Canopy (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.clusterers.AbstractClusterer
- - weka.clusterers.RandomizableClusterer
  - - weka.clusterers.Canopy

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Clusterer, NumberOfClustersRequestable, UpdateableClusterer, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

public class Canopy
extends RandomizableClusterer
implements UpdateableClusterer, NumberOfClustersRequestable, OptionHandler, TechnicalInformationHandler

Cluster data using the capopy clustering algorithm, which requires just one pass over the data. Can run in eitherbatch or incremental mode. Results are generally not as good when running incrementally as the min/max for each numeric attribute is not known in advance. Has a heuristic (based on attribute std. deviations), that can be used in batch mode, for setting the T2 distance. The T2 distance determines how many canopies (clusters) are formed. When the user specifies a specific number (N) of clusters to generate, the algorithm will return the top N canopies (as determined by T2 density) when N < number of canopies (this applies to both batch and incremental learning); when N > number of canopies, the difference is made up by selecting training instances randomly (this can only be done when batch training). For more information see:

A. McCallum, K. Nigam, L.H. Ungar: Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching. In: Proceedings of the sixth ACM SIGKDD internation conference on knowledge discovery and data mining ACM-SIAM symposium on Discrete algorithms, 169-178, 2000.

BibTeX:

 @inproceedings{McCallum2000,
    author = {A. McCallum and K. Nigam and L.H. Ungar},
    booktitle = {Proceedings of the sixth ACM SIGKDD internation conference on knowledge discovery and data mining ACM-SIAM symposium on Discrete algorithms},
    pages = {169-178},
    title = {Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching},
    year = {2000}
 }

Valid options are:

 -N <num>
  Number of clusters.
  (default 2).

 -max-candidates <num>
  Maximum number of candidate canopies to retain in memory
  at any one time. T2 distance plus, data characteristics,
  will determine how many candidate canopies are formed before
  periodic and final pruning are performed, which might result
  in exceess memory consumption. This setting avoids large numbers
  of candidate canopies consuming memory. (default = 100)

 -periodic-pruning <num>
  How often to prune low density canopies. 
  (default = every 10,000 training instances)

 -min-density
  Minimum canopy density, below which a canopy will be pruned
  during periodic pruning. (default = 2 instances)

 -t2
  The T2 distance to use. Values < 0 indicate that
  a heuristic based on attribute std. deviation should be used to set this.
  Note that this heuristic can only be used when batch training
  (default = -1.0)

 -t1
  The T1 distance to use. A value < 0 is taken as a
  positive multiplier for T2. (default = -1.5)

 -M
  Don't replace missing values with mean/mode when running in batch mode.

 -S <num>
  Random number seed.
  (default 1)

 -output-debug-info
  If set, clusterer is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, clusterer capabilities are not checked before clusterer is built
  (use with caution).

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static double`	`DEFAULT_T1`
`static double`	`DEFAULT_T2`
`protected Instances`	`m_canopies` The canopy centers
`protected java.util.List<double[][]>`	`m_canopyCenters`
`protected java.util.List<double[]>`	`m_canopyNumMissingForNumerics`
`protected java.util.List<double[]>`	`m_canopyT2Density` The T2 density of each canopy
`protected java.util.List<long[]>`	`m_clusterCanopies` The list of canopies that each canopy is a member of (according to the T1 radius, which can overlap).
`protected boolean`	`m_didPruneLastTime` True if the pruning operation did remove at least one low density canopy the last time it was invoked
`protected NormalizableDistance`	`m_distanceFunction` The distance function to use
`protected boolean`	`m_dontReplaceMissing` Replace missing values globally when running in batch mode?
`protected int`	`m_instanceCount` Number of training instances seen so far
`protected int`	`m_maxCanopyCandidates` The maximum number of candidate canopies to hold in memory at any one time
`protected double`	`m_minClusterDensity` The minimum cluster density (according to T2 distance) allowed.
`protected Filter`	`m_missingValuesReplacer` If not null, then this is expected to be a filter that can replace missing values immediately (at training and testing time)
`protected int`	`m_numClustersRequested` Default is to let the t2 radius determine how many canopies/clusters are formed
`protected int`	`m_periodicPruningRate` Prune low-density candidate canopies after every x instances have been seen
`protected double`	`m_t1` Outer radius
`protected double`	`m_t2` Inner radius
`protected Instances`	`m_trainingData` Used to pad out number of cluster centers if fewer canopies are generated than the number of requested clusters and we are running in batch mode.
`protected double`	`m_userT1` < 0 indicates the multiplier to use for T2 when setting T1, otherwise the value is take as is
`protected double`	`m_userT2` < 0 means use the heuristic based on std. dev. to set the t2 radius

