weka.classifiers.functions
Class LeastMedSq

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.functions.LeastMedSq

All Implemented Interfaces:

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

public class LeastMedSq

extends Classifier

implements OptionHandler

Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions. The basis of the algorithm is Robust regression and outlier detection Peter J. Rousseeuw, Annick M. Leroy. c1987

Version:

$Revision: 1.9 $

Author:

Tony Voyle (tv6@waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private double	m_bestMedian
private LinearRegression	m_bestRegression
private LinearRegression	m_currentRegression
private Instances	m_Data
private boolean	m_debug
private boolean	m_israndom
private LinearRegression	m_ls
private ReplaceMissingValues	m_MissingFilter
private java.util.Random	m_random
private long	m_randomseed
private double[]	m_Residuals
private Instances	m_RLSData
private int	m_samples
private int	m_samplesize
private double	m_scalefactor
private RemoveRange	m_SplitFilter
private double	m_SSR
private Instances	m_SubSample
private NominalToBinary	m_TransformFilter
private double[]	m_weight

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

LeastMedSq()

Method Summary

void	buildClassifier(Instances data) Build lms regression
private void	buildRLSRegression() Builds a new LinearRegression without the 'bad' data found by buildWeight
private void	buildWeight() Builds a weight function removing instances with an abnormally high scaled residual
double	classifyInstance(Instance instance) Classify a given instance using the best generated LinearRegression Classifier.
private void	cleanUpData(Instances data) Cleans up data
static int	combinations(int n, int r) Produces the combination nCr
private void	findBestRegression() Finds the best regression generated from m_samples random samples from the training data
private void	findResiduals() Finds residuals (squared) for the current regression.
private void	genRegression() Generates a LinearRegression classifier from the current m_SubSample
boolean	getDebug() Returns whether or not debugging output shouild be printed
private void	getMedian() finds the median residual squared for the current regression
java.lang.String[]	getOptions() Gets the current option settings for the OptionHandler.
long	getRandomSeed() get the seed for the random number generator
private void	getSamples() Gets the number of samples to use.
int	getSampleSize() gets number of samples
java.lang.String	globalInfo() Returns a string describing this classifier
java.util.Enumeration	listOptions() Returns an enumeration of all the available options..
static void	main(java.lang.String[] argv) generate a Linear regression predictor for testing
private static int	partition(double[] a, int l, int r) Partitions an array of numbers such that all numbers less than that at index r, between indexes l and r will have a smaller index and all numbers greater than will have a larger index
java.lang.String	randomSeedTipText() Returns the tip text for this property
java.lang.String	sampleSizeTipText() Returns the tip text for this property
private static void	select(double[] a, int l, int r, int k) Finds the kth number in an array
private java.lang.String	selectIndices(Instances data) Returns a string suitable for passing to RemoveRange consisting of m_samplesize indices.
private void	selectSubSample(Instances data) Produces a random sample from m_Data in m_SubSample
void	setDebug(boolean debug) sets whether or not debugging output shouild be printed
void	setOptions(java.lang.String[] options) Sets the OptionHandler's options using the given list.
private void	setRandom() Set up the random number generator
void	setRandomSeed(long randomseed) Set the seed for the random number generator
void	setSampleSize(int samplesize) sets number of samples
java.lang.String	toString() Returns a string representing the best LinearRegression classifier found.

Methods inherited from class weka.classifiers.Classifier

debugTipText, distributionForInstance, forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_Residuals

private double[] m_Residuals

m_weight

private double[] m_weight

m_SSR

private double m_SSR

m_scalefactor

private double m_scalefactor

m_bestMedian

private double m_bestMedian

m_currentRegression

private LinearRegression m_currentRegression

m_bestRegression

private LinearRegression m_bestRegression

m_ls

private LinearRegression m_ls

m_Data

private Instances m_Data

m_RLSData

private Instances m_RLSData

m_SubSample

private Instances m_SubSample

m_MissingFilter

private ReplaceMissingValues m_MissingFilter

m_TransformFilter

private NominalToBinary m_TransformFilter

m_SplitFilter

private RemoveRange m_SplitFilter

m_samplesize

private int m_samplesize

m_samples

private int m_samples

m_israndom

private boolean m_israndom

m_debug

private boolean m_debug

m_random

private java.util.Random m_random

m_randomseed

private long m_randomseed

Constructor Detail

LeastMedSq

public LeastMedSq()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

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

buildClassifier

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

Build lms regression

Specified by:

buildClassifier in class Classifier

Parameters:

data - training data

Throws:

java.lang.Exception - if an error occurs

classifyInstance

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

Classify a given instance using the best generated LinearRegression Classifier.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - instance to be classified

Returns:

class value

Throws:

java.lang.Exception - if an error occurs

cleanUpData

private void cleanUpData(Instances data)
                  throws java.lang.Exception

Cleans up data

Parameters:

data - data to be cleaned up

Throws:

java.lang.Exception - if an error occurs

getSamples

private void getSamples()
                 throws java.lang.Exception

Gets the number of samples to use.

