Two-norm of the input as an objective function. More...

#include <shark/ObjectiveFunctions/Regularizer.h>

Inheritance diagram for shark::TwoNormRegularizer< SearchPointType >:

Public Member Functions
	TwoNormRegularizer (std::size_t numVariables=0)
	Constructor.

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

std::size_t	numberOfVariables () const
	Accesses the number of variables.

bool	hasScalableDimensionality () const

void	setNumberOfVariables (std::size_t numberOfVariables)
	Adjusts the number of variables if the function is scalable.

void	setMask (SearchPointType const &mask)

SearchPointType const &	mask () const

double	eval (SearchPointType const &input) const
	Evaluates the objective function.

double	evalDerivative (SearchPointType const &input, SearchPointType &derivative) const

Public Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
const Features &	features () const

virtual void	updateFeatures ()

bool	hasValue () const
	returns whether this function can calculate it's function value

bool	hasFirstDerivative () const
	returns whether this function can calculate the first derivative

bool	hasSecondDerivative () const
	returns whether this function can calculate the second derivative

bool	canProposeStartingPoint () const
	returns whether this function can propose a starting point.

bool	isConstrained () const
	returns whether this function can return

bool	hasConstraintHandler () const
	returns whether this function can return

bool	canProvideClosestFeasible () const
	Returns whether this function can calculate thee closest feasible to an infeasible point.

bool	isThreadSafe () const
	Returns true, when the function can be usd in parallel threads.

bool	isNoisy () const
	Returns true, when the function can be usd in parallel threads.

	AbstractObjectiveFunction ()
	Default ctor.

virtual	~AbstractObjectiveFunction ()
	Virtual destructor.

virtual void	init ()

void	setRng (random::rng_type *rng)
	Sets the Rng used by the objective function.

virtual std::size_t	numberOfObjectives () const

virtual bool	hasScalableObjectives () const

virtual void	setNumberOfObjectives (std::size_t numberOfObjectives)
	Adjusts the number of objectives if the function is scalable.

std::size_t	evaluationCounter () const
	Accesses the evaluation counter of the function.

AbstractConstraintHandler< SearchPointType > const &	getConstraintHandler () const
	Returns the constraint handler of the function if it has one.

virtual bool	isFeasible (const SearchPointType &input) const
	Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled.

virtual void	closestFeasible (SearchPointType &input) const
	If supported, the supplied point is repaired such that it satisfies all of the function's constraints.

virtual SearchPointType	proposeStartingPoint () const
	Proposes a starting point in the feasible search space of the function.

ResultType	operator() (SearchPointType const &input) const
	Evaluates the function. Useful together with STL-Algorithms like std::transform.

virtual ResultType	evalDerivative (SearchPointType const &input, FirstOrderDerivative &derivative) const
	Evaluates the objective function and calculates its gradient.

virtual ResultType	evalDerivative (SearchPointType const &input, SecondOrderDerivative &derivative) const
	Evaluates the objective function and calculates its gradient.

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

Additional Inherited Members
Public Types inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
enum	Feature { HAS_VALUE = 1 , HAS_FIRST_DERIVATIVE = 2 , HAS_SECOND_DERIVATIVE = 4 , CAN_PROPOSE_STARTING_POINT = 8 , IS_CONSTRAINED_FEATURE = 16 , HAS_CONSTRAINT_HANDLER = 32 , CAN_PROVIDE_CLOSEST_FEASIBLE = 64 , IS_THREAD_SAFE = 128 , IS_NOISY = 256 }
	List of features that are supported by an implementation. More...

typedef PointType	SearchPointType

typedef ResultT	ResultType

typedef boost::mpl::if_< std::is_arithmetic< ResultT >, SearchPointType, RealMatrix >::type	FirstOrderDerivative

typedef TypedFlags< Feature >	Features
	This statement declares the member m_features. See Core/Flags.h for details.

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Protected Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
void	announceConstraintHandler (AbstractConstraintHandler< SearchPointType > const *handler)
	helper function which is called to announce the presence of an constraint handler.