Fields inherited from class weka.clusterers.RandomizableClusterer
m_Seed, m_SeedDefault

Fields inherited from class weka.clusterers.AbstractClusterer
m_Debug, m_DoNotCheckCapabilities

Constructor Summary

Constructors
Constructor and Description

Canopy()

Constructors
Constructor and Description
`Canopy()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected void`	`adjustCanopies(double[] densities)` Adjust the final number of canopies to match the user-requested number (if possible)
`static Canopy`	`aggregateCanopies(java.util.List<Canopy> canopies, double aggregationT1, double aggregationT2, NormalizableDistance finalDistanceFunction, Filter missingValuesReplacer, int finalNumCanopies)` Aggregate the canopies from a list of Canopy clusterers together into one final model.
`long[]`	`assignCanopies(Instance inst)` Uses T1 distance to assign canopies to the supplied instance.
`void`	`buildClusterer(Instances data)` Generates a clusterer.
`void`	`cleanUp()` Save memory
`double[]`	`distributionForInstance(Instance instance)` Predicts the cluster memberships for a given instance.
`java.lang.String`	`dontReplaceMissingValuesTipText()` Returns the tip text for this property.
`double`	`getActualT1()` Get the actual value of T1 (which may be different from the initial value if the heuristic is used)
`double`	`getActualT2()` Get the actual value of T2 (which may be different from the initial value if the heuristic is used)
`Instances`	`getCanopies()` Get the canopies (cluster centers).
`Capabilities`	`getCapabilities()` Returns default capabilities of the clusterer.
`java.util.List<long[]>`	`getClusterCanopyAssignments()` Get the canopies that each canopy (cluster center) is within T1 distance of
`boolean`	`getDontReplaceMissingValues()` Gets whether missing values are to be replaced.
`int`	`getMaxNumCandidateCanopiesToHoldInMemory()` Get the maximum number of candidate canopies to retain in memory during training.
`double`	`getMinimumCanopyDensity()` Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
`int`	`getNumClusters()` Get the number of clusters to generate
`java.lang.String[]`	`getOptions()` Gets the current settings of Canopy.
`int`	`getPeriodicPruningRate()` Get the how often to prune low density canopies during training
`double`	`getT1()` Get the T1 distance.
`double`	`getT2()` Get the T2 distance to use.
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`java.lang.String`	`globalInfo()` Returns a string describing this clusterer.
`void`	`initializeDistanceFunction(Instances init)` Initialize the distance function (i.e set min/max values for numeric attributes) with the supplied instances.
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)`
`java.lang.String`	`maxNumCandidateCanopiesToHoldInMemory()` Returns the tip text for this property.
`java.lang.String`	`minimumCanopyDensityTipText()` Returns the tip text for this property.
`static boolean`	`nonEmptyCanopySetIntersection(long[] first, long[] second)` Tests if two sets of canopies have a non-empty intersection
`int`	`numberOfClusters()` Returns the number of clusters.
`java.lang.String`	`numClustersTipText()` Returns the tip text for this property.
`java.lang.String`	`periodicPruningRateTipText()` Returns the tip text for this property.
`static java.lang.String`	`printCanopyAssignments(Instances dataPoints, java.util.List<long[]> canopyAssignments)` Print the supplied instances and their canopies
`static java.lang.String`	`printSingleAssignment(long[] assignments)`
`protected void`	`pruneCandidateCanopies()` Prune low density candidate canopies
`void`	`setCanopies(Instances canopies)` Set the canopies to use (replaces any learned by this clusterer already)
`void`	`setClusterCanopyAssignments(java.util.List<long[]> clusterCanopies)` Set the canopies that each canopy (cluster center) is within T1 distance of
`void`	`setDontReplaceMissingValues(boolean r)` Sets whether missing values are to be replaced.
`void`	`setMaxNumCandidateCanopiesToHoldInMemory(int max)` Set the maximum number of candidate canopies to retain in memory during training.
`void`	`setMinimumCanopyDensity(double dens)` Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
`void`	`setMissingValuesReplacer(Filter missingReplacer)` Set a ready-to-use missing values replacement filter
`void`	`setNumClusters(int numClusters)` Set the number of clusters to generate
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPeriodicPruningRate(int p)` Set the how often to prune low density canopies during training
`void`	`setT1(double t1)` Set the T1 distance.
`void`	`setT2(double t2)` Set the T2 distance to use.
`protected void`	`setT2T1BasedOnStdDev(Instances trainingBatch)` Pretty hokey heuristic to try and set t2 distance automatically based on standard deviation
`java.lang.String`	`t1TipText()` Tip text for this property
`java.lang.String`	`t2TipText()` Tip text for this property
`java.lang.String`	`toString()`
`java.lang.String`	`toString(boolean header)` Return a textual description of this clusterer
`protected void`	`updateCanopyCenter(Instance newInstance, double[][] center, double[] numMissingNumerics)`
`void`	`updateClusterer(Instance newInstance)` Adds an instance to the clusterer.
`void`	`updateFinished()` Signals the end of the updating.

