weka.clusterers
Class EM

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

All Implemented Interfaces:

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

public class EM

extends DistributionClusterer

implements OptionHandler

Simple EM (estimation 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.

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.

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

EM()
Constructor.

Method Summary

void	buildClusterer(Instances data) Generates a clusterer.
double	densityForInstance(Instance inst) Computes the density for a given instance.
double[]	distributionForInstance(Instance inst) Predicts the cluster memberships for a given instance.
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.
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
protected void	resetOptions() Reset to default options
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.
protected double[]	weightsForInstance(Instance inst) Returns the weights (indicating cluster membership) for a given instance

Methods inherited from class weka.clusterers.DistributionClusterer

clusterInstance

Methods inherited from class weka.clusterers.Clusterer

forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, 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()

resetOptions

protected void resetOptions()

Reset to default options

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.

Overrides:

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.

Overrides:

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

densityForInstance

public double densityForInstance(Instance inst)
                          throws java.lang.Exception

Computes the density for a given instance.

Overrides:

densityForInstance in class DistributionClusterer

Parameters:

inst - the instance to compute the density for

Returns:

the density.

Throws:

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

distributionForInstance

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

Predicts the cluster memberships for a given instance.

Overrides:

distributionForInstance in class DistributionClusterer

Parameters:

data - set of test instances

instance - the instance to be assigned a cluster.

Returns:

an array containing the estimated membership probabilities of the test instance in each cluster (this should sum to at most 1)

Throws:

java.lang.Exception - if distribution could not be computed successfully

weightsForInstance

protected double[] weightsForInstance(Instance inst)
                               throws java.lang.Exception

Returns the weights (indicating cluster membership) for a given instance

Parameters:

inst - the instance to be assigned a cluster

Returns:

an array of weights

Throws:

java.lang.Exception - if weights could not be computed

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]