SimpleMI

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## weka.classifiers.mi
Class SimpleMI

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              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)

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