Optimize by trying out predefined configurations. More...

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

Inheritance diagram for shark::PointSearch:

Collaboration diagram for shark::PointSearch:

Public Member Functions
	PointSearch ()
	Constructor. More...

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

void	configure (const std::vector< RealVector > &points)

void	configure (size_t parameters, size_t samples, double min, double max)
	samples random points in the range [min,max]^parameters More...

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

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

void	init (const ObjectiveFunctionType &objectiveFunction, const SearchPointType &startingPoint)
	If the class wasn't configured before, this method samples random uniform distributed points in [-1,1]^n. More...

void	step (const ObjectiveFunctionType &objectiveFunction)

Public Member Functions inherited from shark::AbstractSingleObjectiveOptimizer< VectorSpace< double > >
virtual void	init (ObjectiveFunctionType const &function)

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

Public Member Functions inherited from shark::AbstractOptimizer< SearchSpaceT, ResultT, SetT >
const Features &	features () const

virtual void	updateFeatures ()

bool	requiresValue () const

bool	requiresFirstDerivative () const

bool	requiresSecondDerivative () const

bool	canSolveConstrained () const

bool	requiresClosestFeasible () const

virtual	~AbstractOptimizer ()
	Empty virtual d'tor. More...

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

virtual void	configure (const PropertyTree &node)
	Configures the component given a property tree. 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 ()

Protected Attributes
std::vector< RealVector >	m_points
	The array holds one parameter configuration in every column. More...

bool	m_configured
	verbosity level More...

Protected Attributes inherited from shark::AbstractSingleObjectiveOptimizer< VectorSpace< double > >
SolutionSetType	m_best
	current solution of the optimizer More...

Protected Attributes inherited from shark::AbstractOptimizer< SearchSpaceT, ResultT, SetT >
Features	m_features

Additional Inherited Members
Public Types inherited from shark::AbstractSingleObjectiveOptimizer< VectorSpace< double > >
typedef base_type::SearchSpaceType	SearchSpaceType

typedef base_type::SearchPointType	SearchPointType

typedef base_type::SolutionSetType	SolutionSetType

typedef base_type::ResultType	ResultType

typedef base_type::ObjectiveFunctionType	ObjectiveFunctionType

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

Detailed Description

Optimize by trying out predefined configurations.

: The PointSearch class is similair to the GridSearch class by the property that it optimizes a model in a single pass just trying out a predefined number of parameter configurations. The main difference is that every parameter configuration has to be explicitly defined. It is not possible to define a set of values for every axis; see GridSearch for this purpose. Thus, the PointSearch class allows for more flexibility.

If no configure method is called, this class just samples random points. They are uniformly distributed in [-1,1]. parameters^2 points but minimum 20 are sampled in this case.

Definition at line 541 of file GridSearch.h.