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

Packages that use Metric
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
 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
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
 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
 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
KNearestNeighborKDTree.Learner(int k, Metric<? super Vectorizable> divergenceFunction, Summarizer<? super OutputType,? extends OutputType> averager)
          Creates a new instance of Learner
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
NearestNeighborKDTree.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
 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
 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
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.
 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.