org.drip.learning.bound

Class EmpiricalLearnerLoss

java.lang.Object

org.drip.function.definition.R1ToR1

org.drip.learning.bound.EmpiricalLearnerLoss

public class EmpiricalLearnerLoss
extends R1ToR1

EmpiricalLearnerLoss Function computes the Empirical Loss of a Learning Operation resulting from the Use of a Learning Function in Conjunction with the corresponding Empirical Realization. The References are: 1) Lugosi, G. (2002): Pattern Classification and Learning Theory, in: L. Györ, editor, Principles of Non-parametric Learning, 5-62, Springer, Wien. 2) Boucheron, S., G. Lugosi, and P. Massart (2003): Concentration Inequalities Using the Entropy Method, Annals of Probability, 31, 1583-1614.

Author:

Lakshmi Krishnamurthy

Constructor Summary

Constructors

Constructor and Description
EmpiricalLearnerLoss(R1ToR1 learner, double dblRealization) EmpiricalLearnerLoss Constructor

Method Summary

Modifier and Type	Method and Description
double	empiricalRealization() Retrieve the Empirical Realization
double	evaluate(double dblVariate) Evaluate for the given variate
R1ToR1	learner() Retrieve the Learning Function
double	loss(double dblVariate) Compute the Loss for the specified Variate

Methods inherited from class org.drip.function.definition.R1ToR1

derivative, differential, differential, integrate, maxima, maxima, minima, minima

Methods inherited from class java.lang.Object

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

Constructor Detail

EmpiricalLearnerLoss

public EmpiricalLearnerLoss(R1ToR1 learner,
                            double dblRealization)
                     throws java.lang.Exception

EmpiricalLearnerLoss Constructor

Parameters:

learner - The Learning Function

dblRealization - The Empirical Outcome

Throws:

java.lang.Exception - Thrown if the Inputs are Invalid

Method Detail

empiricalRealization

public double empiricalRealization()

Retrieve the Empirical Realization

Returns:

The Empirical Realization

learner

public R1ToR1 learner()

Retrieve the Learning Function

Returns:

The Learning Function

loss

public double loss(double dblVariate)
            throws java.lang.Exception

Compute the Loss for the specified Variate

Parameters:

dblVariate - The Variate

Returns:

Loss for the specified Variate

Throws:

java.lang.Exception - Thrown if the Loss cannot be computed

evaluate

public double evaluate(double dblVariate)
                throws java.lang.Exception

Description copied from class: R1ToR1

Evaluate for the given variate

Specified by:

evaluate in class R1ToR1

Parameters:

dblVariate - Variate

Returns:

Returns the calculated value

Throws:

java.lang.Exception - Thrown if evaluation cannot be done