Hinge-loss for large margin regression using th squared two-norm. More...

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

Inheritance diagram for shark::SquaredEpsilonHingeLoss:

Public Member Functions
	SquaredEpsilonHingeLoss (double epsilon)
	constructor

std::string	name () const
	Returns class name "HingeLoss".

double	eval (BatchLabelType const &labels, BatchOutputType const &predictions) const
	calculates the sum of all

double	evalDerivative (BatchLabelType const &labels, BatchOutputType const &predictions, BatchOutputType &gradient) const
	evaluate the loss and the derivative w.r.t. the prediction

Public Member Functions inherited from shark::AbstractLoss< RealVector, RealVector >
	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

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 ()

Additional Inherited Members
Public Types inherited from shark::AbstractLoss< RealVector, RealVector >
typedef RealVector	OutputType

typedef RealVector	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

Protected Attributes inherited from shark::AbstractCost< LabelT, OutputT >
Features	m_features

Detailed Description

Hinge-loss for large margin regression using th squared two-norm.

The loss is defined as \(L_i = 1/2 \max\{0.0, ||f(x_i)-y{i,j}||^2- \epsilon^2\} \)
where \( y_i =(y_{i,1},\dots,y_{i_N} \) is the label of dimension N and \( f_j(x_i) \) is the j-th output of the prediction of the model for the ith input. The loss introduces the concept of a margin to regression, that is, points are not punished when they are sufficiently close to the function.

epsilon describes the distance from the label to the margin that is allowed until the point leaves the margin.

Contrary to th EpsilonHingeLoss, this loss is differentiable.

Definition at line 52 of file SquaredEpsilonHingeLoss.h.

Constructor & Destructor Documentation

◆ SquaredEpsilonHingeLoss()

shark::SquaredEpsilonHingeLoss::SquaredEpsilonHingeLoss ( double epsilon )

inline

constructor

Definition at line 56 of file SquaredEpsilonHingeLoss.h.

References shark::AbstractCost< LabelT, OutputT >::m_features.

Member Function Documentation

◆ eval()

double shark::SquaredEpsilonHingeLoss::eval	(	BatchLabelType const &	labels,
		BatchOutputType const &	predictions
	)		const

inlinevirtual

calculates the sum of all

Implements shark::AbstractLoss< RealVector, RealVector >.

Definition at line 66 of file SquaredEpsilonHingeLoss.h.

References SIZE_CHECK.

◆ evalDerivative()

double shark::SquaredEpsilonHingeLoss::evalDerivative	(	BatchLabelType const &	target,
		BatchOutputType const &	prediction,
		BatchOutputType &	gradient
	)		const

inlinevirtual

evaluate the loss and the derivative w.r.t. the prediction

: The default implementations throws an exception. If you overwrite this method, don't forget to set the flag HAS_FIRST_DERIVATIVE.

Parameters

target	target value
prediction	prediction, typically made by a model
gradient	the gradient of the loss function with respect to the prediction

Reimplemented from shark::AbstractLoss< RealVector, RealVector >.

Definition at line 73 of file SquaredEpsilonHingeLoss.h.

References SIZE_CHECK.

◆ name()

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

inlinevirtual

Returns class name "HingeLoss".

Reimplemented from shark::INameable.

Definition at line 61 of file SquaredEpsilonHingeLoss.h.

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

include/shark/ObjectiveFunctions/Loss/SquaredEpsilonHingeLoss.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ SquaredEpsilonHingeLoss()

Member Function Documentation

◆ eval()

◆ evalDerivative()

◆ name()