RBFNetwork

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.functions.RBFNetwork

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler
```
public class RBFNetwork
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.WeightedInstancesHandler
```
Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance.
Valid options are:
```
 -B <number>
  Set the number of clusters (basis functions) to generate. (default = 2).
 
```
```
 -S <seed>
  Set the random seed to be used by K-means. (default = 1).
 
```
```
 -R <ridge>
  Set the ridge value for the logistic or linear regression.
 
```
```
 -M <number>
  Set the maximum number of iterations for the logistic regression. (default -1, until convergence).
 
```
```
 -W <number>
  Set the minimum standard deviation for the clusters. (default 0.1).
 
```
Version:

$Revision: 10951 $

Author:

Mark Hall, Eibe Frank

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

RBFNetwork()

Constructors
Constructor and Description
`RBFNetwork()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances instances)` Builds the classifier
`java.lang.String`	`clusteringSeedTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(weka.core.Instance instance)` Computes the distribution for a given instance
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier, i.e., and "or" of Logistic and LinearRegression.
`int`	`getClusteringSeed()` Get the random seed used by K-means.
`int`	`getMaxIts()` Get the value of MaxIts.
`double`	`getMinStdDev()` Get the MinStdDev value.
`int`	`getNumClusters()` Return the number of clusters to generate.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getRidge()` Gets the ridge value.
`java.lang.String`	`globalInfo()` Returns a string describing this classifier
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`maxItsTipText()` Returns the tip text for this property
`java.lang.String`	`minStdDevTipText()` Returns the tip text for this property
`java.lang.String`	`numClustersTipText()` Returns the tip text for this property
`java.lang.String`	`ridgeTipText()` Returns the tip text for this property
`void`	`setClusteringSeed(int seed)` Set the random seed to be passed on to K-means.
`void`	`setMaxIts(int newMaxIts)` Set the value of MaxIts.
`void`	`setMinStdDev(double newMinStdDev)` Set the MinStdDev value.
`void`	`setNumClusters(int numClusters)` Set the number of clusters for K-means to generate.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setRidge(double ridge)` Sets the ridge value for logistic or linear regression.
`java.lang.String`	`toString()` Returns a description of this classifier as a String

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - RBFNetwork
```
public RBFNetwork()
```
- 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
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier, i.e., and "or" of Logistic and LinearRegression.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Logistic, LinearRegression
  - buildClassifier
```
public void buildClassifier(weka.core.Instances instances)
                     throws java.lang.Exception
```
    Builds the classifier
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    instances - the training data
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Computes the distribution for a given instance
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - toString
```
public java.lang.String toString()
```
    Returns a description of this classifier as a String
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of this classifier
  - maxItsTipText
```
public java.lang.String maxItsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMaxIts
```
public int getMaxIts()
```
    Get the value of MaxIts.
    
    Returns:
    
    Value of MaxIts.
  - setMaxIts
```
public void setMaxIts(int newMaxIts)
```
    Set the value of MaxIts.
    
    Parameters:
    
    newMaxIts - Value to assign to MaxIts.
  - ridgeTipText
```
public java.lang.String ridgeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setRidge
```
public void setRidge(double ridge)
```
    Sets the ridge value for logistic or linear regression.
    
    Parameters:
    
    ridge - the ridge
  - getRidge
```
public double getRidge()
```
    Gets the ridge value.
    
    Returns:
    
    the ridge
  - numClustersTipText
```
public java.lang.String numClustersTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumClusters
```
public void setNumClusters(int numClusters)
```
    Set the number of clusters for K-means to generate.
    
    Parameters:
    
    numClusters - the number of clusters to generate.
  - getNumClusters
```
public int getNumClusters()
```
    Return the number of clusters to generate.
    
    Returns:
    
    the number of clusters to generate.
  - clusteringSeedTipText
```
public java.lang.String clusteringSeedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setClusteringSeed
```
public void setClusteringSeed(int seed)
```
    Set the random seed to be passed on to K-means.
    
    Parameters:
    
    seed - a seed value.
  - getClusteringSeed
```
public int getClusteringSeed()
```
    Get the random seed used by K-means.
    
    Returns:
    
    the seed value.
  - minStdDevTipText
```
public java.lang.String minStdDevTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMinStdDev
```
public double getMinStdDev()
```
    Get the MinStdDev value.
    
    Returns:
    
    the MinStdDev value.
  - setMinStdDev
```
public void setMinStdDev(double newMinStdDev)
```
    Set the MinStdDev value.
    
    Parameters:
    
    newMinStdDev - The new MinStdDev value.
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    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:
```
 -B <number>
  Set the number of clusters (basis functions) to generate. (default = 2).
 
```
```
 -S <seed>
  Set the random seed to be used by K-means. (default = 1).
 
```
```
 -R <ridge>
  Set the ridge value for the logistic or linear regression.
 
```
```
 -M <number>
  Set the maximum number of iterations for the logistic regression. (default -1, until convergence).
 
```
```
 -W <number>
  Set the minimum standard deviation for the clusters. (default 0.1).
 
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    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.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain the command line arguments to the scheme (see Evaluation)

Class RBFNetwork

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

RBFNetwork

Method Detail

globalInfo

getCapabilities

buildClassifier

distributionForInstance

toString

maxItsTipText

getMaxIts

setMaxIts

ridgeTipText

setRidge

getRidge

numClustersTipText

setNumClusters

getNumClusters

clusteringSeedTipText

setClusteringSeed

getClusteringSeed

minStdDevTipText

getMinStdDev

setMinStdDev

listOptions

setOptions

getOptions

getRevision

main