weka.classifiers.lazy
Class IB1

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.lazy.IB1

All Implemented Interfaces:

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

public class IB1

extends Classifier

implements UpdateableClassifier

IB1-type classifier. Uses a simple distance measure to find the training instance closest to the given test instance, and predicts the same class as this training instance. If multiple instances are the same (smallest) distance to the test instance, the first one found is used. For more information, see

Aha, D., and D. Kibler (1991) "Instance-based learning algorithms", Machine Learning, vol.6, pp. 37-66.

Version:

$Revision: 1.12 $

Author:

Stuart Inglis (singlis@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private double[]	m_MaxArray The maximum values for numeric attributes.
private double[]	m_MinArray The minimum values for numeric attributes.
private Instances	m_Train The training instances used for classification.

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

IB1()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance instance) Classifies the given test instance.
private double	distance(Instance first, Instance second) Calculates the distance between two instances
java.lang.String	globalInfo() Returns a string describing classifier
static void	main(java.lang.String[] argv) Main method for testing this class.
private double	norm(double x, int i) Normalizes a given value of a numeric attribute.
java.lang.String	toString() Returns a description of this classifier.
void	updateClassifier(Instance instance) Updates the classifier.
private void	updateMinMax(Instance instance) Updates the minimum and maximum values for all the attributes based on a new instance.

Methods inherited from class weka.classifiers.Classifier

debugTipText, distributionForInstance, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

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

Field Detail

m_Train

private Instances m_Train

The training instances used for classification.

m_MinArray

private double[] m_MinArray

The minimum values for numeric attributes.

m_MaxArray

private double[] m_MaxArray

The maximum values for numeric attributes.

Constructor Detail

IB1

public IB1()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

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

updateClassifier

public void updateClassifier(Instance instance)
                      throws java.lang.Exception

Updates the classifier.

Specified by:

updateClassifier in interface UpdateableClassifier

Parameters:

instance - the instance to be put into the classifier

Throws:

java.lang.Exception - if the instance could not be included successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws java.lang.Exception

Classifies the given test instance.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the predicted class for the instance

Throws:

java.lang.Exception - if the instance can't be classified

toString

public java.lang.String toString()

Returns a description of this classifier.

Returns:

a description of this classifier as a string.

distance

private double distance(Instance first,
                        Instance second)

Calculates the distance between two instances

Parameters:

first - the first instance

second - the second instance

Returns:

the distance between the two given instances

norm

private double norm(double x,
                    int i)

Normalizes a given value of a numeric attribute.

Parameters:

x - the value to be normalized

i - the attribute's index

updateMinMax

private void updateMinMax(Instance instance)

Updates the minimum and maximum values for all the attributes based on a new instance.

Parameters:

instance - the new instance

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).