Objective function for supervised learning. More...

#include <shark/ObjectiveFunctions/ErrorFunction.h>

Inheritance diagram for shark::ErrorFunction< SearchPointType >:

Public Types
typedef FunctionType::ResultType	ResultType

typedef FunctionType::FirstOrderDerivative	FirstOrderDerivative

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

Public Member Functions
template<class InputType , class LabelType , class OutputType >
	ErrorFunction (LabeledData< InputType, LabelType > const &dataset, AbstractModel< InputType, OutputType, SearchPointType > model, AbstractLoss< LabelType, OutputType > loss, bool useMiniBatches=false)

template<class InputType , class LabelType , class OutputType >
	ErrorFunction (WeightedLabeledData< InputType, LabelType > const &dataset, AbstractModel< InputType, OutputType, SearchPointType > model, AbstractLoss< LabelType, OutputType > loss)

	ErrorFunction (ErrorFunction const &op)

ErrorFunction &	operator= (ErrorFunction const &op)

std::string	name () const
	returns the name of the object

void	setRegularizer (double factor, FunctionType *regularizer)

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

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

void	init ()

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

ResultType	evalDerivative (SearchPointType const &input, FirstOrderDerivative &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.

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

virtual bool	hasScalableDimensionality () const

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

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.

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
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::ErrorFunction< SearchPointType >

Objective function for supervised learning.

: An ErrorFunction object is an objective function for learning the parameters of a model from data by means of minimization of a cost function. The value of the objective function is the cost of the model predictions on the training data, given the targets.

: It supports mini-batch learning using an optional fourth argument to The constructor. With mini-batch learning enabled, each iteration a random batch is taken from the dataset. Thus the size of the minibatch is the size of the batches in the datasets. Normalization ensures that batches of different sizes have approximately the same magnitude of error and derivative.

: It automatically infers the input und label type from the given dataset and the output type of the model in the constructor and ensures that Model and loss match. Thus the user does not need to provide the types as template parameters.

Definition at line 69 of file ErrorFunction.h.

Member Typedef Documentation

◆ FirstOrderDerivative

template<class SearchPointType = RealVector>

typedef FunctionType::FirstOrderDerivative shark::ErrorFunction< SearchPointType >::FirstOrderDerivative

Definition at line 75 of file ErrorFunction.h.

◆ ResultType

template<class SearchPointType = RealVector>

typedef FunctionType::ResultType shark::ErrorFunction< SearchPointType >::ResultType

Definition at line 74 of file ErrorFunction.h.

Constructor & Destructor Documentation

◆ ErrorFunction() [1/3]

template<class SearchPointType = RealVector>

template<class InputType , class LabelType , class OutputType >

shark::ErrorFunction< SearchPointType >::ErrorFunction	(	LabeledData< InputType, LabelType > const &	dataset,
		AbstractModel< InputType, OutputType, SearchPointType > *	model,
		AbstractLoss< LabelType, OutputType > *	loss,
		bool	useMiniBatches = `false`
	)

inline

Definition at line 78 of file ErrorFunction.h.

References shark::AbstractObjectiveFunction< PointType, ResultT >::features(), and shark::AbstractObjectiveFunction< PointType, ResultT >::m_features.

◆ ErrorFunction() [2/3]

template<class SearchPointType = RealVector>

template<class InputType , class LabelType , class OutputType >

shark::ErrorFunction< SearchPointType >::ErrorFunction	(	WeightedLabeledData< InputType, LabelType > const &	dataset,
		AbstractModel< InputType, OutputType, SearchPointType > *	model,
		AbstractLoss< LabelType, OutputType > *	loss
	)