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Java Source Code / Java Documentation » Science » weka » weka.classifiers.mi

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Class Diagram

Java Document (Java Doc)

weka.classifiers .RandomizableClassifier

weka.classifiers.mi .TLDSimple

TLDSimple

public class TLDSimple extends RandomizableClassifier implements OptionHandler,MultiInstanceCapabilitiesHandler,TechnicalInformationHandler(Code)

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

author:
   Eibe Frank (eibe@cs.waikato.ac.nz)
author:
   Xin Xu (xx5@cs.waikato.ac.nz)
version:
   $Revision: 1.5 $

Field Summary
public static double	ZERO
protected double[]	m_Class
protected double	m_Cutoff
protected int	m_Dimension The dimension of each exemplar, i.e.
protected double[][]	m_MeanN
protected double[][]	m_MeanP
protected int	m_NumClasses
protected double[]	m_ParamsN
protected double[]	m_ParamsP
protected int	m_Run
protected double[]	m_SgmSqN
protected double[]	m_SgmSqP
protected double[][]	m_SumN
protected double[][]	m_SumP
protected boolean	m_UseEmpiricalCutOff
final static long	serialVersionUID

Method Summary
public void	buildClassifier(Instances exs)
public double	classifyInstance(Instance ex)
public double[]	distributionForInstance(Instance ex)
public Capabilities	getCapabilities() Returns default capabilities of the classifier.
public Capabilities	getMultiInstanceCapabilities() Returns the capabilities of this multi-instance classifier for the relational data.
public int	getNumRuns() Returns the number of runs to perform.
public String[]	getOptions() Gets the current settings of the Classifier.
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.
public boolean	getUsingCutOff()
public String	globalInfo()
public Enumeration	listOptions()
public static void	main(String[] args) Main method for testing.
public String	numRunsTipText()
public void	setNumRuns(int numRuns) Sets the number of runs to perform.
public void	setOptions(String[] options) Parses a given list of options.
public void	setUsingCutOff(boolean cutOff) Sets whether to use an empirical cutoff.
public String	toString() Gets a string describing the classifier.
public String	usingCutOffTipText()

Field Detail

ZERO
public static double ZERO(Code)
	The very small number representing zero

m_Class
protected double[] m_Class(Code)
	The class label of each exemplar

m_Cutoff
protected double m_Cutoff(Code)

m_Dimension
protected int m_Dimension(Code)
	The dimension of each exemplar, i.e. (numAttributes-2)

m_MeanN
protected double[][] m_MeanN(Code)
	The mean for each attribute of each negative exemplar

m_MeanP
protected double[][] m_MeanP(Code)
	The mean for each attribute of each positive exemplar

m_NumClasses
protected int m_NumClasses(Code)
	The number of class labels in the data

m_ParamsN
protected double[] m_ParamsN(Code)
	The parameters to be estimated for each negative exemplar

m_ParamsP
protected double[] m_ParamsP(Code)
	The parameters to be estimated for each positive exemplar

m_Run
protected int m_Run(Code)

m_SgmSqN
protected double[] m_SgmSqN(Code)
	Estimated sigma^2 in negative bags

m_SgmSqP
protected double[] m_SgmSqP(Code)
	Estimated sigma^2 in positive bags

m_SumN
protected double[][] m_SumN(Code)
	The effective sum of weights of each negative exemplar in each dimension

m_SumP
protected double[][] m_SumP(Code)
	The effective sum of weights of each positive exemplar in each dimension

m_UseEmpiricalCutOff
protected boolean m_UseEmpiricalCutOff(Code)

serialVersionUID
final static long serialVersionUID(Code)
	for serialization

Method Detail

buildClassifier
public void buildClassifier(Instances exs) throws Exception(Code)
	Parameters: exs - the training exemplars throws: Exception - if the model cannot be built properly

classifyInstance
public double classifyInstance(Instance ex) throws Exception(Code)
	Parameters: ex - the given test exemplar the classification throws: Exception - if the exemplar could not be classifiedsuccessfully

distributionForInstance
public double[] distributionForInstance(Instance ex) throws Exception(Code)
	Computes the distribution for a given exemplar Parameters: ex - the exemplar for which distribution is computed the distribution throws: Exception - if the distribution can't be computed successfully

getCapabilities
public Capabilities getCapabilities()(Code)
	Returns default capabilities of the classifier. the capabilities of this classifier

getMultiInstanceCapabilities
public Capabilities getMultiInstanceCapabilities()(Code)
	Returns the capabilities of this multi-instance classifier for the relational data. the capabilities of this object See Also: Capabilities

getNumRuns
public int getNumRuns()(Code)
	Returns the number of runs to perform. the number of runs to perform

getOptions
public String[] getOptions()(Code)
	Gets the current settings of the Classifier. an array of strings suitable for passing to setOptions

getTechnicalInformation
public TechnicalInformation getTechnicalInformation()(Code)
	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. the technical information about this class

getUsingCutOff
public boolean getUsingCutOff()(Code)
	Returns whether an empirical cutoff is used true if an empirical cutoff is used

globalInfo
public String globalInfo()(Code)
	Returns a string describing this filter a description of the filter suitable fordisplaying in the explorer/experimenter gui

listOptions
public Enumeration listOptions()(Code)
	Returns an enumeration describing the available options an enumeration of all the available options

main
public static void main(String[] args)(Code)
	Main method for testing. Parameters: args - the options for the classifier

numRunsTipText
public String numRunsTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

setNumRuns
public void setNumRuns(int numRuns)(Code)
	Sets the number of runs to perform. Parameters: numRuns - the number of runs to perform

setOptions

public void setOptions(String[] options) throws Exception(Code)

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

Parameters:
options - the list of options as an array of strings
throws:
Exception - if an option is not supported

setUsingCutOff
public void setUsingCutOff(boolean cutOff)(Code)
	Sets whether to use an empirical cutoff. Parameters: cutOff - whether to use an empirical cutoff

toString
public String toString()(Code)
	Gets a string describing the classifier. a string describing the classifer built.

usingCutOffTipText
public String usingCutOffTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

Fields inherited from weka.classifiers.RandomizableClassifier

protected int m_Seed(Code)(Java Doc)

Methods inherited from weka.classifiers.RandomizableClassifier

public String[] getOptions()(Code)(Java Doc)
public int getSeed()(Code)(Java Doc)
public Enumeration listOptions()(Code)(Java Doc)
public String seedTipText()(Code)(Java Doc)
public void setOptions(String[] options) throws Exception(Code)(Java Doc)
public void setSeed(int seed)(Code)(Java Doc)

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