Package org.drip.learning.svm

Class KernelRdDecisionFunction

java.lang.Object

org.drip.function.definition.RdToR1

org.drip.learning.svm.RdDecisionFunction

org.drip.learning.svm.KernelRdDecisionFunction

public abstract class KernelRdDecisionFunction
extends RdDecisionFunction

KernelRdDecisionFunction implements the Kernel-based R^d Decision Function-Based SVM Functionality for Classification and Regression.

• Module = Computational Core Module
• Library = Statistical Learning
• Project = Agnostic Learning Bounds under Empirical Loss Minimization Schemes
• Package = Kernel SVM Decision Function Operator

Author:

Lakshmi Krishnamurthy

Constructor Summary

Constructors

Constructor	Description
KernelRdDecisionFunction(RdNormed rdInverseMargin, double[] adblInverseMarginWeight, double dblB, SymmetricRdToNormedRdKernel kernel, double[][] aadblKernelPredictorPivot)	KernelRdDecisionFunction Constructor

Method Summary

Modifier and Type	Method	Description
double	evaluate(double[] adblX)	Evaluate for the given Input Variates
SymmetricRdToNormedRdKernel	kernel()	Retrieve the Decision Kernel
double[][]	kernelPredictorPivot()	Retrieve the Decision Kernel Predictor Pivot Nodes

Methods inherited from class org.drip.learning.svm.RdDecisionFunction

classify, entropyNumberUpperBounds, inverseMarginSpace, inverseMarginWeights, logEntropyNumberAsymptote, offset, optimizeClassificationHyperplane, optimizeRegressionHyperplane, predictorSpace, regress

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

conditionNumber, conditionNumberL2, conditionNumberLInfinity, conditionNumberLp, derivative, differential, dimension, 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

KernelRdDecisionFunction

public KernelRdDecisionFunction(RdNormed rdInverseMargin,
double[] adblInverseMarginWeight,
double dblB,
SymmetricRdToNormedRdKernel kernel,
double[][] aadblKernelPredictorPivot)
                         throws java.lang.Exception

KernelRdDecisionFunction Constructor

Parameters:

rdInverseMargin - The Inverse Margin Weights R^d Space

adblInverseMarginWeight - Array of Inverse Margin Weights

dblB - The Kernel Offset

kernel - The Kernel

aadblKernelPredictorPivot - Array of the Kernel R^d Predictor Pivot Nodes

Throws:

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

Method Details

evaluate

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

Description copied from class: RdToR1

Evaluate for the given Input Variates

Specified by:

evaluate in class RdToR1

Parameters:

adblX - Array of Input Variates

Returns:

The Calculated Value

Throws:

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

kernel

public SymmetricRdToNormedRdKernel kernel()

Retrieve the Decision Kernel

Returns:

The Decision Kernel

kernelPredictorPivot

public double[][] kernelPredictorPivot()

Retrieve the Decision Kernel Predictor Pivot Nodes

Returns:

The Decision Kernel Predictor Pivot Nodes