shark::DiscreteLoss Class Reference

flexible loss for classification More...

#include <shark/ObjectiveFunctions/Loss/DiscreteLoss.h>

Inheritance diagram for shark::DiscreteLoss:

Public Member Functions
SHARK_EXPORT_SYMBOL	DiscreteLoss (RealMatrix const &cost)
std::string	name () const
	From INameable: return the class name.
SHARK_EXPORT_SYMBOL double	eval (BatchLabelType const &target, BatchOutputType const &prediction) const
	inherited from AbstractLoss, evaluation of the loss function
SHARK_EXPORT_SYMBOL void	defineCostMatrix (RealMatrix const &cost)
SHARK_EXPORT_SYMBOL void	defineBalancedCost (UnlabeledData< unsigned int > const &labels)
Public Member Functions inherited from shark::AbstractLoss< unsigned int, unsigned int >
	AbstractLoss ()
virtual double	eval (ConstLabelReference target, ConstOutputReference prediction) const
	evaluate the loss for a target and a prediction
double	eval (Data< LabelType > const &targets, Data< OutputType > const &predictions) const
virtual double	evalDerivative (ConstLabelReference target, ConstOutputReference prediction, OutputType &gradient) const
	evaluate the loss and its derivative for a target and a prediction
virtual double	evalDerivative (ConstLabelReference target, ConstOutputReference prediction, OutputType &gradient, MatrixType &hessian) const
	evaluate the loss and its first and second derivative for a target and a prediction
virtual double	evalDerivative (BatchLabelType const &target, BatchOutputType const &prediction, BatchOutputType &gradient) const
	evaluate the loss and the derivative w.r.t. the prediction
double	operator() (LabelType const &target, OutputType const &prediction) const
	evaluate the loss for a target and a prediction
double	operator() (BatchLabelType const &target, BatchOutputType const &prediction) const
Public Member Functions inherited from shark::AbstractCost< LabelT, OutputT >
virtual	~AbstractCost ()
const Features &	features () const
virtual void	updateFeatures ()
bool	hasFirstDerivative () const
	returns true when the first parameter derivative is implemented
bool	isLossFunction () const
	returns true when the cost function is in fact a loss function
virtual double	eval (Data< LabelType > const &targets, Data< OutputType > const &predictions) const =0
double	operator() (Data< LabelType > const &targets, Data< OutputType > const &predictions) const
Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Protected Attributes
RealMatrix	m_cost
	cost matrix
Protected Attributes inherited from shark::AbstractCost< LabelT, OutputT >
Features	m_features

Additional Inherited Members
Public Types inherited from shark::AbstractLoss< unsigned int, unsigned int >
typedef unsigned int	OutputType
typedef unsigned int	LabelType
typedef RealMatrix	MatrixType
typedef Batch< OutputType >::type	BatchOutputType
typedef Batch< LabelType >::type	BatchLabelType
typedef ConstProxyReference< LabelTypeconst >::type	ConstLabelReference
	Const references to LabelType.
typedef ConstProxyReference< OutputTypeconst >::type	ConstOutputReference
	Const references to OutputType.
Public Types inherited from shark::AbstractCost< LabelT, OutputT >
enum	Feature { HAS_FIRST_DERIVATIVE = 1 , HAS_SECOND_DERIVATIVE = 2 , IS_LOSS_FUNCTION = 4 }
	list of features a cost function can have More...
typedef OutputT	OutputType
typedef LabelT	LabelType
typedef Batch< OutputType >::type	BatchOutputType
typedef Batch< LabelType >::type	BatchLabelType
typedef TypedFlags< Feature >	Features
typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Detailed Description

flexible loss for classification

The DiscreteLoss class allows for the definition of a cost matrix applied to a finite number of classes. The cost of correct classification must be zero, all other costs must be non-negative.

Note: As a special case, this loss can be used to provide a balanced error signal for unbalanced data sets.

Definition at line 57 of file DiscreteLoss.h.

Constructor & Destructor Documentation

DiscreteLoss()

SHARK_EXPORT_SYMBOL shark::DiscreteLoss::DiscreteLoss

(

RealMatrix const &

cost

)

Constructor

Parameters

cost	cost matrix in the format (target, prediction).

Member Function Documentation

defineBalancedCost()

SHARK_EXPORT_SYMBOL void shark::DiscreteLoss::defineBalancedCost

(

UnlabeledData< unsigned int > const &

labels

)

Define a new cost structure so that the cost of misclassifying a pattern is anti-proportional to the frequency of its class. This amounts to balancing the class-wise cost in unbalanced data sets (i.e., where one class is far more frequent than another).

Parameters

labels

label set to which the balanced loss should be adapted

defineCostMatrix()

SHARK_EXPORT_SYMBOL void shark::DiscreteLoss::defineCostMatrix

(

RealMatrix const &

cost

)

Define a new cost structure given by an explicit cost matrix.

Parameters

cost	cost matrix in the format (target, prediction).

eval()

SHARK_EXPORT_SYMBOL double shark::DiscreteLoss::eval ( BatchLabelType const &  target, BatchOutputType const &  prediction  ) const

virtual

inherited from AbstractLoss, evaluation of the loss function

Implements shark::AbstractLoss< unsigned int, unsigned int >.

name()

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

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 67 of file DiscreteLoss.h.

Member Data Documentation

m_cost

RealMatrix shark::DiscreteLoss::m_cost

protected

cost matrix

Definition at line 88 of file DiscreteLoss.h.

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

• include/shark/ObjectiveFunctions/Loss/DiscreteLoss.h