Throws:

java.lang.Exception

setRandom

private void setRandom()

Set up the random number generator

findBestRegression

private void findBestRegression()
                         throws java.lang.Exception

Finds the best regression generated from m_samples random samples from the training data

Throws:

java.lang.Exception - if an error occurs

genRegression

private void genRegression()
                    throws java.lang.Exception

Generates a LinearRegression classifier from the current m_SubSample

Throws:

java.lang.Exception - if an error occurs

findResiduals

private void findResiduals()
                    throws java.lang.Exception

Finds residuals (squared) for the current regression.

Throws:

java.lang.Exception - if an error occurs

getMedian

private void getMedian()
                throws java.lang.Exception

finds the median residual squared for the current regression

Throws:

java.lang.Exception - if an error occurs

toString

public java.lang.String toString()

Returns a string representing the best LinearRegression classifier found.

Returns:

String representing the regression

buildWeight

private void buildWeight()
                  throws java.lang.Exception

Builds a weight function removing instances with an abnormally high scaled residual

Throws:

java.lang.Exception

buildRLSRegression

private void buildRLSRegression()
                         throws java.lang.Exception

Builds a new LinearRegression without the 'bad' data found by buildWeight

Throws:

java.lang.Exception

select

private static void select(double[] a,
                           int l,
                           int r,
                           int k)

Finds the kth number in an array

Parameters:

a - an array of numbers

l - left pointer

r - right pointer

k - position of number to be found

partition

private static int partition(double[] a,
                             int l,
                             int r)

Partitions an array of numbers such that all numbers less than that at index r, between indexes l and r will have a smaller index and all numbers greater than will have a larger index

Parameters:

a - an array of numbers

l - left pointer

r - right pointer

Returns:

final index of number originally at r

selectSubSample

private void selectSubSample(Instances data)
                      throws java.lang.Exception

Produces a random sample from m_Data in m_SubSample

Parameters:

data - data from which to take sample

Throws:

java.lang.Exception - if an error occurs

selectIndices

private java.lang.String selectIndices(Instances data)

Returns a string suitable for passing to RemoveRange consisting of m_samplesize indices.

Parameters:

data - dataset from which to take indicese

Returns:

string of indices suitable for passing to RemoveRange

sampleSizeTipText

public java.lang.String sampleSizeTipText()

Returns the tip text for this property

Returns:

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

setSampleSize

public void setSampleSize(int samplesize)

sets number of samples

Parameters:

samplesize - value

getSampleSize

public int getSampleSize()

gets number of samples

Returns:

value

randomSeedTipText

public java.lang.String randomSeedTipText()

Returns the tip text for this property

Returns:

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

setRandomSeed

public void setRandomSeed(long randomseed)

Set the seed for the random number generator

Parameters:

randomseed - the seed

getRandomSeed

public long getRandomSeed()

get the seed for the random number generator

Returns:

the seed value

setDebug

public void setDebug(boolean debug)

sets whether or not debugging output shouild be printed

Overrides:

setDebug in class Classifier

Parameters:

debug - true if debugging output selected

getDebug

public boolean getDebug()

Returns whether or not debugging output shouild be printed

Overrides:

getDebug in class Classifier

Returns:

true if debuging output selected

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration of all the available options..

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

Returns:

an enumeration of all available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Sets the OptionHandler's options using the given list. All options will be set (or reset) during this call (i.e. incremental setting of options is not possible).

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 option settings for the OptionHandler.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class Classifier

Returns:

the list of current option settings as an array of strings

combinations

public static int combinations(int n,
                               int r)
                        throws java.lang.Exception

Produces the combination nCr

Parameters:

n -

Returns:

the combination

Throws:

java.lang.Exception - if r is greater than n

main

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

generate a Linear regression predictor for testing

Parameters:

argv - options