Methods inherited from class weka.clusterers.RandomizableClusterer
getSeed, seedTipText, setSeed

Methods inherited from class weka.clusterers.AbstractClusterer
clusterInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getRevision, makeCopies, makeCopy, postExecution, preExecution, run, runClusterer, setDebug, setDoNotCheckCapabilities

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

- Field Detail
  - m_canopies
```
protected Instances m_canopies
```
    The canopy centers
  - m_canopyT2Density
```
protected java.util.List<double[]> m_canopyT2Density
```
    The T2 density of each canopy
  - m_canopyCenters
```
protected java.util.List<double[][]> m_canopyCenters
```
  - m_canopyNumMissingForNumerics
```
protected java.util.List<double[]> m_canopyNumMissingForNumerics
```
  - m_clusterCanopies
```
protected java.util.List<long[]> m_clusterCanopies
```
    The list of canopies that each canopy is a member of (according to the T1 radius, which can overlap). Each bit position in the long values corresponds to one canopy. Outer list order corresponds to the order of the instances that store the actual canopy centers
  - DEFAULT_T2
```
public static final double DEFAULT_T2
```
    See Also:
    
    Constant Field Values
  - DEFAULT_T1
```
public static final double DEFAULT_T1
```
    See Also:
    
    Constant Field Values
  - m_userT2
```
protected double m_userT2
```
    < 0 means use the heuristic based on std. dev. to set the t2 radius
  - m_userT1
```
protected double m_userT1
```
    < 0 indicates the multiplier to use for T2 when setting T1, otherwise the value is take as is
  - m_t1
```
protected double m_t1
```
    Outer radius
  - m_t2
```
protected double m_t2
```
    Inner radius
  - m_periodicPruningRate
```
protected int m_periodicPruningRate
```
    Prune low-density candidate canopies after every x instances have been seen
  - m_minClusterDensity
```
protected double m_minClusterDensity
```
    The minimum cluster density (according to T2 distance) allowed. Used when periodically pruning candidate canopies
  - m_maxCanopyCandidates
```
protected int m_maxCanopyCandidates
```
    The maximum number of candidate canopies to hold in memory at any one time
  - m_didPruneLastTime
```
protected boolean m_didPruneLastTime
```
    True if the pruning operation did remove at least one low density canopy the last time it was invoked
  - m_instanceCount
```
protected int m_instanceCount
```
    Number of training instances seen so far
  - m_numClustersRequested
```
protected int m_numClustersRequested
```
    Default is to let the t2 radius determine how many canopies/clusters are formed
  - m_missingValuesReplacer
```
protected Filter m_missingValuesReplacer
```
    If not null, then this is expected to be a filter that can replace missing values immediately (at training and testing time)
  - m_dontReplaceMissing
```
protected boolean m_dontReplaceMissing
```
    Replace missing values globally when running in batch mode?
  - m_distanceFunction
```
protected NormalizableDistance m_distanceFunction
```
    The distance function to use
  - m_trainingData
```
protected Instances m_trainingData
```
    Used to pad out number of cluster centers if fewer canopies are generated than the number of requested clusters and we are running in batch mode.
- Constructor Detail
  - Canopy
```
public Canopy()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this clusterer.
    
    Returns:
    
    a description of the evaluator suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Description copied from interface: TechnicalInformationHandler
    
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the clusterer.
    
    Specified by:
    
    getCapabilities in interface Clusterer
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClusterer
    
    Returns:
    
    the capabilities of this clusterer
    
    See Also:
    
    Capabilities
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClusterer
    
    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:
```
 -N <num>
  Number of clusters.
  (default 2).
```
```
 -max-candidates <num>
  Maximum number of candidate canopies to retain in memory
  at any one time. T2 distance plus, data characteristics,
  will determine how many candidate canopies are formed before
  periodic and final pruning are performed, which might result
  in exceess memory consumption. This setting avoids large numbers
  of candidate canopies consuming memory. (default = 100)
```
```
 -periodic-pruning <num>
  How often to prune low density canopies. 
