shark::TwoNormRegularizer Class Reference

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

#include <shark/ObjectiveFunctions/Regularizer.h>

+ Inheritance diagram for shark::TwoNormRegularizer:

Public Types

typedef RealVector SearchPointType
 
typedef double ResultType
 
typedef AbstractObjectiveFunction< RealVector, double > super
 
- Public Types inherited from shark::AbstractObjectiveFunction< RealVector, double >
enum  Feature
 List of features that are supported by an implementation. More...
 
typedef RealVector SearchPointType
 
typedef double ResultType
 
typedef boost::mpl::if_< std::is_arithmetic< double >, SearchPointType, RealMatrix >::type FirstOrderDerivative
 
typedef TypedFlags< FeatureFeatures
 This statement declares the member m_features. See Core/Flags.h for details. More...
 
typedef TypedFeatureNotAvailableException< FeatureFeatureNotAvailableException
 

Public Member Functions

 TwoNormRegularizer (std::size_t numVariables=0)
 Constructor. More...
 
std::string name () const
 From INameable: return the class name. More...
 
std::size_t numberOfVariables () const
 Accesses the number of variables. More...
 
bool hasScalableDimensionality () const
 
void setNumberOfVariables (std::size_t numberOfVariables)
 Adjusts the number of variables if the function is scalable. More...
 
void setMask (const RealVector &mask)
 
const RealVector & mask () const
 
virtual double eval (RealVector const &input) const
 Evaluates the objective function. More...
 
virtual double evalDerivative (RealVector const &input, FirstOrderDerivative &derivative) const
 
virtual ResultType evalDerivative (const SearchPointType &input, SecondOrderDerivative &derivative) const
 
- Public Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double >
const Featuresfeatures () const
 
virtual void updateFeatures ()
 
bool hasValue () const
 returns whether this function can calculate it's function value More...
 
bool hasFirstDerivative () const
 returns whether this function can calculate the first derivative More...
 
bool hasSecondDerivative () const
 returns whether this function can calculate the second derivative More...
 
bool canProposeStartingPoint () const
 returns whether this function can propose a starting point. More...
 
bool isConstrained () const
 returns whether this function can return More...
 
bool hasConstraintHandler () const
 returns whether this function can return More...
 
bool canProvideClosestFeasible () const
 Returns whether this function can calculate thee closest feasible to an infeasible point. More...
 
bool isThreadSafe () const
 Returns true, when the function can be usd in parallel threads. More...
 
bool isNoisy () const
 Returns true, when the function can be usd in parallel threads. More...
 
 AbstractObjectiveFunction ()
 Default ctor. More...
 
virtual ~AbstractObjectiveFunction ()
 Virtual destructor. More...
 
virtual void init ()
 
void setRng (random::rng_type *rng)
 Sets the Rng used by the objective function. More...
 
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. More...
 
std::size_t evaluationCounter () const
 Accesses the evaluation counter of the function. More...
 
AbstractConstraintHandler< SearchPointType > const & getConstraintHandler () const
 Returns the constraint handler of the function if it has one. More...
 
virtual bool isFeasible (const SearchPointType &input) const
 Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled. More...
 
virtual void closestFeasible (SearchPointType &input) const
 If supported, the supplied point is repaired such that it satisfies all of the function's constraints. More...
 
virtual SearchPointType proposeStartingPoint () const
 Proposes a starting point in the feasible search space of the function. More...
 
ResultType operator() (SearchPointType const &input) const
 Evaluates the function. Useful together with STL-Algorithms like std::transform. More...
 
- Public Member Functions inherited from shark::INameable
virtual ~INameable ()
 

Additional Inherited Members

- Protected Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double >
void announceConstraintHandler (AbstractConstraintHandler< SearchPointType > const *handler)
 helper function which is called to announce the presence of an constraint handler. More...
 
- Protected Attributes inherited from shark::AbstractObjectiveFunction< RealVector, double >
Features m_features
 
std::size_t m_evaluationCounter
 Evaluation counter, default value: 0. More...
 
AbstractConstraintHandler< SearchPointType > const * m_constraintHandler
 
random::rng_type * mep_rng
 

Detailed Description

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 147 of file Regularizer.h.

Member Typedef Documentation

◆ ResultType

Definition at line 151 of file Regularizer.h.

◆ SearchPointType

Definition at line 150 of file Regularizer.h.

◆ super

Definition at line 153 of file Regularizer.h.

Constructor & Destructor Documentation

◆ TwoNormRegularizer()

Member Function Documentation

◆ eval()

virtual double shark::TwoNormRegularizer::eval ( RealVector const &  input) const
inlinevirtual

Evaluates the objective function.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 186 of file Regularizer.h.

◆ evalDerivative() [1/2]

virtual double shark::TwoNormRegularizer::evalDerivative ( RealVector const &  input,
FirstOrderDerivative derivative 
) const
inlinevirtual

Evaluates the objective function and calculates its gradient.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 198 of file Regularizer.h.

References shark::OneNormRegularizer::eval().

◆ evalDerivative() [2/2]

virtual ResultType shark::TwoNormRegularizer::evalDerivative ( const SearchPointType input,
SecondOrderDerivative &  derivative 
) const
inlinevirtual

Evaluates the objective function and calculates its gradient and its Hessian.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 211 of file Regularizer.h.

◆ hasScalableDimensionality()

bool shark::TwoNormRegularizer::hasScalableDimensionality ( ) const
inlinevirtual

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 170 of file Regularizer.h.

◆ mask()

const RealVector& shark::TwoNormRegularizer::mask ( ) const
inline

Definition at line 181 of file Regularizer.h.

◆ name()

std::string shark::TwoNormRegularizer::name ( ) const
inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 163 of file Regularizer.h.

◆ numberOfVariables()

std::size_t shark::TwoNormRegularizer::numberOfVariables ( ) const
inlinevirtual

Accesses the number of variables.

Implements shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 166 of file Regularizer.h.

◆ setMask()

void shark::TwoNormRegularizer::setMask ( const RealVector &  mask)
inline

Definition at line 178 of file Regularizer.h.

References shark::OneNormRegularizer::mask().

◆ setNumberOfVariables()

void shark::TwoNormRegularizer::setNumberOfVariables ( std::size_t  numberOfVariables)
inlinevirtual

Adjusts the number of variables if the function is scalable.

Parameters
[in]numberOfVariablesThe new dimension.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 174 of file Regularizer.h.

References shark::OneNormRegularizer::numberOfVariables().


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