Computes the negative log likelihood of a dataset under a model. More...
#include <shark/ObjectiveFunctions/NegativeLogLikelihood.h>
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. More... | |
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. More... | |
SearchPointType | proposeStartingPoint () const |
Proposes a starting point in the feasible search space of the function. More... | |
std::size_t | numberOfVariables () const |
Accesses the number of variables. More... | |
ResultType | eval (RealVector const &input) const |
Evaluates the objective function for the supplied argument. More... | |
ResultType | evalDerivative (SearchPointType const &input, FirstOrderDerivative &derivative) const |
Evaluates the objective function and calculates its gradient. More... | |
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 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 bool | hasScalableDimensionality () const |
virtual void | setNumberOfVariables (std::size_t numberOfVariables) |
Adjusts the number of variables if the function is scalable. 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... | |
ResultType | operator() (SearchPointType const &input) const |
Evaluates the function. Useful together with STL-Algorithms like std::transform. More... | |
virtual ResultType | evalDerivative (SearchPointType const &input, SecondOrderDerivative &derivative) const |
Evaluates the objective function and calculates its gradient. 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 |
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
Definition at line 55 of file NegativeLogLikelihood.h.
typedef UnlabeledData<RealVector> shark::NegativeLogLikelihood::DatasetType |
Definition at line 58 of file NegativeLogLikelihood.h.
|
inline |
Definition at line 60 of file NegativeLogLikelihood.h.
References shark::AbstractObjectiveFunction< RealVector, double >::CAN_PROPOSE_STARTING_POINT, shark::AbstractObjectiveFunction< RealVector, double >::HAS_FIRST_DERIVATIVE, shark::AbstractModel< InputTypeT, OutputTypeT, ParameterType >::hasFirstParameterDerivative(), and shark::AbstractObjectiveFunction< RealVector, double >::m_features.
|
inlinevirtual |
Evaluates the objective function for the supplied argument.
[in] | input | The argument for which the function shall be evaluated. |
FeatureNotAvailableException | in the default implementation and if a function does not support this feature. |
Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 81 of file NegativeLogLikelihood.h.
References shark::Data< Type >::batch(), shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, shark::Data< Type >::numberOfBatches(), numberOfVariables(), shark::IParameterizable< VectorType >::setParameterVector(), SHARK_PARALLEL_FOR, and SIZE_CHECK.
|
inlinevirtual |
Evaluates the objective function and calculates its gradient.
[in] | input | The argument to eval the function for. |
[out] | derivative | The derivate is placed here. |
FeatureNotAvailableException | in the default implementation and if a function does not support this feature. |
Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 99 of file NegativeLogLikelihood.h.
References shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, shark::Data< Type >::numberOfBatches(), shark::Data< Type >::numberOfElements(), numberOfVariables(), shark::IParameterizable< VectorType >::setParameterVector(), and SIZE_CHECK.
|
inlinevirtual |
From INameable: return the class name.
Reimplemented from shark::INameable.
Definition at line 70 of file NegativeLogLikelihood.h.
|
inlinevirtual |
Accesses the number of variables.
Implements shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 77 of file NegativeLogLikelihood.h.
References shark::IParameterizable< VectorType >::numberOfParameters().
Referenced by eval(), and evalDerivative().
|
inlinevirtual |
Proposes a starting point in the feasible search space of the function.
FeatureNotAvailableException | in the default implementation and if a function does not support this feature. |
Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 73 of file NegativeLogLikelihood.h.
References shark::IParameterizable< VectorType >::parameterVector().