jsat.lossfunctions

Class LogisticLoss

java.lang.Object

jsat.lossfunctions.LogisticLoss

All Implemented Interfaces:

java.io.Serializable, LossC, LossFunc

Direct Known Subclasses:

SoftmaxLoss

public class LogisticLoss
extends java.lang.Object
implements LossC

The LogisticLoss loss function for classification L(x, y) = log(1+exp(-y*x)).
This function is twice differentiable.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticLoss()

Method Summary

Modifier and Type	Method and Description
static CategoricalResults	classify(double score)
LogisticLoss	clone()
static double	deriv(double pred, double y) Computes the first derivative of the logistic loss
static double	deriv2(double pred, double y) Computes the second derivative of the logistic loss
CategoricalResults	getClassification(double score) Given the score value of a data point, this returns the classification results.
double	getConjugate(double b, double pred, double y) Computes the result of the conjugate function of this 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 classification problem.
double	lipschitz() If this loss is L-Lipschitz (1/L Lipschitz smooth), this method will return the value of L.
static double	loss(double pred, double y) Computes the logistic loss

Methods inherited from class java.lang.Object

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

Constructor Detail

LogisticLoss

public LogisticLoss()

Method Detail

loss

public static double loss(double pred,
                          double y)

Computes the logistic loss

Parameters:

pred - the predicted value

y - the true value

Returns:

the logistic loss

deriv

public static double deriv(double pred,
                           double y)

Computes the first derivative of the logistic loss

Parameters:

pred - the predicted value

y - the true value

Returns:

the first derivative of the logistic loss

deriv2

public static double deriv2(double pred,
                            double y)

Computes the second derivative of the logistic loss

Parameters:

pred - the predicted value

y - the true value

Returns:

the second derivative of the logistic loss

classify

public static CategoricalResults classify(double score)

getLoss

public double getLoss(double pred,
                      double y)

Description copied from interface: LossC

Computes the getLoss for a classification problem.

Specified by:

getLoss in interface LossC

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

public double getDeriv(double pred,
                       double y)

Description copied from interface: LossC

Computes the first derivative of the getLoss function.

Specified by:

getDeriv in interface LossC

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

public double getDeriv2(double pred,
                        double y)

Description copied from interface: LossC

Computes the second derivative of the getLoss function.

Specified by:

getDeriv2 in interface LossC

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

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 LogisticLoss clone()

Specified by:

clone in interface LossC

Specified by:

clone in interface LossFunc

Overrides:

clone in class java.lang.Object

getClassification

public CategoricalResults getClassification(double score)

Description copied from interface: LossC

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

Specified by:

getClassification in interface LossC

Parameters:

score - the score for a data point

Returns:

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

getConjugate

public double getConjugate(double b,
                           double pred,
                           double y)

Description copied from interface: LossFunc

Computes the result of the conjugate function of this loss. This function is generally optional, and should return Double.NaN if not properly implemented. Many optimization algorithms do require a working implementation though.

Specified by:

getConjugate in interface LossFunc

Parameters:

b - the primary input to the function

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

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

Returns:

the result of the conjugate function of this loss

lipschitz

public double lipschitz()

Description copied from interface: LossFunc

If this loss is L-Lipschitz (1/L Lipschitz smooth), this method will return the value of L. If it is not L-Lipschitz, a value of 0 will be returned.

Specified by:

lipschitz in interface LossFunc

Returns:

the L-Lipschitz constant, or 0 if this loss is not L-Lipschitz;