Protected Attributes inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
Features	m_features

std::size_t	m_evaluationCounter
	Evaluation counter, default value: 0.

AbstractConstraintHandler< SearchPointType > const *	m_constraintHandler

random::rng_type *	mep_rng

Detailed Description

template<class SearchPointType = RealVector>
class shark::TwoNormRegularizer< SearchPointType >

Two-norm of the input as an objective function.

: The TwoNormRegularizer is intended to be used together with other objective functions within a CombinedObjectiveFunction, in order to obtain a more smooth solution.

Definition at line 122 of file Regularizer.h.

Constructor & Destructor Documentation

◆ TwoNormRegularizer()

template<class SearchPointType = RealVector>

shark::TwoNormRegularizer< SearchPointType >::TwoNormRegularizer ( std::size_t numVariables = 0 )

inline

Constructor.

Definition at line 128 of file Regularizer.h.

References shark::AbstractObjectiveFunction< SearchPointType, double >::HAS_FIRST_DERIVATIVE, and shark::AbstractObjectiveFunction< PointType, ResultT >::m_features.

Member Function Documentation

◆ eval()

template<class SearchPointType = RealVector>

double shark::TwoNormRegularizer< SearchPointType >::eval ( SearchPointType const & input ) const

inlinevirtual

Evaluates the objective function.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 156 of file Regularizer.h.

References shark::sqr().

Referenced by shark::TwoNormRegularizer< SearchPointType >::evalDerivative().

◆ evalDerivative()

template<class SearchPointType = RealVector>

double shark::TwoNormRegularizer< SearchPointType >::evalDerivative	(	SearchPointType const &	input,
		SearchPointType &	derivative
	)		const

inline

Evaluates the objective function and calculates its gradient.

Definition at line 167 of file Regularizer.h.

References shark::TwoNormRegularizer< SearchPointType >::eval().

◆ hasScalableDimensionality()

template<class SearchPointType = RealVector>

bool shark::TwoNormRegularizer< SearchPointType >::hasScalableDimensionality ( ) const

inlinevirtual

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 140 of file Regularizer.h.

◆ mask()

template<class SearchPointType = RealVector>

SearchPointType const & shark::TwoNormRegularizer< SearchPointType >::mask ( ) const

inline

Definition at line 151 of file Regularizer.h.

Referenced by shark::TwoNormRegularizer< SearchPointType >::setMask().

◆ name()

template<class SearchPointType = RealVector>

std::string shark::TwoNormRegularizer< SearchPointType >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 133 of file Regularizer.h.

◆ numberOfVariables()

template<class SearchPointType = RealVector>

std::size_t shark::TwoNormRegularizer< SearchPointType >::numberOfVariables ( ) const

inlinevirtual

Accesses the number of variables.

Implements shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 136 of file Regularizer.h.

Referenced by shark::TwoNormRegularizer< SearchPointType >::setNumberOfVariables().

◆ setMask()

template<class SearchPointType = RealVector>

void shark::TwoNormRegularizer< SearchPointType >::setMask ( SearchPointType const & mask )

inline

Definition at line 148 of file Regularizer.h.

References shark::TwoNormRegularizer< SearchPointType >::mask().

◆ setNumberOfVariables()

template<class SearchPointType = RealVector>

void shark::TwoNormRegularizer< SearchPointType >::setNumberOfVariables ( std::size_t numberOfVariables )

inlinevirtual

Adjusts the number of variables if the function is scalable.

Parameters

[in] numberOfVariables The new dimension.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 144 of file Regularizer.h.

References shark::TwoNormRegularizer< SearchPointType >::numberOfVariables().

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

include/shark/ObjectiveFunctions/Regularizer.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ TwoNormRegularizer()

Member Function Documentation

◆ eval()

◆ evalDerivative()

◆ hasScalableDimensionality()

◆ mask()

◆ name()

◆ numberOfVariables()

◆ setMask()

◆ setNumberOfVariables()