Class SimpleMI

Method Details

• globalInfo

  public String globalInfo()

  Returns a string describing this filter

  Returns:

  a description of the filter suitable for displaying in the explorer/experimenter gui

• listOptions

  public 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(String[] options) throws 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:

  Exception - if an option is not supported

• getOptions

  public 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 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 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:

  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 Exception

  Builds the classifier

  Specified by:

  buildClassifier in class Classifier

  Parameters:

  train - the training data to be used for generating the boosted classifier.

  Throws:

  Exception - if the classifier could not be built successfully

• distributionForInstance

  public double[] distributionForInstance(Instance newBag) throws 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:

  Exception - if the distribution can't be computed successfully

• toString

  public String toString()

  Gets a string describing the classifier.

  Overrides:

  toString in class Object

  Returns:

  a string describing the classifer built.

• getRevision

  public 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(String[] argv)

  Main method for testing this class.

  Parameters:

  argv - should contain the command line arguments to the scheme (see Evaluation)