Holder of the set of evolving solutions from running an instance of CMA-ES. More...

#include <libcmaes/cmasolutions.h>

Public Member Functions
	CMASolutions ()
	dummy constructor.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>
	CMASolutions (Parameters< TGenoPheno > &p)
	initializes solutions from stochastic optimization parameters.

void	sort_candidates ()
	sorts the current internal set of solution candidates.

void	update_best_candidates ()
	updates the history of best candidates, as well as other meaningful values, typically used in termination criteria.

void	update_eigenv (const dVec &eigenvalues, const dMat &eigenvectors)
	updates reference eigenvalue and eigenvectors, for use in termination criteria.

Candidate	best_candidate () const
	returns current best solution candidate. NOTE: candidates MUST be sorted

Candidate	get_best_seen_candidate () const
	returns the best seen candidate.

Candidate	get_worst_seen_candidate () const
	returns the worst seen candidate.

Candidate &	get_candidate (const int &r)
	get a reference to the r-th candidate in current set

Candidate	get_candidate (const int &r) const

std::vector< Candidate > &	candidates ()
	get a reference to the full candidate set

int	size () const
	number of candidate solutions.

void	reset ()
	resets the solution object in order to restart from the current solution with fresh covariance matrix. Note: experimental.

void	reset_as_fixed (const int &k)
	re-arrange solution object such that parameter 'k' is fixed (i.e. removed).

bool	get_pli (const int &k, pli &p) const
	get profile likelihood if previously computed.

int	dim () const
	return problem dimension.

double	edm () const
	returns expected distance to minimum.

dMat	cov () const
	returns error covariance matrix

const dMat &	cov_ref () const
	returns reference to error covariance matrix

const double *	cov_data () const
	returns pointer to covariance matrix array

dMat	full_cov () const
	returns full covariance matrix. Similar to cov() but in case of linear-sized algorithms like sep and vd, returns the full covariance matrix anyways.

dMat	sepcov () const
	returns separable covariance diagonal matrix, only applicable to sep-CMA-ES algorithms.

const dMat &	sepcov_ref () const
	returns reference to separable covariance diagonal vector, only applicable to sep-CMA-ES algorithms.

const double *	sepcov_data () const
	returns pointer to covariance diagnoal vector

dMat	csqinv () const
	returns inverse root square of covariance matrix

dMat	sepcsqinv () const
	returns inverse root square of separable covariance diagonal matrix, only applicable to sep-CMA-ES algorithms.

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>
dVec	stds (const CMAParameters< TGenoPheno > &cmaparams) const

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>
dVec	errors (const CMAParameters< TGenoPheno > &cmaparams) const

dMat	corr () const
	returns correlation matrix

double	corr (const int &i, const int &j) const
	returns correlation between parameter i and j (useful in large-scale settings)

double	sigma () const
	returns current value of step-size sigma

void	set_sigma (const double &sigma)
	sets new step-size value, use with care

dVec	xmean () const
	returns current distribution's mean in parameter space

void	set_xmean (const dVec &xmean)
	sets the current distributions' mean in parameter space

int	run_status () const
	returns current optimization status.

std::string	status_msg () const
	returns current optimization status' message.

int	elapsed_time () const
	returns currently elapsed time spent on optimization

int	elapsed_last_iter () const
	returns time spent on last iteration

int	niter () const
	returns current number of iterations

int	nevals () const
	returns current budget (number of objective function calls)

double	min_eigenv () const
	returns current minimal eigen value

double	max_eigenv () const
	returns current maximal eigen value

bool	updated_eigen () const
	returns whether the last update is lazy

int	fevals () const
	returns current number of objective function evaluations

dVec	eigenvalues () const
	returns last computed eigenvalues

dMat	eigenvectors () const
	returns last computed eigenvectors

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>
std::ostream &	print (std::ostream &out, const int &verb_level=0, const TGenoPheno &gp=TGenoPheno()) const
	print the solution object out.

