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

Constructors
Constructor and Description

NormalMixture()
Contructs an empty NormalMixture

Constructors
Constructor and Description
`NormalMixture()` Contructs an empty NormalMixture

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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

Class NormalMixture

Field Summary

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

Constructor Summary

Method Summary

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

Methods inherited from class java.lang.Object

Constructor Detail

NormalMixture

Method Detail

getSeparatingThreshold

setSeparatingThreshold

getTrimingThreshold

setTrimingThreshold

separable

supportPoints

fittingIntervals

probabilityMatrix

empiricalBayesEstimate

empiricalBayesEstimate

nestedEstimate

subsetEstimate

trim

hf

h

h

f

f

toString

getRevision

main