first iterator
Bbegin	the beginning of the second iterator

Template Parameters

T	the type of object de-referenced by the iterator
ISROWWISE	a boolean to indicate if the iterator operates row-wise in the matrix

Returns: the distance squared

◆ calculateSOptimal()

template<class T >

double lhslib::calculateSOptimal ( const bclib::matrix< T > & mat )

Calculate the S optimality measure

Parameters

mat	the matrix to calculate S optimality for

Template Parameters

the	type of object contained in the matrix

Returns: the S optimality measure

◆ copyMatrix()

template<class T >

void lhslib::copyMatrix	(	bclib::matrix< T > &	copyTo,
		const bclib::matrix< T > &	copyFrom
	)

Copy a matrix

Parameters

copyTo	the matrix to copy to
copyFrom	the matrix to copy from

Template Parameters

the	type of object contained in the matrix

◆ geneticLHS()

void lhslib::geneticLHS	(	int	n,
		int	k,
		int	pop,
		int	gen,
		double	pMut,
		const std::string &	criterium,
		bool	bVerbose,
		bclib::matrix< double > &	result,
		bclib::CRandom< double > &	oRandom
	)

Create a latin hypercube sample optimized by some criteria with a genetic algorithm

Parameters

n	number of rows / samples in the lhs
k	number parameters / columns in the lhs
pop	the population of the genetic algorithm in each iteration
gen	the number of generations to use
pMut	the mutation rate
criterium	the optimization criterium
bVerbose	should messages be printed?
result	the lhs
oRandom	the random number stream

◆ improvedLHS()

void lhslib::improvedLHS	(	int	n,
		int	k,
		int	dup,
		bclib::matrix< int > &	result,
		bclib::CRandom< double > &	oRandom
	)

Improved Latin hypercube sample algorithm

Parameters

n	number of rows / samples in the lha
k	number parameters / columns in the lhs
dup	A factor that determines the number of candidate points used in the search.
result	the result matrix
oRandom	the random number stream

◆ initializeAvailableMatrix()

void lhslib::initializeAvailableMatrix ( bclib::matrix< int > & avail )

Initialize the matrix of available points

Parameters

avail the matrix of available points

◆ isValidLHS() [1/2]

bool lhslib::isValidLHS ( const bclib::matrix< double > & result )

is the Latin hypercube sample valid?

Parameters

result the latin hypercube sample

Returns: the result of the validity check

◆ isValidLHS() [2/2]

bool lhslib::isValidLHS ( const bclib::matrix< int > & result )

is the Latin hypercube sample valid

Parameters

result the latin hypercube sample with integer values

Returns: the result of the validity check

◆ lhsPrint()

template<class T >

void lhslib::lhsPrint ( const bclib::matrix< T > & A )

print the Latin hypercube sample

Parameters

A	the matrix to print

Template Parameters

T	the type of matrix to print

◆ maximinLHS()

void lhslib::maximinLHS	(	int	n,
		int	k,
		int	dup,
		bclib::matrix< int > &	result,
		bclib::CRandom< double > &	oRandom
	)

Latin hypercube sample algorithm with maximin criterion

Parameters

n	number of rows / samples in the lha
k	number parameters / columns in the lhs
dup	A factor that determines the number of candidate points used in the search.
result	the result matrix
oRandom	the random number stream

◆ optimumLHS()

void lhslib::optimumLHS	(	int	n,
		int	k,
		int	maxSweeps,
		double	eps,
		bclib::matrix< int > &	outlhs,
		int	optimalityRecordLength,
		bclib::CRandom< double > &	oRandom,
		bool	bVerbose
	)

Optimum Latin hypercube sample algorithm

Parameters

n	number of rows / samples in the lha
k	number parameters / columns in the lhs
maxSweeps	the maximum number of sweeps to use in the algorithm
eps	The optimal stopping criterion
outlhs	the resultant lhs
optimalityRecordLength	the length of a vector used in the calculations
oRandom	the random number stream
bVerbose	should messages be printed?

