MixtureDistribution (original) (raw)

java.lang.Object
- weka.classifiers.functions.pace.MixtureDistribution
All Implemented Interfaces:
RevisionHandler, TechnicalInformationHandler
Direct Known Subclasses:
ChisqMixture, NormalMixture

public abstract class MixtureDistribution
extends java.lang.Object
implements TechnicalInformationHandler, RevisionHandler
Abtract class for manipulating 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:1.5Revision: 1.5 Revision:1.5
Author:
Yong Wang (yongwang@cs.waikato.ac.nz)

Field Summary

Fields

Modifier and Type	Field and Description
static int	NNMMethod The nonnegative-measure-based method
static int	PMMethod The probability-measure-based method

Constructor Summary

Constructors

Constructor and Description
MixtureDistribution()

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods

Modifier and Type	Method and Description
PaceMatrix	empiricalProbability(DoubleVector data,PaceMatrix intervals) Computes the empirical probabilities of the data over a set of intervals.
void	fit(DoubleVector data) Fits the mixture (or mixing) distribution to the data.
void	fit(DoubleVector data, int method) Fits the mixture (or mixing) distribution to the data.
DiscreteFunction	fitForSingleCluster(DoubleVector data, int method) Fits the mixture (or mixing) distribution to the data.
abstract PaceMatrix	fittingIntervals(DoubleVector data) Contructs the set of fitting intervals for mixture estimation.
DiscreteFunction	getMixingDistribution() Gets the mixing distribution
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
abstract PaceMatrix	probabilityMatrix(DoubleVector s,PaceMatrix intervals) Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
abstract 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	setMixingDistribution(DiscreteFunction d) Sets the mixing distribution
abstract DoubleVector	supportPoints(DoubleVector data, int ne) Contructs the set of support points for mixture estimation.
java.lang.String	toString() Converts to a string

   * ### Methods inherited from class java.lang.Object  
   `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`  
   * ### Methods inherited from interface weka.core.[RevisionHandler](../../../../weka/core/RevisionHandler.html "interface in weka.core")  
   `[getRevision](../../../../weka/core/RevisionHandler.html#getRevision--)`

Field Detail

 * #### NNMMethod  
 public static final int NNMMethod  
 The nonnegative-measure-based method  
 See Also:  
 [Constant Field Values](../../../../constant-values.html#weka.classifiers.functions.pace.MixtureDistribution.NNMMethod)  
 * #### PMMethod  
 public static final int PMMethod  
 The probability-measure-based method  
 See Also:  
 [Constant Field Values](../../../../constant-values.html#weka.classifiers.functions.pace.MixtureDistribution.PMMethod)

Constructor Detail

* #### MixtureDistribution  
public MixtureDistribution()

Method Detail

* #### getTechnicalInformation  
public [TechnicalInformation](../../../../weka/core/TechnicalInformation.html "class in weka.core") getTechnicalInformation()  
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.  
Specified by:  
`[getTechnicalInformation](../../../../weka/core/TechnicalInformationHandler.html#getTechnicalInformation--)` in interface `[TechnicalInformationHandler](../../../../weka/core/TechnicalInformationHandler.html "interface in weka.core")`  
Returns:  
the technical information about this class  
* #### getMixingDistribution  
public [DiscreteFunction](../../../../weka/classifiers/functions/pace/DiscreteFunction.html "class in weka.classifiers.functions.pace") getMixingDistribution()  
Gets the mixing distribution  
Returns:  
the mixing distribution  
* #### setMixingDistribution  
public void setMixingDistribution([DiscreteFunction](../../../../weka/classifiers/functions/pace/DiscreteFunction.html "class in weka.classifiers.functions.pace") d)  
Sets the mixing distribution  
Parameters:  
`d` \- the mixing distribution  
* #### fit  
public void fit([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") data)  
Fits the mixture (or mixing) distribution to the data. The default method is the nonnegative-measure-based method.  
Parameters:  
`data` \- the data, supposedly generated from the mixture model  
* #### fit  
public void fit([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") data,  
                int method)  
Fits the mixture (or mixing) distribution to the data.  
Parameters:  
`data` \- the data supposedly generated from the mixture  
`method` \- the method to be used. Refer to the static final variables of this class.  
* #### fitForSingleCluster  
public [DiscreteFunction](../../../../weka/classifiers/functions/pace/DiscreteFunction.html "class in weka.classifiers.functions.pace") fitForSingleCluster([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") data,  
                                            int method)  
Fits the mixture (or mixing) distribution to the data. The data is not pre-clustered for computational efficiency.  
Parameters:  
`data` \- the data supposedly generated from the mixture  
`method` \- the method to be used. Refer to the static final variables of this class.  
Returns:  
the generated distribution  
* #### separable  
public abstract boolean separable([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") 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  
Returns:  
true if a value can be considered  
* #### supportPoints  
public abstract [DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") supportPoints([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") 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  
Returns:  
the set of support points  
* #### fittingIntervals  
public abstract [PaceMatrix](../../../../weka/classifiers/functions/pace/PaceMatrix.html "class in weka.classifiers.functions.pace") fittingIntervals([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") data)  
Contructs the set of fitting intervals for mixture estimation.  
Parameters:  
`data` \- the data supposedly generated from the mixture  
Returns:  
the set of fitting intervals  
* #### probabilityMatrix  
public abstract [PaceMatrix](../../../../weka/classifiers/functions/pace/PaceMatrix.html "class in weka.classifiers.functions.pace") probabilityMatrix([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") s,  
                                             [PaceMatrix](../../../../weka/classifiers/functions/pace/PaceMatrix.html "class in weka.classifiers.functions.pace") 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  
Returns:  
the probability matrix  
* #### empiricalProbability  
public [PaceMatrix](../../../../weka/classifiers/functions/pace/PaceMatrix.html "class in weka.classifiers.functions.pace") empiricalProbability([DoubleVector](../../../../weka/core/matrix/DoubleVector.html "class in weka.core.matrix") data,  
                                       [PaceMatrix](../../../../weka/classifiers/functions/pace/PaceMatrix.html "class in weka.classifiers.functions.pace") intervals)  
Computes the empirical probabilities of the data over a set of intervals.  
Parameters:  
`data` \- the data  
`intervals` \- the intervals  
Returns:  
the empirical probabilities  
* #### toString  
public java.lang.String toString()  
Converts to a string  
Overrides:  
`toString` in class `java.lang.Object`  
Returns:  
a string representation

MixtureDistribution (original) (raw)

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail