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Class weka.classifiers.functions.pace.MixtureDistribution 

java.lang.Object
    |
    +----weka.classifiers.functions.pace.MixtureDistribution
public abstract class MixtureDistribution
extends java.lang.Object
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: 1.1 $
Author:
Yong Wang (yongwang@cs.waikato.ac.nz)

Variable Index

o NNMMethod
The nonnegative-measure-based method
o PMMethod
The probability-measure-based method

Constructor Index

o MixtureDistribution()
 

Method Index

o empiricalProbability(DoubleVector, PaceMatrix)
Computes the empirical probabilities of the data over a set of intervals.
o fit(DoubleVector)
Fits the mixture (or mixing) distribution to the data.
o fit(DoubleVector, int)
Fits the mixture (or mixing) distribution to the data.
o fitForSingleCluster(DoubleVector, int)
Fits the mixture (or mixing) distribution to the data.
o fittingIntervals(DoubleVector)
Contructs the set of fitting intervals for mixture estimation.
o getMixingDistribution()
Gets the mixing distribution
o probabilityMatrix(DoubleVector, PaceMatrix)
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
o 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
o setMixingDistribution(DiscreteFunction)
Sets the mixing distribution
o supportPoints(DoubleVector, int)
Contructs the set of support points for mixture estimation.
o toString()
Converts to a string

Field Detail

o NNMMethod
public static final int NNMMethod
          The nonnegative-measure-based method
o PMMethod
public static final int PMMethod
          The probability-measure-based method

Constructor Detail

o MixtureDistribution
public MixtureDistribution()

Method Detail

o getMixingDistribution
public DiscreteFunction getMixingDistribution()
          Gets the mixing distribution
o setMixingDistribution
public void setMixingDistribution(DiscreteFunction d)
          Sets the mixing distribution
Parameters:
d - the mixing distribution
o fit
public void fit(DoubleVector 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
o fit
public void fit(DoubleVector 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.
o fitForSingleCluster
public DiscreteFunction fitForSingleCluster(DoubleVector 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.
o separable
public 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
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
o supportPoints
public abstract 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
o fittingIntervals
public abstract PaceMatrix fittingIntervals(DoubleVector data)
          Contructs the set of fitting intervals for mixture estimation.
Parameters:
data - the data supposedly generated from the mixture
o probabilityMatrix
public 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.
Parameters:
s - the set of support points
intervals - the intervals
o empiricalProbability
public PaceMatrix empiricalProbability(DoubleVector data,
                                       PaceMatrix intervals)
          Computes the empirical probabilities of the data over a set of intervals.
Parameters:
data - the data
intervals - the intervals
o toString
public java.lang.String toString()
          Converts to a string
Overrides:
toString in class java.lang.Object
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