weka.classifiers.mi

Class SimpleMI

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.mi.SimpleMI

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 SimpleMI
extends weka.classifiers.SingleClassifierEnhancer
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler

Reduces MI data into mono-instance data.

Valid options are:

 -M [1|2|3]
  The method used in transformation:
  1.arithmatic average; 2.geometric centor;
  3.using minimax combined features of a bag (default: 1)
 
  Method 3:
  Define s to be the vector of the coordinate-wise maxima
  and minima of X, ie., 
  s(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform
  the exemplars into mono-instance which contains attributes
  s(X)
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)
 

 Options specific to classifier weka.classifiers.rules.ZeroR:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Version:

$Revision: 10369 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz), Lin Dong (ld21@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static weka.core.Tag[]	TAGS_TRANSFORMMETHOD the transformation methods
static int	TRANSFORMMETHOD_ARITHMETIC arithmetic average
static int	TRANSFORMMETHOD_GEOMETRIC geometric average
static int	TRANSFORMMETHOD_MINIMAX using minimax combined features of a bag

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
SimpleMI()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances train) Builds the classifier
double[]	distributionForInstance(weka.core.Instance newBag) Computes the distribution for a given exemplar
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.
weka.core.SelectedTag	getTransformMethod() Get the method used in transformation.
java.lang.String	globalInfo() Returns a string describing this filter
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.
static double[]	minimax(weka.core.Instances data, int attIndex) Get the minimal and maximal value of a certain attribute in a certain data
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setTransformMethod(weka.core.SelectedTag newMethod) Set the method used in transformation.
java.lang.String	toString() Gets a string describing the classifier.
weka.core.Instances	transform(weka.core.Instances train) Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together
java.lang.String	transformMethodTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, 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

TRANSFORMMETHOD_ARITHMETIC

public static final int TRANSFORMMETHOD_ARITHMETIC

arithmetic average

See Also:

Constant Field Values

TRANSFORMMETHOD_GEOMETRIC

public static final int TRANSFORMMETHOD_GEOMETRIC

geometric average

See Also:

Constant Field Values

TRANSFORMMETHOD_MINIMAX

public static final int TRANSFORMMETHOD_MINIMAX

using minimax combined features of a bag

See Also:

Constant Field Values

TAGS_TRANSFORMMETHOD

public static final weka.core.Tag[] TAGS_TRANSFORMMETHOD

the transformation methods

Constructor Detail

SimpleMI

public SimpleMI()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this filter

Returns:

a description of the filter 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.SingleClassifierEnhancer

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:

 -M [1|2|3]
  The method used in transformation:
  1.arithmatic average; 2.geometric centor;
  3.using minimax combined features of a bag (default: 1)
 
  Method 3:
  Define s to be the vector of the coordinate-wise maxima
  and minima of X, ie., 
  s(X)=(minx1, ..., minxm, maxx1, ...,maxxm), transform
  the exemplars into mono-instance which contains attributes
  s(X)
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)
 

 Options specific to classifier weka.classifiers.rules.ZeroR:
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.SingleClassifierEnhancer

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.SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

transformMethodTipText

public java.lang.String transformMethodTipText()

Returns the tip text for this property

Returns:

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

setTransformMethod

public void setTransformMethod(weka.core.SelectedTag newMethod)

Set the method used in transformation.

Parameters:

newMethod - the index of method to use.

getTransformMethod

public weka.core.SelectedTag getTransformMethod()

Get the method used in transformation.

Returns:

the index of method used.

transform

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

Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together

Parameters:

train - the multi-instance dataset (with relational attribute)

Returns:

the transformed dataset with each bag contain mono-instance (without relational attribute) so that any classifier not for MI dataset can be applied on it.

Throws:

java.lang.Exception - if the transformation fails

minimax

public static double[] minimax(weka.core.Instances data,
                               int attIndex)

Get the minimal and maximal value of a certain attribute in a certain data

Parameters:

data - the data

attIndex - the index of the attribute

Returns:

the double array containing in entry 0 for min and 1 for max.

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.SingleClassifierEnhancer

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 newBag)
                                 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:

newBag - 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)