EM

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## weka.clusterers
Class EM

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

All Implemented Interfaces:
  java.io.Serializable, java.lang.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.

     -I <num>
      max iterations.
     (default 100)

     -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)

     -S <num>
      Random number seed.
      (default 100)

Version:
  $Revision: 1.44 $
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.
`java.lang.String` | `debugTipText()` Returns the tip text for this property
`java.lang.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` | `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.
`java.lang.String` | `getRevision()` Returns the revision string.
`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
`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` | `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` | `setMinStdDevPerAtt(double[] m)`
`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.
`java.lang.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, forName, makeCopies, makeCopy`

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 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.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `RandomizableDensityBasedClusterer`
  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.

  Valid options are:

       -N <num>
        number of clusters. If omitted or -1 specified, then
        cross validation is used to select the number of clusters.

       -I <num>
        max iterations.
       (default 100)

       -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)

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

### displayModelInOldFormatTipText

    public java.lang.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 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

### setMinStdDevPerAtt

    public void setMinStdDevPerAtt(double[] m)

### getMinStdDev

    public double getMinStdDev()

  Get the minimum allowable standard deviation.

  Returns:
    the minumum allowable standard deviation

### 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).

  Specified by:
    `setNumClusters` in interface `NumberOfClustersRequestable`
  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

### debugTipText

    public java.lang.String debugTipText()

  Returns the tip text for this property

  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

  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`
  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 java.lang.String toString()

  Outputs the generated clusters into a string.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    the clusterer in string representation

### numberOfClusters

    public int numberOfClusters()
                         throws java.lang.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:
    `java.lang.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 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 interface `Clusterer`
  Specified by:
    `buildClusterer` in class `AbstractClusterer`
  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 interface `DensityBasedClusterer`
  Specified by:
    `clusterPriors` in class `AbstractDensityBasedClusterer`
  Returns:
    the cluster priors

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

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Returns:
    the revision

### 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]

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