Package weka.clusterers
Class MakeDensityBasedClusterer
- java.lang.Object
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- weka.clusterers.AbstractClusterer
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- weka.clusterers.AbstractDensityBasedClusterer
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- weka.clusterers.MakeDensityBasedClusterer
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,Clusterer
,DensityBasedClusterer
,NumberOfClustersRequestable
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,WeightedInstancesHandler
public class MakeDensityBasedClusterer extends AbstractDensityBasedClusterer implements NumberOfClustersRequestable, 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. Supports the NumberOfClustersRequestable interface only if the wrapped Clusterer does. Valid options are:-M <num> minimum allowable standard deviation for normal density computation (default 1e-6)
-W <clusterer name> Clusterer to wrap. (default weka.clusterers.SimpleKMeans)
Options specific to clusterer weka.clusterers.SimpleKMeans:
-N <num> number of clusters. (default 2).
-V Display std. deviations for centroids.
-M Replace missing values with mean/mode.
-S <num> Random number seed. (default 10)
Options after "--" are passed on to the base clusterer.- Version:
- $Revision: 5538 $
- 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
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Constructor Summary
Constructors Constructor Description MakeDensityBasedClusterer()
Default constructor.MakeDensityBasedClusterer(Clusterer toWrap)
Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClusterer(Instances data)
Builds a clusterer for a set of instances.java.lang.String
clustererTipText()
Returns the tip text for this propertydouble[]
clusterPriors()
Returns the cluster priors.Capabilities
getCapabilities()
Returns default capabilities of the clusterer (i.e., of the wrapper clusterer).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.lang.String
getRevision()
Returns the revision string.java.lang.String
globalInfo()
Returns a string describing classifierjava.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.static void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
minStdDevTipText()
Returns the tip text for this propertyint
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
setNumClusters(int n)
Set the number of clusters to generate.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.AbstractDensityBasedClusterer
distributionForInstance, logDensityForInstance, logJointDensitiesForInstance, makeCopies
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Methods inherited from class weka.clusterers.AbstractClusterer
clusterInstance, forName, makeCopies, makeCopy
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Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface weka.clusterers.Clusterer
clusterInstance
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Constructor Detail
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MakeDensityBasedClusterer
public MakeDensityBasedClusterer()
Default constructor.
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MakeDensityBasedClusterer
public MakeDensityBasedClusterer(Clusterer toWrap)
Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.- Parameters:
toWrap
- the clusterer to wrap around
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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setNumClusters
public void setNumClusters(int n) throws java.lang.Exception
Set the number of clusters to generate.- Specified by:
setNumClusters
in interfaceNumberOfClustersRequestable
- Parameters:
n
- the number of clusters to generate- Throws:
java.lang.Exception
- if the wrapped clusterer has not been set, or if the wrapped clusterer does not implement this facility.
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the clusterer (i.e., of the wrapper clusterer).- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Specified by:
getCapabilities
in interfaceClusterer
- Overrides:
getCapabilities
in classAbstractClusterer
- Returns:
- the capabilities of this clusterer
- See Also:
Capabilities
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buildClusterer
public void buildClusterer(Instances data) throws java.lang.Exception
Builds a clusterer for a set of instances.- Specified by:
buildClusterer
in interfaceClusterer
- Specified by:
buildClusterer
in classAbstractClusterer
- Parameters:
data
- the instances to train the clusterer with- Throws:
java.lang.Exception
- if the clusterer hasn't been set or something goes wrong
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clusterPriors
public double[] clusterPriors()
Returns the cluster priors.- Specified by:
clusterPriors
in interfaceDensityBasedClusterer
- Specified by:
clusterPriors
in classAbstractDensityBasedClusterer
- Returns:
- the cluster priors
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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 interfaceDensityBasedClusterer
- Specified by:
logDensityPerClusterForInstance
in classAbstractDensityBasedClusterer
- Parameters:
inst
- the instance to compute the density for- Returns:
- an array containing the estimated densities
- Throws:
java.lang.Exception
- if the density could not be computed successfully
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numberOfClusters
public int numberOfClusters() throws java.lang.Exception
Returns the number of clusters.- Specified by:
numberOfClusters
in interfaceClusterer
- Specified by:
numberOfClusters
in classAbstractClusterer
- Returns:
- the number of clusters generated for a training dataset.
- Throws:
java.lang.Exception
- if number of clusters could not be returned successfully
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toString
public java.lang.String toString()
Returns a description of the clusterer.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string containing a description of the clusterer
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clustererTipText
public java.lang.String clustererTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setClusterer
public void setClusterer(Clusterer toWrap)
Sets the clusterer to wrap.- Parameters:
toWrap
- the clusterer
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getClusterer
public Clusterer getClusterer()
Gets the clusterer being wrapped.- Returns:
- the clusterer
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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
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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
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getMinStdDev
public double getMinStdDev()
Get the minimum allowable standard deviation.- Returns:
- the minumum allowable standard deviation
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options..- Specified by:
listOptions
in interfaceOptionHandler
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-M <num> minimum allowable standard deviation for normal density computation (default 1e-6)
-W <clusterer name> Clusterer to wrap. (default weka.clusterers.SimpleKMeans)
Options specific to clusterer weka.clusterers.SimpleKMeans:
-N <num> number of clusters. (default 2).
-V Display std. deviations for centroids.
-M Replace missing values with mean/mode.
-S <num> Random number seed. (default 10)
- Specified by:
setOptions
in interfaceOptionHandler
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the clusterer.- Specified by:
getOptions
in interfaceOptionHandler
- Returns:
- an array of strings suitable for passing to setOptions()
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classAbstractClusterer
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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