probabilityMachine
Class VPMSimpleTypeDistMetaLearner

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--probabilityMachine.VennProbabilityClassifier
              |
              +--probabilityMachine.VPMSimpleTypeDistMetaLearner

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class VPMSimpleTypeDistMetaLearner

extends VennProbabilityClassifier

implements OptionHandler

The first implementation of a VPM Distribution Classifier Meta Learner. Uses Gaussians to model types!

Version:

$Revision: 1.0 $

Author:

David Lindsay (davidl@cs.rhul.ac.uk)

See Also:

Serialized Form

Constructor Summary

VPMSimpleTypeDistMetaLearner()
The amazing VPMBartsRMI classifier

VPMSimpleTypeDistMetaLearner(int vennt)
The amazing VPMSimpleTypeDistMetaLearner classifier, used to show how to adapt a classifer to the VPM meta learning framework!

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
java.lang.String[]	calculateTypesForExamples(Instances train, Instances[] classPreds, int[][] predLabelArray) This function will calculate the types for each example using the Gaussians calculated above.
java.lang.String	createTypeDetailString(int[] closestGaussianVennType, int[] typeDetails, int classAssigned) Output type membership details
int	getNumberVennTypes() Gets the number of Venn probability types used!
boolean	getOptimizeBins() Returns whether bin optimisation is switched on.
java.lang.String[]	getOptions() Gets the current settings of VPM.
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	printArray(int[] array) Debugging function
java.lang.String	printArray(java.lang.String[] array) Debugging function
void	setNumberVennTypes(int vennt) Sets the number of Venn probability types used!
void	setOptimizeBins(boolean optBin) Switches bin optimisation on.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns a description of this classifier.
Matrix	vennProbsForInstance(Instance instance) Returns the Venn probability matrix for a given test instance.

Methods inherited from class probabilityMachine.VennProbabilityClassifier

classifyInstance, computeRowOfVPMMatrix, distributionForInstance, printArray, returnDistribution, returnUpperAndLowerProbability

Methods inherited from class weka.classifiers.Classifier

debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

VPMSimpleTypeDistMetaLearner

public VPMSimpleTypeDistMetaLearner(int vennt)

The amazing VPMSimpleTypeDistMetaLearner classifier, used to show how to adapt a classifer to the VPM meta learning framework!

The options for this wonderful algorithm are as follows:
-y num Max number of Venn types
Sets the number of types used for the VPM

-W classifier name
Specify the name of the distribution classifier name that you wish to specify -u Optimise bins
Optimise number of bins using leave one out entropy.

Parameters:

vennt - the number of Venn probability types used

VPMSimpleTypeDistMetaLearner

public VPMSimpleTypeDistMetaLearner()

The amazing VPMBartsRMI classifier

Method Detail

setNumberVennTypes

public void setNumberVennTypes(int vennt)

Sets the number of Venn probability types used!

getNumberVennTypes

public int getNumberVennTypes()

Gets the number of Venn probability types used!

setOptimizeBins

public void setOptimizeBins(boolean optBin)

Switches bin optimisation on.

getOptimizeBins

public boolean getOptimizeBins()

Returns whether bin optimisation is switched on.

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

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:

-y num Venn types
Sets the number of types used for the VPM

-W classifier name
Specify the name of the distribution classifier name that you wish to specify -u Optimise bins
Optimise number of bins using leave one out entropy.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

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 VPM.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions()

calculateTypesForExamples

public java.lang.String[] calculateTypesForExamples(Instances train,
                                                    Instances[] classPreds,
                                                    int[][] predLabelArray)
                                             throws java.lang.Exception

This function will calculate the types for each example using the Gaussians calculated above.

Parameters:

train - this is the training data

predLabelArray - this is a horrible hack to allow discretization of predictions of each class label

Returns:

the type definitions for each example

java.lang.Exception

vennProbsForInstance

public Matrix vennProbsForInstance(Instance instance)
                            throws java.lang.Exception

Returns the Venn probability matrix for a given test instance.

Specified by:

vennProbsForInstance in class VennProbabilityClassifier

Parameters:

instance - the instance to be classified

Returns:

the Venn probability matrix

Throws:

java.lang.Exception - no training instances

printArray

public java.lang.String printArray(java.lang.String[] array)

Debugging function

printArray

public java.lang.String printArray(int[] array)

Debugging function

Overrides:

printArray in class VennProbabilityClassifier

createTypeDetailString

public java.lang.String createTypeDetailString(int[] closestGaussianVennType,
                                               int[] typeDetails,
                                               int classAssigned)

Output type membership details

toString

public java.lang.String toString()

Returns a description of this classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of this classifier as a string.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain command line arguments for evaluation (see Evaluation).