Private Attributes
dMat	_cov

dMat	_csqinv

dMat	_sepcov

dMat	_sepcsqinv

dVec	_xmean

dVec	_psigma

dVec	_pc

short	_hsig = 1

double	_sigma

std::vector< Candidate >	_candidates

std::vector< Candidate >	_best_candidates_hist

int	_max_hist = -1

double	_max_eigenv = 0.0

double	_min_eigenv = 0.0

dVec	_leigenvalues

dMat	_leigenvectors

int	_niter = 0

int	_nevals = 0

int	_kcand = 1

std::vector< Candidate >	_k_best_candidates_hist

std::vector< double >	_bfvalues

std::vector< double >	_median_fvalues

int	_eigeniter = 0

bool	_updated_eigen = true

int	_run_status = 0

int	_elapsed_time = 0

int	_elapsed_last_iter = 0

std::map< int, pli >	_pls

double	_edm = 0.0

Candidate	_best_seen_candidate

int	_best_seen_iter

Candidate	_worst_seen_candidate

Candidate	_initial_candidate

dVec	_v

std::vector< RankedCandidate >	_candidates_uh

int	_lambda_reev

double	_suh

double	_tpa_s = 0.0

int	_tpa_p1 = 0

int	_tpa_p2 = 1

dVec	_tpa_x1

dVec	_tpa_x2

dVec	_xmean_prev

Friends
template<class U , class V >
class	CMAStrategy

template<class U , class V , class W >
class	ESOptimizer

template<class U , class V , class W >
class	ESOStrategy

template<class U >
class	CMAStopCriteria

template<class U , class V >
class	IPOPCMAStrategy

template<class U , class V >
class	BIPOPCMAStrategy

class	CovarianceUpdate

class	ACovarianceUpdate

template<class U >
class	errstats

class	VDCMAUpdate

Detailed Description

Holder of the set of evolving solutions from running an instance of CMA-ES.

Constructor & Destructor Documentation

◆ CMASolutions()

template<class TGenoPheno >

libcmaes::CMASolutions::CMASolutions ( Parameters< TGenoPheno > & p )

initializes solutions from stochastic optimization parameters.

Parameters

p	parameters

Member Function Documentation

◆ best_candidate()

Candidate libcmaes::CMASolutions::best_candidate ( ) const

inline

returns current best solution candidate. NOTE: candidates MUST be sorted

Returns: current best candidate

See also: CMASolutions::sort_candidates

◆ corr() [1/2]

dMat libcmaes::CMASolutions::corr ( ) const

returns correlation matrix

Returns: correlation matrix

◆ corr() [2/2]

double libcmaes::CMASolutions::corr	(	const int &	i,
		const int &	j
	)		const

returns correlation between parameter i and j (useful in large-scale settings)

Returns: correlation between parameter i and j

◆ cov()

dMat libcmaes::CMASolutions::cov ( ) const

inline

returns error covariance matrix

Returns: error covariance matrix

◆ cov_data()

const double * libcmaes::CMASolutions::cov_data ( ) const

inline

returns pointer to covariance matrix array

Returns: pointer to covariance matrix array

◆ cov_ref()

const dMat & libcmaes::CMASolutions::cov_ref ( ) const

inline

returns reference to error covariance matrix

Returns: error covariance matrix

◆ csqinv()

dMat libcmaes::CMASolutions::csqinv ( ) const

inline

returns inverse root square of covariance matrix

Returns: square root of error covariance matrix

◆ dim()

int libcmaes::CMASolutions::dim ( ) const

inline

return problem dimension.

Returns: problem dimension

◆ edm()

double libcmaes::CMASolutions::edm ( ) const

inline

returns expected distance to minimum.

