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## weka.classifiers.mi
Class MIDD

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.mi.MIDD

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

    public class MIDD

extends AbstractClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler

Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998). Learning from ambiguity.
O. Maron, T. Lozano-Perez (1998). A Framework for Multiple Instance Learning. Neural Information Processing Systems. 10.

BibTeX:

     @phdthesis{Maron1998,
        author = {Oded Maron},
        school = {Massachusetts Institute of Technology},
        title = {Learning from ambiguity},
        year = {1998}
     }

     @article{Maron1998,
        author = {O. Maron and T. Lozano-Perez},
        journal = {Neural Information Processing Systems},
        title = {A Framework for Multiple Instance Learning},
        volume = {10},
        year = {1998}
     }

Valid options are:

     -D
      Turn on debugging output.

     -N <num>
      Whether to 0=normalize/1=standardize/2=neither.
      (default 1=standardize)

Version:
  $Revision: 5928 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static int` | `FILTER_NONE` No normalization/standardization
`static int` | `FILTER_NORMALIZE` Normalize training data
`static int` | `FILTER_STANDARDIZE` Standardize training data
`static Tag[]` | `TAGS_FILTER` The filter to apply to the training data

Constructor Summary
`MIDD()`

Method Summary
`void` | `buildClassifier(Instances train)` Builds the classifier
`double[]` | `distributionForInstance(Instance exmp)` Computes the distribution for a given exemplar
`java.lang.String` | `filterTypeTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`SelectedTag` | `getFilterType()` Gets how the training data will be transformed.
`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.
`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.
`void` | `setFilterType(SelectedTag newType)` Sets how the training data will be transformed.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`java.lang.String` | `toString()` Gets a string describing the classifier.

Methods inherited from class weka.classifiers.AbstractClassifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Field Detail

### FILTER_NORMALIZE

    public static final int FILTER_NORMALIZE

  Normalize training data

  See Also:
    Constant Field Values

### FILTER_STANDARDIZE

    public static final int FILTER_STANDARDIZE

  Standardize training data

  See Also:
    Constant Field Values

### FILTER_NONE

    public static final int FILTER_NONE

  No normalization/standardization

  See Also:
    Constant Field Values

### TAGS_FILTER

    public static final Tag[] TAGS_FILTER

  The filter to apply to the training data

Constructor Detail

### MIDD

    public MIDD()

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 `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.

  Valid options are:

       -D
        Turn on debugging output.

       -N <num>
        Whether to 0=normalize/1=standardize/2=neither.
        (default 1=standardize)

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `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 `OptionHandler`
  Overrides:
    `getOptions` in class `AbstractClassifier`
  Returns:
    an array of strings suitable for passing to setOptions

### filterTypeTipText

    public java.lang.String filterTypeTipText()

  Returns the tip text for this property

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

### getFilterType

    public SelectedTag getFilterType()

  Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.

  Returns:
    the filtering mode

### setFilterType

    public void setFilterType(SelectedTag newType)

  Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.

  Parameters:
    `newType` - the new filtering mode

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `Classifier`
  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `AbstractClassifier`
  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 interface `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 exmp)
                                     throws java.lang.Exception

  Computes the distribution for a given exemplar

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `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 `RevisionHandler`
  Overrides:
    `getRevision` in class `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)

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