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## 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)

Field Summary

Fields inherited from class weka.classifiers.functions.pace.MixtureDistribution
`NNMMethod, PMMethod`

Constructor Summary
`NormalMixture()` Contructs an empty NormalMixture

Method Summary
`double` | `empiricalBayesEstimate(double x)` Returns the empirical Bayes estimate of a single value.
`DoubleVector` | `empiricalBayesEstimate(DoubleVector x)` Returns the empirical Bayes estimate of a vector.
`double` | `f(double x)` Computes the value of f(x) given the mixture.
`DoubleVector` | `f(DoubleVector x)` Computes the value of f(x) given the mixture, where x is a vector.
`PaceMatrix` | `fittingIntervals(DoubleVector data)` Contructs the set of fitting intervals for mixture estimation.
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `getSeparatingThreshold()` Gets the separating threshold value.
`double` | `getTrimingThreshold()` Gets the triming thresholding value.
`double` | `h(double x)` Computes the value of h(x) given the mixture.
`DoubleVector` | `h(DoubleVector x)` Computes the value of h(x) given the mixture, where x is a vector.
`double` | `hf(double x)` Computes the value of h(x) / f(x) given the mixture.
`static void` | `main(java.lang.String[] args)` Method to test this class
`DoubleVector` | `nestedEstimate(DoubleVector x)` Returns the optimal nested model estimate of a vector.
`PaceMatrix` | `probabilityMatrix(DoubleVector s, PaceMatrix intervals)` Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
`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
`void` | `setSeparatingThreshold(double t)` Sets the separating threshold value
`void` | `setTrimingThreshold(double t)` Sets the triming thresholding value.
`DoubleVector` | `subsetEstimate(DoubleVector x)` Returns the estimate of optimal subset selection.
`DoubleVector` | `supportPoints(DoubleVector data, int ne)` Contructs the set of support points for mixture estimation.
`java.lang.String` | `toString()` Converts to a string
`void` | `trim(DoubleVector x)` Trims the small values of the estaimte

Methods inherited from class weka.classifiers.functions.pace.MixtureDistribution
`empiricalProbability, fit, fit, fitForSingleCluster, getMixingDistribution, getTechnicalInformation, setMixingDistribution`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

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

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