weka.classifiers.mi

Class TLDSimple

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.RandomizableClassifier

weka.classifiers.mi.TLDSimple

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, 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: 5527 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	ZERO The very small number representing zero

Constructor Summary

Constructors

Constructor and Description
TLDSimple()

Method Summary

Methods

Modifier and Type	Method and Description
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.Classifier

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 CapabilitiesHandler

Overrides:

getCapabilities in class Classifier

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 class: 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 class 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: Classifier

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().

Overrides:

classifyInstance in class Classifier

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

Overrides:

distributionForInstance in class Classifier

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 Classifier

Returns:

the revision

main

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

Main method for testing.

Parameters:

args - the options for the classifier