Implements the Cross Entropy Method. More...

#include <shark/Algorithms/DirectSearch/CrossEntropyMethod.h>

Inheritance diagram for shark::CrossEntropyMethod:

Classes
class	ConstantNoise
	Constant noise term z_t = noise. More...

class	INoiseType
	Interface class for noise type. More...

class	LinearNoise
	Linear noise term z_t = a + t / b. More...

Public Types
typedef boost::shared_ptr< INoiseType >	StrongNoisePtr
	Smart pointer for noise type. More...

Public Types inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
typedef base_type::SearchPointType	SearchPointType

typedef base_type::SolutionType	SolutionType

typedef base_type::ResultType	ResultType

typedef base_type::ObjectiveFunctionType	ObjectiveFunctionType

Public Types inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
enum	Feature
	Models features that the optimizer requires from the objective function. More...

typedef RealVector	SearchPointType

typedef double	ResultType

typedef SingleObjectiveResultSet< RealVector >	SolutionType

typedef AbstractObjectiveFunction< RealVector, ResultType >	ObjectiveFunctionType

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Public Member Functions
SHARK_EXPORT_SYMBOL	CrossEntropyMethod ()
	Default c'tor. More...

std::string	name () const
	From INameable: return the class name. More...

void	read (InArchive &archive)
	Read the component from the supplied archive. More...

void	write (OutArchive &archive) const
	Write the component to the supplied archive. More...

SHARK_EXPORT_SYMBOL void	init (ObjectiveFunctionType const &function, SearchPointType const &p)
	Initializes the algorithm for the supplied objective function and the initial mean p. More...

SHARK_EXPORT_SYMBOL void	init (ObjectiveFunctionType const &function, SearchPointType const &initialSearchPoint, unsigned int populationSize, unsigned int selectionSize, RealVector initialSigma)
	Initializes the algorithm for the supplied objective function. More...

SHARK_EXPORT_SYMBOL void	step (ObjectiveFunctionType const &function)
	Executes one iteration of the algorithm. More...

RealVector const &	variance () const
	Access the current variance. More...

void	setVariance (RealVector variance)
	Set the variance to a vector. More...

void	setVariance (double variance)
	Set all variance values. More...

RealVector const &	mean () const
	Access the current population mean. More...

unsigned int	selectionSize () const
	Returns the size of the parent population. More...

unsigned int &	selectionSize ()
	Returns a mutable reference to the size of the parent population. More...

unsigned int	populationSize () const
	Returns a immutable reference to the size of the population. More...

unsigned int &	populationSize ()
	Returns a mutable reference to the size of the population. More...

void	setNoiseType (INoiseType *noiseType)
	Set the noise type from a raw pointer. More...

const INoiseType &	getNoiseType () const
	Get an immutable reference to Noise Type. More...

Public Member Functions inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
std::size_t	numInitPoints () const
	By default most single objective optimizers only require a single point. More...

virtual void	init (ObjectiveFunctionType const &function, std::vector< SearchPointType > const &initPoints)
	Initialize the optimizer for the supplied objective function using a set of initialisation points. More...

virtual const SolutionType &	solution () const
	returns the current solution of the optimizer More...

Public Member Functions inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
const Features &	features () const

virtual void	updateFeatures ()

bool	requiresValue () const

bool	requiresFirstDerivative () const

bool	requiresSecondDerivative () const

bool	canSolveConstrained () const

bool	requiresClosestFeasible () const

virtual	~AbstractOptimizer ()

virtual void	init (ObjectiveFunctionType const &function)
	Initialize the optimizer for the supplied objective function. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Static Public Member Functions
static SHARK_EXPORT_SYMBOL unsigned	suggestPopulationSize ()
	Sets default value for Population size. More...

static SHARK_EXPORT_SYMBOL unsigned	suggestSelectionSize (unsigned int populationSize)
	Calculates selection size for the supplied population size. More...

Protected Types
typedef Individual< RealVector, double, RealVector >	IndividualType

Protected Member Functions
SHARK_EXPORT_SYMBOL void	updateStrategyParameters (std::vector< IndividualType > const &parents)
	Updates the strategy parameters based on the supplied parent population. More...

Protected Member Functions inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
void	checkFeatures (ObjectiveFunctionType const &objectiveFunction)
	Convenience function that checks whether the features of the supplied objective function match with the required features of the optimizer. More...

Protected Attributes
std::size_t	m_numberOfVariables
	Stores the dimensionality of the search space. More...

unsigned int	m_selectionSize
	Number of vectors chosen when updating distribution parameters. More...

unsigned int	m_populationSize
	Number of vectors sampled in a generation. More...

RealVector	m_variance
	Variance for sample parameters. More...

