Static Public Member Functions
static pli	profile_likelihood (FitFunc &func, const CMAParameters< TGenoPheno > &parameters, CMASolutions &cmasol, const int &k, const bool &curve=false, const int &samplesize=10, const double &fup=0.1, const double &delta=0.1, const int &maxiters=1e4)
	computes the profile likelihood in dimension k around a previously found optima

static CMASolutions	optimize_vpk (FitFunc &func, const CMAParameters< TGenoPheno > &parameters, const CMASolutions &cmasol, const std::vector< int > &k, const std::vector< double > &vk, const dVec &x0, const bool &pheno_x0=true, const bool &pheno_vk=true)
	optimizes an objective function while fixing the value of parameters in several dimensions

static CMASolutions	optimize_pk (FitFunc &func, const CMAParameters< TGenoPheno > &parameters, const CMASolutions &cmasol, const int &k, const double &vk, const dVec &x0, const bool &pheno_x0=true, const bool &pheno_vk=true)
	optimizes an objective function while fixing the value of parameters in dimension k

static contour	contour_points (FitFunc &func, const int &px, const int &py, const int &npoints, const double &fup, const CMAParameters< TGenoPheno > &parameters, CMASolutions &cmasol, const double &delta=0.1, const int &maxiters=1e4)
	computes a set of contour points around a function minima, for a deviation fup in objective function value

Static Private Member Functions
static void	profile_likelihood_search (FitFunc &func, const CMAParameters< TGenoPheno > &parameters, pli &le, const CMASolutions &cmasol, const int &k, const bool &neg, const int &samplesize, const double &fup, const double &delta, const int &maxiters, const bool &curve)
	computes and search the profile likelihood points in dimension k around a previously found optima and in a given direction

static void	take_linear_step (FitFunc &func, const CMAParameters< TGenoPheno > &parameters, const int &k, const double &minfvalue, const double &fup, const double &delta, const int &n, const bool &linit, const dMat &eigenve, double &d, dVec &x)
	take a linesearch step in a given direction Note: the search takes place in geno-space

static fcross	cross (FitFunc &func, const CMAParameters< TGenoPheno > &parameters, CMASolutions &cmasol, const double &fup, const std::vector< int > &par, const std::vector< double > &pmid, const std::vector< double > &pdir, const double &ftol)
	finds crossing point

Member Function Documentation

◆ contour_points()

template<class TGenoPheno >

contour libcmaes::errstats< TGenoPheno >::contour_points	(	FitFunc &	func,
		const int &	px,
		const int &	py,
		const int &	npoints,
		const double &	fup,
		const CMAParameters< TGenoPheno > &	parameters,
		CMASolutions &	cmasol,
		const double &	delta = `0.1`,
		const int &	maxiters = `1e4`
	)

static

computes a set of contour points around a function minima, for a deviation fup in objective function value

Parameters

func	objective function
px	first dimension for contour points computation
py	second dimension for contour points computation
npoints	number of points to be computed in contour
fup	the function deviation for which to compute the contour
parameters	stochastic search parameters
cmasol	solution object that contains the previously found optima
delta	tolerance around fvalue + fup for which to compute the profile likelihood
maxiters	maximum number of linesearch tentatives for computing the profile likelihood

Returns: contour object that contains the contour points

◆ cross()

template<class TGenoPheno >

fcross libcmaes::errstats< TGenoPheno >::cross	(	FitFunc &	func,
		const CMAParameters< TGenoPheno > &	parameters,
		CMASolutions &	cmasol,
		const double &	fup,
		const std::vector< int > &	par,
		const std::vector< double > &	pmid,
		const std::vector< double > &	pdir,
		const double &	ftol
	)

staticprivate

finds crossing point

Parameters

parameters	stochastic search parameters
cmasol	solution object that contains the previously foundoptima
fup	the function deviation for which to compute the contour
par	pair of dimensions to work in
pmid	middle point in both dimensions
pdir	direction in both dimensions
ftol	tolerance around fvalue + fup

Returns: crossing object

◆ optimize_pk()

template<class TGenoPheno >

CMASolutions libcmaes::errstats< TGenoPheno >::optimize_pk	(	FitFunc &	func,
		const CMAParameters< TGenoPheno > &	parameters,
		const CMASolutions &	cmasol,
		const int &	k,
		const double &	vk,
		const dVec &	x0,
		const bool &	pheno_x0 = `true`,
		const bool &	pheno_vk = `true`
	)

static

optimizes an objective function while fixing the value of parameters in dimension k

