Uses of Interface
gov.sandia.cognition.learning.algorithm.clustering.divergence.ClusterDivergenceFunction

Packages that use ClusterDivergenceFunction
gov.sandia.cognition.learning.algorithm.clustering Provides clustering algorithms. 
gov.sandia.cognition.learning.algorithm.clustering.divergence Provides divergence functions for use in clustering. 
gov.sandia.cognition.learning.function.cost Provides cost functions. 
 

Uses of ClusterDivergenceFunction in gov.sandia.cognition.learning.algorithm.clustering
 

Fields in gov.sandia.cognition.learning.algorithm.clustering declared as ClusterDivergenceFunction
protected  ClusterDivergenceFunction<? super ClusterType,? super DataType> KMeansClusterer.divergenceFunction
          The divergence function between cluster being used.
protected  ClusterDivergenceFunction<? super ClusterType,? super DataType> PartitionalClusterer.divergenceFunction
          The divergence function used to find the distance between two clusters.
 

Methods in gov.sandia.cognition.learning.algorithm.clustering that return ClusterDivergenceFunction
 ClusterDivergenceFunction<? super ClusterType,? super DataType> KMeansClusterer.getDivergenceFunction()
          Gets the divergence function used in clustering.
 ClusterDivergenceFunction<? super ClusterType,? super DataType> PartitionalClusterer.getDivergenceFunction()
          Gets the divergence function used in clustering.
 

Methods in gov.sandia.cognition.learning.algorithm.clustering with parameters of type ClusterDivergenceFunction
 void KMeansClusterer.setDivergenceFunction(ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction)
          Sets the divergence function.
 void PartitionalClusterer.setDivergenceFunction(ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction)
          Sets the divergence function.
 

Constructors in gov.sandia.cognition.learning.algorithm.clustering with parameters of type ClusterDivergenceFunction
KMeansClusterer(int numRequestedClusters, int maxIterations, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator)
          Creates a new instance of KMeansClusterer using the given parameters.
KMeansClustererWithRemoval(int numRequestedClusters, int maxIterations, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<ClusterType,DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, double removalThreshold)
          Creates a new instance of KMeansClusterer using the given parameters.
ParallelizedKMeansClusterer(int numRequestedClusters, int maxIterations, ThreadPoolExecutor threadPool, FixedClusterInitializer<ClusterType,DataType> initializer, ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator)
          Creates a new instance of ParallelizedKMeansClusterer2
PartitionalClusterer(ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, IncrementalClusterCreator<ClusterType,DataType> creator, int minClusterSize, double maxCriterionDecrease, Random random)
          Initializes the clustering to use the given metric between clusters, the given cluster creator, and the minimum number of elements per cluster to allow.
PartitionalClusterer(ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, IncrementalClusterCreator<ClusterType,DataType> creator, int maxIterations, int minClusterSize, double maxCriterionDecrease, Random random)
          Initializes the clustering to use the given metric between clusters, the given cluster creator, the minimum number of elements per cluster to allow, and the maximum decrease in the training criterion during partition to allow.
PartitionalClusterer(ClusterDivergenceFunction<? super ClusterType,? super DataType> divergenceFunction, IncrementalClusterCreator<ClusterType,DataType> creator, Random random)
          Initializes the clustering to use the given metric between clusters, and the given cluster creator.
 

Uses of ClusterDivergenceFunction in gov.sandia.cognition.learning.algorithm.clustering.divergence
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.divergence that implement ClusterDivergenceFunction
 class CentroidClusterDivergenceFunction<DataType>
          The CentroidClusterDivergenceFunction class implements a divergence function between a cluster and an object by computing the divergence between the center of the cluster and the object.
 class GaussianClusterDivergenceFunction
          The GaussianClusterDivergenceFunction class implements a divergence function between a Gaussian cluster and a vector, which is calculated by finding the likelihood that the vector was generated from that Gaussian and then returning the negative of the likelihood since it is a divergence measure, not a similarity measure.
 

Uses of ClusterDivergenceFunction in gov.sandia.cognition.learning.function.cost
 

Methods in gov.sandia.cognition.learning.function.cost that return ClusterDivergenceFunction
 ClusterDivergenceFunction<? super ClusterType,? super DataType> ClusterDistortionMeasure.getCostParameters()
           
 

Methods in gov.sandia.cognition.learning.function.cost with parameters of type ClusterDivergenceFunction
 void ClusterDistortionMeasure.setCostParameters(ClusterDivergenceFunction<? super ClusterType,? super DataType> costParameters)
           
 

Constructors in gov.sandia.cognition.learning.function.cost with parameters of type ClusterDivergenceFunction
ClusterDistortionMeasure(ClusterDivergenceFunction<ClusterType,DataType> costParameters)
          Creates a new instance of ClusterDistortionMeasure
ParallelClusterDistortionMeasure(ClusterDivergenceFunction<ClusterType,DataType> costParameters)
          Creates a new instance of ClusterDistortionMeasure