Uses of Interface
gov.sandia.cognition.math.DivergenceFunction

Packages that use DivergenceFunction

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.
gov.sandia.cognition.learning.function.kernel	Provides kernel functions.
gov.sandia.cognition.math	Provides classes for mathematical computation.
gov.sandia.cognition.text.relation	Provides classes for relationships involving text.

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

Fields in gov.sandia.cognition.learning.algorithm.clustering declared as DivergenceFunction

Modifier and Type	Field and Description
protected DivergenceFunction<? super DataType,? super DataType>	AffinityPropagation.divergence The divergence function to use.
protected DivergenceFunction<? super ClusterType,? super DataType>	PartitionalClusterer.divergenceFunction An optional DivergenceFunction that is used to create a WithinClusterDivergence function via a WithinClusterDivergenceWrapper.

Methods in gov.sandia.cognition.learning.algorithm.clustering that return DivergenceFunction

Modifier and Type	Method and Description
DivergenceFunction<? super DataType,? super DataType>	AffinityPropagation.getDivergence() Gets the divergence function used by the algorithm.
DivergenceFunction<? super DataType,? super DataType>	AffinityPropagation.getDivergenceFunction()
DivergenceFunction<? super ClusterType,? super DataType>	PartitionalClusterer.getDivergenceFunction() Gets the stored metric between a cluster and a point.

Methods in gov.sandia.cognition.learning.algorithm.clustering with parameters of type DivergenceFunction

Modifier and Type	Method and Description
void	AffinityPropagation.setDivergence(DivergenceFunction<? super DataType,? super DataType> divergence) Sets the divergence function used by the algorithm.
void	PartitionalClusterer.setDivergenceFunction(DivergenceFunction<? super ClusterType,? super DataType> divergenceFunction) Use a metric between a cluster and a point to update the metric on clusters.

Constructors in gov.sandia.cognition.learning.algorithm.clustering with parameters of type DivergenceFunction

Constructor and Description
AffinityPropagation(DivergenceFunction<? super DataType,? super DataType> divergence, double selfDivergence) Creates a new instance of AffinityPropagation.
AffinityPropagation(DivergenceFunction<? super DataType,? super DataType> divergence, double selfDivergence, double dampingFactor) Creates a new instance of AffinityPropagation.
AffinityPropagation(DivergenceFunction<? super DataType,? super DataType> divergence, double selfDivergence, double dampingFactor, int maxIterations) Creates a new instance of AffinityPropagation.

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

Constructors in gov.sandia.cognition.learning.algorithm.clustering.cluster with parameters of type DivergenceFunction

Constructor and Description
MedoidClusterCreator(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of MedoidClusterCreator

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

Subinterfaces of DivergenceFunction in gov.sandia.cognition.learning.algorithm.clustering.divergence

Modifier and Type	Interface and Description
interface	ClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> The ClusterDivergenceFunction interface defines a function that computes the divergence between a cluster and some other object.
interface	ClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType> The ClusterToClusterDivergenceFunction defines a DivergenceFunction between two clusters of the same data type.

Classes in gov.sandia.cognition.learning.algorithm.clustering.divergence that implement DivergenceFunction

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.
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.

Fields in gov.sandia.cognition.learning.algorithm.clustering.divergence declared as DivergenceFunction

Modifier and Type	Field and Description
protected DivergenceFunction<? super ClusterType,? super DataType>	WithinClusterDivergenceWrapper.divergenceFunction The divergence function.

Constructors in gov.sandia.cognition.learning.algorithm.clustering.divergence with parameters of type DivergenceFunction

Constructor and Description
AbstractClusterToClusterDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of AbstractClusterToClusterDivergenceFunction
CentroidClusterDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of CentroidClusterDivergenceFunction.
ClusterCentroidDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of ClusterCompleteLinkDivergenceFunction using the given divergence function for elements.
ClusterCompleteLinkDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of ClusterCompleteLinkDivergenceFunction using the given divergence function for elements.
ClusterMeanLinkDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of ClusterMeanLinkDivergenceFunction using the given divergence function for elements.
ClusterSingleLinkDivergenceFunction(DivergenceFunction<? super DataType,? super DataType> divergenceFunction) Creates a new instance of ClusterSingleLinkDivergenceFunction using the given divergence function for elements.
WithinClusterDivergenceWrapper(DivergenceFunction<? super ClusterType,? super DataType> divergenceFunction) Creates a new WithinClusterDivergenceWrapper.