RealVector	m_mean
	The mean of the population. More...

unsigned	m_counter
	Counter for generations. More...

StrongNoisePtr	m_noise
	Noise type to apply in the update of distribution parameters. More...

Protected Attributes inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
SolutionType	m_best
	Current solution of the optimizer. More...

Protected Attributes inherited from shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >
Features	m_features

Detailed Description

Implements the Cross Entropy Method.

This class implements the noisy cross entropy method as descibed in the following article.

Christophe Thiery, Bruno Scherrer. Improvements on Learning Tetris with Cross Entropy. International Computer Games Association Journal, ICGA, 2009, 32. <inria-00418930>

The algorithm aims to minimize an objective function through stochastic search. It works iteratively until a certain stopping criteria is met. At each iteration, it samples a number of vectors from a Gaussian distribution and evaluates each of these against the supplied objective function. Based on the return value from the objective function, a subset of the the best ranked vectors are chosen to update the search parameters of the next generation.

The mean of the Gaussian distribution is set to the centroid of the best ranked vectors, and the variance is set to the variance of the best ranked vectors in each individual dimension.

Definition at line 70 of file CrossEntropyMethod.h.

Member Typedef Documentation

◆ IndividualType

typedef Individual<RealVector, double, RealVector> shark::CrossEntropyMethod::IndividualType

protected

Definition at line 232 of file CrossEntropyMethod.h.

◆ StrongNoisePtr

typedef boost::shared_ptr<INoiseType> shark::CrossEntropyMethod::StrongNoisePtr

Smart pointer for noise type.

Definition at line 86 of file CrossEntropyMethod.h.

Constructor & Destructor Documentation

◆ CrossEntropyMethod()

SHARK_EXPORT_SYMBOL shark::CrossEntropyMethod::CrossEntropyMethod ( )

Default c'tor.

Referenced by shark::CrossEntropyMethod::LinearNoise::name().

Member Function Documentation

◆ getNoiseType()

const INoiseType& shark::CrossEntropyMethod::getNoiseType ( ) const

inline

Get an immutable reference to Noise Type.

Definition at line 226 of file CrossEntropyMethod.h.

References m_noise.

◆ init() [1/2]

SHARK_EXPORT_SYMBOL void shark::CrossEntropyMethod::init	(	ObjectiveFunctionType const &	function,
		SearchPointType const &	p
	)

virtual

Initializes the algorithm for the supplied objective function and the initial mean p.

Implements shark::AbstractSingleObjectiveOptimizer< RealVector >.

Referenced by name().

◆ init() [2/2]

SHARK_EXPORT_SYMBOL void shark::CrossEntropyMethod::init	(	ObjectiveFunctionType const &	function,
		SearchPointType const &	initialSearchPoint,
		unsigned int	populationSize,
		unsigned int	selectionSize,
		RealVector	initialSigma
	)

Initializes the algorithm for the supplied objective function.

◆ mean()

RealVector const& shark::CrossEntropyMethod::mean ( ) const

inline

Access the current population mean.

Definition at line 183 of file CrossEntropyMethod.h.

References m_mean.

◆ name()

std::string shark::CrossEntropyMethod::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 128 of file CrossEntropyMethod.h.

References init(), populationSize(), read(), selectionSize(), SHARK_EXPORT_SYMBOL, step(), suggestPopulationSize(), suggestSelectionSize(), and write().

◆ populationSize() [1/2]

unsigned int shark::CrossEntropyMethod::populationSize ( ) const

inline

Returns a immutable reference to the size of the population.

Definition at line 204 of file CrossEntropyMethod.h.

References m_populationSize.

Referenced by name().

◆ populationSize() [2/2]

unsigned int& shark::CrossEntropyMethod::populationSize ( )

inline

Returns a mutable reference to the size of the population.

Definition at line 211 of file CrossEntropyMethod.h.

References m_populationSize.

◆ read()

void shark::CrossEntropyMethod::read ( InArchive & archive )

virtual

Read the component from the supplied archive.

Parameters

[in,out] archive The archive to read from.

Reimplemented from shark::ISerializable.

Referenced by name().

◆ selectionSize() [1/2]

unsigned int shark::CrossEntropyMethod::selectionSize ( ) const

inline

Returns the size of the parent population.

Definition at line 190 of file CrossEntropyMethod.h.

References m_selectionSize.

Referenced by name().

◆ selectionSize() [2/2]

unsigned int& shark::CrossEntropyMethod::selectionSize ( )

inline

Returns a mutable reference to the size of the parent population.

Definition at line 197 of file CrossEntropyMethod.h.

References m_selectionSize.

◆ setNoiseType()

void shark::CrossEntropyMethod::setNoiseType ( INoiseType * noiseType )

inline

Set the noise type from a raw pointer.

