LeastMedSq

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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler
```
public class LeastMedSq
extends Classifier
implements OptionHandler, TechnicalInformationHandler
```
Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model.

The basis of the algorithm is

Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection. .
BibTeX:
```
 @book{Rousseeuw1987,
    author = {Peter J. Rousseeuw and Annick M. Leroy},
    title = {Robust regression and outlier detection},
    year = {1987}
 }
 
```
Valid options are:
```
 -S <sample size>
  Set sample size
  (default: 4)
 
```
```
 -G <seed>
  Set the seed used to generate samples
  (default: 0)
 
```
```
 -D
  Produce debugging output
  (default no debugging output)
 
```
Version:

$Revision: 5523 $

Author:

Tony Voyle (tv6@waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

LeastMedSq()

Constructors
Constructor and Description
`LeastMedSq()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`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
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSampleSize()` gets number of samples
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`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, makeCopy

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
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - 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). Valid options are:
```
 -S <sample size>
  Set sample size
  (default: 4)
 
```
```
 -G <seed>
  Set the seed used to generate samples
  (default: 0)
 
```
```
 -D
  Produce debugging output
  (default no debugging output)
 
```
    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 -
    
    r -
    
    Returns:
    
    the combination
    
    Throws:
    
    java.lang.Exception - if r is greater than n
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    generate a Linear regression predictor for testing
    
    Parameters:
    
    argv - options

Class LeastMedSq

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

LeastMedSq

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

classifyInstance

toString

sampleSizeTipText

setSampleSize

getSampleSize

randomSeedTipText

setRandomSeed

getRandomSeed

setDebug

getDebug

listOptions

setOptions

getOptions

combinations

getRevision

main