weka.classifiers.functions.pace
Class ChisqMixture

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

public class ChisqMixture

extends MixtureDistribution

Class for manipulating chi-square 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

protected int	fittingIntervalLength
protected double	fittingIntervalThreshold
protected int	maxNumSupportPoints
protected double	separatingThreshold
protected double	supportThreshold
protected double	trimingThreshold

Fields inherited from class weka.classifiers.functions.pace.MixtureDistribution

mixingDistribution, NNMMethod, PMMethod

Constructor Summary

ChisqMixture()
Contructs an empty ChisqMixture

Method Summary

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 AHat) Computes the value of h(x) given the mixture.
DoubleVector	h(DoubleVector AHat) Computes the value of h(x) given the mixture, where x is a vector.
double	hf(double AHat) Computes the value of h(x) / f(x) given the mixture.
static void	main(java.lang.String[] args) Method to test this class
DoubleVector	pace2(DoubleVector x) Returns the pace2 estimate of a vector.
DoubleVector	pace4(DoubleVector x) Returns the pace4 estimate of a vector.
double	pace6(double x) Returns the pace6 estimate of a single value.
DoubleVector	pace6(DoubleVector x) Returns the pace6 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	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

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

separatingThreshold

protected double separatingThreshold

trimingThreshold

protected double trimingThreshold

supportThreshold

protected double supportThreshold

maxNumSupportPoints

protected int maxNumSupportPoints

fittingIntervalLength

protected int fittingIntervalLength

fittingIntervalThreshold

protected double fittingIntervalThreshold

Constructor Detail

ChisqMixture

public ChisqMixture()

Contructs an empty ChisqMixture

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

pace6

public double pace6(double x)

Returns the pace6 estimate of a single value.

Parameters:

x - the value

pace6

public DoubleVector pace6(DoubleVector x)

Returns the pace6 estimate of a vector.

Parameters:

x - the vector

pace2

public DoubleVector pace2(DoubleVector x)

Returns the pace2 estimate of a vector.

Parameters:

x - the vector

pace4

public DoubleVector pace4(DoubleVector x)

Returns the pace4 estimate of a vector.

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 AHat)

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

h

public double h(double AHat)

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

h

public DoubleVector h(DoubleVector AHat)

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

Parameters:

AHat - 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