Returns: edm

◆ eigenvalues()

dVec libcmaes::CMASolutions::eigenvalues ( ) const

inline

returns last computed eigenvalues

Returns: last computed eigenvalues

◆ eigenvectors()

dMat libcmaes::CMASolutions::eigenvectors ( ) const

inline

returns last computed eigenvectors

Returns: last computed eigenvectors

◆ elapsed_last_iter()

int libcmaes::CMASolutions::elapsed_last_iter ( ) const

inline

returns time spent on last iteration

Returns: time spent on last iteration

◆ elapsed_time()

int libcmaes::CMASolutions::elapsed_time ( ) const

inline

returns currently elapsed time spent on optimization

Returns: time spent on optimization

◆ errors()

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

dVec libcmaes::CMASolutions::errors ( const CMAParameters< TGenoPheno > & cmaparams ) const

inline

Returns: standard deviation vector

Parameters

cmaparams parameter object that hold the genotype/phenotype transform

Returns: standard deviation vector

◆ fevals()

int libcmaes::CMASolutions::fevals ( ) const

inline

returns current number of objective function evaluations

Returns: number of objective function evaluations

◆ full_cov()

dMat libcmaes::CMASolutions::full_cov ( ) const

returns full covariance matrix. Similar to cov() but in case of linear-sized algorithms like sep and vd, returns the full covariance matrix anyways.

Returns: full size covariance matrix

◆ get_best_seen_candidate()

Candidate libcmaes::CMASolutions::get_best_seen_candidate ( ) const

inline

returns the best seen candidate.

Returns: best seen candidate

◆ get_candidate()

Candidate & libcmaes::CMASolutions::get_candidate ( const int & r )

inline

get a reference to the r-th candidate in current set

Parameters

r	candidate position

◆ get_worst_seen_candidate()

Candidate libcmaes::CMASolutions::get_worst_seen_candidate ( ) const

inline

returns the worst seen candidate.

Returns: worst seen candidate

◆ max_eigenv()

double libcmaes::CMASolutions::max_eigenv ( ) const

inline

returns current maximal eigen value

Returns: maximal eigen value

◆ min_eigenv()

double libcmaes::CMASolutions::min_eigenv ( ) const

inline

returns current minimal eigen value

Returns: minimal eigen value

◆ nevals()

int libcmaes::CMASolutions::nevals ( ) const

inline

returns current budget (number of objective function calls)

Returns: number of objective function calls

◆ niter()

int libcmaes::CMASolutions::niter ( ) const

inline

returns current number of iterations

Returns: number of iterations

◆ print()

template<class TGenoPheno >

template CMAES_EXPORT std::ostream & libcmaes::CMASolutions::print	(	std::ostream &	out,
		const int &	verb_level = `0`,
		const TGenoPheno &	gp = `TGenoPheno()`
	)		const

print the solution object out.

Parameters

out	output stream
verb_level	verbosity level: 0 for short, 1 for debug.

◆ reset_as_fixed()

void libcmaes::CMASolutions::reset_as_fixed ( const int & k )

re-arrange solution object such that parameter 'k' is fixed (i.e. removed).

Parameters

k	index of the parameter to remove.

◆ run_status()

int libcmaes::CMASolutions::run_status ( ) const

inline

returns current optimization status.

Returns: status

◆ sepcov()

dMat libcmaes::CMASolutions::sepcov ( ) const

inline

returns separable covariance diagonal matrix, only applicable to sep-CMA-ES algorithms.

Returns: error covariance diagonal vector

◆ sepcov_data()

const double * libcmaes::CMASolutions::sepcov_data ( ) const

inline

returns pointer to covariance diagnoal vector

Returns: pointer to covariance diagonal array

◆ sepcov_ref()

const dMat & libcmaes::CMASolutions::sepcov_ref ( ) const

inline

returns reference to separable covariance diagonal vector, only applicable to sep-CMA-ES algorithms.

Returns: error covariance diagonal vector

◆ sepcsqinv()

dMat libcmaes::CMASolutions::sepcsqinv ( ) const

inline

returns inverse root square of separable covariance diagonal matrix, only applicable to sep-CMA-ES algorithms.

