jsat.lossfunctions

Interface LossR

All Superinterfaces:

LossFunc, Serializable

All Known Implementing Classes:

AbsoluteLoss, EpsilonInsensitiveLoss, HuberLoss, SquaredLoss

public interface LossR
extends LossFunc

Specifies a getLoss function for regression problems.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
LossR	clone()
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	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.

Methods inherited from interface jsat.lossfunctions.LossFunc

getDeriv2Max

Method Detail

getLoss

double getLoss(double pred,
               double y)

Computes the getLoss for a regression problem.

Specified by:

getLoss in interface LossFunc

Parameters:

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

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

Returns:

the getLoss in [0, Inf)

getDeriv

double getDeriv(double pred,
                double y)

Computes the first derivative of the getLoss function.

Specified by:

getDeriv in interface LossFunc

Parameters:

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

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

Returns:

the first derivative of the getLoss

getDeriv2

double getDeriv2(double pred,
                 double y)

Computes the second derivative of the getLoss function.

Specified by:

getDeriv2 in interface LossFunc

Parameters:

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

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

Returns:

the second derivative of the getLoss function

getRegression

double getRegression(double score)

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

Parameters:

score - the score for a data point

Returns:

the correct numeric regression value for this loss function

clone

LossR clone()

Specified by:

clone in interface LossFunc