Computes the negative log likelihood of a dataset under a model. More...

#include <shark/ObjectiveFunctions/NegativeLogLikelihood.h>

Inheritance diagram for shark::NegativeLogLikelihood:

Public Types
typedef UnlabeledData< RealVector >	DatasetType

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

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Public Member Functions
	NegativeLogLikelihood (DatasetType const &data, AbstractModel< RealVector, RealVector > *model)

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

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.

ResultType	eval (RealVector const &input) const
	Evaluates the objective function for the supplied argument.

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

Public Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double >
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 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, 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< RealVector, double >
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< RealVector, double >
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

Computes the negative log likelihood of a dataset under a model.

The negative log likelihood is defined as

\[ L(\theta) = -\frac 1 N \sum_{i=1}^N log(p_{\theta}(x_i)) \]

where \( \theta \) is the vector of parameters of the model \( p \) and \( x \) are the datapoints of the training set. Minimizing this maximizes the probability of the datast under p. This error measure is closely related to the Kulback-Leibler-Divergence.

For this error function, the model is only allowed to have a single output

the probability of the sample. The distribution must be normalized as otherwise the likeelihood does not mean anything.

Definition at line 56 of file NegativeLogLikelihood.h.

Member Typedef Documentation

◆ DatasetType

typedef UnlabeledData<RealVector> shark::NegativeLogLikelihood::DatasetType

Definition at line 59 of file NegativeLogLikelihood.h.

Constructor & Destructor Documentation

◆ NegativeLogLikelihood()

shark::NegativeLogLikelihood::NegativeLogLikelihood	(	DatasetType const &	data,
		AbstractModel< RealVector, RealVector > *	model
	)

inline

Definition at line 61 of file NegativeLogLikelihood.h.

References shark::AbstractObjectiveFunction< RealVector, double >::CAN_PROPOSE_STARTING_POINT, shark::AbstractObjectiveFunction< RealVector, double >::HAS_FIRST_DERIVATIVE, shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >::hasFirstParameterDerivative(), and shark::AbstractObjectiveFunction< RealVector, double >::m_features.

Member Function Documentation

◆ eval()

ResultType shark::NegativeLogLikelihood::eval ( RealVector const & input ) const

inlinevirtual

Evaluates the objective function for the supplied argument.

Parameters

[in] input The argument for which the function shall be evaluated.

Returns: The result of evaluating the function for the supplied argument.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

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

Definition at line 82 of file NegativeLogLikelihood.h.

References shark::Data< Type >::batch(), shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, shark::Data< Type >::numberOfBatches(), shark::Data< Type >::numberOfElements(), numberOfVariables(), shark::IParameterizable< VectorType >::setParameterVector(), SHARK_CRITICAL_REGION, SHARK_PARALLEL_FOR, and SIZE_CHECK.

◆ evalDerivative()

ResultType shark::NegativeLogLikelihood::evalDerivative	(	SearchPointType const &	input,
		FirstOrderDerivative &	derivative
	)		const

inlinevirtual

Evaluates the objective function and calculates its gradient.

Parameters

[in]	input	The argument to eval the function for.
[out]	derivative	The derivate is placed here.

Returns: The result of evaluating the function for the supplied argument.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

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

Definition at line 100 of file NegativeLogLikelihood.h.

References shark::Data< Type >::batch(), shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >::createState(), shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >::eval(), shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, shark::Data< Type >::numberOfBatches(), shark::Data< Type >::numberOfElements(), numberOfVariables(), shark::IParameterizable< VectorType >::setParameterVector(), SHARK_CRITICAL_REGION, SHARK_NUM_THREADS, SHARK_PARALLEL_FOR, SIZE_CHECK, and shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >::weightedParameterDerivative().

◆ name()

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

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 71 of file NegativeLogLikelihood.h.

◆ numberOfVariables()

std::size_t shark::NegativeLogLikelihood::numberOfVariables ( ) const

inlinevirtual

Accesses the number of variables.

Implements shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 78 of file NegativeLogLikelihood.h.

References shark::IParameterizable< VectorType >::numberOfParameters().

Referenced by eval(), and evalDerivative().

◆ proposeStartingPoint()

SearchPointType shark::NegativeLogLikelihood::proposeStartingPoint ( ) const

inlinevirtual

Proposes a starting point in the feasible search space of the function.

Returns: The generated starting point.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

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

Definition at line 74 of file NegativeLogLikelihood.h.

References shark::IParameterizable< VectorType >::parameterVector().

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

include/shark/ObjectiveFunctions/NegativeLogLikelihood.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ DatasetType

Constructor & Destructor Documentation

◆ NegativeLogLikelihood()

Member Function Documentation

◆ eval()

◆ evalDerivative()

◆ name()

◆ numberOfVariables()

◆ proposeStartingPoint()