Returns: square root of error covariance diagonal matrix

◆ set_sigma()

void libcmaes::CMASolutions::set_sigma ( const double & sigma )

inline

sets new step-size value, use with care

Parameters

sigma step-size value

◆ set_xmean()

void libcmaes::CMASolutions::set_xmean ( const dVec & xmean )

inline

sets the current distributions' mean in parameter space

Parameters

xmean mean vector

◆ sigma()

double libcmaes::CMASolutions::sigma ( ) const

inline

returns current value of step-size sigma

Returns: current step-size

◆ size()

int libcmaes::CMASolutions::size ( ) const

inline

number of candidate solutions.

Returns: current number of solution candidates.

◆ status_msg()

std::string libcmaes::CMASolutions::status_msg ( ) const

inline

returns current optimization status' message.

Returns: status message

◆ stds()

template<class TGenoPheno = GenoPheno<NoBoundStrategy>>

dVec libcmaes::CMASolutions::stds ( const CMAParameters< TGenoPheno > & cmaparams ) const

inline

Returns: the unscaled standard deviation vector Note: this is only useful to compare amond standard deviations To get the true rescaled estimate of the error, use errors()

Parameters

cmaparams parameter object that hold the genotype/phenotype transform

Returns: unscaled standard deviation vector

◆ update_best_candidates()

void libcmaes::CMASolutions::update_best_candidates ( )

updates the history of best candidates, as well as other meaningful values, typically used in termination criteria.

See also: CMAStopCriteria

◆ update_eigenv()

void libcmaes::CMASolutions::update_eigenv	(	const dVec &	eigenvalues,
		const dMat &	eigenvectors
	)

updates reference eigenvalue and eigenvectors, for use in termination criteria.

See also: CMAStopCriteria

◆ updated_eigen()

bool libcmaes::CMASolutions::updated_eigen ( ) const

inline

returns whether the last update is lazy

Returns: whether the last update is lazy

◆ xmean()

dVec libcmaes::CMASolutions::xmean ( ) const

inline

returns current distribution's mean in parameter space

Returns: mean

Member Data Documentation

◆ _best_candidates_hist

std::vector<Candidate> libcmaes::CMASolutions::_best_candidates_hist

private

history of best candidate solutions.

◆ _best_seen_candidate

Candidate libcmaes::CMASolutions::_best_seen_candidate

private

best seen candidate along the run.

◆ _bfvalues

std::vector<double> libcmaes::CMASolutions::_bfvalues

private

best function values over the past 20 steps, for termination criteria.

◆ _candidates

std::vector<Candidate> libcmaes::CMASolutions::_candidates

private

current set of candidate solutions.

◆ _candidates_uh

std::vector<RankedCandidate> libcmaes::CMASolutions::_candidates_uh

private

temporary set of candidates used by uncertainty handling scheme.

◆ _cov

dMat libcmaes::CMASolutions::_cov

private

covariance matrix.

◆ _edm

double libcmaes::CMASolutions::_edm = 0.0

private

expected vertical distance to the minimum.

◆ _eigeniter

int libcmaes::CMASolutions::_eigeniter = 0

private

eigenvalues computation last step, lazy-update only.

◆ _elapsed_last_iter

int libcmaes::CMASolutions::_elapsed_last_iter = 0

private

time consumed during last iteration.

◆ _elapsed_time

int libcmaes::CMASolutions::_elapsed_time = 0

private

final elapsed time of stochastic optimization.

◆ _hsig

short libcmaes::CMASolutions::_hsig = 1

private

0 or 1.

◆ _k_best_candidates_hist

std::vector<Candidate> libcmaes::CMASolutions::_k_best_candidates_hist

private

k-th best candidate history, for termination criteria, k is kcand=1+floor(0.1+lambda/4).

◆ _lambda_reev

int libcmaes::CMASolutions::_lambda_reev

private

number of reevaluated solutions at current step.

◆ _leigenvalues

dVec libcmaes::CMASolutions::_leigenvalues

private

last computed eigenvalues, for termination criteria.

◆ _leigenvectors

dMat libcmaes::CMASolutions::_leigenvectors

private

last computed eigenvectors, for termination criteria.

◆ _max_eigenv

double libcmaes::CMASolutions::_max_eigenv = 0.0

private

max eigenvalue, for termination criteria.

