Tukey's Biweight-loss for robust regression. More...

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

Inheritance diagram for shark::TukeyBiweightLoss:

Public Member Functions
	TukeyBiweightLoss (double k=1.0)
	constructor More...

std::string	name () const
	Returns class name "HuberLoss". 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 Features &	features () 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< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

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

Detailed Description

Tukey's Biweight-loss for robust regression.

Tukey's Biweight-loss is a robust regression function. For predictions close to the correct classification, it is convex, but close to a value k, it approaches a constant value. for differences greater than k, the function is constant and has gradient 0. This effectively ignores large outliers at the cost of loosing the convexity of the loss function. The 1-dimensional loss is defined as

\[ f(x)= \frac {x^6}{6k^4} - \frac {x^4} {2k^2}+\frac {x^2} {2} \]

for \( x \in [-k,k]\). outside it is the constant function \(\frac {k^2}{6}\).

For multidimensional problems we define it with x being the two-norm of the difference between the label and predicted values.

Definition at line 50 of file TukeyBiweightLoss.h.

Constructor & Destructor Documentation

◆ TukeyBiweightLoss()

shark::TukeyBiweightLoss::TukeyBiweightLoss ( double k = 1.0 )

inline

constructor

Definition at line 54 of file TukeyBiweightLoss.h.

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

Member Function Documentation

◆ eval()

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

inlinevirtual

calculates the sum of all

Implements shark::AbstractLoss< RealVector, RealVector >.

Definition at line 64 of file TukeyBiweightLoss.h.

References SIZE_CHECK, and shark::sqr().

◆ evalDerivative()

double shark::TukeyBiweightLoss::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 87 of file TukeyBiweightLoss.h.

References SIZE_CHECK, and shark::sqr().

◆ name()

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

inlinevirtual

Returns class name "HuberLoss".

Reimplemented from shark::INameable.

Definition at line 59 of file TukeyBiweightLoss.h.

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

include/shark/ObjectiveFunctions/Loss/TukeyBiweightLoss.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ TukeyBiweightLoss()

Member Function Documentation

◆ eval()

◆ evalDerivative()

◆ name()