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## weka.classifiers.functions.pace
Class NormalMixture

    java.lang.Object
      weka.classifiers.functions.pace.MixtureDistribution
          weka.classifiers.functions.pace.NormalMixture

    public class NormalMixture

extends MixtureDistribution

Class for manipulating normal 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: 1.1 $
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.
`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, 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

### 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.

### setTrimingThreshold

    public void setTrimingThreshold(double t)

  Sets the triming thresholding value.

### 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

### 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

### 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

### 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

### empiricalBayesEstimate

    public double empiricalBayesEstimate(double x)

  Returns the empirical Bayes estimate of a single value.

  Parameters:
    `x` - the value

### empiricalBayesEstimate

    public DoubleVector empiricalBayesEstimate(DoubleVector x)

  Returns the empirical Bayes estimate of a vector.

  Parameters:
    `x` - the vector

### nestedEstimate

    public DoubleVector nestedEstimate(DoubleVector x)

  Returns the optimal nested model estimate of a vector.

  Parameters:
    `x` - the vector

### subsetEstimate

    public DoubleVector subsetEstimate(DoubleVector x)

  Returns the estimate of optimal subset selection.

  Parameters:
    `x` - the vector

### 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

### h

    public double h(double x)

  Computes the value of h(x) given the mixture.

  Parameters:
    `x` - the value

### h

    public DoubleVector h(DoubleVector x)

  Computes the value of h(x) given the mixture, where x is a vector.

  Parameters:
    `x` - the vector

### f

    public double f(double x)

  Computes the value of f(x) given the mixture.

  Parameters:
    `x` - the value

### f

    public DoubleVector f(DoubleVector x)

  Computes the value of f(x) given the mixture, where x is a vector.

  Parameters:
    `x` - the vector

### toString

    public java.lang.String toString()

  Converts to a string

  Overrides:
    `toString` in class `MixtureDistribution`

### main

    public static void main(java.lang.String[] args)

  Method to test this class

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