milk.classifiers
Class DD

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

All Implemented Interfaces:

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

public class DD

extends MIClassifier

implements weka.core.OptionHandler

Re-implement the Diverse Density algorithm, changes the testing procedure.

See Also:

Serialized Form

Field Summary

protected weka.core.Instances	m_Attributes All attribute names
protected int[]	m_Classes Class labels for each bag
protected int	m_ClassIndex The index of the class attribute
protected double[][][]	m_Data MI data
protected boolean	m_Debug Debugging output
protected int	m_IdIndex
protected int	m_NumClasses The number of the class labels
protected double[]	m_Par

Constructor Summary

DD()

Method Summary

void	buildClassifier(Exemplars train) Builds the classifier
double[]	distributionForExemplar(Exemplar exmp) Computes the distribution for a given exemplar
static double	divBy100(double d)
boolean	getDebug() Gets whether debugging output will be printed.
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	setDebug(boolean debug) Sets whether debugging output will be printed.
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_Par

protected double[] m_Par

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_Data

protected double[][][] m_Data

MI data

m_Attributes

protected weka.core.Instances m_Attributes

All attribute names

Constructor Detail

DD

public DD()

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.

-P precision
Set the precision of stopping criteria in Newton method.

-R ridge
Set the ridge parameter for the log-likelihood.

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

buildClassifier

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

Builds the classifier

Specified by:

buildClassifier in class MIClassifier

Parameters:

train - the training data to be used for generating the boosted classifier.

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 distribution

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)

divBy100

public static double divBy100(double d)