EM (weka-dev r10501 API)

java.lang.Object
- weka.clusterers.AbstractClusterer
- - weka.clusterers.AbstractDensityBasedClusterer
  - - weka.clusterers.RandomizableDensityBasedClusterer
    - - weka.clusterers.EM

All Implemented Interfaces:

Serializable, Cloneable, Clusterer, DensityBasedClusterer, NumberOfClustersRequestable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler
```
public class EM
extends RandomizableDensityBasedClusterer
implements NumberOfClustersRequestable, 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:
```
 -N <num>
  number of clusters. If omitted or -1 specified, then 
  cross validation is used to select the number of clusters.
 
```
```
 -X <num>
  Number of folds to use when cross-validating to find the best number of clusters.
 
```
```
 -max <num>
  Maximum number of clusters to consider during cross-validation. If omitted or -1 specified, then 
  there is no upper limit on the number of clusters.
 
```
```
 -ll-cv <num>
  Minimum improvement in cross-validated log likelihood required
  to consider increasing the number of clusters.
  (default 1e-6)
 
```
```
 -I <num>
  max iterations.
  (default 100)
 
```
```
 -ll-iter <num>
  Minimum improvement in log likelihood required
  to perform another iteration of the E and M steps.
  (default 1e-6)
 
```
```
 -V
  verbose.
 
```
```
 -M <num>
  minimum allowable standard deviation for normal density
  computation
  (default 1e-6)
 
```
```
 -O
  Display model in old format (good when there are many clusters)
 
```
```
 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
 
```
```
 -S <num>
  Random number seed.
  (default 100)
 
```
Version:

$Revision: 10203 $

Author:

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

See Also:
Serialized Form

Constructor Summary

Constructors
Constructor and Description

EM()
Constructor.

Constructors
Constructor and Description
`EM()` Constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClusterer(Instances data)` Generates a clusterer.
`double[]`	`clusterPriors()` Returns the cluster priors.
`String`	`debugTipText()` Returns the tip text for this property
`String`	`displayModelInOldFormatTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the clusterer (i.e., the ones of SimpleKMeans).
`double[][][]`	`getClusterModelsNumericAtts()` Return the normal distributions for the cluster models
`double[]`	`getClusterPriors()` Return the priors for the clusters
`boolean`	`getDebug()` Get debug mode
`boolean`	`getDisplayModelInOldFormat()` Get whether to display model output in the old, original format.
`int`	`getMaximumNumberOfClusters()` Get the maximum number of clusters to consider when cross-validating
`int`	`getMaxIterations()` Get the maximum number of iterations
`double`	`getMinLogLikelihoodImprovementCV()` Get the minimum improvement in cross-validated log likelihood required to consider increasing the number of clusters when cross-validating to find the best number of clusters
`double`	`getMinLogLikelihoodImprovementIterating()` Get the minimum improvement in log likelihood necessary to perform another iteration of the E and M steps.
`double`	`getMinStdDev()` Get the minimum allowable standard deviation.
`int`	`getNumClusters()` Get the number of clusters
`int`	`getNumExecutionSlots()` Get the degree of parallelism to use.
`int`	`getNumFolds()` Get the number of folds to use when cross-validating to find the best number of clusters.
`String[]`	`getOptions()` Gets the current settings of EM.
`String`	`getRevision()` Returns the revision string.
`String`	`globalInfo()` Returns a string describing this clusterer
`Enumeration<Option>`	`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(String[] argv)` Main method for testing this class.
`String`	`maximumNumberOfClustersTipText()` Returns the tip text for this property
`String`	`maxIterationsTipText()` Returns the tip text for this property
`String`	`minLogLikelihoodImprovementCVTipText()` Returns the tip text for this property
`String`	`minLogLikelihoodImprovementIteratingTipText()` Returns the tip text for this property
`String`	`minStdDevTipText()` Returns the tip text for this property
`int`	`numberOfClusters()` Returns the number of clusters.
`String`	`numClustersTipText()` Returns the tip text for this property
`String`	`numExecutionSlotsTipText()` Returns the tip text for this property
`String`	`numFoldsTipText()` Returns the tip text for this property
`void`	`setDebug(boolean v)` Set debug mode - verbose output
`void`	`setDisplayModelInOldFormat(boolean d)` Set whether to display model output in the old, original format.
`void`	`setMaximumNumberOfClusters(int n)` Set the maximum number of clusters to consider when cross-validating
`void`	`setMaxIterations(int i)` Set the maximum number of iterations to perform
`void`	`setMinLogLikelihoodImprovementCV(double min)` Set the minimum improvement in cross-validated log likelihood required to consider increasing the number of clusters when cross-validating to find the best number of clusters
`void`	`setMinLogLikelihoodImprovementIterating(double min)` Set the minimum improvement in log likelihood necessary to perform another iteration of the E and M steps.
`void`	`setMinStdDev(double m)` Set the minimum value for standard deviation when calculating normal density.
`void`	`setMinStdDevPerAtt(double[] m)`
`void`	`setNumClusters(int n)` Set the number of clusters (-1 to select by CV).
`void`	`setNumExecutionSlots(int slots)` Set the degree of parallelism to use.
`void`	`setNumFolds(int folds)` Set the number of folds to use when cross-validating to find the best number of clusters.
`void`	`setOptions(String[] options)` Parses a given list of options.
`String`	`toString()` Outputs the generated clusters into a string.

Methods inherited from class weka.clusterers.RandomizableDensityBasedClusterer
getSeed, seedTipText, setSeed

Methods inherited from class weka.clusterers.AbstractDensityBasedClusterer
distributionForInstance, logDensityForInstance, logJointDensitiesForInstance, makeCopies

Methods inherited from class weka.clusterers.AbstractClusterer
clusterInstance, doNotCheckCapabilitiesTipText, forName, getDoNotCheckCapabilities, makeCopies, makeCopy, runClusterer, setDoNotCheckCapabilities

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.clusterers.Clusterer
clusterInstance

- Constructor Detail
  - EM
```
public EM()
```
    Constructor.
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing this clusterer
    
    Returns:
    a description of the evaluator suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableDensityBasedClusterer
    
    Returns:
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -N <num>
  number of clusters. If omitted or -1 specified, then 
  cross validation is used to select the number of clusters.
 
