Package org.drip.sequence.custom

Class GlivenkoCantelliFunctionSupremum

java.lang.Object

org.drip.function.definition.RdToR1

org.drip.sequence.functional.MultivariateRandom

org.drip.sequence.custom.GlivenkoCantelliFunctionSupremum

All Implemented Interfaces:

SeparableMultivariateRandom

public class GlivenkoCantelliFunctionSupremum
extends MultivariateRandom
implements SeparableMultivariateRandom

GlivenkoCantelliFunctionSupremum contains the Implementation of the Supremum Class Objective Function dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of Bounded Univariate Functions acting on each Random Variate.

• Module = Numerical Core Module
• Library = Statistical Learning Library
• Project = Sequence
• Package = Custom

Author:

Lakshmi Krishnamurthy

Constructor Summary

Constructors

Constructor	Description
GlivenkoCantelliFunctionSupremum(FunctionSupremumUnivariateRandom fsur, double[] adblWeight)	GlivenkoCantelliFunctionSupremum Constructor

Method Summary

Modifier and Type	Method	Description
static GlivenkoCantelliFunctionSupremum	Create(FunctionSupremumUnivariateRandom fsur, int iNumSample)	Construct an Instance of GlivenkoCantelliFunctionSupremum from the Sample
int	dimension()	Retrieve the Dimension of the Input Variate
double	evaluate(double[] adblVariate)	Evaluate for the given Input Variates
FunctionSupremumUnivariateRandom	separableUnivariateRandom()	Retrieve the Supremum Univariate Random Function
double	targetVariateVariance(int iTargetVariateIndex)	Compute the Variance associated with the Target Variate Function
double[]	weights()	Retrieve the Weights

Methods inherited from class org.drip.sequence.functional.MultivariateRandom

conditionalTargetVariateMetrics, conditionalTargetVariateMetrics, ghostTargetVariateMetrics, ghostTargetVariateMetrics, ghostTargetVariateMetrics, unconditionalTargetVariateMetrics

Methods inherited from class org.drip.function.definition.RdToR1

derivative, differential, gradient, gradientModulus, gradientModulusFunction, hessian, integrate, jacobian, maxima, minima, ValidateInput

Methods inherited from class java.lang.Object

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

Constructor Details

GlivenkoCantelliFunctionSupremum

public GlivenkoCantelliFunctionSupremum(FunctionSupremumUnivariateRandom fsur,
double[] adblWeight)
                                 throws java.lang.Exception

GlivenkoCantelliFunctionSupremum Constructor

Parameters:

fsur - The Supremum Univariate Random Function

adblWeight - Array of Variable Weights

Throws:

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

Method Details

Create

public static final GlivenkoCantelliFunctionSupremum Create(FunctionSupremumUnivariateRandom fsur,
int iNumSample)

Construct an Instance of GlivenkoCantelliFunctionSupremum from the Sample

Parameters:

fsur - The Supremum Univariate Random Function

iNumSample - Number of Empirical Samples

Returns:

The GlivenkoCantelliFunctionSupremum Instance

separableUnivariateRandom

public FunctionSupremumUnivariateRandom separableUnivariateRandom()

Retrieve the Supremum Univariate Random Function

Returns:

The Supremum Univariate Random Function

weights

public double[] weights()

Retrieve the Weights

Returns:

The Weights

dimension

public int dimension()

Description copied from class: RdToR1

Retrieve the Dimension of the Input Variate

Specified by:

dimension in class RdToR1

Returns:

The Dimension of the Input Variate

evaluate

public double evaluate(double[] adblVariate)
                throws java.lang.Exception

Description copied from class: RdToR1

Evaluate for the given Input Variates

Specified by:

evaluate in class RdToR1

Parameters:

adblVariate - Array of Input Variates

Returns:

The Calculated Value

Throws:

java.lang.Exception - Thrown if the Evaluation cannot be done

targetVariateVariance

public double targetVariateVariance(int iTargetVariateIndex)
                             throws java.lang.Exception

Description copied from interface: SeparableMultivariateRandom

Compute the Variance associated with the Target Variate Function

Specified by:

targetVariateVariance in interface SeparableMultivariateRandom

Parameters:

iTargetVariateIndex - The Target Variate Index

Returns:

Variance associated with the Target Variate Function

Throws:

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