| 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. | 
| org.apache.commons.math3.ml.neuralnet.twod.util | Utilities to visualize two-dimensional neural networks. | 
| Modifier and Type | Method and Description | 
|---|---|
| DistanceMeasure | Clusterer. getDistanceMeasure()Returns the  DistanceMeasureinstance used by this clusterer. | 
| 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. | 
| Constructor and Description | 
|---|
| ClusterEvaluator(DistanceMeasure measure)Creates a new cluster evaluator with the given distance measure. | 
| SumOfClusterVariances(DistanceMeasure measure) | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | CanberraDistanceCalculates the Canberra distance between two points. | 
| class  | ChebyshevDistanceCalculates the L∞ (max of abs) distance between two points. | 
| class  | EarthMoversDistanceCalculates the Earh Mover's distance (also known as Wasserstein metric) between two distributions. | 
| class  | EuclideanDistanceCalculates the L2 (Euclidean) distance between two points. | 
| class  | ManhattanDistanceCalculates the L1 (sum of abs) distance between two points. | 
| 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. | 
| static Neuron[] | MapUtils. sort(double[] features,
    Iterable<Neuron> neurons,
    DistanceMeasure distance)Creates a list of neurons sorted in increased order of the distance
 to the given  features. | 
| Constructor and Description | 
|---|
| KohonenUpdateAction(DistanceMeasure distance,
                   LearningFactorFunction learningFactor,
                   NeighbourhoodSizeFunction neighbourhoodSize) | 
| Constructor and Description | 
|---|
| HitHistogram(boolean normalizeCount,
            DistanceMeasure distance) | 
| QuantizationError(DistanceMeasure distance) | 
| SmoothedDataHistogram(int smoothingBins,
                     DistanceMeasure distance) | 
| TopographicErrorHistogram(boolean relativeCount,
                         DistanceMeasure distance) | 
| UnifiedDistanceMatrix(boolean individualDistances,
                     DistanceMeasure distance)Simple constructor. | 
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