Simple surrogate strategy: trains every n steps, and exploits in between, mostly as an example and for testing / debugging surrogates. This strategy overrides the ask/eval/tell functions of the base optimization strategy. More...

#include <libcmaes/surrogatestrategy.h>

Inheritance diagram for libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >:

Public Member Functions
	SimpleSurrogateStrategy (FitFunc &func, CMAParameters< TGenoPheno > &parameters)
	constructor

void	eval (const dMat &candidates, const dMat &phenocandidates=dMat(0, 0))
	Evaluates a set of candiates against the objective function or the surrogate model, as needed.

void	tell ()
	Updates the state of the stochastic search, and prepares for the next iteration by training the surrogate model, as needed.

int	optimize ()
	Finds the minimum of the objective function. It makes alternate calls to ask(), tell() and stop() until one of the termination criteria triggers.

int	compute_lifelength ()
	estimates surrogate lifelength

bool	do_train () const
	whether to train the model

Public Member Functions inherited from libcmaes::SurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >
	SurrogateStrategy (FitFunc &func, CMAParameters< TGenoPheno > &parameters)
	constructor

int	train (const std::vector< Candidate > &candidates, const dMat &cov)
	train a surrogate model

int	predict (std::vector< Candidate > &candidates, const dMat &cov)
	predict from a surrogate model

double	compute_error (const std::vector< Candidate > &test_set, const dMat &cov=dMat(0, 0))
	compute surrogate model error (copies and sorts the test_set)

bool	do_train () const
	conditionals on training, to be specialized in inherited surrogate strategies

void	set_ftrain (const CSurrFunc &train)
	sets the training function

void	set_fpredict (const SurrFunc &predict)
	sets the prediction function

void	set_l (const int &l)
	sets the size of the training set (number of points)

int	get_l () const
	gets the size of the training set (number of points)

void	set_exploit (const bool &exploit)
	sets whether to exploit the surrogate model

bool	get_exploit () const
	gets the state of surrogate model exploitation

double	get_train_error () const
	returns the surrogate model training error

double	get_test_error () const
	returns the surrogate model test error

void	set_train_error (const double &err)
	sets training error

void	set_test_error (const double &err)
	sets the test error and updates the smoothed test err.

void	add_to_training_set (const Candidate &c)
	adds a point to the training set (candidate = points + objective function value)

void	set_nsteps (const int &nsteps)
	sets the lifelength of the surrogate, i.e. the number of steps in between to training steps

void	reset_training_set ()
	resets training set and related information, useful when using algorithms with restarts

int	get_nsteps () const
	returns the current surrogate lifelength

Public Attributes
double	_terr = 0.45

int	_nmax = 20

Additional Inherited Members
Protected Attributes inherited from libcmaes::SurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >
bool	_exploit = true

int	_l = 200

std::vector< Candidate >	_tset

CSurrFunc	_train

SurrFunc	_predict

double	_train_err = 0.0

double	_test_err = 0.0

double	_smooth_test_err = 0.5

double	_beta_err = 0.2

int	_nsteps = 1

int	_auto_nsteps = false

Detailed Description

template<template< class U, class V > class TStrategy, class TCovarianceUpdate = CovarianceUpdate, class TGenoPheno = GenoPheno<NoBoundStrategy>>
class libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >

Simple surrogate strategy: trains every n steps, and exploits in between, mostly as an example and for testing / debugging surrogates. This strategy overrides the ask/eval/tell functions of the base optimization strategy.

Constructor & Destructor Documentation

◆ SimpleSurrogateStrategy()

template<template< class U, class V > class TStrategy, class TCovarianceUpdate , class TGenoPheno >

libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::SimpleSurrogateStrategy	(	FitFunc &	func,
		CMAParameters< TGenoPheno > &	parameters
	)

constructor

Parameters

func	objective function to minimize
parameters	optimization parameters

Member Function Documentation

◆ compute_lifelength()

template<template< class U, class V > class TStrategy, class TCovarianceUpdate , class TGenoPheno >

int libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::compute_lifelength ( )

estimates surrogate lifelength

Returns: estimated surrogate lifelength

◆ do_train()

template<template< class U, class V > class TStrategy, class TCovarianceUpdate = CovarianceUpdate, class TGenoPheno = GenoPheno<NoBoundStrategy>>

bool libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::do_train ( ) const

inline

whether to train the model

Returns: whether to train the model

◆ eval()

template<template< class U, class V > class TStrategy, class TCovarianceUpdate , class TGenoPheno >

void libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::eval	(	const dMat &	candidates,
		const dMat &	phenocandidates = `dMat(0,0)`
	)

Evaluates a set of candiates against the objective function or the surrogate model, as needed.

Note: this function overrides the default CMAStrategy::eval

Parameters

candidates	A matrix whose rows contain the candidates.
phenocandidates	The candidates transformed into phenotype, leave empty if no pheno transform.

◆ optimize()

template<template< class U, class V > class TStrategy, class TCovarianceUpdate , class TGenoPheno >

int libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::optimize ( )

Finds the minimum of the objective function. It makes alternate calls to ask(), tell() and stop() until one of the termination criteria triggers.

Returns: success or error code, as defined in opti_err.h Note: the termination criteria code is held by _solutions._run_status

◆ tell()

template<template< class U, class V > class TStrategy, class TCovarianceUpdate , class TGenoPheno >

void libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::tell ( )

Updates the state of the stochastic search, and prepares for the next iteration by training the surrogate model, as needed.

Note: this function overrides the default CMAStrategy::tell

Member Data Documentation

◆ _terr

template<template< class U, class V > class TStrategy, class TCovarianceUpdate = CovarianceUpdate, class TGenoPheno = GenoPheno<NoBoundStrategy>>

double libcmaes::SimpleSurrogateStrategy< TStrategy, TCovarianceUpdate, TGenoPheno >::_terr = 0.45

error threshold for estimating optimal nsteps

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

include/libcmaes/surrogatestrategy.h
src/surrogatestrategy.cc

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ SimpleSurrogateStrategy()

Member Function Documentation

◆ compute_lifelength()

◆ do_train()

◆ eval()

◆ optimize()

◆ tell()

Member Data Documentation

◆ _terr