## Uses of Interface
org.apache.commons.math3.ml.distance.DistanceMeasure

- Packages that use DistanceMeasure
  Package | Description
  --- | ---
  org.apache.commons.math3.ml.clustering | Clustering algorithms.
  org.apache.commons.math3.ml.clustering.evaluation | Cluster evaluation methods.
  org.apache.commons.math3.ml.distance | Common distance measures.
  org.apache.commons.math3.ml.neuralnet | Neural networks.
  org.apache.commons.math3.ml.neuralnet.sofm | Self Organizing Feature Map.

- - ### Uses of DistanceMeasure in org.apache.commons.math3.ml.clustering

    Methods in org.apache.commons.math3.ml.clustering that return DistanceMeasure
    Modifier and Type | Method and Description
    --- | ---
    `DistanceMeasure` | Clusterer.`getDistanceMeasure()` Returns the `DistanceMeasure` instance used by this clusterer.

    Constructors in org.apache.commons.math3.ml.clustering with parameters of type DistanceMeasure
    Constructor and Description
    `Clusterer(DistanceMeasure measure)` Build a new clusterer with the given `DistanceMeasure`.
    `DBSCANClusterer(double eps, int minPts, DistanceMeasure measure)` Creates a new instance of a DBSCANClusterer.
    `FuzzyKMeansClusterer(int k, double fuzziness, int maxIterations, DistanceMeasure measure)` Creates a new instance of a FuzzyKMeansClusterer.
    `FuzzyKMeansClusterer(int k, double fuzziness, int maxIterations, DistanceMeasure measure, double epsilon, RandomGenerator random)` Creates a new instance of a FuzzyKMeansClusterer.
    `KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure)` Build a clusterer.
    `KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random)` Build a clusterer.
    `KMeansPlusPlusClusterer(int k, int maxIterations, DistanceMeasure measure, RandomGenerator random, KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)` Build a clusterer.

  - ### Uses of DistanceMeasure in org.apache.commons.math3.ml.clustering.evaluation

    Constructors in org.apache.commons.math3.ml.clustering.evaluation with parameters of type DistanceMeasure
    Constructor and Description
    `ClusterEvaluator(DistanceMeasure measure)` Creates a new cluster evaluator with the given distance measure.
    `SumOfClusterVariances(DistanceMeasure measure)`

  - ### Uses of DistanceMeasure in org.apache.commons.math3.ml.distance

    Classes in org.apache.commons.math3.ml.distance that implement DistanceMeasure
    Modifier and Type | Class and Description
    --- | ---
    `class` | `CanberraDistance` Calculates the Canberra distance between two points.
    `class` | `ChebyshevDistance` Calculates the L∞ (max of abs) distance between two points.
    `class` | `EarthMoversDistance` Calculates the Earh Mover's distance (also known as Wasserstein metric) between two distributions.
    `class` | `EuclideanDistance` Calculates the L2 (Euclidean) distance between two points.
    `class` | `ManhattanDistance` Calculates the L1 (sum of abs) distance between two points.

  - ### Uses of DistanceMeasure in org.apache.commons.math3.ml.neuralnet

    Methods in org.apache.commons.math3.ml.neuralnet with parameters of type DistanceMeasure
    Modifier and Type | Method and Description
    --- | ---
    `static int[][]` | MapUtils.`computeHitHistogram(Iterable<double[]> data, NeuronSquareMesh2D map, DistanceMeasure distance)` Computes the "hit" histogram of a two-dimensional map.
    `static double` | MapUtils.`computeQuantizationError(Iterable<double[]> data, Iterable<Neuron> neurons, DistanceMeasure distance)` Computes the quantization error.
    `static double` | MapUtils.`computeTopographicError(Iterable<double[]> data, Network net, DistanceMeasure distance)` Computes the topographic error.
    `static double[][]` | MapUtils.`computeU(NeuronSquareMesh2D map, DistanceMeasure distance)` Computes the U-matrix of a two-dimensional map.
    `static Neuron` | MapUtils.`findBest(double[] features, Iterable<Neuron> neurons, DistanceMeasure distance)` Finds the neuron that best matches the given features.
    `static Pair<Neuron,Neuron>` | MapUtils.`findBestAndSecondBest(double[] features, Iterable<Neuron> neurons, DistanceMeasure distance)` Finds the two neurons that best match the given features.

  - ### Uses of DistanceMeasure in org.apache.commons.math3.ml.neuralnet.sofm

    Constructors in org.apache.commons.math3.ml.neuralnet.sofm with parameters of type DistanceMeasure
    Constructor and Description
    `KohonenUpdateAction(DistanceMeasure distance, LearningFactorFunction learningFactor, NeighbourhoodSizeFunction neighbourhoodSize)`

Copyright © 2003–2014 The Apache Software Foundation. All rights reserved.