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Class weka.classifiers.LWR 

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.LWR
public class LWR
extends Classifier
implements OptionHandler, UpdateableClassifier, WeightedInstancesHandler
Locally-weighted regression. Uses an instance-based algorithm to assign instance weights which are then used by a linear regression model. For more information, see

Atkeson, C., A. Moore, and S. Schaal (1996) Locally weighted learning download postscript.

Valid options are:

-D
Produce debugging output.

-K num
Set the number of neighbours used for setting kernel bandwidth. (default all)

-W num
Set the weighting kernel shape to use. 1 = Inverse, 2 = Gaussian. (default 0 = Linear)

Version:
$Revision: 1.6 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz)

Constructor Index

o LWR()

Method Index

o buildClassifier(Instances)
Generates the classifier.
o classifyInstance(Instance)
Predicts the class value for the given test instance.
o getDebug()
SGts whether debugging output should be produced
o getKNN()
Gets the number of neighbours used for kernel bandwidth setting.
o getOptions()
Gets the current settings of the classifier.
o getWeightingKernel()
Gets the kernel weighting method to use.
o listOptions()
Returns an enumeration describing the available options
o main(String[])
Main method for testing this class.
o setDebug(boolean)
Sets whether debugging output should be produced
o setKNN(int)
Sets the number of neighbours used for kernel bandwidth setting.
o setOptions(String[])
Parses a given list of options.
o setWeightingKernel(int)
Sets the kernel weighting method to use.
o toString()
Returns a description of this classifier.
o updateClassifier(Instance)
Adds the supplied instance to the training set

Constructors

o LWR 
 public LWR()

Methods

o listOptions 
 public Enumeration listOptions()
Returns an enumeration describing the available options

Returns:
an enumeration of all the available options
o setOptions 
 public void setOptions(String options[]) throws Exception
Parses a given list of options. Valid options are:

-D
Produce debugging output.

-K num
Set the number of neighbours used for setting kernel bandwidth. (default all)

-W num
Set the weighting kernel shape to use. 1 = Inverse, 2 = Gaussian. (default 0 = Linear)

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
o getOptions 
 public String[] getOptions()
Gets the current settings of the classifier.

Returns:
an array of strings suitable for passing to setOptions
o setDebug 
 public void setDebug(boolean debug)
Sets whether debugging output should be produced

Parameters:
debug - true if debugging output should be printed
o getDebug 
 public boolean getDebug()
SGts whether debugging output should be produced

Returns:
true if debugging output should be printed
o setKNN 
 public void setKNN(int knn)
Sets the number of neighbours used for kernel bandwidth setting. The bandwidth is taken as the distance to the kth neighbour.

Parameters:
knn - the number of neighbours included inside the kernel bandwidth, or 0 to specify using all neighbors.
o getKNN 
 public int getKNN()
Gets the number of neighbours used for kernel bandwidth setting. The bandwidth is taken as the distance to the kth neighbour.

Returns:
the number of neighbours included inside the kernel bandwidth, or 0 for all neighbours
o setWeightingKernel 
 public void setWeightingKernel(int kernel)
Sets the kernel weighting method to use. Must be one of LINEAR, INVERSE, or GAUSS, other values are ignored.

Parameters:
kernel - the new kernel method to use. Must be one of LINEAR, INVERSE, or GAUSS
o getWeightingKernel 
 public int getWeightingKernel()
Gets the kernel weighting method to use.

Returns:
the new kernel method to use. Will be one of LINEAR, INVERSE, or GAUSS
o buildClassifier 
 public void buildClassifier(Instances instances) throws Exception
Generates the classifier.

Parameters:
instances - set of instances serving as training data
Throws: Exception
if the classifier has not been generated successfully
Overrides:
buildClassifier in class Classifier
o updateClassifier 
 public void updateClassifier(Instance instance) throws Exception
Adds the supplied instance to the training set

Parameters:
instance - the instance to add
Throws: Exception
if instance could not be incorporated successfully
o classifyInstance 
 public double classifyInstance(Instance instance) throws Exception
Predicts the class value for the given test instance.

Parameters:
instance - the instance to be classified
Returns:
the predicted class value
Throws: Exception
if an error occurred during the prediction
Overrides:
classifyInstance in class Classifier
o toString 
 public String toString()
Returns a description of this classifier.

Returns:
a description of this classifier as a string.
Overrides:
toString in class Object
o main 
 public static void main(String argv[])
Main method for testing this class.

Parameters:
argv - the options 
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