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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: 1.9 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz)
LWR()
-
buildClassifier(Instances)
- Generates the classifier.
classifyInstance(Instance)
- Predicts the class value for the given test instance.
getDebug()
- SGts whether debugging output should be produced
getKNN()
- Gets the number of neighbours used for kernel bandwidth setting.
getOptions()
- Gets the current settings of the classifier.
getWeightingKernel()
- Gets the kernel weighting method to use.
listOptions()
- Returns an enumeration describing the available options
main(String[])
- Main method for testing this class.
setDebug(boolean)
- Sets whether debugging output should be produced
setKNN(int)
- Sets the number of neighbours used for kernel bandwidth setting.
setOptions(String[])
- Parses a given list of options.
setWeightingKernel(int)
- Sets the kernel weighting method to use.
toString()
- Returns a description of this classifier.
updateClassifier(Instance)
- Adds the supplied instance to the training set
LWR
public LWR()
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
- Returns:
- an enumeration of all the available options
setOptions
public void setOptions(java.lang.String options[]) throws java.lang.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:
- java.lang.Exception - if an option is not supported
getOptions
public java.lang.String[] getOptions()
Gets the current settings of the classifier.
- Returns:
- an array of strings suitable for passing to setOptions
setDebug
public void setDebug(boolean debug)
Sets whether debugging output should be produced
- Parameters:
debug
- true if debugging output should be printed
getDebug
public boolean getDebug()
SGts whether debugging output should be produced
- Returns:
- true if debugging output should be printed
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.
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
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
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
buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Generates the classifier.
- Parameters:
instances
- set of instances serving as training data
- Throws:
- java.lang.Exception - if the classifier has not been generated successfully
- Overrides:
- buildClassifier in class Classifier
updateClassifier
public void updateClassifier(Instance instance) throws java.lang.Exception
Adds the supplied instance to the training set
- Parameters:
instance
- the instance to add
- Throws:
- java.lang.Exception - if instance could not be incorporated
successfully
classifyInstance
public double classifyInstance(Instance instance) throws java.lang.Exception
Predicts the class value for the given test instance.
- Parameters:
instance
- the instance to be classified
- Returns:
- the predicted class value
- Throws:
- java.lang.Exception - if an error occurred during the prediction
- Overrides:
- classifyInstance in class Classifier
toString
public java.lang.String toString()
Returns a description of this classifier.
- Returns:
- a description of this classifier as a string.
- Overrides:
- toString in class java.lang.Object
main
public static void main(java.lang.String argv[])
Main method for testing this class.
- Parameters:
argv
- the options
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