Package org.drip.learning.svm

Class RdDecisionFunction

java.lang.Object
org.drip.function.definition.RdToR1
org.drip.learning.svm.RdDecisionFunction
Direct Known Subclasses:
KernelRdDecisionFunction, LinearRdDecisionFunction
public abstract class RdDecisionFunction
extends RdToR1
RdDecisionFunction exposes the Rd Decision-Function Based SVM Functionality for Classification and Regression.

The References are:

  • Shawe-Taylor, J., P. L. Bartlett, R. C. Williamson, and M. Anthony (1996): A Framework for Structural Risk Minimization, in: Proceedings of the 9th Annual Conference on Computational Learning Theory ACM New York 68-76
  • Vapnik, V., and A. Chervonenkis (1974): Theory of Pattern Recognition (in Russian) Nauka Moscow USSR
  • Vapnik, V. (1995): The Nature of Statistical Learning Springer-Verlag New York


Author:
Lakshmi Krishnamurthy

  • Constructor Details

    • RdDecisionFunction

      public RdDecisionFunction​(RdGeneralizedVector rdPredictor, RdNormed rdInverseMargin, double[] adblInverseMarginWeight, double dblB) throws java.lang.Exception
      RdDecisionFunction Constructor
      Parameters:
      rdPredictor - The R^d Metric Input Predictor Space
      rdInverseMargin - The Inverse Margin Weights R^d Space
      adblInverseMarginWeight - Array of Inverse Margin Weights
      dblB - The Kernel Offset
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

  • Method Details

    • predictorSpace

      public RdGeneralizedVector predictorSpace()
      Retrieve the Input Predictor Metric Vector Space
      Returns:
      The Input Predictor Metric Vector Space

    • inverseMarginSpace

      public RdNormed inverseMarginSpace()
      Retrieve the Inverse Margin Weight Metric Vector Space
      Returns:
      The Inverse Margin Weight Metric Vector Space

    • inverseMarginWeights

      public double[] inverseMarginWeights()
      Retrieve the Decision Kernel Weights
      Returns:
      The Decision Kernel Weights

    • offset

      public double offset()
      Retrieve the Offset
      Returns:
      The Offset

    • classify

      public short classify​(double[] adblX) throws java.lang.Exception
      Classify the Specified Multi-dimensional Point
      Parameters:
      adblX - The Multi-dimensional Input Point
      Returns:
      +1/-1 Boolean Space Output Equivalents
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

    • regress

      public double regress​(double[] adblX) throws java.lang.Exception
      Regress on the Specified Multi-dimensional Point
      Parameters:
      adblX - The Multi-dimensional Input Point
      Returns:
      The Regression Output
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

    • entropyNumberUpperBounds

      public DecisionFunctionOperatorBounds entropyNumberUpperBounds​(DiagonalScalingOperator dsoFactorizer, double dblFeatureSpaceMaureyConstant)
      Compute the Entropy Number Upper Bounds Instance for the Specified Inputs
      Parameters:
      dsoFactorizer - The Factorizing Diagonal Scaling Operator
      dblFeatureSpaceMaureyConstant - The Feature Space Maurey Constant
      Returns:
      The Entropy Number Upper Bounds Instance

    • logEntropyNumberAsymptote

      public double logEntropyNumberAsymptote​(DiagonalScalingOperator dsoFactorizer) throws java.lang.Exception
      Compute the Decision Function's Asymptotic Exponent for the Entropy Number
      Parameters:
      dsoFactorizer - The Factorizing Diagonal Scaling Operator
      Returns:
      The Decision Function's Asymptotic Exponent for the Entropy Number
      Throws:
      java.lang.Exception - Thrown if the Asymptotoc Exponent cannot be computed

    • optimizeRegressionHyperplane

      public abstract boolean optimizeRegressionHyperplane​(double[] adblEmpirical, double dblMargin, double dblInverseWidthNormConstraint)
      Optimize the Hyper-plane for the Purposes of Regression
      Parameters:
      adblEmpirical - The Empirical Observation Array
      dblMargin - The Optimization Margin
      dblInverseWidthNormConstraint - The Inverse Width Norm Constraint
      Returns:
      TRUE - The Hyper-plane has been successfully Optimized for Regression

    • optimizeClassificationHyperplane

      public abstract boolean optimizeClassificationHyperplane​(short[] asEmpirical, double dblMargin, double dblInverseWidthNormConstraint)
      Optimize the Hyper-plane for the Purposes of Classification
      Parameters:
      asEmpirical - The Empirical Observation Array
      dblMargin - The Optimization Margin
      dblInverseWidthNormConstraint - The Inverse Width Norm Constraint
      Returns:
      TRUE - The Hyper-plane has been successfully Optimized for Classification