Package weka.classifiers.functions.pace

Class NormalMixture

java.lang.Object
weka.classifiers.functions.pace.MixtureDistribution
weka.classifiers.functions.pace.NormalMixture
All Implemented Interfaces:
RevisionHandler, TechnicalInformationHandler
public class NormalMixture extends MixtureDistribution
Class for manipulating normal mixture distributions.

For more information see:

Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand.

Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002. BibTeX: 

 @phdthesis{Wang2000,
    address = {Hamilton, New Zealand},
    author = {Wang, Y},
    school = {Department of Computer Science, University of Waikato},
    title = {A new approach to fitting linear models in high dimensional spaces},
    year = {2000}
 }
 
 @inproceedings{Wang2002,
    address = {Sydney, Australia},
    author = {Wang, Y. and Witten, I. H.},
    booktitle = {Proceedings of the Nineteenth International Conference in Machine Learning},
    pages = {650-657},
    title = {Modeling for optimal probability prediction},
    year = {2002}
 }

Version:
$Revision: 1.5 $
Author:
Yong Wang (yongwang@cs.waikato.ac.nz)

  • Constructor Details

    • NormalMixture

      public NormalMixture()
      Contructs an empty NormalMixture

  • Method Details

    • getSeparatingThreshold

      public double getSeparatingThreshold()
      Gets the separating threshold value. This value is used by the method separatable
      Returns:
      the separating threshold

    • setSeparatingThreshold

      public void setSeparatingThreshold(double t)
      Sets the separating threshold value
      Parameters:
      t - the threshold value

    • getTrimingThreshold

      public double getTrimingThreshold()
      Gets the triming thresholding value. This value is usef by the method trim.
      Returns:
      the triming thresholding

    • setTrimingThreshold

      public void setTrimingThreshold(double t)
      Sets the triming thresholding value.
      Parameters:
      t - the triming thresholding

    • separable

      public boolean separable(DoubleVector data, int i0, int i1, double x)
      Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
      Specified by:
      separable in class MixtureDistribution
      Parameters:
      data - the data supposedly generated from the mixture
      i0 - the index of the first element in the group
      i1 - the index of the last element in the group
      x - the value
      Returns:
      true if the value can be considered

    • supportPoints

      public DoubleVector supportPoints(DoubleVector data, int ne)
      Contructs the set of support points for mixture estimation.
      Specified by:
      supportPoints in class MixtureDistribution
      Parameters:
      data - the data supposedly generated from the mixture
      ne - the number of extra data that are suppposedly discarded earlier and not passed into here
      Returns:
      the set of support points

    • fittingIntervals

      public PaceMatrix fittingIntervals(DoubleVector data)
      Contructs the set of fitting intervals for mixture estimation.
      Specified by:
      fittingIntervals in class MixtureDistribution
      Parameters:
      data - the data supposedly generated from the mixture
      Returns:
      the set of fitting intervals

    • probabilityMatrix

      public PaceMatrix probabilityMatrix(DoubleVector s, PaceMatrix intervals)
      Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
      Specified by:
      probabilityMatrix in class MixtureDistribution
      Parameters:
      s - the set of support points
      intervals - the intervals
      Returns:
      the probability matrix

    • empiricalBayesEstimate

      public double empiricalBayesEstimate(double x)
      Returns the empirical Bayes estimate of a single value.
      Parameters:
      x - the value
      Returns:
      the empirical Bayes estimate

    • empiricalBayesEstimate

      public DoubleVector empiricalBayesEstimate(DoubleVector x)
      Returns the empirical Bayes estimate of a vector.
      Parameters:
      x - the vector
      Returns:
      the empirical Bayes estimate

    • nestedEstimate

      public DoubleVector nestedEstimate(DoubleVector x)
      Returns the optimal nested model estimate of a vector.
      Parameters:
      x - the vector
      Returns:
      the optimal nested model estimate

    • subsetEstimate

      public DoubleVector subsetEstimate(DoubleVector x)
      Returns the estimate of optimal subset selection.
      Parameters:
      x - the vector
      Returns:
      the estimate of optimal subset selection

    • trim

      public void trim(DoubleVector x)
      Trims the small values of the estaimte
      Parameters:
      x - the estimate vector

    • hf

      public double hf(double x)
      Computes the value of h(x) / f(x) given the mixture. The implementation avoided overflow.
      Parameters:
      x - the value
      Returns:
      the value of h(x) / f(x)

    • h

      public double h(double x)
      Computes the value of h(x) given the mixture.
      Parameters:
      x - the value
      Returns:
      the value of h(x)

    • h

      public DoubleVector h(DoubleVector x)
      Computes the value of h(x) given the mixture, where x is a vector.
      Parameters:
      x - the vector
      Returns:
      the value of h(x)

    • f

      public double f(double x)
      Computes the value of f(x) given the mixture.
      Parameters:
      x - the value
      Returns:
      the value of f(x)

    • f

      public DoubleVector f(DoubleVector x)
      Computes the value of f(x) given the mixture, where x is a vector.
      Parameters:
      x - the vector
      Returns:
      the value of f(x)

    • toString

      public String toString()
      Converts to a string
      Overrides:
      toString in class MixtureDistribution
      Returns:
      a string representation

    • getRevision

      public String getRevision()
      Returns the revision string.
      Returns:
      the revision

    • main

      public static void main(String[] args)
      Method to test this class
      Parameters:
      args - the commandline arguments - ignored