public abstract class MixtureDistribution extends java.lang.Object implements TechnicalInformationHandler, RevisionHandler
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.
Modifier and Type | Field and Description |
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static int |
NNMMethod
The nonnegative-measure-based method
|
static int |
PMMethod
The probability-measure-based method
|
Constructor and Description |
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MixtureDistribution() |
Modifier and Type | Method and Description |
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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
|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
getRevision
public static final int NNMMethod
public static final int PMMethod
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public DiscreteFunction getMixingDistribution()
public void setMixingDistribution(DiscreteFunction d)
d
- the mixing distributionpublic void fit(DoubleVector data)
data
- the data, supposedly generated from the mixture modelpublic void fit(DoubleVector data, int method)
data
- the data supposedly generated from the mixturemethod
- the method to be used. Refer to the static final
variables of this class.public DiscreteFunction fitForSingleCluster(DoubleVector data, int method)
data
- the data supposedly generated from the mixturemethod
- the method to be used. Refer to the static final
variables of this class.public abstract boolean separable(DoubleVector data, int i0, int i1, double x)
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 valuepublic abstract DoubleVector supportPoints(DoubleVector data, int ne)
data
- the data supposedly generated from the mixturene
- the number of extra data that are suppposedly discarded
earlier and not passed into herepublic abstract PaceMatrix fittingIntervals(DoubleVector data)
data
- the data supposedly generated from the mixturepublic abstract PaceMatrix probabilityMatrix(DoubleVector s, PaceMatrix intervals)
s
- the set of support pointsintervals
- the intervalspublic PaceMatrix empiricalProbability(DoubleVector data, PaceMatrix intervals)
data
- the dataintervals
- the intervalspublic java.lang.String toString()
toString
in class java.lang.Object