weka.classifiers.mi
Class SimpleMI

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.SingleClassifierEnhancer
          extended by weka.classifiers.mi.SimpleMI
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler

public class SimpleMI
extends SingleClassifierEnhancer
implements OptionHandler, 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: 1.6 $
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
static 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
 
Constructor Summary
SimpleMI()
           
 
Method Summary
 void buildClassifier(Instances train)
          Builds the classifier
 double[] distributionForInstance(Instance newBag)
          Computes the distribution for a given exemplar
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 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.
 SelectedTag getTransformMethod()
          Get the method used in transformation.
 java.lang.String globalInfo()
          Returns a string describing this filter
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
static void main(java.lang.String[] argv)
          Main method for testing this class.
static double[] minimax(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(SelectedTag newMethod)
          Set the method used in transformation.
 java.lang.String toString()
          Gets a string describing the classifier.
 Instances transform(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, setClassifier
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

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 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 listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class 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)
 -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

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class 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 OptionHandler
Overrides:
getOptions in class 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(SelectedTag newMethod)
Set the method used in transformation.

Parameters:
newMethod - the index of method to use.

getTransformMethod

public SelectedTag getTransformMethod()
Get the method used in transformation.

Returns:
the index of method used.

transform

public Instances transform(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(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 Capabilities getCapabilities()
Returns default capabilities of the classifier.

Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class SingleClassifierEnhancer
Returns:
the capabilities of this classifier
See Also:
Capabilities

getMultiInstanceCapabilities

public Capabilities getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data.

Specified by:
getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandler
Returns:
the capabilities of this object
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances train)
                     throws java.lang.Exception
Builds the classifier

Specified by:
buildClassifier in class 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(Instance newBag)
                                 throws java.lang.Exception
Computes the distribution for a given exemplar

Overrides:
distributionForInstance in class Classifier
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 RevisionHandler
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)