milk.classifiers
Class TLDSimple

java.lang.Object
  |
  +--milk.classifiers.MIClassifier
        |
        +--milk.classifiers.TLDSimple

All Implemented Interfaces:

java.lang.Cloneable, weka.core.OptionHandler, java.io.Serializable

public class TLDSimple

extends MIClassifier

implements weka.core.OptionHandler

0657.594 Thesis A simpler version of TLD, \mu random but \sigma^2 fixed and estimated via data

See Also:

Serialized Form

Field Summary

protected double[]	m_Class The class label of each exemplar
protected double	m_Cutoff
protected int	m_Dimension The dimension of each exemplar, i.e.
protected double[][]	m_MeanN The mean for each attribute of each negative exemplar
protected double[][]	m_MeanP The mean for each attribute of each positive exemplar
protected int	m_NumClasses The number of class labels in the data
protected double[]	m_ParamsN The parameters to be estimated for each negative exemplar
protected double[]	m_ParamsP The parameters to be estimated for each positive exemplar
protected int	m_Run
protected long	m_Seed
protected double[]	m_SgmSqN Estimated sigma^2 in negative bags
protected double[]	m_SgmSqP Estimated sigma^2 in positive bags
protected double[][]	m_SumN The effective sum of weights of each negative exemplar in each dimension
protected double[][]	m_SumP The effective sum of weights of each positive exemplar in each dimension
protected boolean	m_UseEmpiricalCutOff
static double	ZERO The very small number representing zero

Constructor Summary

TLDSimple()

Method Summary

void	buildClassifier(Exemplars exs) Generates a classifier.
double	classifyExemplar(Exemplar e) Classifies the given test instance.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options Valid options are:
static void	main(java.lang.String[] args) Main method for testing.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Gets a string describing the classifier.

Methods inherited from class milk.classifiers.MIClassifier

distributionForExemplar, forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_MeanP

protected double[][] m_MeanP

The mean for each attribute of each positive exemplar

m_MeanN

protected double[][] m_MeanN

The mean for each attribute of each negative exemplar

m_SumP

protected double[][] m_SumP

The effective sum of weights of each positive exemplar in each dimension

m_SumN

protected double[][] m_SumN

The effective sum of weights of each negative exemplar in each dimension

m_SgmSqP

protected double[] m_SgmSqP

Estimated sigma^2 in positive bags

m_SgmSqN

protected double[] m_SgmSqN

Estimated sigma^2 in negative bags

m_ParamsP

protected double[] m_ParamsP

The parameters to be estimated for each positive exemplar

m_ParamsN

protected double[] m_ParamsN

The parameters to be estimated for each negative exemplar

m_Dimension

protected int m_Dimension

The dimension of each exemplar, i.e. (numAttributes-2)

m_Class

protected double[] m_Class

The class label of each exemplar

m_NumClasses

protected int m_NumClasses

The number of class labels in the data

ZERO

public static double ZERO

The very small number representing zero

m_Run

protected int m_Run

m_Seed

protected long m_Seed

m_Cutoff

protected double m_Cutoff

m_UseEmpiricalCutOff

protected boolean m_UseEmpiricalCutOff

Constructor Detail

TLDSimple

public TLDSimple()

Method Detail

buildClassifier

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

Description copied from class: MIClassifier

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 MIClassifier

Parameters:

exs - the training exemplars

Throws:

if - the model cannot be built properly

java.lang.Exception - if the classifier has not been generated successfully

classifyExemplar

public double classifyExemplar(Exemplar e)
                        throws java.lang.Exception

Description copied from class: MIClassifier

Classifies the given test instance. The instance has to belong to a dataset when it's being classified. Classifiers either have to implement this or distributionForExemplar().

Overrides:

classifyExemplar in class MIClassifier

Parameters:

e - the instance to be classified

Returns:

the classification

Throws:

java.lang.Exception - if the exemplar could not be classified successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options Valid options are:

-C Set whether or not use empirical log-odds cut-off instead of 0 (default: Not use) -R Set the number of multiple runs needed for searching the MLE. (default: 1)

Specified by:

listOptions in interface weka.core.OptionHandler

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.

Specified by:

setOptions in interface weka.core.OptionHandler

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 weka.core.OptionHandler

Returns:

an array of strings suitable for passing to setOptions

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.

main

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

Main method for testing.

Parameters:

args - the options for the classifier