Definition at line 218 of file CrossEntropyMethod.h.

References m_noise.

◆ setVariance() [1/2]

void shark::CrossEntropyMethod::setVariance ( RealVector variance )

inline

Set the variance to a vector.

Definition at line 172 of file CrossEntropyMethod.h.

References m_variance, and variance().

◆ setVariance() [2/2]

void shark::CrossEntropyMethod::setVariance ( double variance )

inline

Set all variance values.

Definition at line 178 of file CrossEntropyMethod.h.

References m_variance.

◆ step()

SHARK_EXPORT_SYMBOL void shark::CrossEntropyMethod::step ( ObjectiveFunctionType const & function )

virtual

Executes one iteration of the algorithm.

Implements shark::AbstractOptimizer< RealVector, double, SingleObjectiveResultSet< RealVector > >.

Referenced by name().

◆ suggestPopulationSize()

static SHARK_EXPORT_SYMBOL unsigned shark::CrossEntropyMethod::suggestPopulationSize ( )

static

Sets default value for Population size.

Referenced by name().

◆ suggestSelectionSize()

static SHARK_EXPORT_SYMBOL unsigned shark::CrossEntropyMethod::suggestSelectionSize ( unsigned int populationSize )

static

Calculates selection size for the supplied population size.

Referenced by name().

◆ updateStrategyParameters()

SHARK_EXPORT_SYMBOL void shark::CrossEntropyMethod::updateStrategyParameters ( std::vector< IndividualType > const & parents )

protected

Updates the strategy parameters based on the supplied parent population.

◆ variance()

RealVector const& shark::CrossEntropyMethod::variance ( ) const

inline

Access the current variance.

Definition at line 167 of file CrossEntropyMethod.h.

References m_variance.

Referenced by setVariance().

◆ write()

void shark::CrossEntropyMethod::write ( OutArchive & archive ) const

virtual

Write the component to the supplied archive.

Parameters

[in,out] archive The archive to write to.

Reimplemented from shark::ISerializable.

Referenced by name().

Member Data Documentation

◆ m_counter

unsigned shark::CrossEntropyMethod::m_counter

protected

Counter for generations.

Definition at line 247 of file CrossEntropyMethod.h.

◆ m_mean

RealVector shark::CrossEntropyMethod::m_mean

protected

The mean of the population.

Definition at line 245 of file CrossEntropyMethod.h.

Referenced by mean().

◆ m_noise

StrongNoisePtr shark::CrossEntropyMethod::m_noise

protected

Noise type to apply in the update of distribution parameters.

Definition at line 249 of file CrossEntropyMethod.h.

Referenced by shark::CrossEntropyMethod::ConstantNoise::ConstantNoise(), getNoiseType(), shark::CrossEntropyMethod::ConstantNoise::name(), and setNoiseType().

◆ m_numberOfVariables

std::size_t shark::CrossEntropyMethod::m_numberOfVariables

protected

Stores the dimensionality of the search space.

Definition at line 238 of file CrossEntropyMethod.h.

◆ m_populationSize

unsigned int shark::CrossEntropyMethod::m_populationSize

protected

Number of vectors sampled in a generation.

Definition at line 240 of file CrossEntropyMethod.h.

Referenced by populationSize().

◆ m_selectionSize

unsigned int shark::CrossEntropyMethod::m_selectionSize

protected

Number of vectors chosen when updating distribution parameters.

Definition at line 239 of file CrossEntropyMethod.h.

Referenced by selectionSize().

◆ m_variance

RealVector shark::CrossEntropyMethod::m_variance

protected

Variance for sample parameters.

Definition at line 242 of file CrossEntropyMethod.h.

Referenced by setVariance(), and variance().

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

include/shark/Algorithms/DirectSearch/CrossEntropyMethod.h

Classes

Public Types

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ IndividualType

◆ StrongNoisePtr

Constructor & Destructor Documentation

◆ CrossEntropyMethod()

Member Function Documentation

◆ getNoiseType()

◆ init() [1/2]

◆ init() [2/2]

◆ mean()

◆ name()

◆ populationSize() [1/2]

◆ populationSize() [2/2]

◆ read()

◆ selectionSize() [1/2]

◆ selectionSize() [2/2]

◆ setNoiseType()

◆ setVariance() [1/2]

◆ setVariance() [2/2]

◆ step()

◆ suggestPopulationSize()

◆ suggestSelectionSize()

◆ updateStrategyParameters()

◆ variance()

◆ write()

Member Data Documentation

◆ m_counter

◆ m_mean

◆ m_noise

◆ m_numberOfVariables

◆ m_populationSize

◆ m_selectionSize

◆ m_variance