weka.classifiers.mi

Class MILR

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.mi.MILR

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler

public class MILR
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler

Uses either standard or collective multi-instance assumption, but within linear regression. For the collective assumption, it offers arithmetic or geometric mean for the posteriors.

Valid options are:

 -D
  Turn on debugging output.
 

 -R <ridge>
  Set the ridge in the log-likelihood.
 

 -A [0|1|2]
  Defines the type of algorithm:
   0. standard MI assumption
   1. collective MI assumption, arithmetic mean for posteriors
   2. collective MI assumption, geometric mean for posteriors
 

Version:

$Revision: 10369 $

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 int	ALGORITHMTYPE_ARITHMETIC collective MI assumption, arithmetic mean for posteriors
static int	ALGORITHMTYPE_DEFAULT standard MI assumption
static int	ALGORITHMTYPE_GEOMETRIC collective MI assumption, geometric mean for posteriors
static weka.core.Tag[]	TAGS_ALGORITHMTYPE the types of algorithms

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
MILR()

Method Summary

Modifier and Type	Method and Description
java.lang.String	algorithmTypeTipText() Returns the tip text for this property
void	buildClassifier(weka.core.Instances train) Builds the classifier
double[]	distributionForInstance(weka.core.Instance exmp) Computes the distribution for a given exemplar
weka.core.SelectedTag	getAlgorithmType() Gets the type of algorithm.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
weka.core.Capabilities	getMultiInstanceCapabilities() Returns the capabilities of this multi-instance classifier for the relational data.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
double	getRidge() Gets the ridge in the log-likelihood.
java.lang.String	globalInfo() Returns the tip text for this property
java.util.Enumeration<weka.core.Option>	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	ridgeTipText() Returns the tip text for this property
void	setAlgorithmType(weka.core.SelectedTag newType) Sets the algorithm type.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRidge(double ridge) Sets the ridge in the log-likelihood.
java.lang.String	toString() Gets a string describing the classifier.

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Field Detail

ALGORITHMTYPE_DEFAULT

public static final int ALGORITHMTYPE_DEFAULT

standard MI assumption

See Also:

Constant Field Values

ALGORITHMTYPE_ARITHMETIC

public static final int ALGORITHMTYPE_ARITHMETIC

collective MI assumption, arithmetic mean for posteriors

See Also:

Constant Field Values

ALGORITHMTYPE_GEOMETRIC

public static final int ALGORITHMTYPE_GEOMETRIC

collective MI assumption, geometric mean for posteriors

See Also:

Constant Field Values

TAGS_ALGORITHMTYPE

public static final weka.core.Tag[] TAGS_ALGORITHMTYPE

the types of algorithms

Constructor Detail

MILR

public MILR()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration<weka.core.Option> listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.AbstractClassifier

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

Overrides:

setOptions in class weka.classifiers.AbstractClassifier

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

Overrides:

getOptions in class weka.classifiers.AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

ridgeTipText

public java.lang.String ridgeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setRidge

public void setRidge(double ridge)

Sets the ridge in the log-likelihood.

Parameters:

ridge - the ridge

getRidge

public double getRidge()

Gets the ridge in the log-likelihood.

Returns:

the ridge

algorithmTypeTipText

public java.lang.String algorithmTypeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getAlgorithmType

public weka.core.SelectedTag getAlgorithmType()

Gets the type of algorithm.

Returns:

the algorithm type

setAlgorithmType

public void setAlgorithmType(weka.core.SelectedTag newType)

Sets the algorithm type.

Parameters:

newType - the new algorithm type

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this classifier

getMultiInstanceCapabilities

public weka.core.Capabilities getMultiInstanceCapabilities()

Returns the capabilities of this multi-instance classifier for the relational data.

Specified by:

getMultiInstanceCapabilities in interface weka.core.MultiInstanceCapabilitiesHandler

Returns:

the capabilities of this object

See Also:

Capabilities

buildClassifier

public void buildClassifier(weka.core.Instances train)
                     throws java.lang.Exception

Builds the classifier

Specified by:

buildClassifier in interface weka.classifiers.Classifier

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

distributionForInstance

public double[] distributionForInstance(weka.core.Instance exmp)
                                 throws java.lang.Exception

Computes the distribution for a given exemplar

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

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.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

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)