```
```
 -X <num>
  Number of folds to use when cross-validating to find the best number of clusters.
 
```
```
 -max <num>
  Maximum number of clusters to consider during cross-validation. If omitted or -1 specified, then 
  there is no upper limit on the number of clusters.
 
```
```
 -ll-cv <num>
  Minimum improvement in cross-validated log likelihood required
  to consider increasing the number of clusters.
  (default 1e-6)
 
```
```
 -I <num>
  max iterations.
  (default 100)
 
```
```
 -ll-iter <num>
  Minimum improvement in log likelihood required
  to perform another iteration of the E and M steps.
  (default 1e-6)
 
```
```
 -V
  verbose.
 
```
```
 -M <num>
  minimum allowable standard deviation for normal density
  computation
  (default 1e-6)
 
```
```
 -O
  Display model in old format (good when there are many clusters)
 
```
```
 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
 
```
```
 -S <num>
  Random number seed.
  (default 100)
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableDensityBasedClusterer
    
    Parameters:
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - numFoldsTipText
```
public String numFoldsTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumFolds
```
public void setNumFolds(int folds)
```
    Set the number of folds to use when cross-validating to find the best number of clusters.
    
    Parameters:
    folds - the number of folds to use
  - getNumFolds
```
public int getNumFolds()
```
    Get the number of folds to use when cross-validating to find the best number of clusters.
    