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

Constructors in gov.sandia.cognition.learning.algorithm.clustering.initializer with parameters of type DivergenceFunction

Constructor and Description
AbstractMinDistanceFixedClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, java.util.Random random) Creates a new instance of AbstractMinDistanceFixedClusterInitializer.
DistanceSamplingClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, java.util.Random random) Creates a new instance of MinDistanceSamplingClusterInitializer.
GreedyClusterInitializer(DivergenceFunction<? super DataType,? super DataType> divergenceFunction, ClusterCreator<ClusterType,DataType> creator, java.util.Random random) Creates a new instance of GreedyClusterInitializer.

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

Methods in gov.sandia.cognition.learning.algorithm.nearest that return DivergenceFunction

Modifier and Type	Method and Description
DivergenceFunction<? super InputType,? super InputType>	NearestNeighbor.getDivergenceFunction() Getter for divergenceFunction

Methods in gov.sandia.cognition.learning.algorithm.nearest with parameters of type DivergenceFunction

Modifier and Type	Method and Description
void	KNearestNeighborKDTree.setDivergenceFunction(DivergenceFunction<? super InputType,? super InputType> divergenceFunction)
void	NearestNeighborKDTree.setDivergenceFunction(DivergenceFunction<? super InputType,? super InputType> divergenceFunction)

Constructors in gov.sandia.cognition.learning.algorithm.nearest with parameters of type DivergenceFunction

Constructor and Description
AbstractKNearestNeighbor(int k, DivergenceFunction<? super InputType,? super InputType> divergenceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of KNearestNeighbor
AbstractNearestNeighbor(DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Creates a new instance of AbstractNearestNeighbor
KNearestNeighborExhaustive(int k, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, DivergenceFunction<? super InputType,? super InputType> divergenceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of KNearestNeighborExhaustive
Learner(DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Creates a new instance of NearestNeighborExhaustive.Learner.
Learner(int k, DivergenceFunction<? super InputType,? super InputType> divergenceFunction, Summarizer<? super OutputType,OutputType> averager) Creates a new instance of Learner
NearestNeighborExhaustive(DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Creates a new instance of NearestNeighborExhaustive.
NearestNeighborExhaustive(DivergenceFunction<? super InputType,? super InputType> divergenceFunction, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a new instance of NearestNeighborExhaustive.
NearestNeighborKDTree(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data, DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Creates a new instance of NearestNeighborKDTree

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

Classes in gov.sandia.cognition.learning.function.distance that implement DivergenceFunction

Modifier and Type	Class and Description
class	ChebyshevDistanceMetric An implementation of the Chebyshev distance, which is the absolute value of the largest difference between two vectors in a single dimension.
class	CosineDistanceMetric The CosineDistanceMetric class implements a semimetric between two vectors based on the cosine between the vectors.
class	EuclideanDistanceMetric The EuclideanDistanceMetric implements a distance metric that computes the Euclidean distance between two points.
class	EuclideanDistanceSquaredMetric The EuclideanDistanceSquaredMetric implements a distance metric that computes the squared Euclidean distance between two points.
class	IdentityDistanceMetric A distance metric that is 0 if two objects are equal and 1 if they are not.
class	ManhattanDistanceMetric The ManhattanDistanceMetric class implements a distance metric between two vectors that is implemented as the sum of the absolute value of the difference between the elements in the vectors.
class	MinkowskiDistanceMetric An implementation of the Minkowski distance metric.
class	WeightedEuclideanDistanceMetric A distance metric that weights each dimension of a vector differently before computing Euclidean distance.

