Class MixtureDistribution

java.lang.Object
weka.classifiers.functions.pace.MixtureDistribution
All Implemented Interfaces:
RevisionHandler, TechnicalInformationHandler
Direct Known Subclasses:
ChisqMixture, NormalMixture

public abstract class MixtureDistribution extends Object implements TechnicalInformationHandler, RevisionHandler
Abtract class for manipulating mixture distributions.

REFERENCES

Wang, Y. (2000). "A new approach to fitting linear models in high dimensional spaces." PhD Thesis. Department of Computer Science, University of Waikato, New Zealand.

Wang, Y. and Witten, I. H. (2002). "Modeling for optimal probability prediction." Proceedings of ICML'2002. Sydney.

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

    • NNMMethod

      public static final int NNMMethod
      The nonnegative-measure-based method
      See Also:
    • PMMethod

      public static final int PMMethod
      The probability-measure-based method
      See Also:
  • Constructor Details

    • MixtureDistribution

      public MixtureDistribution()
  • Method Details

    • getTechnicalInformation

      public TechnicalInformation getTechnicalInformation()
      Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
      Specified by:
      getTechnicalInformation in interface TechnicalInformationHandler
      Returns:
      the technical information about this class
    • getMixingDistribution

      public DiscreteFunction getMixingDistribution()
      Gets the mixing distribution
      Returns:
      the mixing distribution
    • setMixingDistribution

      public void setMixingDistribution(DiscreteFunction d)
      Sets the mixing distribution
      Parameters:
      d - the mixing distribution
    • fit

      public void fit(DoubleVector data)
      Fits the mixture (or mixing) distribution to the data. The default method is the nonnegative-measure-based method.
      Parameters:
      data - the data, supposedly generated from the mixture model
    • fit

      public void fit(DoubleVector data, int method)
      Fits the mixture (or mixing) distribution to the data.
      Parameters:
      data - the data supposedly generated from the mixture
      method - the method to be used. Refer to the static final variables of this class.
    • fitForSingleCluster

      public DiscreteFunction fitForSingleCluster(DoubleVector data, int method)
      Fits the mixture (or mixing) distribution to the data. The data is not pre-clustered for computational efficiency.
      Parameters:
      data - the data supposedly generated from the mixture
      method - the method to be used. Refer to the static final variables of this class.
      Returns:
      the generated distribution
    • separable

      public abstract 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
      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 a value can be considered
    • supportPoints

      public abstract DoubleVector supportPoints(DoubleVector data, int ne)
      Contructs the set of support points for mixture estimation.
      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 abstract PaceMatrix fittingIntervals(DoubleVector data)
      Contructs the set of fitting intervals for mixture estimation.
      Parameters:
      data - the data supposedly generated from the mixture
      Returns:
      the set of fitting intervals
    • probabilityMatrix

      public abstract PaceMatrix probabilityMatrix(DoubleVector s, PaceMatrix intervals)
      Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
      Parameters:
      s - the set of support points
      intervals - the intervals
      Returns:
      the probability matrix
    • empiricalProbability

      public PaceMatrix empiricalProbability(DoubleVector data, PaceMatrix intervals)
      Computes the empirical probabilities of the data over a set of intervals.
      Parameters:
      data - the data
      intervals - the intervals
      Returns:
      the empirical probabilities
    • toString

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