weka.clusterers
Class EM

java.lang.Object
  |
  +--weka.clusterers.Clusterer
        |
        +--weka.clusterers.DensityBasedClusterer
              |
              +--weka.clusterers.EM

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class EM

extends DensityBasedClusterer

implements OptionHandler, WeightedInstancesHandler

Simple EM (expectation maximisation) class.

EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. EM can decide how many clusters to create by cross validation, or you may specify apriori how many clusters to generate.

The cross validation performed to determine the number of clusters is done in the following steps:
1. the number of clusters is set to 1
2. the training set is split randomly into 10 folds.
3. EM is performed 10 times using the 10 folds the usual CV way.
4. the loglikelihood is averaged over all 10 results.
5. if loglikelihood has increased the number of clusters is increased by 1 and the program continues at step 2.

The number of folds is fixed to 10, as long as the number of instances in the training set is not smaller 10. If this is the case the number of folds is set equal to the number of instances.

Valid options are:

-V
Verbose.

-N
Specify the number of clusters to generate. If omitted, EM will use cross validation to select the number of clusters automatically.

-I
Terminate after this many iterations if EM has not converged.

-S
Specify random number seed.

-M
Set the minimum allowable standard deviation for normal density calculation.

Version:

$Revision: 1.25 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

EM()
Constructor.

Method Summary

void	buildClusterer(Instances data) Generates a clusterer.
double[]	clusterPriors() Returns the cluster priors.
double[][][]	getClusterModelsNumericAtts() Return the normal distributions for the cluster models
double[]	getClusterPriors() Return the priors for the clusters
boolean	getDebug() Get debug mode
int	getMaxIterations() Get the maximum number of iterations
double	getMinStdDev() Get the minimum allowable standard deviation.
int	getNumClusters() Get the number of clusters
java.lang.String[]	getOptions() Gets the current settings of EM.
int	getSeed() Get the random number seed
java.lang.String	globalInfo() Returns a string describing this clusterer
java.util.Enumeration	listOptions() Returns an enumeration describing the available options..
double[]	logDensityPerClusterForInstance(Instance inst) Computes the log of the conditional density (per cluster) for a given instance.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	maxIterationsTipText() Returns the tip text for this property
java.lang.String	minStdDevTipText() Returns the tip text for this property
int	numberOfClusters() Returns the number of clusters.
java.lang.String	numClustersTipText() Returns the tip text for this property
java.lang.String	seedTipText() Returns the tip text for this property
void	setDebug(boolean v) Set debug mode - verbose output
void	setMaxIterations(int i) Set the maximum number of iterations to perform
void	setMinStdDev(double m) Set the minimum value for standard deviation when calculating normal density.
void	setNumClusters(int n) Set the number of clusters (-1 to select by CV).
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int s) Set the random number seed
java.lang.String	toString() Outputs the generated clusters into a string.

Methods inherited from class weka.clusterers.DensityBasedClusterer

distributionForInstance, logDensityForInstance

Methods inherited from class weka.clusterers.Clusterer

clusterInstance, forName, makeCopies

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

EM

public EM()

Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this clusterer

Returns:

a description of the evaluator suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options..

Valid options are:

-V
Verbose.

-N
Specify the number of clusters to generate. If omitted, EM will use cross validation to select the number of clusters automatically.

-I
Terminate after this many iterations if EM has not converged.

-S
Specify random number seed.

-M
Set the minimum allowable standard deviation for normal density calculation.

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

minStdDevTipText

public java.lang.String minStdDevTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMinStdDev

public void setMinStdDev(double m)

Set the minimum value for standard deviation when calculating normal density. Reducing this value can help prevent arithmetic overflow resulting from multiplying large densities (arising from small standard deviations) when there are many singleton or near singleton values.

Parameters:

m - minimum value for standard deviation

getMinStdDev

public double getMinStdDev()

Get the minimum allowable standard deviation.

Returns:

the minumum allowable standard deviation

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setSeed

public void setSeed(int s)

Set the random number seed

Parameters:

s - the seed

getSeed

public int getSeed()

Get the random number seed

Returns:

the seed

numClustersTipText

public java.lang.String numClustersTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumClusters

public void setNumClusters(int n)
                    throws java.lang.Exception

Set the number of clusters (-1 to select by CV).

Parameters:

n - the number of clusters

Throws:

java.lang.Exception - if n is 0

getNumClusters

public int getNumClusters()

Get the number of clusters

Returns:

the number of clusters.

maxIterationsTipText

public java.lang.String maxIterationsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMaxIterations

public void setMaxIterations(int i)
                      throws java.lang.Exception

Set the maximum number of iterations to perform

Parameters:

i - the number of iterations

Throws:

java.lang.Exception - if i is less than 1

getMaxIterations

public int getMaxIterations()

Get the maximum number of iterations

Returns:

the number of iterations

setDebug

public void setDebug(boolean v)

Set debug mode - verbose output

Parameters:

v - true for verbose output

getDebug

public boolean getDebug()

Get debug mode

Returns:

true if debug mode is set

getOptions

public java.lang.String[] getOptions()

Gets the current settings of EM.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions()

getClusterModelsNumericAtts

public double[][][] getClusterModelsNumericAtts()

Return the normal distributions for the cluster models

Returns:

a double[][][] value

getClusterPriors

public double[] getClusterPriors()

Return the priors for the clusters

Returns:

a double[] value

toString

public java.lang.String toString()

Outputs the generated clusters into a string.

Overrides:

toString in class java.lang.Object

numberOfClusters

public int numberOfClusters()
                     throws java.lang.Exception

Returns the number of clusters.

Specified by:

numberOfClusters in class Clusterer

Returns:

the number of clusters generated for a training dataset.

Throws:

java.lang.Exception - if number of clusters could not be returned successfully

buildClusterer

public void buildClusterer(Instances data)
                    throws java.lang.Exception

Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.

Specified by:

buildClusterer in class Clusterer

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the clusterer has not been generated successfully

clusterPriors

public double[] clusterPriors()

Returns the cluster priors.

Specified by:

clusterPriors in class DensityBasedClusterer

Returns:

the prior probability for each cluster

logDensityPerClusterForInstance

public double[] logDensityPerClusterForInstance(Instance inst)
                                         throws java.lang.Exception

Computes the log of the conditional density (per cluster) for a given instance.

Specified by:

logDensityPerClusterForInstance in class DensityBasedClusterer

Parameters:

inst - the instance to compute the density for

Returns:

the density.

Throws:

java.lang.Exception - if the density could not be computed successfully

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the following arguments:

-t training file [-T test file] [-N number of clusters] [-S random seed]