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## weka.classifiers.mi
Class TLDSimple

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.RandomizableClassifier
              weka.classifiers.mi.TLDSimple

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

    public class TLDSimple

extends RandomizableClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler

A simpler version of TLD, mu random but sigma^2 fixed and estimated via data.
For more information see:
Xin Xu (2003). Statistical learning in multiple instance problem. Hamilton, NZ.

BibTeX:

     @mastersthesis{Xu2003,
        address = {Hamilton, NZ},
        author = {Xin Xu},
        note = {0657.594},
        school = {University of Waikato},
        title = {Statistical learning in multiple instance problem},
        year = {2003}
     }

Valid options are:

     -C
      Set whether or not use empirical
      log-odds cut-off instead of 0

     -R <numOfRuns>
      Set the number of multiple runs
      needed for searching the MLE.

     -S <num>
      Random number seed.
      (default 1)

     -D
      If set, classifier is run in debug mode and
      may output additional info to the console

Version:
  $Revision: 5481 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static double` | `ZERO` The very small number representing zero

Constructor Summary
`TLDSimple()`

Method Summary
`void` | `buildClassifier(Instances exs)` Generates a classifier.
`double` | `classifyInstance(Instance ex)` Classifies the given test instance.
`double[]` | `distributionForInstance(Instance ex)` Computes the distribution for a given exemplar
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`Capabilities` | `getMultiInstanceCapabilities()` Returns the capabilities of this multi-instance classifier for the relational data.
`int` | `getNumRuns()` Returns the number of runs to perform.
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`boolean` | `getUsingCutOff()` Returns whether an empirical cutoff is used
`java.lang.String` | `globalInfo()` Returns a string describing this filter
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options
`static void` | `main(java.lang.String[] args)` Main method for testing.
`java.lang.String` | `numRunsTipText()` Returns the tip text for this property
`void` | `setNumRuns(int numRuns)` Sets the number of runs to perform.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setUsingCutOff(boolean cutOff)` Sets whether to use an empirical cutoff.
`java.lang.String` | `toString()` Gets a string describing the classifier.
`java.lang.String` | `usingCutOffTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableClassifier
`getSeed, seedTipText, setSeed`

Methods inherited from class weka.classifiers.AbstractClassifier
`debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

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

Field Detail

### ZERO

    public static double ZERO

  The very small number representing zero

Constructor Detail

### TLDSimple

    public TLDSimple()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this filter

  Returns:
    a description of the filter suitable for displaying in the explorer/experimenter gui

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `Classifier`
  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `AbstractClassifier`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### getMultiInstanceCapabilities

    public Capabilities getMultiInstanceCapabilities()

  Returns the capabilities of this multi-instance classifier for the relational data.

  Specified by:
    `getMultiInstanceCapabilities` in interface `MultiInstanceCapabilitiesHandler`
  Returns:
    the capabilities of this object
  See Also:
    `Capabilities`

### buildClassifier

    public void buildClassifier(Instances exs)
                         throws java.lang.Exception

  Description copied from interface: `Classifier`
  Generates a classifier. Must initialize all fields of the classifier that are not being set via options (ie. multiple calls of buildClassifier must always lead to the same result). Must not change the dataset in any way.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `exs` - the training exemplars
  Throws:
    `java.lang.Exception` - if the model cannot be built properly

### classifyInstance

    public double classifyInstance(Instance ex)
                            throws java.lang.Exception

  Description copied from class: `AbstractClassifier`
  Classifies the given test instance. The instance has to belong to a dataset when it's being classified. Note that a classifier MUST implement either this or distributionForInstance().

  Specified by:
    `classifyInstance` in interface `Classifier`
  Overrides:
    `classifyInstance` in class `AbstractClassifier`
  Parameters:
    `ex` - the given test exemplar
  Returns:
    the classification
  Throws:
    `java.lang.Exception` - if the exemplar could not be classified successfully

### distributionForInstance

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

  Computes the distribution for a given exemplar

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `AbstractClassifier`
  Parameters:
    `ex` - the exemplar for which distribution is computed
  Returns:
    the distribution
  Throws:
    `java.lang.Exception` - if the distribution can't be computed successfully

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `RandomizableClassifier`
  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:

       -C
        Set whether or not use empirical
        log-odds cut-off instead of 0

       -R <numOfRuns>
        Set the number of multiple runs
        needed for searching the MLE.

       -S <num>
        Random number seed.
        (default 1)

       -D
        If set, classifier is run in debug mode and
        may output additional info to the console

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `RandomizableClassifier`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the Classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `RandomizableClassifier`
  Returns:
    an array of strings suitable for passing to setOptions

### numRunsTipText

    public java.lang.String numRunsTipText()

  Returns the tip text for this property

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

### setNumRuns

    public void setNumRuns(int numRuns)

  Sets the number of runs to perform.

  Parameters:
    `numRuns` - the number of runs to perform

### getNumRuns

    public int getNumRuns()

  Returns the number of runs to perform.

  Returns:
    the number of runs to perform

### usingCutOffTipText

    public java.lang.String usingCutOffTipText()

  Returns the tip text for this property

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

### setUsingCutOff

    public void setUsingCutOff(boolean cutOff)

  Sets whether to use an empirical cutoff.

  Parameters:
    `cutOff` - whether to use an empirical cutoff

### getUsingCutOff

    public boolean getUsingCutOff()

  Returns whether an empirical cutoff is used

  Returns:
    true if an empirical cutoff is used

### toString

    public java.lang.String toString()

  Gets a string describing the classifier.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a string describing the classifer built.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `AbstractClassifier`
  Returns:
    the revision

### main

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

  Main method for testing.

  Parameters:
    `args` - the options for the classifier

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