Package org.drip.sequence.custom

Class GlivenkoCantelliUniformDeviation

java.lang.Object

org.drip.function.definition.RdToR1

org.drip.sequence.functional.MultivariateRandom

org.drip.sequence.functional.BoundedMultivariateRandom

org.drip.sequence.custom.GlivenkoCantelliUniformDeviation

All Implemented Interfaces:

SeparableMultivariateRandom

public class GlivenkoCantelliUniformDeviation
extends BoundedMultivariateRandom
implements SeparableMultivariateRandom

GlivenkoCantelliUniformDeviation contains the Implementation of the Bounded 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
GlivenkoCantelliUniformDeviation(BoundedIdempotentUnivariateRandom biur, double[] adblWeight)	GlivenkoCantelliUniformDeviation Constructor

Method Summary

Modifier and Type	Method	Description
static GlivenkoCantelliUniformDeviation	Create(BoundedIdempotentUnivariateRandom biur, int iNumSample)	GlivenkoCantelliUniformDeviation Constructor
int	dimension()	Retrieve the Dimension of the Input Variate
double	evaluate(double[] adblVariate)	Evaluate for the given Input Variates
BoundedIdempotentUnivariateRandom	separableUnivariateRandom()	Retrieve the Separable Bounded Idempotent Univariate Random Function
double	targetVariateVariance(int iTargetVariateIndex)	Compute the Variance associated with the Target Variate Function
double	targetVariateVarianceBound(int iTargetVariateIndex)	Retrieve the Maximal Agnostic Variance Bound over the Non-target Variate Space for the Target Variate
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

GlivenkoCantelliUniformDeviation

public GlivenkoCantelliUniformDeviation(BoundedIdempotentUnivariateRandom biur,
double[] adblWeight)
                                 throws java.lang.Exception

GlivenkoCantelliUniformDeviation Constructor

Parameters:

biur - The Bounded Idempotent Univariate Random Function

adblWeight - Array of Variable Weights

Throws:

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

Method Details

Create

public static final GlivenkoCantelliUniformDeviation Create(BoundedIdempotentUnivariateRandom biur,
int iNumSample)

GlivenkoCantelliUniformDeviation Constructor

Parameters:

biur - The Bounded Idempotent Univariate Random Function

iNumSample - Number of Empirical Samples

Returns:

The GlivenkoCantelliUniformDeviation Instance

separableUnivariateRandom

public BoundedIdempotentUnivariateRandom separableUnivariateRandom()

Retrieve the Separable Bounded Idempotent Univariate Random Function

Returns:

The Separable Bounded Idempotent 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

targetVariateVarianceBound

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

Description copied from class: BoundedMultivariateRandom

Retrieve the Maximal Agnostic Variance Bound over the Non-target Variate Space for the Target Variate

Specified by:

targetVariateVarianceBound in class BoundedMultivariateRandom

Parameters:

iTargetVariateIndex - The Index corresponding to the Variate on which the Bound is sought

Returns:

The Maximal Agnostic Bound over the Non-target Variate Space for the Target Variate

Throws:

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

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