Uses of Interface
gov.sandia.cognition.learning.function.distance.DivergenceFunctionContainer

Packages that use DivergenceFunctionContainer
gov.sandia.cognition.learning.algorithm.clustering Provides clustering algorithms. 
gov.sandia.cognition.learning.algorithm.clustering.cluster Provides implementations of different types of clusters. 
gov.sandia.cognition.learning.algorithm.clustering.divergence Provides divergence functions for use in clustering. 
gov.sandia.cognition.learning.algorithm.clustering.initializer Provides implementations of methods for selecting initial clusters. 
gov.sandia.cognition.learning.algorithm.nearest Provides algorithms for Nearest-Neighbor memory-based functions. 
gov.sandia.cognition.learning.function.distance Provides distance functions. 
 

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

Classes in gov.sandia.cognition.learning.algorithm.clustering that implement DivergenceFunctionContainer
 class AffinityPropagation<DataType>
          The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
 class AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>>
          The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
 class KMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
 class KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>>
          Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
 class OptimizedKMeansClusterer<DataType>
          This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
 class ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          This is a parallel implementation of the k-means clustering algorithm.
 class PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>>
          The PartitionClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.
 

Uses of DivergenceFunctionContainer in gov.sandia.cognition.learning.algorithm.clustering.cluster
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.cluster that implement DivergenceFunctionContainer
 class MedoidClusterCreator<DataType>
          The MedoidClusterCreator class creates a CentroidCluster at the sample that minimizes the sum of the divergence to the objects assigned to the cluster.
 

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

Classes in gov.sandia.cognition.learning.algorithm.clustering.divergence that implement DivergenceFunctionContainer
 class AbstractClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The AbstractClusterToClusterDivergenceFunction class is an abstract class that helps out implementations of ClusterToClusterDivergenceFunction implementations by holding a DivergenceFunction between elements of a cluster.
 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 ClusterCentroidDivergenceFunction<DataType>
          The ClusterCentroidDivergenceFunction class implements the distance between two clusters by computing the distance between the cluster's centroid.
 class ClusterCompleteLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterCompleteLinkDivergenceFunction class implements the complete linkage distance metric between two clusters.
 class ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterMeanLinkDivergenceFunction class implements the mean linkage distance metric between two clusters.
 class ClusterSingleLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterSingleLinkDivergenceFunction class implements the complete linkage distance metric between two clusters.
 

Uses of DivergenceFunctionContainer in gov.sandia.cognition.learning.algorithm.clustering.initializer
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.initializer that implement DivergenceFunctionContainer
 class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.
 class DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements FixedClusterInitializer that initializes clusters by first selecting a random point for the first cluster and then randomly sampling each successive cluster based on the squared minimum distance from the point to the existing selected clusters.
 class GreedyClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements a FixedClusterInitializer that greedily attempts to create the initial clusters.
 

Uses of DivergenceFunctionContainer in gov.sandia.cognition.learning.algorithm.nearest
 

Classes in gov.sandia.cognition.learning.algorithm.nearest that implement DivergenceFunctionContainer
 class AbstractKNearestNeighbor<InputType,OutputType>
          Partial implementation of KNearestNeighbor.
 class AbstractNearestNeighbor<InputType,OutputType>
          Partial implementation of KNearestNeighbor.
 class KNearestNeighborExhaustive<InputType,OutputType>
          A generic k-nearest-neighbor classifier.
static class KNearestNeighborExhaustive.Learner<InputType,OutputType>
          This is a BatchLearner interface for creating a new KNearestNeighborExhaustive from a given dataset, simply a pass-through to the constructor of KNearestNeighborExhaustive
 class KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>
          A KDTree-based implementation of the k-nearest neighbor algorithm.
static class KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
          This is a BatchLearner interface for creating a new KNearestNeighbor from a given dataset, simply a pass-through to the constructor of KNearestNeighbor
 class NearestNeighborExhaustive<InputType,OutputType>
          The NearestNeighborExhaustive class implements a simple evaluator that looks up a given input object in a collection of input-output pair examples and returns the output associated with the most similar input.
static class NearestNeighborExhaustive.Learner<InputType,OutputType>
          The NearestNeighborExhaustive.Learner class implements a batch learner for the NearestNeighborExhaustive class.
 class NearestNeighborKDTree<InputType extends Vectorizable,OutputType>
          A KDTree-based implementation of the nearest neighbor algorithm.
static class NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
          This is a BatchLearner interface for creating a new NearestNeighbor from a given dataset, simply a pass-through to the constructor of NearestNeighbor
 

Uses of DivergenceFunctionContainer in gov.sandia.cognition.learning.function.distance
 

Classes in gov.sandia.cognition.learning.function.distance that implement DivergenceFunctionContainer
 class DefaultDivergenceFunctionContainer<FirstType,SecondType>
          The DefaultDivergenceFunctionContainer class implements an object that holds a divergence function.
 class DivergencesEvaluator<InputType,ValueType>
          Evaluates the divergence (distance) between an input and a list of values, storing the resulting divergence values in a vector.
static class DivergencesEvaluator.Learner<DataType,InputType,ValueType>
          A learner adapter for the DivergencesEvaluator.