Fields in gov.sandia.cognition.learning.function.distance declared as DivergenceFunction

Modifier and Type	Field and Description
protected DivergenceFunction<? super FirstType,? super SecondType>	DefaultDivergenceFunctionContainer.divergenceFunction The internal divergence function for the object to use.
protected DivergenceFunction<? super ValueType,? super InputType>	DivergencesEvaluator.divergenceFunction The divergence function to apply between the data and the input.
protected DivergenceFunction<? super ValueType,? super InputType>	DivergencesEvaluator.Learner.divergenceFunction The divergence function to apply between the data and the input.

Methods in gov.sandia.cognition.learning.function.distance that return DivergenceFunction

Modifier and Type	Method and Description
DivergenceFunction<? super FirstType,? super SecondType>	DefaultDivergenceFunctionContainer.getDivergenceFunction() Gets the divergence function used by this object.
DivergenceFunction<? super FirstType,? super SecondType>	DivergenceFunctionContainer.getDivergenceFunction() Gets the divergence function used by this object.
DivergenceFunction<? super ValueType,? super InputType>	DivergencesEvaluator.getDivergenceFunction()
DivergenceFunction<? super ValueType,? super InputType>	DivergencesEvaluator.Learner.getDivergenceFunction()

Methods in gov.sandia.cognition.learning.function.distance with parameters of type DivergenceFunction

Modifier and Type	Method and Description
static <DataType,InputType,ValueType> DivergencesEvaluator.Learner<DataType,InputType,ValueType>	DivergencesEvaluator.Learner.create(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Convenience method for creating a DivergencesEvaluator.Learner.
static <InputType,ValueType> DivergencesEvaluator<InputType,ValueType>	DivergencesEvaluator.create(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, java.util.Collection<ValueType> values) Convenience method for creation a DivergeceEvaluator.
void	DefaultDivergenceFunctionContainer.setDivergenceFunction(DivergenceFunction<? super FirstType,? super SecondType> divergenceFunction) Sets the divergence function used by this object.
void	DivergencesEvaluator.setDivergenceFunction(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Sets the divergence function to use from the values to the inputs.
void	DivergencesEvaluator.Learner.setDivergenceFunction(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Sets the divergence function to use from the values to the inputs.

Constructors in gov.sandia.cognition.learning.function.distance with parameters of type DivergenceFunction

Constructor and Description
DefaultDivergenceFunctionContainer(DivergenceFunction<? super FirstType,? super SecondType> divergenceFunction) Creates a new instance of DefaultDivergenceFunctionContainer.
DivergencesEvaluator(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, java.util.Collection<ValueType> values) Creates a new DivergencesEvaluator with the given divergence and values.
DivergencesEvaluator(DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, java.util.Collection<ValueType> values, VectorFactory<?> vectorFactory) Creates a new DivergencesEvaluator with the given divergence and values.
Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Creates a new DivergenceFunction.Learner with the given properties.
Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, VectorFactory<?> vectorFactory) Creates a new DivergenceFunction.Learner with the given properties.

Uses of DivergenceFunction in gov.sandia.cognition.learning.function.kernel

Classes in gov.sandia.cognition.learning.function.kernel that implement DivergenceFunction

Modifier and Type	Class and Description
class	KernelDistanceMetric<InputType> The KernelDistanceMetric class implements a distance metric that utilizes an underlying Kernel for computing the distance.

Uses of DivergenceFunction in gov.sandia.cognition.math

Subinterfaces of DivergenceFunction in gov.sandia.cognition.math

Modifier and Type	Interface and Description
interface	Metric<EvaluatedType> A metric is a non-negative function that satisfies the following properties g(x, y) + g(y, z) >= g(x, z) g(x, y) == g(y, x) g(x, x) == 0.
interface	Semimetric<InputType> A semimetric is a divergence function that takes inputs from the same set (domain) and is positive definite and symmetric.

Uses of DivergenceFunction in gov.sandia.cognition.text.relation

Methods in gov.sandia.cognition.text.relation that return DivergenceFunction

Modifier and Type	Method and Description
DivergenceFunction<FromType,ToType>	SimilarityFunction.asDivergence() Converts the similarity function into a divergence function.