  (default = every 10,000 training instances)
```
```
 -min-density
  Minimum canopy density, below which a canopy will be pruned
  during periodic pruning. (default = 2 instances)
```
```
 -t2
  The T2 distance to use. Values < 0 indicate that
  a heuristic based on attribute std. deviation should be used to set this.
  Note that this heuristic can only be used when batch training
  (default = -1.0)
```
```
 -t1
  The T1 distance to use. A value < 0 is taken as a
  positive multiplier for T2. (default = -1.5)
```
```
 -M
  Don't replace missing values with mean/mode when running in batch mode.
 
```
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -output-debug-info
  If set, clusterer is run in debug mode and
  may output additional info to the console
```
```
 -do-not-check-capabilities
  If set, clusterer capabilities are not checked before clusterer is built
  (use with caution).
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClusterer
    
    Parameters:
    
    options - the list of options as an array of strings throws Exception if an option is not supported
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of Canopy.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableClusterer
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - nonEmptyCanopySetIntersection
```
public static boolean nonEmptyCanopySetIntersection(long[] first,
                                                    long[] second)
                                             throws java.lang.Exception
```
    Tests if two sets of canopies have a non-empty intersection
    
    Parameters:
    
    first - the first canopy set
    
    second - the second canopy set
    
    Returns:
    
    true if the intersection is non-empty
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - assignCanopies
```
public long[] assignCanopies(Instance inst)
                      throws java.lang.Exception
```
    Uses T1 distance to assign canopies to the supplied instance. If the instance does not fall within T1 distance of any canopies then the instance has the closest canopy assigned to it.
    
    Parameters:
    
    inst - the instance to find covering canopies for
    
    Returns:
    
    a set of canopies that contain this instance according to T1 distance
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - updateCanopyCenter
```
protected void updateCanopyCenter(Instance newInstance,
                                  double[][] center,
                                  double[] numMissingNumerics)
```
  - updateClusterer
```
public void updateClusterer(Instance newInstance)
                     throws java.lang.Exception
```
    Description copied from interface: UpdateableClusterer
    
    Adds an instance to the clusterer.
    
    Specified by:
    
    updateClusterer in interface UpdateableClusterer
    
    Parameters:
    
    newInstance - the instance to be added
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - pruneCandidateCanopies
```
protected void pruneCandidateCanopies()
```
    Prune low density candidate canopies
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Description copied from class: AbstractClusterer
    
    Predicts the cluster memberships for a given instance. Either this or clusterInstance() needs to be implemented by subclasses.
    
    Specified by:
    
    distributionForInstance in interface Clusterer
    
    Overrides:
    
    distributionForInstance in class AbstractClusterer
    
    Parameters:
    
    instance - the instance to be assigned a cluster.
    
    Returns:
    
    an array containing the estimated membership probabilities of the test instance in each cluster (this should sum to at most 1)
    
    Throws:
    
    java.lang.Exception - if distribution could not be computed successfully
  - adjustCanopies
```
protected void adjustCanopies(double[] densities)
```
    Adjust the final number of canopies to match the user-requested number (if possible)
    
    Parameters:
    
    densities - the density of each of the canopies
  - updateFinished
```
public void updateFinished()
```
    Description copied from interface: UpdateableClusterer
    
    Signals the end of the updating.
    
    Specified by:
    
    updateFinished in interface UpdateableClusterer
  - initializeDistanceFunction
```
public void initializeDistanceFunction(Instances init)
                                throws java.lang.Exception
```
    Initialize the distance function (i.e set min/max values for numeric attributes) with the supplied instances.
    
