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

Packages that use DivergenceFunctionContainer

Package	Description
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

Modifier and Type	Class and Description
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	MiniBatchKMeansClusterer<DataType extends Vector> Approximates k-means clustering by working on random subsets of the data.
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 PartitionalClusterer 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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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.