Parameters

func	objective function
parameters	stochastic search parameters
cmasol	solution object that contains the previously found optima
k	dimension into which to fix the parameter (i.e. search takes place in all other dimensions)
vk	fixed value of parameter k
x0	initial parameter values
pheno_x0	whether x0 is in phenotype
pheno_vk	whether vk is in phenotype

Returns: optimization solution partial object with a single candidate that is the best candidate in full dimension

◆ optimize_vpk()

template<class TGenoPheno >

CMASolutions libcmaes::errstats< TGenoPheno >::optimize_vpk	(	FitFunc &	func,
		const CMAParameters< TGenoPheno > &	parameters,
		const CMASolutions &	cmasol,
		const std::vector< int > &	k,
		const std::vector< double > &	vk,
		const dVec &	x0,
		const bool &	pheno_x0 = `true`,
		const bool &	pheno_vk = `true`
	)

static

optimizes an objective function while fixing the value of parameters in several dimensions

Parameters

func	objective function
parameters	stochastic search parameters
cmasol	solution object that contains the previously found optima
k	dimensions in which to fix parameters (i.e. search takes place in all other dimensions)
vk	fixed values of parameters in dimensions of set k
x0	initial parameter values
pheno_x0	whether x0 is in phenotype
pheno_vk	whether vk is in phenotype

Returns: optimization solution partial object with a single candidate that is the best candidate in full dimension

◆ profile_likelihood()

template<class TGenoPheno >

pli libcmaes::errstats< TGenoPheno >::profile_likelihood	(	FitFunc &	func,
		const CMAParameters< TGenoPheno > &	parameters,
		CMASolutions &	cmasol,
		const int &	k,
		const bool &	curve = `false`,
		const int &	samplesize = `10`,
		const double &	fup = `0.1`,
		const double &	delta = `0.1`,
		const int &	maxiters = `1e4`
	)

static

computes the profile likelihood in dimension k around a previously found optima

Parameters

func	objective function
parameters	stochastic search parameters
cmasol	solution object that contains the previously found optima
k	dimension in which to compute profile likelihood points
curve	whether to store all points during search in order to build a profile likelihood curve
samplesize	number of steps of linesearch in every direction in dimension k
fup	the function deviation for which to compute the profile likelihood
delta	tolerance around fvalue + fup for which to compute the profile likelihood
maxiters	maximum number of linesearch tentatives for computing the profile likelihood

Returns: profile likelihood object

See also: pli

◆ profile_likelihood_search()

template<class TGenoPheno >

void libcmaes::errstats< TGenoPheno >::profile_likelihood_search	(	FitFunc &	func,
		const CMAParameters< TGenoPheno > &	parameters,
		pli &	le,
		const CMASolutions &	cmasol,
		const int &	k,
		const bool &	neg,
		const int &	samplesize,
		const double &	fup,
		const double &	delta,
		const int &	maxiters,
		const bool &	curve
	)

staticprivate

computes and search the profile likelihood points in dimension k around a previously found optima and in a given direction

Parameters

func	objective function
parameters	stochastic search parameters
cmasol	solution object that contains the previously found optima
k	dimension in which to compute profile likelihood points
neg	whether to go on the right (i.e. search direction)
curve	whether to store all points during search in order to build a profile likelihood curve
samplesize	number of steps of linesearch in every direction in dimension k
fup	the function deviation for which to compute the profile likelihood
delta	tolerance around fvalue + fup for which to compute the profile likelihood
maxiters	maximum number of linesearch tentatives for computing the profile likelihood

◆ take_linear_step()

template<class TGenoPheno >

void libcmaes::errstats< TGenoPheno >::take_linear_step	(	FitFunc &	func,
		const CMAParameters< TGenoPheno > &	parameters,
		const int &	k,
		const double &	minfvalue,
		const double &	fup,
		const double &	delta,
		const int &	n,
		const bool &	linit,
		const dMat &	eigenve,
		double &	d,
		dVec &	x
	)

staticprivate

take a linesearch step in a given direction Note: the search takes place in geno-space

Parameters

func	objective function
parameters	stochastic search parameters
k	dimension in which to compute profile likelihood points
minfvalue	current objective function min value
n	number of steps allowed in linesearch
fup	the function deviation for which to compute the profile likelihood
delta	tolerance around fvalue + fup for which to compute the profile likelihood
linit	whether this is the first linesearch call
eigenve	eigenvectors
d	step
x	vector on the line

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

include/libcmaes/errstats.h
src/errstats.cc

Static Public Member Functions

Static Private Member Functions

Member Function Documentation

◆ contour_points()

◆ cross()

◆ optimize_pk()

◆ optimize_vpk()

◆ profile_likelihood()

◆ profile_likelihood_search()

◆ take_linear_step()