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

Packages that use Semimetric

Package	Description
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
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.

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

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

Modifier and Type	Method and Description
Semimetric<? super DataType>	DBSCANClusterer.getMetric() Gets the distance metric the clustering uses.

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

Modifier and Type	Method and Description
static ParallelizedKMeansClusterer<Vector,CentroidCluster<Vector>>	KMeansFactory.create(int numClusters, Semimetric<? super Vector> distanceMetric, java.util.Random random) Creates a new parallelized k-means clustering algorithm for vector data with the given number of clusters (k), distance metric, and random number generator.
void	DBSCANClusterer.setMetric(Semimetric<? super DataType> metric) Sets the distance metric the clustering uses.

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

Constructor and Description
Builder(int numClusters, Semimetric<? super Vector> metric) Create a mini-batch k-means clusterer builder and set it to the given number of clusters.
DBSCANClusterer(double eps, int minSamples, Semimetric<? super DataType> metric, ClusterCreator<ClusterType,DataType> creator) Creates a new instance of AffinityPropagation.
DBSCANClusterer(Semimetric<? super DataType> metric, ClusterCreator<ClusterType,DataType> creator) Creates a new instance of DBSCANClusterer.
MiniBatchKMeansClusterer(int numClusters, int maxIterations, FixedClusterInitializer<MiniBatchCentroidCluster,Vector> initializer, Semimetric<? super Vector> metric, ClusterCreator<MiniBatchCentroidCluster,Vector> creator, java.util.Random random) Creates a new MiniBatchKMeansClusterer.

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

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

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.

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

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

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 Semimetric in gov.sandia.cognition.math

Subinterfaces of Semimetric 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.