    Parameters:
    
    init - the instances to initialize with
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - setT2T1BasedOnStdDev
```
protected void setT2T1BasedOnStdDev(Instances trainingBatch)
                             throws java.lang.Exception
```
    Pretty hokey heuristic to try and set t2 distance automatically based on standard deviation
    
    Parameters:
    
    trainingBatch - the training instances
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - buildClusterer
```
public void buildClusterer(Instances data)
                    throws java.lang.Exception
```
    Description copied from class: AbstractClusterer
    
    Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.
    
    Specified by:
    
    buildClusterer in interface Clusterer
    
    Specified by:
    
    buildClusterer in class AbstractClusterer
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the clusterer has not been generated successfully
  - numberOfClusters
```
public int numberOfClusters()
                     throws java.lang.Exception
```
    Description copied from class: AbstractClusterer
    
    Returns the number of clusters.
    
    Specified by:
    
    numberOfClusters in interface Clusterer
    
    Specified by:
    
    numberOfClusters in class AbstractClusterer
    
    Returns:
    
    the number of clusters generated for a training dataset.
    
    Throws:
    
    java.lang.Exception - if number of clusters could not be returned successfully
  - setMissingValuesReplacer
```
public void setMissingValuesReplacer(Filter missingReplacer)
```
    Set a ready-to-use missing values replacement filter
    
    Parameters:
    
    missingReplacer - the missing values replacement filter to use
  - getCanopies
```
public Instances getCanopies()
```
    Get the canopies (cluster centers).
    
    Returns:
    
    the canopies
  - setCanopies
```
public void setCanopies(Instances canopies)
```
    Set the canopies to use (replaces any learned by this clusterer already)
    
    Parameters:
    
    canopies - the canopies to use
  - getClusterCanopyAssignments
```
public java.util.List<long[]> getClusterCanopyAssignments()
```
    Get the canopies that each canopy (cluster center) is within T1 distance of
    
    Returns:
    
    a list of canopies for each cluster center
  - setClusterCanopyAssignments
```
public void setClusterCanopyAssignments(java.util.List<long[]> clusterCanopies)
```
    Set the canopies that each canopy (cluster center) is within T1 distance of
    
    Parameters:
    
    clusterCanopies - the list canopies for each cluster center
  - getActualT2
```
public double getActualT2()
```
    Get the actual value of T2 (which may be different from the initial value if the heuristic is used)
    
    Returns:
    
    the actual value of T2
  - getActualT1
```
public double getActualT1()
```
    Get the actual value of T1 (which may be different from the initial value if the heuristic is used)
    
    Returns:
    
    the actual value of T1
  - t1TipText
```
public java.lang.String t1TipText()
```
    Tip text for this property
    
    Returns:
    
    the tip text for this property
  - setT1
```
public void setT1(double t1)
```
    Set the T1 distance. Values < 0 are taken as a positive multiplier for the T2 distance - e.g. T1_actual = Math.abs(t1) * t2;
    
    Parameters:
    
    t1 - the T1 distance to use
  - getT1
```
public double getT1()
```
    Get the T1 distance. Values < 0 are taken as a positive multiplier for the T2 distance - e.g. T1_actual = Math.abs(t1) * t2;
    
    Returns:
    
    the T1 distance to use
  - t2TipText
```
public java.lang.String t2TipText()
```
    Tip text for this property
    
    Returns:
    
    the tip text for this property
  - setT2
```
public void setT2(double t2)
```
    Set the T2 distance to use. Values < 0 indicate that a heuristic based on attribute standard deviation should be used to set this (note that the heuristic is only applicable when batch training).
    
    Parameters:
    
    t2 - the T2 distance to use
  - getT2
```
public double getT2()
```
    Get the T2 distance to use. Values < 0 indicate that a heuristic based on attribute standard deviation should be used to set this (note that the heuristic is only applicable when batch training).
    
    Returns:
    
    the T2 distance to use
  - 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)
                    throws java.lang.Exception
```
    Description copied from interface: NumberOfClustersRequestable
    
