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Class weka.clusterers.EM

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

public class EM

extends DistributionClusterer

implements OptionHandler

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

-S
Specify random number seed.

Version:

$Revision: 1.6 $

Author:

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

EM()

Constructor.

buildClusterer(Instances)

Classifies a given instance.

distributionForInstance(Instance)

Predicts the cluster memberships for a given instance.

getDebug()

Get debug mode

getMaxIterations()

Get the maximum number of iterations

getNumClusters()

Get the number of clusters

getOptions()

Gets the current settings of EM.

getSeed()

Get the random number seed

listOptions()

Returns an enumeration describing the available options.

main(String[])

Main method for testing this class.

numberOfClusters()

Returns the number of clusters.

setDebug(boolean)

Set debug mode - verbose output

setMaxIterations(int)

Set the maximum number of iterations to perform

setNumClusters(int)

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

setOptions(String[])

Parses a given list of options.

setSeed(int)

Set the random number seed

toString()

Outputs the generated clusters into a string.

 public EM()

Constructor.

 public Enumeration listOptions()

Returns an enumeration describing the available options.

Valid options are:

-V
Verbose.

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

-S
Specify random number seed.

Returns:

an enumeration of all the available options

 public void setOptions(String options[]) throws Exception

Parses a given list of options.

Parameters:

options - the list of options as an array of strings

Throws: Exception

if an option is not supported

 public void setSeed(int s)

Set the random number seed

Parameters:

s - the seed

 public int getSeed()

Get the random number seed

Returns:

the seed

 public void setNumClusters(int n) throws Exception

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

Parameters:

n - the number of clusters

Throws: Exception

if n is 0

 public int getNumClusters()

Get the number of clusters

Returns:

the number of clusters.

 public void setMaxIterations(int i) throws Exception

Set the maximum number of iterations to perform

Parameters:

i - the number of iterations

Throws: Exception

if i is less than 1

 public int getMaxIterations()

Get the maximum number of iterations

Returns:

the number of iterations

 public void setDebug(boolean v)

Set debug mode - verbose output

Parameters:

v - true for verbose output

 public boolean getDebug()

Get debug mode

Returns:

true if debug mode is set

 public String[] getOptions()

Gets the current settings of EM.

Returns:

an array of strings suitable for passing to setOptions()

 public String toString()

Outputs the generated clusters into a string.

Overrides:

toString in class Object

 public int numberOfClusters() throws Exception

Returns the number of clusters.

Returns:

the number of clusters generated for a training dataset.

Throws: Exception

if number of clusters could not be returned successfully

Overrides:

numberOfClusters in class Clusterer

 public void buildClusterer(Instances data) throws Exception

Classifies a given instance.

Parameters:

instance - the instance to be assigned to a cluster

Returns:

the number of the assigned cluster as an interger if the class is enumerated, otherwise the predicted value

Throws: Exception

if instance could not be classified successfully

Overrides:

buildClusterer in class Clusterer

 public double[] distributionForInstance(Instance inst) throws Exception

Predicts the cluster memberships for a given instance.

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: Exception

if distribution could not be computed successfully

Overrides:

distributionForInstance in class DistributionClusterer

 public static void main(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]

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