Package weka.classifiers.functions.pace

Class 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 Detail

      • NormalMixture

        public NormalMixture()
        Contructs an empty NormalMixture

    • Method Detail

      • 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 java.lang.String toString()
        Converts to a string
        Overrides:
        toString in class MixtureDistribution
        Returns:
        a string representation

      • getRevision

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

      • main

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