    Returns:
    the number of folds to use
  - minLogLikelihoodImprovementCVTipText
```
public String minLogLikelihoodImprovementCVTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinLogLikelihoodImprovementCV
```
public void setMinLogLikelihoodImprovementCV(double min)
```
    Set the minimum improvement in cross-validated log likelihood required to consider increasing the number of clusters when cross-validating to find the best number of clusters
    
    Parameters:
    min - the minimum improvement in log likelihood
  - getMinLogLikelihoodImprovementCV
```
public double getMinLogLikelihoodImprovementCV()
```
    Get the minimum improvement in cross-validated log likelihood required to consider increasing the number of clusters when cross-validating to find the best number of clusters
    
    Returns:
    the minimum improvement in log likelihood
  - minLogLikelihoodImprovementIteratingTipText
```
public String minLogLikelihoodImprovementIteratingTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinLogLikelihoodImprovementIterating
```
public void setMinLogLikelihoodImprovementIterating(double min)
```
    Set the minimum improvement in log likelihood necessary to perform another iteration of the E and M steps.
    
    Parameters:
    min - the minimum improvement in log likelihood
  - getMinLogLikelihoodImprovementIterating
```
public double getMinLogLikelihoodImprovementIterating()
```
    Get the minimum improvement in log likelihood necessary to perform another iteration of the E and M steps.
    
    Returns:
    the minimum improvement in log likelihood
  - numExecutionSlotsTipText
```
public String numExecutionSlotsTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumExecutionSlots
```
public void setNumExecutionSlots(int slots)
```
    Set the degree of parallelism to use.
    
    Parameters:
    slots - the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds
  - getNumExecutionSlots
```
public int getNumExecutionSlots()
```
    Get the degree of parallelism to use.
    
    Returns:
    the number of tasks to run in parallel when computing the nearest neighbors and evaluating different values of k between the lower and upper bounds
  - displayModelInOldFormatTipText
```
public String displayModelInOldFormatTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDisplayModelInOldFormat
```
public void setDisplayModelInOldFormat(boolean d)
```
    Set whether to display model output in the old, original format.
    
    Parameters:
    d - true if model ouput is to be shown in the old format
  - getDisplayModelInOldFormat
```
public boolean getDisplayModelInOldFormat()
```
    Get whether to display model output in the old, original format.
    
    Returns:
    true if model ouput is to be shown in the old format
  - minStdDevTipText
```
public 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
  - setMinStdDevPerAtt
```
public void setMinStdDevPerAtt(double[] m)
```
  - getMinStdDev
```
public double getMinStdDev()
```
    Get the minimum allowable standard deviation.
    
    Returns:
    the minumum allowable standard deviation
  - numClustersTipText
```
public 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 Exception
```
    Set the number of clusters (-1 to select by CV).
    
    Specified by:
    
    setNumClusters in interface NumberOfClustersRequestable
    
    Parameters:
    n - the number of clusters
    
    Throws:
    
    Exception - if n is 0
  - getNumClusters
```
public int getNumClusters()
```
    Get the number of clusters
    
    Returns:
    the number of clusters.
  - setMaximumNumberOfClusters
```
public void setMaximumNumberOfClusters(int n)
```
    Set the maximum number of clusters to consider when cross-validating
    
    Parameters:
    n - the maximum number of clusters to consider
  - getMaximumNumberOfClusters
```
public int getMaximumNumberOfClusters()
```
    Get the maximum number of clusters to consider when cross-validating
    
    Returns:
    the maximum number of clusters to consider
  - maximumNumberOfClustersTipText
```
public String maximumNumberOfClustersTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - maxIterationsTipText
```
public 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 Exception
```
    Set the maximum number of iterations to perform
    
    Parameters:
    i - the number of iterations
    
    Throws:
    
    Exception - if i is less than 1
  - getMaxIterations
```
public int getMaxIterations()
```
    Get the maximum number of iterations
    
    Returns:
    the number of iterations
  - debugTipText
```
public String debugTipText()
```
    Returns the tip text for this property
    
    Overrides:
    
    debugTipText in class AbstractClusterer
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebug
```
public void setDebug(boolean v)
```
    Set debug mode - verbose output
    
