weka.clusterers
Class MakeDensityBasedClusterer

java.lang.Object
  weka.clusterers.Clusterer
      weka.clusterers.DensityBasedClusterer
          weka.clusterers.MakeDensityBasedClusterer

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class MakeDensityBasedClusterer

extends DensityBasedClusterer

implements OptionHandler, WeightedInstancesHandler

Class for wrapping a Clusterer to make it return a distribution and density. Fits normal distributions and discrete distributions within each cluster produced by the wrapped clusterer.

Version:

$Revision: 1.1 $

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz), Mark Hall (mhall@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

private double	m_minStdDev default minimum standard deviation
private DiscreteEstimator[][]	m_model discrete distributions fitted to each discrete attribute in each cluster
private double[][][]	m_modelNormal normal distributions fitted to each numeric attribute in each cluster
private static double	m_normConst Constant for normal distribution.
private double[]	m_priors prior probabilities for the fitted clusters
private Instances	m_theInstances holds training instances header information
private Clusterer	m_wrappedClusterer The clusterer being wrapped

Constructor Summary

MakeDensityBasedClusterer()
Default constructor.

MakeDensityBasedClusterer(Clusterer toWrap)
Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.

Method Summary

void	buildClusterer(Instances data) Builds a clusterer for a set of instances.
double[]	clusterPriors() Returns the cluster priors.
Clusterer	getClusterer() Gets the clusterer being wrapped.
double	getMinStdDev() Get the minimum allowable standard deviation.
java.lang.String[]	getOptions() Gets the current settings of the clusterer.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options..
double[]	logDensityPerClusterForInstance(Instance inst) Computes the log of the conditional density (per cluster) for a given instance.
private double	logNormalDens(double x, double mean, double stdDev) Density function of normal distribution.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	minStdDevTipText() Returns the tip text for this property
int	numberOfClusters() Returns the number of clusters.
void	setClusterer(Clusterer toWrap) Sets the clusterer to wrap.
void	setMinStdDev(double m) Set the minimum value for standard deviation when calculating normal density.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns a description of the clusterer.

Methods inherited from class weka.clusterers.DensityBasedClusterer

distributionForInstance, logDensityForInstance, logJointDensitiesForInstance

Methods inherited from class weka.clusterers.Clusterer

clusterInstance, forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_theInstances

private Instances m_theInstances

holds training instances header information

m_priors

private double[] m_priors

prior probabilities for the fitted clusters

m_modelNormal

private double[][][] m_modelNormal

normal distributions fitted to each numeric attribute in each cluster

m_model

private DiscreteEstimator[][] m_model

discrete distributions fitted to each discrete attribute in each cluster

m_minStdDev

private double m_minStdDev

default minimum standard deviation

m_wrappedClusterer

private Clusterer m_wrappedClusterer

The clusterer being wrapped

m_normConst

private static double m_normConst

Constant for normal distribution.

Constructor Detail

MakeDensityBasedClusterer

public MakeDensityBasedClusterer()

Default constructor.

MakeDensityBasedClusterer

public MakeDensityBasedClusterer(Clusterer toWrap)

Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.

Parameters:

toWrap - the clusterer to wrap around

Method Detail

buildClusterer

public void buildClusterer(Instances data)
                    throws java.lang.Exception

Builds a clusterer for a set of instances.

Specified by:

buildClusterer in class Clusterer

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the clusterer hasn't been set or something goes wrong

clusterPriors

public double[] clusterPriors()

Returns the cluster priors.

Specified by:

clusterPriors in class DensityBasedClusterer

Returns:

the prior probability for each cluster

logDensityPerClusterForInstance

public double[] logDensityPerClusterForInstance(Instance inst)
                                         throws java.lang.Exception

Computes the log of the conditional density (per cluster) for a given instance.

Specified by:

logDensityPerClusterForInstance in class DensityBasedClusterer

Parameters:

inst - the instance to compute the density for

Returns:

the density.

Throws:

java.lang.Exception - if the density could not be computed successfully

logNormalDens

private double logNormalDens(double x,
                             double mean,
                             double stdDev)

Density function of normal distribution.

Parameters:

x - input value

mean - mean of distribution

stdDev - standard deviation of distribution

numberOfClusters

public int numberOfClusters()
                     throws java.lang.Exception

Returns the number of clusters.

Specified by:

numberOfClusters in class Clusterer

Returns:

the number of clusters generated for a training dataset.

Throws:

java.lang.Exception - if number of clusters could not be returned successfully

toString

public java.lang.String toString()

Returns a description of the clusterer.

Returns:

a string containing a description of the clusterer

setClusterer

public void setClusterer(Clusterer toWrap)

Sets the clusterer to wrap.

Parameters:

toWrap - the clusterer

getClusterer

public Clusterer getClusterer()

Gets the clusterer being wrapped.

Returns:

the clusterer

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

setMinStdDev

public void setMinStdDev(double m)

Set the minimum value for standard deviation when calculating normal density. Reducing this value can help prevent arithmetic overflow resulting from multiplying large densities (arising from small standard deviations) when there are many singleton or near singleton values.

Parameters:

m - minimum value for standard deviation

getMinStdDev

public double getMinStdDev()

Get the minimum allowable standard deviation.

Returns:

the minumum allowable standard deviation

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options..

Specified by:

listOptions in interface OptionHandler

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:

-W clusterer name
Clusterer to wrap. (required)

-M
Set the minimum allowable standard deviation for normal density calculation.

Specified by:

setOptions in interface OptionHandler

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 clusterer.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions()

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options