## weka.classifiers.functions.pace Class MixtureDistribution

```java.lang.Object
weka.classifiers.functions.pace.MixtureDistribution
```
Direct Known Subclasses:
ChisqMixture, NormalMixture

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)

 Field Summary `protected  DiscreteFunction` `mixingDistribution` `static int` `NNMMethod`           The nonnegative-measure-based method `static int` `PMMethod`           The probability-measure-based method

 Constructor Summary `MixtureDistribution()`

 Method Summary ` 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 `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 `clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait`

 Field Detail

### mixingDistribution

`protected DiscreteFunction mixingDistribution`

### NNMMethod

`public static final int NNMMethod`
The nonnegative-measure-based method

Constant Field Values

### PMMethod

`public static final int PMMethod`
The probability-measure-based method

Constant Field Values
 Constructor Detail

### MixtureDistribution

`public MixtureDistribution()`
 Method Detail

### getMixingDistribution

`public DiscreteFunction getMixingDistribution()`
Gets the mixing distribution

### setMixingDistribution

`public void setMixingDistribution(DiscreteFunction d)`
Sets the mixing distribution

Parameters:
`d` - the mixing distribution

### 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

### 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.

### 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.

### 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

### 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

### 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

### 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

### 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

### toString

`public java.lang.String toString()`
Converts to a string