◆ optSeededLHS()

void lhslib::optSeededLHS	(	int	n,
		int	k,
		int	maxSweeps,
		double	eps,
		bclib::matrix< double > &	oldHypercube,
		int	optimalityRecordLength,
		bool	bVerbose
	)

Application of the optimum lhs method to a seeded Latin hypercube

Parameters

n	number of rows / samples in the lhs
k	number parameters / columns in the lhs
maxSweeps	the maximum number of sweeps to use in the algorithm
eps	The optimal stopping criterion
oldHypercube	the seeded lhs
optimalityRecordLength	the length of a vector used in the calculations
bVerbose	should messages be printed?

◆ randomLHS() [1/2]

void lhslib::randomLHS	(	int	n,
		int	k,
		bclib::matrix< int > &	result,
		bclib::CRandom< double > &	oRandom
	)

Create a random latin hypercube sample with integer values

Parameters

n	number of rows / samples in the lhs
k	number parameters / columns in the lhs
result	the lhs
oRandom	the random number stream

◆ randomLHS() [2/2]

void lhslib::randomLHS	(	int	n,
		int	k,
		bool	bPreserveDraw,
		bclib::matrix< double > &	result,
		bclib::CRandom< double > &	oRandom
	)

Create a random latin hypercube sample

Parameters

n	number of rows / samples in the lhs
k	number parameters / columns in the lhs
bPreserveDraw	should the order of the draw be preserved if less columns are selected
result	the lhs
oRandom	the random number stream

◆ rank()

template<class T >

void lhslib::rank	(	const std::vector< T > &	toRank,
		std::vector< int > &	ranks
	)

rank a vector of objects

Parameters

toRank	the vector of objects to rank
ranks	the ranks of the vector of values

Template Parameters

the	type of element in the vectors

◆ runif_std()

void lhslib::runif_std	(	unsigned int	n,
		std::vector< double > &	output,
		bclib::CRandom< double > &	oRandom
	)

Create a vector of random values on (0,1)

Parameters

n	the number of random values
output	the output vector of random values
oRandom	the pseudo random number generator

◆ runifint() [1/2]

template<class T1 >

void lhslib::runifint	(	T1	min,
		T1	max,
		T1 *	output,
		bclib::CRandom< double > &	oRandom
	)

Create a random integer like values

Parameters

min	the minimum integer value
max	the maximum integer value
output	the output value
oRandom	the pseudo random number generator

Template Parameters

T1	the integer valued type like `int, unsigned int, long, long long, unsigned long`

◆ runifint() [2/2]

template<class T1 >

void lhslib::runifint	(	unsigned int	n,
		T1	min,
		T1	max,
		std::vector< T1 > &	output,
		bclib::CRandom< double > &	oRandom
	)

Create a vector of random integer like values

Parameters

n	the length of the random vector
min	the minimum integer value
max	the maximum integer value
output	the output vector of values
oRandom	the pseudo random number generator.

Template Parameters

T1	the integer valued type like `int, unsigned int, long, long long, unsigned long`

◆ sumInvDistance()

template<class T >

double lhslib::sumInvDistance ( const bclib::matrix< T > & A )

sum of the inverse distance between points in a matrix

Parameters

A	the matrix

Template Parameters

T	the type of object contained in the matrix

Returns: the sum of the inverse distance between points

◆ sumInvDistance_deprecated()

template<class T >

double lhslib::sumInvDistance_deprecated ( const bclib::matrix< T > & A )

Sum of the inverse distance between points

Parameters

A	the matrix to sum

Template Parameters

T	the type of object in the matrix

Returns: the sum of the inverse distance between points

Classes

Typedefs

Functions

Detailed Description

Typedef Documentation

◆ msize_type

◆ vsize_type

Function Documentation

◆ calculateDistance()

◆ calculateDistanceSquared() [1/2]

◆ calculateDistanceSquared() [2/2]

◆ calculateSOptimal()

◆ copyMatrix()

◆ geneticLHS()

◆ improvedLHS()

◆ initializeAvailableMatrix()

◆ isValidLHS() [1/2]

◆ isValidLHS() [2/2]

◆ lhsPrint()

◆ maximinLHS()

◆ optimumLHS()

◆ optSeededLHS()

◆ randomLHS() [1/2]

◆ randomLHS() [2/2]

◆ rank()

◆ runif_std()

◆ runifint() [1/2]

◆ runifint() [2/2]

◆ sumInvDistance()

◆ sumInvDistance_deprecated()