    Set the number of clusters to generate
    
    Specified by:
    
    setNumClusters in interface NumberOfClustersRequestable
    
    Parameters:
    
    numClusters - the number of clusters to generate
    
    Throws:
    
    java.lang.Exception - if the requested number of clusters in inapropriate
  - getNumClusters
```
public int getNumClusters()
```
    Get the number of clusters to generate
    
    Returns:
    
    the number of clusters to generate
  - periodicPruningRateTipText
```
public java.lang.String periodicPruningRateTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPeriodicPruningRate
```
public void setPeriodicPruningRate(int p)
```
    Set the how often to prune low density canopies during training
    
    Parameters:
    
    p - how often (every p instances) to prune low density canopies
  - getPeriodicPruningRate
```
public int getPeriodicPruningRate()
```
    Get the how often to prune low density canopies during training
    
    Returns:
    
    how often (every p instances) to prune low density canopies
  - minimumCanopyDensityTipText
```
public java.lang.String minimumCanopyDensityTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinimumCanopyDensity
```
public void setMinimumCanopyDensity(double dens)
```
    Set the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    
    Parameters:
    
    dens - the minimum canopy density
  - getMinimumCanopyDensity
```
public double getMinimumCanopyDensity()
```
    Get the minimum T2-based density below which a canopy will be pruned during periodic pruning.
    
    Returns:
    
    the minimum canopy density
  - maxNumCandidateCanopiesToHoldInMemory
```
public java.lang.String maxNumCandidateCanopiesToHoldInMemory()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMaxNumCandidateCanopiesToHoldInMemory
```
public void setMaxNumCandidateCanopiesToHoldInMemory(int max)
```
    Set the maximum number of candidate canopies to retain in memory during training. T2 distance and data characteristics determine how many candidate canopies are formed before periodic and final pruning are performed. There may not be enough memory available if T2 is set too low.
    
    Parameters:
    
    max - the maximum number of candidate canopies to retain in memory during training
  - getMaxNumCandidateCanopiesToHoldInMemory
```
public int getMaxNumCandidateCanopiesToHoldInMemory()
```
    Get the maximum number of candidate canopies to retain in memory during training. T2 distance and data characteristics determine how many candidate canopies are formed before periodic and final pruning are performed. There may not be enough memory available if T2 is set too low.
    
    Returns:
    
    the maximum number of candidate canopies to retain in memory during training
  - dontReplaceMissingValuesTipText
```
public java.lang.String dontReplaceMissingValuesTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDontReplaceMissingValues
```
public void setDontReplaceMissingValues(boolean r)
```
    Sets whether missing values are to be replaced.
    
