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java.lang.Objectweka.classifiers.functions.pace.MixtureDistribution
weka.classifiers.functions.pace.NormalMixture
public class NormalMixture
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.@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} }
Field Summary |
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Fields inherited from class weka.classifiers.functions.pace.MixtureDistribution |
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NNMMethod, PMMethod |
Constructor Summary | |
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NormalMixture()
Contructs an empty NormalMixture |
Method Summary | |
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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 |
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empiricalProbability, fit, fit, fitForSingleCluster, getMixingDistribution, getTechnicalInformation, setMixingDistribution |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public NormalMixture()
Method Detail |
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public double getSeparatingThreshold()
public void setSeparatingThreshold(double t)
t
- the threshold valuepublic double getTrimingThreshold()
public void setTrimingThreshold(double t)
t
- the triming thresholdingpublic boolean separable(DoubleVector data, int i0, int i1, double x)
separable
in class MixtureDistribution
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
public DoubleVector supportPoints(DoubleVector data, int ne)
supportPoints
in class MixtureDistribution
data
- the data supposedly generated from the mixturene
- the number of extra data that are suppposedly discarded
earlier and not passed into here
public PaceMatrix fittingIntervals(DoubleVector data)
fittingIntervals
in class MixtureDistribution
data
- the data supposedly generated from the mixture
public PaceMatrix probabilityMatrix(DoubleVector s, PaceMatrix intervals)
probabilityMatrix
in class MixtureDistribution
s
- the set of support pointsintervals
- the intervals
public double empiricalBayesEstimate(double x)
x
- the value
public DoubleVector empiricalBayesEstimate(DoubleVector x)
x
- the vector
public DoubleVector nestedEstimate(DoubleVector x)
x
- the vector
public DoubleVector subsetEstimate(DoubleVector x)
x
- the vector
public void trim(DoubleVector x)
x
- the estimate vectorpublic double hf(double x)
x
- the value
public double h(double x)
x
- the value
public DoubleVector h(DoubleVector x)
x
- the vector
public double f(double x)
x
- the value
public DoubleVector f(DoubleVector x)
x
- the vector
public java.lang.String toString()
toString
in class MixtureDistribution
public java.lang.String getRevision()
public static void main(java.lang.String[] args)
args
- the commandline arguments - ignored
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