milk.classifiers
Class MIBoost

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

All Implemented Interfaces:

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

public class MIBoost

extends MIClassifier

implements weka.core.OptionHandler

MI AdaBoost method, consider the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function. Exact derivation from Hastie et al. paper

See Also:

Serialized Form

Field Summary

protected weka.core.Instances	m_Attributes All attribute names
protected double[]	m_Beta Voting weights of models
protected int[]	m_Classes Class labels for each bag
protected weka.classifiers.Classifier	m_Classifier The model base classifier to use
protected int	m_ClassIndex The index of the class attribute
protected boolean	m_Debug Debugging output
protected int	m_DiscretizeBin
protected weka.filters.unsupervised.attribute.Discretize	m_Filter
protected int	m_IdIndex
protected int	m_MaxIterations
protected weka.classifiers.Classifier[]	m_Models
protected int	m_NumClasses The number of the class labels

Constructor Summary

MIBoost()

Method Summary

void	buildClassifier(Exemplars exps) Builds the classifier
double[]	distributionForExemplar(Exemplar exmp) Computes the distribution for a given exemplar
weka.classifiers.Classifier	getClassifier() Get the classifier used as the classifier
boolean	getDebug() Gets whether debugging output will be printed.
int	getDiscretizeBin() Get the number of bins in discretization
int	getMaxIterations() Get the maximum number of boost iterations
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setClassifier(weka.classifiers.Classifier newClassifier) Set the classifier for boosting.
void	setDebug(boolean debug) Sets whether debugging output will be printed.
void	setDiscretizeBin(int bin) Set the number of bins in discretization
void	setMaxIterations(int maxIterations) Set the maximum number of boost iterations
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

classifyExemplar, forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_ClassIndex

protected int m_ClassIndex

The index of the class attribute

m_Models

protected weka.classifiers.Classifier[] m_Models

m_NumClasses

protected int m_NumClasses

The number of the class labels

m_IdIndex

protected int m_IdIndex

m_Debug

protected boolean m_Debug

Debugging output

m_Classes

protected int[] m_Classes

Class labels for each bag

m_Attributes

protected weka.core.Instances m_Attributes

All attribute names

m_Classifier

protected weka.classifiers.Classifier m_Classifier

The model base classifier to use

m_Beta

protected double[] m_Beta

Voting weights of models

m_MaxIterations

protected int m_MaxIterations

m_DiscretizeBin

protected int m_DiscretizeBin

m_Filter

protected weka.filters.unsupervised.attribute.Discretize m_Filter

Constructor Detail

MIBoost

public MIBoost()

Method Detail

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

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. Valid options are:

-D
Turn on debugging output.

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

setDebug

public void setDebug(boolean debug)

Sets whether debugging output will be printed.

Parameters:

debug - true if debugging output should be printed

getDebug

public boolean getDebug()

Gets whether debugging output will be printed.

Returns:

true if debugging output will be printed

setClassifier

public void setClassifier(weka.classifiers.Classifier newClassifier)

Set the classifier for boosting.

Parameters:

newClassifier - the Classifier to use.

getClassifier

public weka.classifiers.Classifier getClassifier()

Get the classifier used as the classifier

Returns:

the classifier used as the base classifier

setMaxIterations

public void setMaxIterations(int maxIterations)

Set the maximum number of boost iterations

Parameters:

maxIterations - the maximum number of boost iterations

getMaxIterations

public int getMaxIterations()

Get the maximum number of boost iterations

Returns:

the maximum number of boost iterations

setDiscretizeBin

public void setDiscretizeBin(int bin)

Set the number of bins in discretization

Parameters:

bin - the number of bins in discretization

getDiscretizeBin

public int getDiscretizeBin()

Get the number of bins in discretization

Returns:

the number of bins in discretization

buildClassifier

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

Builds the classifier

Specified by:

buildClassifier in class MIClassifier

Parameters:

exps - set of exemplars serving as training data

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForExemplar

public double[] distributionForExemplar(Exemplar exmp)
                                 throws java.lang.Exception

Computes the distribution for a given exemplar

Overrides:

distributionForExemplar in class MIClassifier

Parameters:

exmp - the exemplar for which distribution is computed

Returns:

the classification

Throws:

java.lang.Exception - if the distribution can't be computed successfully

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[] argv)

Main method for testing this class.

Parameters:

argv - should contain the command line arguments to the scheme (see Evaluation)