    Parameters:
    
    r - true if missing values are to be replaced
  - getDontReplaceMissingValues
```
public boolean getDontReplaceMissingValues()
```
    Gets whether missing values are to be replaced.
    
    Returns:
    
    true if missing values are to be replaced
  - printSingleAssignment
```
public static java.lang.String printSingleAssignment(long[] assignments)
```
  - printCanopyAssignments
```
public static java.lang.String printCanopyAssignments(Instances dataPoints,
                                                      java.util.List<long[]> canopyAssignments)
```
    Print the supplied instances and their canopies
    
    Parameters:
    
    dataPoints - the instances to print
    
    canopyAssignments - the canopy assignments, one assignment array for each instance
    
    Returns:
    
    a string containing the printed assignments
  - toString
```
public java.lang.String toString(boolean header)
```
    Return a textual description of this clusterer
    
    Parameters:
    
    header - true if the header should be printed
    
    Returns:
    
    a string describing the result of the clustering
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
  - cleanUp
```
public void cleanUp()
```
    Save memory
  - aggregateCanopies
```
public static Canopy aggregateCanopies(java.util.List<Canopy> canopies,
                                       double aggregationT1,
                                       double aggregationT2,
                                       NormalizableDistance finalDistanceFunction,
                                       Filter missingValuesReplacer,
                                       int finalNumCanopies)
```
    Aggregate the canopies from a list of Canopy clusterers together into one final model.
    
    Parameters:
    
    canopies - the list of Canopy clusterers to aggregate
    
    aggregationT1 - the T1 distance to use for the aggregated classifier
    
    aggregationT2 - the T2 distance to use when aggregating canopies
    
    finalDistanceFunction - the distance function to use with the final Canopy clusterer
    
    missingValuesReplacer - the missing value replacement filter to use with the final clusterer (can be null for no missing value replacement)
    
    finalNumCanopies - the final number of canopies
    
    Returns:
    
    a Canopy clusterer that aggregates all the canopies
  - main
```
public static void main(java.lang.String[] args)
```

Class Canopy

Field Summary

Fields inherited from class weka.clusterers.RandomizableClusterer

Fields inherited from class weka.clusterers.AbstractClusterer

Constructor Summary

Method Summary

Methods inherited from class weka.clusterers.RandomizableClusterer

Methods inherited from class weka.clusterers.AbstractClusterer

Methods inherited from class java.lang.Object

Field Detail

m_canopies

m_canopyT2Density

m_canopyCenters

m_canopyNumMissingForNumerics

m_clusterCanopies

DEFAULT_T2

DEFAULT_T1

m_userT2

m_userT1

m_t1

m_t2

m_periodicPruningRate

m_minClusterDensity

m_maxCanopyCandidates

m_didPruneLastTime

m_instanceCount

m_numClustersRequested

m_missingValuesReplacer

m_dontReplaceMissing

m_distanceFunction

m_trainingData

Constructor Detail

Canopy

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

listOptions

setOptions

getOptions

nonEmptyCanopySetIntersection

assignCanopies

updateCanopyCenter

updateClusterer

pruneCandidateCanopies

distributionForInstance

adjustCanopies

updateFinished

initializeDistanceFunction

setT2T1BasedOnStdDev

buildClusterer

numberOfClusters

setMissingValuesReplacer

getCanopies

setCanopies

getClusterCanopyAssignments

setClusterCanopyAssignments

getActualT2

getActualT1

t1TipText

setT1

getT1

t2TipText

setT2

getT2

numClustersTipText

setNumClusters

getNumClusters

periodicPruningRateTipText

setPeriodicPruningRate

getPeriodicPruningRate

minimumCanopyDensityTipText

setMinimumCanopyDensity

getMinimumCanopyDensity

maxNumCandidateCanopiesToHoldInMemory

setMaxNumCandidateCanopiesToHoldInMemory

getMaxNumCandidateCanopiesToHoldInMemory

dontReplaceMissingValuesTipText

setDontReplaceMissingValues

getDontReplaceMissingValues