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## weka.classifiers.mi
Class MIWrapper

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.mi.MIWrapper

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class MIWrapper

extends SingleClassifierEnhancer
implements MultiInstanceCapabilitiesHandler, OptionHandler, TechnicalInformationHandler

A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.

BibTeX:

     @techreport{Frank2003,
        address = {Department of Computer Science, University of Waikato, Hamilton, NZ},
        author = {E. T. Frank and X. Xu},
        institution = {University of Waikato},
        month = {06},
        title = {Applying propositional learning algorithms to multi-instance data},
        year = {2003}
     }

Valid options are:

     -P [1|2|3]
      The method used in testing:
      1.arithmetic average
      2.geometric average
      3.max probability of positive bag.
      (default: 1)

     -A [0|1|2|3]
      The type of weight setting for each single-instance:
      0.keep the weight to be the same as the original value;
      1.weight = 1.0
      2.weight = 1.0/Total number of single-instance in the
       corresponding bag
      3. weight = Total number of single-instance / (Total
       number of bags * Total number of single-instance
       in the corresponding bag).
      (default: 3)

     -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: 1.5 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static Tag[]` | `TAGS_TESTMETHOD` the test methods
`static int` | `TESTMETHOD_ARITHMETIC` arithmetic average
`static int` | `TESTMETHOD_GEOMETRIC` geometric average
`static int` | `TESTMETHOD_MAXPROB` max probability of positive bag

Constructor Summary
`MIWrapper()`

Method Summary
`void` | `buildClassifier(Instances data)` Builds the classifier
`double[]` | `distributionForInstance(Instance exmp)` Computes the distribution for a given exemplar
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`SelectedTag` | `getMethod()` Get the method used in testing.
`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.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`SelectedTag` | `getWeightMethod()` Returns the current weighting method for instances.
`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.
`java.lang.String` | `methodTipText()` Returns the tip text for this property
`void` | `setMethod(SelectedTag method)` Set the method used in testing.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setWeightMethod(SelectedTag method)` The new method for weighting the instances.
`java.lang.String` | `toString()` Gets a string describing the classifier.
`java.lang.String` | `weightMethodTipText()` 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

### TESTMETHOD_ARITHMETIC

    public static final int TESTMETHOD_ARITHMETIC

  arithmetic average

  See Also:
    Constant Field Values

### TESTMETHOD_GEOMETRIC

    public static final int TESTMETHOD_GEOMETRIC

  geometric average

  See Also:
    Constant Field Values

### TESTMETHOD_MAXPROB

    public static final int TESTMETHOD_MAXPROB

  max probability of positive bag

  See Also:
    Constant Field Values

### TAGS_TESTMETHOD

    public static final Tag[] TAGS_TESTMETHOD

  the test methods

Constructor Detail

### MIWrapper

    public MIWrapper()

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

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### 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:

       -P [1|2|3]
        The method used in testing:
        1.arithmetic average
        2.geometric average
        3.max probability of positive bag.
        (default: 1)

       -A [0|1|2|3]
        The type of weight setting for each single-instance:
        0.keep the weight to be the same as the original value;
        1.weight = 1.0
        2.weight = 1.0/Total number of single-instance in the
         corresponding bag
        3. weight = Total number of single-instance / (Total
         number of bags * Total number of single-instance
         in the corresponding bag).
        (default: 3)

       -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

### weightMethodTipText

    public java.lang.String weightMethodTipText()

  Returns the tip text for this property

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

### setWeightMethod

    public void setWeightMethod(SelectedTag method)

  The new method for weighting the instances.

  Parameters:
    `method` - the new method

### getWeightMethod

    public SelectedTag getWeightMethod()

  Returns the current weighting method for instances.

  Returns:
    the current weighting method

### methodTipText

    public java.lang.String methodTipText()

  Returns the tip text for this property

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

### setMethod

    public void setMethod(SelectedTag method)

  Set the method used in testing.

  Parameters:
    `method` - the index of method to use.

### getMethod

    public SelectedTag getMethod()

  Get the method used in testing.

  Returns:
    the index of method used in testing.

### 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 data)
                         throws java.lang.Exception

  Builds the classifier

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - 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 exmp)
                                     throws java.lang.Exception

  Computes the distribution for a given exemplar

  Overrides:
    `distributionForInstance` in class `Classifier`
  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 `RevisionHandler`
  Overrides:
    `getRevision` in class `Classifier`
  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)

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