milk.classifiers
Class SimpleMI

java.lang.Object
  |
  +--milk.classifiers.MIClassifier
        |
        +--milk.classifiers.MIWrapper
              |
              +--milk.classifiers.SimpleMI

All Implemented Interfaces:

java.lang.Cloneable, MITransform, weka.core.OptionHandler, java.io.Serializable

public class SimpleMI

extends MIWrapper

implements weka.core.OptionHandler, MITransform

Avg of feature data and reduce MI data into mono-instance Valid options are:

-M method index
Set which method to use in transformation: 1.arithmatic average; 2.geometric centor for each bag. (default: 1)

See Also:

Serialized Form

Field Summary

protected Exemplars	m_Exemplars
protected int	m_TransformMethod

Fields inherited from class milk.classifiers.MIWrapper

m_Attributes, m_Classifier, m_ClassIndex, m_Debug, m_IdIndex, m_Method, m_NumClasses

Constructor Summary

SimpleMI()

Method Summary

void	buildClassifier(Exemplars train) Builds the classifier
double	classifyExemplar(Exemplar exmp) Computes the distribution for a given exemplar
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
int	getTransformMethod() Get the method used in transformation.
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(weka.core.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(int newMethod) Set the method used in transformation.
java.lang.String	toString() Gets a string describing the classifier.
weka.core.Instances	transform(Exemplars train) transform the given exemplars to an Instances object.

Methods inherited from class milk.classifiers.MIWrapper

distributionForExemplar, getClassifier, getDebug, getMethod, setClassifier, setDebug, setMethod

Methods inherited from class milk.classifiers.MIClassifier

forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_TransformMethod

protected int m_TransformMethod

m_Exemplars

protected Exemplars m_Exemplars

Constructor Detail

SimpleMI

public SimpleMI()

Method Detail

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class MIWrapper

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 method_index
Set which method to use in transformation: 1.arithmatic average; 2.geometric centor of a bag (default: 1)

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class MIWrapper

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 weka.core.OptionHandler

Overrides:

getOptions in class MIWrapper

Returns:

an array of strings suitable for passing to setOptions

setTransformMethod

public void setTransformMethod(int newMethod)

Set the method used in transformation.

Parameters:

newMethod - the index of method to use.

getTransformMethod

public int getTransformMethod()

Get the method used in transformation.

Returns:

the index of method used.

transform

public weka.core.Instances transform(Exemplars train)
                              throws java.lang.Exception

Description copied from interface: MITransform

transform the given exemplars to an Instances object.

Specified by:

transform in interface MITransform

Overrides:

transform in class MIWrapper

Returns:

the transformed instances

Throws:

java.lang.Exception - if transformation is not successful

minimax

public static double[] minimax(weka.core.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.

buildClassifier

public void buildClassifier(Exemplars train)
                     throws java.lang.Exception

Builds the classifier

Overrides:

buildClassifier in class MIWrapper

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

classifyExemplar

public double classifyExemplar(Exemplar exmp)
                        throws java.lang.Exception

Computes the distribution for a given exemplar

Overrides:

classifyExemplar in class MIClassifier

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 MIWrapper

Returns:

a string describing the classifer built.

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)