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

Constructor Summary

LeastMedSq()

Method Summary

void	buildClassifier(Instances data) Build lms regression
double	classifyInstance(Instance instance) Classify a given instance using the best generated LinearRegression Classifier.
static int	combinations(int n, int r) Produces the combination nCr
boolean	getDebug() Returns whether or not debugging output shouild be printed
java.lang.String[]	getOptions() Gets the current option settings for the OptionHandler.
long	getRandomSeed() get the seed for the random number generator
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
java.lang.String	randomSeedTipText() Returns the tip text for this property
java.lang.String	sampleSizeTipText() Returns the tip text for this property
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.
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

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

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

toString

public java.lang.String toString()

Returns a string representing the best LinearRegression classifier found.

Overrides:

toString in class java.lang.Object

Returns:

String representing the regression

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