Package weka.classifiers.functions.pace
Class NormalMixture
java.lang.Object
weka.classifiers.functions.pace.MixtureDistribution
weka.classifiers.functions.pace.NormalMixture
- All Implemented Interfaces:
RevisionHandler
,TechnicalInformationHandler
Class for manipulating normal mixture distributions.
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:
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)
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Field Summary
Fields inherited from class weka.classifiers.functions.pace.MixtureDistribution
NNMMethod, PMMethod
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptiondouble
empiricalBayesEstimate
(double x) Returns the empirical Bayes estimate of a single value.Returns the empirical Bayes estimate of a vector.double
f
(double x) Computes the value of f(x) given the mixture.f
(DoubleVector x) Computes the value of f(x) given the mixture, where x is a vector.fittingIntervals
(DoubleVector data) Contructs the set of fitting intervals for mixture estimation.Returns the revision string.double
Gets the separating threshold value.double
Gets the triming thresholding value.double
h
(double x) Computes the value of h(x) given the mixture.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
Method to test this classReturns the optimal nested model estimate of a vector.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 i1void
setSeparatingThreshold
(double t) Sets the separating threshold valuevoid
setTrimingThreshold
(double t) Sets the triming thresholding value.Returns the estimate of optimal subset selection.supportPoints
(DoubleVector data, int ne) Contructs the set of support points for mixture estimation.toString()
Converts to a stringvoid
trim
(DoubleVector x) Trims the small values of the estaimteMethods inherited from class weka.classifiers.functions.pace.MixtureDistribution
empiricalProbability, fit, fit, fitForSingleCluster, getMixingDistribution, getTechnicalInformation, setMixingDistribution
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Constructor Details
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NormalMixture
public NormalMixture()Contructs an empty NormalMixture
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Method Details
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getSeparatingThreshold
public double getSeparatingThreshold()Gets the separating threshold value. This value is used by the method separatable- Returns:
- the separating threshold
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setSeparatingThreshold
public void setSeparatingThreshold(double t) Sets the separating threshold value- Parameters:
t
- the threshold value
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getTrimingThreshold
public double getTrimingThreshold()Gets the triming thresholding value. This value is usef by the method trim.- Returns:
- the triming thresholding
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setTrimingThreshold
public void setTrimingThreshold(double t) Sets the triming thresholding value.- Parameters:
t
- the triming thresholding
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separable
Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1- Specified by:
separable
in classMixtureDistribution
- Parameters:
data
- the data supposedly generated from the mixturei0
- the index of the first element in the groupi1
- the index of the last element in the groupx
- the value- Returns:
- true if the value can be considered
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supportPoints
Contructs the set of support points for mixture estimation.- Specified by:
supportPoints
in classMixtureDistribution
- Parameters:
data
- the data supposedly generated from the mixturene
- the number of extra data that are suppposedly discarded earlier and not passed into here- Returns:
- the set of support points
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fittingIntervals
Contructs the set of fitting intervals for mixture estimation.- Specified by:
fittingIntervals
in classMixtureDistribution
- Parameters:
data
- the data supposedly generated from the mixture- Returns:
- the set of fitting intervals
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probabilityMatrix
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.- Specified by:
probabilityMatrix
in classMixtureDistribution
- Parameters:
s
- the set of support pointsintervals
- the intervals- Returns:
- the probability matrix
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empiricalBayesEstimate
public double empiricalBayesEstimate(double x) Returns the empirical Bayes estimate of a single value.- Parameters:
x
- the value- Returns:
- the empirical Bayes estimate
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empiricalBayesEstimate
Returns the empirical Bayes estimate of a vector.- Parameters:
x
- the vector- Returns:
- the empirical Bayes estimate
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nestedEstimate
Returns the optimal nested model estimate of a vector.- Parameters:
x
- the vector- Returns:
- the optimal nested model estimate
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subsetEstimate
Returns the estimate of optimal subset selection.- Parameters:
x
- the vector- Returns:
- the estimate of optimal subset selection
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trim
Trims the small values of the estaimte- Parameters:
x
- the estimate vector
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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)
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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)
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h
Computes the value of h(x) given the mixture, where x is a vector.- Parameters:
x
- the vector- Returns:
- the value of h(x)
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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)
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f
Computes the value of f(x) given the mixture, where x is a vector.- Parameters:
x
- the vector- Returns:
- the value of f(x)
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toString
Converts to a string- Overrides:
toString
in classMixtureDistribution
- Returns:
- a string representation
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getRevision
Returns the revision string.- Returns:
- the revision
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main
Method to test this class- Parameters:
args
- the commandline arguments - ignored
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