◆ _max_hist

int libcmaes::CMASolutions::_max_hist = -1

private

max size of the history, keeps memory requirements fixed.

◆ _median_fvalues

std::vector<double> libcmaes::CMASolutions::_median_fvalues

private

median function values of some steps, in the past, for termination criteria.

◆ _min_eigenv

double libcmaes::CMASolutions::_min_eigenv = 0.0

private

min eigenvalue, for termination criteria.

◆ _nevals

int libcmaes::CMASolutions::_nevals = 0

private

number of function calls to reach the current solution.

◆ _niter

int libcmaes::CMASolutions::_niter = 0

private

number of iterations to reach this solution, for termination criteria.

◆ _pc

dVec libcmaes::CMASolutions::_pc

private

cumulation for covariance.

◆ _pls

std::map<int,pli> libcmaes::CMASolutions::_pls

private

profile likelihood for parameters it has been computed for.

◆ _psigma

dVec libcmaes::CMASolutions::_psigma

private

cumulation for sigma.

◆ _run_status

int libcmaes::CMASolutions::_run_status = 0

private

current status of the stochastic optimization (e.g. running, or stopped under termination criteria).

◆ _sepcov

dMat libcmaes::CMASolutions::_sepcov

private

inverse root square of covariance matrix.

◆ _sigma

double libcmaes::CMASolutions::_sigma

private

step size.

◆ _suh

double libcmaes::CMASolutions::_suh

private

uncertainty level computed by uncertainty handling procedure.

◆ _updated_eigen

bool libcmaes::CMASolutions::_updated_eigen = true

private

last update is not lazy.

◆ _v

dVec libcmaes::CMASolutions::_v

private

complementary vector for use in vdcma.

◆ _xmean

dVec libcmaes::CMASolutions::_xmean

private

distribution mean.

◆ _xmean_prev

dVec libcmaes::CMASolutions::_xmean_prev

private

previous step's mean vector.

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

include/libcmaes/cmasolutions.h
src/cmasolutions.cc

Public Member Functions

Private Attributes

Friends

Detailed Description

Constructor & Destructor Documentation

◆ CMASolutions()

Member Function Documentation

◆ best_candidate()

◆ corr() [1/2]

◆ corr() [2/2]

◆ cov()

◆ cov_data()

◆ cov_ref()

◆ csqinv()

◆ dim()

◆ edm()

◆ eigenvalues()

◆ eigenvectors()

◆ elapsed_last_iter()

◆ elapsed_time()

◆ errors()

◆ fevals()

◆ full_cov()

◆ get_best_seen_candidate()

◆ get_candidate()

◆ get_worst_seen_candidate()

◆ max_eigenv()

◆ min_eigenv()

◆ nevals()

◆ niter()

◆ print()

◆ reset_as_fixed()

◆ run_status()

◆ sepcov()

◆ sepcov_data()

◆ sepcov_ref()

◆ sepcsqinv()

◆ set_sigma()

◆ set_xmean()

◆ sigma()

◆ size()

◆ status_msg()

◆ stds()

◆ update_best_candidates()

◆ update_eigenv()

◆ updated_eigen()

◆ xmean()

Member Data Documentation

◆ _best_candidates_hist

◆ _best_seen_candidate

◆ _bfvalues

◆ _candidates

◆ _candidates_uh

◆ _cov

◆ _edm

◆ _eigeniter

◆ _elapsed_last_iter

◆ _elapsed_time

◆ _hsig

◆ _k_best_candidates_hist

◆ _lambda_reev

◆ _leigenvalues

◆ _leigenvectors

◆ _max_eigenv

◆ _max_hist

◆ _median_fvalues

◆ _min_eigenv

◆ _nevals

◆ _niter

◆ _pc

◆ _pls

◆ _psigma

◆ _run_status

◆ _sepcov

◆ _sigma

◆ _suh

◆ _updated_eigen

◆ _v

◆ _xmean

◆ _xmean_prev