jsat.lossfunctions

Class AbsoluteLoss

java.lang.Object

jsat.lossfunctions.AbsoluteLoss

All Implemented Interfaces:

Serializable, LossFunc, LossR

public class AbsoluteLoss
extends Object
implements LossR

The AbsoluteLoss loss function for regression L(x, y) = |x-y|.
This function is only one differentiable.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AbsoluteLoss()

Method Summary

Modifier and Type	Method and Description
AbsoluteLoss	clone()
static double	deriv(double pred, double y) Returns the derivative of the absolute loss
double	getDeriv(double pred, double y) Computes the first derivative of the getLoss function.
double	getDeriv2(double pred, double y) Computes the second derivative of the getLoss function.
double	getDeriv2Max() Returns an upper bound on the maximum value of the second derivative.
double	getLoss(double pred, double y) Computes the getLoss for a regression problem.
double	getRegression(double score) Given the score value of a data point, this returns the correct numeric result.
static double	loss(double pred, double y) Computes the absolute loss
static double	regress(double score)

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AbsoluteLoss

public AbsoluteLoss()

Method Detail

loss

public static double loss(double pred,
                          double y)

Computes the absolute loss

Parameters:

pred - the predicted value

y - the target value

Returns:

the loss for the functions

deriv

public static double deriv(double pred,
                           double y)

Returns the derivative of the absolute loss

Parameters:

pred - the predicted value

y - the target value

Returns:

the derivative of the loss function

regress

public static double regress(double score)

getLoss

public double getLoss(double pred,
                      double y)

Description copied from interface: LossR

Computes the getLoss for a regression problem.

Specified by:

getLoss in interface LossFunc

Specified by:

getLoss in interface LossR

Parameters:

pred - the predicted value in (-Infinity, Infinity)

y - the true target value in (-Infinity, Infinity)

Returns:

the getLoss in [0, Inf)

getDeriv

public double getDeriv(double pred,
                       double y)

Description copied from interface: LossR

Computes the first derivative of the getLoss function.

Specified by:

getDeriv in interface LossFunc

Specified by:

getDeriv in interface LossR

Parameters:

pred - the predicted value in (-Infinity, Infinity)

y - the true target value in (-Infinity, Infinity)

Returns:

the first derivative of the getLoss

getDeriv2

public double getDeriv2(double pred,
                        double y)

Description copied from interface: LossR

Computes the second derivative of the getLoss function.

Specified by:

getDeriv2 in interface LossFunc

Specified by:

getDeriv2 in interface LossR

Parameters:

pred - the predicted value in (-Infinity, Infinity)

y - the true target value in (-Infinity, Infinity)

Returns:

the second derivative of the getLoss function

getDeriv2Max

public double getDeriv2Max()

Description copied from interface: LossFunc

Returns an upper bound on the maximum value of the second derivative. If the second derivative does not exist, Double.NaN is a valid result. It is also possible for 0 and Double.POSITIVE_INFINITY to be valid results, and must be checked for.

Specified by:

getDeriv2Max in interface LossFunc

Returns:

the max value of LossFunc.getDeriv2(double, double)

clone

public AbsoluteLoss clone()

Specified by:

clone in interface LossFunc

Specified by:

clone in interface LossR

Overrides:

clone in class Object

getRegression

public double getRegression(double score)

Description copied from interface: LossR

Given the score value of a data point, this returns the correct numeric result. For most regression problems this simply returns the score value.

Specified by:

getRegression in interface LossR

Parameters:

score - the score for a data point

Returns:

the correct numeric regression value for this loss function