    Overrides:
    
    setDebug in class AbstractClusterer
    
    Parameters:
    v - true for verbose output
  - getDebug
```
public boolean getDebug()
```
    Get debug mode
    
    Overrides:
    
    getDebug in class AbstractClusterer
    
    Returns:
    true if debug mode is set
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of EM.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableDensityBasedClusterer
    
    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 String toString()
```
    Outputs the generated clusters into a string.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    the clusterer in string representation
  - numberOfClusters
```
public int numberOfClusters()
                     throws Exception
```
    Returns the number of clusters.
    
    Specified by:
    
    numberOfClusters in interface Clusterer
    
    Specified by:
    
    numberOfClusters in class AbstractClusterer
    
    Returns:
    the number of clusters generated for a training dataset.
    
    Throws:
    
    Exception - if number of clusters could not be returned successfully
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the clusterer (i.e., the ones of SimpleKMeans).
    
    Specified by:
    
    getCapabilities in interface Clusterer
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClusterer
    
    Returns:
    the capabilities of this clusterer
    See Also:
    Capabilities
  - buildClusterer
```
public void buildClusterer(Instances data)
                    throws Exception
```
    Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.
    
    Specified by:
    
    buildClusterer in interface Clusterer
    
    Specified by:
    
    buildClusterer in class AbstractClusterer
    
    Parameters:
    data - set of instances serving as training data
    
    Throws:
    
    Exception - if the clusterer has not been generated successfully
  - clusterPriors
```
public double[] clusterPriors()
```
    Returns the cluster priors.
    
    Specified by:
    
    clusterPriors in interface DensityBasedClusterer
    
    Specified by:
    
    clusterPriors in class AbstractDensityBasedClusterer
    
    Returns:
    the cluster priors
  - logDensityPerClusterForInstance
```
public double[] logDensityPerClusterForInstance(Instance inst)
                                         throws Exception
```
    Computes the log of the conditional density (per cluster) for a given instance.
    
    Specified by:
    
    logDensityPerClusterForInstance in interface DensityBasedClusterer
    
    Specified by:
    
    logDensityPerClusterForInstance in class AbstractDensityBasedClusterer
    
    Parameters:
    inst - the instance to compute the density for
    
    Returns:
    an array containing the estimated densities
    
    Throws:
    
    Exception - if the density could not be computed successfully
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClusterer
    
    Returns:
    the revision
  - main
```
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]

Class EM

Constructor Summary

Method Summary

Methods inherited from class weka.clusterers.RandomizableDensityBasedClusterer

Methods inherited from class weka.clusterers.AbstractDensityBasedClusterer

Methods inherited from class weka.clusterers.AbstractClusterer

Methods inherited from class java.lang.Object

Methods inherited from interface weka.clusterers.Clusterer

Constructor Detail

EM

Method Detail

globalInfo

listOptions

setOptions

numFoldsTipText

setNumFolds

getNumFolds

minLogLikelihoodImprovementCVTipText

setMinLogLikelihoodImprovementCV

getMinLogLikelihoodImprovementCV

minLogLikelihoodImprovementIteratingTipText

setMinLogLikelihoodImprovementIterating

getMinLogLikelihoodImprovementIterating

numExecutionSlotsTipText

setNumExecutionSlots

getNumExecutionSlots

displayModelInOldFormatTipText

setDisplayModelInOldFormat

getDisplayModelInOldFormat

minStdDevTipText

setMinStdDev

setMinStdDevPerAtt

getMinStdDev

numClustersTipText

setNumClusters

getNumClusters

setMaximumNumberOfClusters

getMaximumNumberOfClusters

maximumNumberOfClustersTipText

maxIterationsTipText

setMaxIterations

getMaxIterations

debugTipText

setDebug

getDebug

getOptions

getClusterModelsNumericAtts

getClusterPriors

toString

numberOfClusters

getCapabilities

buildClusterer

clusterPriors

logDensityPerClusterForInstance

getRevision

main