jsat.lossfunctions

Interface LossC

All Superinterfaces:

LossFunc, java.io.Serializable

All Known Subinterfaces:

LossMC

All Known Implementing Classes:

HingeLoss, LogisticLoss, SoftmaxLoss

public interface LossC
extends LossFunc

Specifies a loss function for binary classification problems.

Method Summary

Modifier and Type	Method and Description
LossC	clone()
CategoricalResults	getClassification(double score) Given the score value of a data point, this returns the classification results.
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 classification problem.

Methods inherited from interface jsat.lossfunctions.LossFunc

getConjugate, getDeriv2Max, lipschitz

Method Detail

getLoss

double getLoss(double pred,
               double y)

Computes the getLoss for a classification problem.

Specified by:

getLoss in interface LossFunc

Parameters:

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

y - the true class label in {-1, 1}

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 score in (-Infinity, Infinity)

y - the true class label in {-1, 1}

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 score in (-Infinity, Infinity)

y - the true class label in {-1, 1}

Returns:

the second derivative of the getLoss function

getClassification

CategoricalResults getClassification(double score)

Given the score value of a data point, this returns the classification results.

Parameters:

score - the score for a data point

Returns:

the categorical results with the correct probability values for this loss function.

clone

LossC clone()

Specified by:

clone in interface LossFunc