shark::EpsilonHingeLoss Class Reference

Hinge-loss for large margin regression. More...

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

+ Inheritance diagram for shark::EpsilonHingeLoss:

Public Member Functions

 EpsilonHingeLoss (double epsilon)
 constructor More...
 
std::string name () const
 Returns class name "HingeLoss". More...
 
double eval (BatchLabelType const &labels, BatchOutputType const &predictions) const
 calculates the sum of all More...
 
double evalDerivative (BatchLabelType const &labels, BatchOutputType const &predictions, BatchOutputType &gradient) const
 evaluate the loss and the derivative w.r.t. the prediction More...
 
- 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 More...
 
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 More...
 
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 More...
 
double operator() (LabelType const &target, OutputType const &prediction) const
 evaluate the loss for a target and a prediction More...
 
double operator() (BatchLabelType const &target, BatchOutputType const &prediction) const
 
- Public Member Functions inherited from shark::AbstractCost< RealVector, RealVector >
virtual ~AbstractCost ()
 
const Featuresfeatures () const
 
virtual void updateFeatures ()
 
bool hasFirstDerivative () const
 returns true when the first parameter derivative is implemented More...
 
bool isLossFunction () const
 returns true when the cost function is in fact a loss function More...
 
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< LabelType const >::type ConstLabelReference
 Const references to LabelType. More...
 
typedef ConstProxyReference< OutputType const >::type ConstOutputReference
 Const references to OutputType. More...
 
- Public Types inherited from shark::AbstractCost< RealVector, RealVector >
enum  Feature
 list of features a cost function can have More...
 
typedef RealVector OutputType
 
typedef RealVector LabelType
 
typedef Batch< OutputType >::type BatchOutputType
 
typedef Batch< LabelType >::type BatchLabelType
 
typedef TypedFlags< FeatureFeatures
 
typedef TypedFeatureNotAvailableException< FeatureFeatureNotAvailableException
 
- Protected Attributes inherited from shark::AbstractCost< RealVector, RealVector >
Features m_features
 

Detailed Description

Hinge-loss for large margin regression.

The loss is defined as \(L_i = \sum_j^N \max\{0.0, |f_j(x_i)-y_{i,j}|-\epsilon\} \) 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. Points which are outside of the margin are linearly punished, that is the loss is outlier resistant.

Epsilon describes the size of the margin.

The hinge-loss is not differentiable at the points y_{i,j}+epsilon and y_{i,j}-epsilon.

Definition at line 51 of file EpsilonHingeLoss.h.

Constructor & Destructor Documentation

◆ EpsilonHingeLoss()

shark::EpsilonHingeLoss::EpsilonHingeLoss ( double  epsilon)
inline

constructor

Definition at line 55 of file EpsilonHingeLoss.h.

References shark::AbstractCost< RealVector, RealVector >::m_features.

Member Function Documentation

◆ eval()

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

calculates the sum of all

Implements shark::AbstractLoss< RealVector, RealVector >.

Definition at line 65 of file EpsilonHingeLoss.h.

References SIZE_CHECK.

◆ evalDerivative()

double shark::EpsilonHingeLoss::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
targettarget value
predictionprediction, typically made by a model
gradientthe gradient of the loss function with respect to the prediction

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

Definition at line 71 of file EpsilonHingeLoss.h.

References SIZE_CHECK.

◆ name()

std::string shark::EpsilonHingeLoss::name ( ) const
inlinevirtual

Returns class name "HingeLoss".

Reimplemented from shark::INameable.

Definition at line 60 of file EpsilonHingeLoss.h.


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