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Class weka.classifiers.functions.pace.NormalMixture

java.lang.Object
    |
    +----weka.classifiers.functions.pace.MixtureDistribution
            |
            +----weka.classifiers.functions.pace.NormalMixture

public class NormalMixture

extends MixtureDistribution

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)

NormalMixture()

Contructs an empty NormalMixture

empiricalBayesEstimate(double)

Returns the empirical Bayes estimate of a single value.

empiricalBayesEstimate(DoubleVector)

Returns the empirical Bayes estimate of a vector.

f(double)

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

f(DoubleVector)

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

fittingIntervals(DoubleVector)

Contructs the set of fitting intervals for mixture estimation.

getSeparatingThreshold()

Gets the separating threshold value.

getTrimingThreshold()

Gets the triming thresholding value.

h(double)

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

h(DoubleVector)

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

hf(double)

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

main(String[])

Method to test this class

nestedEstimate(DoubleVector)

Returns the optimal nested model estimate of a vector.

probabilityMatrix(DoubleVector, PaceMatrix)

Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.

separable(DoubleVector, int, int, double)

Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1

setSeparatingThreshold(double)

Sets the separating threshold value

setTrimingThreshold(double)

Sets the triming thresholding value.

subsetEstimate(DoubleVector)

Returns the estimate of optimal subset selection.

supportPoints(DoubleVector, int)

Contructs the set of support points for mixture estimation.

toString()

Converts to a string

trim(DoubleVector)

Trims the small values of the estaimte

public NormalMixture()

          Contructs an empty NormalMixture

public double getSeparatingThreshold()

          Gets the separating threshold value. This value is used by the method separatable

public void setSeparatingThreshold(double t)

          Sets the separating threshold value

Parameters:

t - the threshold value

public double getTrimingThreshold()

          Gets the triming thresholding value. This value is usef by the method trim.

public void setTrimingThreshold(double t)

          Sets the triming thresholding value.

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

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

Overrides:

separable in class MixtureDistribution

public 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

Overrides:

supportPoints in class MixtureDistribution

public PaceMatrix fittingIntervals(DoubleVector data)

          Contructs the set of fitting intervals for mixture estimation.

Parameters:

data - the data supposedly generated from the mixture

Overrides:

fittingIntervals in class MixtureDistribution

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.

Parameters:

s - the set of support points

intervals - the intervals

Overrides:

probabilityMatrix in class MixtureDistribution

public double empiricalBayesEstimate(double x)

          Returns the empirical Bayes estimate of a single value.

Parameters:

x - the value

public DoubleVector empiricalBayesEstimate(DoubleVector x)

          Returns the empirical Bayes estimate of a vector.

Parameters:

x - the vector

public DoubleVector nestedEstimate(DoubleVector x)

          Returns the optimal nested model estimate of a vector.

Parameters:

x - the vector

public DoubleVector subsetEstimate(DoubleVector x)

          Returns the estimate of optimal subset selection.

Parameters:

x - the vector

public void trim(DoubleVector x)

          Trims the small values of the estaimte

Parameters:

x - the estimate vector

public double hf(double x)

          Computes the value of h(x) / f(x) given the mixture. The implementation avoided overflow.

Parameters:

x - the value

public double h(double x)

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

Parameters:

x - the value

public DoubleVector h(DoubleVector x)

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

Parameters:

x - the vector

public double f(double x)

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

Parameters:

x - the value

public DoubleVector f(DoubleVector x)

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

Parameters:

x - the vector

public java.lang.String toString()

          Converts to a string

Overrides:

toString in class MixtureDistribution

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

          Method to test this class

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