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

Packages that use Metric

Package	Description
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
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.geometry	Provides classes and interfaces for computational geometry.

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

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

Modifier and Type	Method and Description
Metric<? super DataType>	OptimizedKMeansClusterer.getMetric() Gets the metric being used by the algorithm.

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

Constructor and Description
OptimizedKMeansClusterer(int numClusters, int maxIterations, FixedClusterInitializer<CentroidCluster<DataType>,DataType> initializer, Metric<? super DataType> metric, ClusterCreator<CentroidCluster<DataType>,DataType> creator) Creates a new instance of OptimizedKMeansClusterer.

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

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

Modifier and Type	Method and Description
Metric<? super InputType>	KNearestNeighborKDTree.getDivergenceFunction() Setter for distanceFunction
Metric<? super InputType>	NearestNeighborKDTree.getDivergenceFunction() Setter for distanceFunction

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

Modifier and Type	Method and Description
void	KNearestNeighborKDTree.setDivergenceFunction(Metric<? super InputType> divergenceFunction) Sets the Metric to use.
void	NearestNeighborKDTree.setDivergenceFunction(Metric<? super InputType> divergenceFunction) Sets the Metric to use.

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

Constructor and Description
KNearestNeighborKDTree(int k, KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data, Metric<? super InputType> distanceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of KNearestNeighborKDTree
Learner(int k, Metric<? super Vectorizable> divergenceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of Learner
Learner(Metric<? super Vectorizable> divergenceFunction) Creates a new instance of Learner

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

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

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	EuclideanDistanceMetric The EuclideanDistanceMetric implements a distance metric that computes the 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.

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

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

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 Metric in gov.sandia.cognition.math.geometry

Methods in gov.sandia.cognition.math.geometry with parameters of type Metric

Modifier and Type	Method and Description
protected void	KDTree.findNearest(VectorType key, int k, KDTree.Neighborhood<VectorType,DataType,PairType> neighborhood, Metric<? super VectorType> metric) Finds the "num" nearest neighbors to the given "key" stored in the KDTree.
java.util.Collection<PairType>	KDTree.findNearest(VectorType key, int k, Metric<? super VectorType> metric) Finds the "num" nearest neighbors to the given "key" stored in the KDTree.
protected void	KDTree.findNearestWithinRadius(VectorType key, double radius, KDTree.Neighborhood<VectorType,DataType,PairType> neighborhood, Metric<? super VectorType> metric) Finds the neighbors within a given distance to the given "key" stored in the KDTree.
java.util.Collection<PairType>	KDTree.findNearestWithinRadius(VectorType key, double radius, Metric<? super VectorType> metric) Finds the neighbors within a given distance to the given "key" stored in the KDTree.