Index (Hipparchus::Cluster Algorithms 3.1 API)

A C D E F G H I K L M O S T V
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A

addPoint(T) - Method in class org.hipparchus.clustering.Cluster: Add a point to this cluster.

C

CanberraDistance - Class in org.hipparchus.clustering.distance: Calculates the Canberra distance between two points.
CanberraDistance() - Constructor for class org.hipparchus.clustering.distance.CanberraDistance: Empty constructor.
CentroidCluster<T extends Clusterable> - Class in org.hipparchus.clustering: A Cluster used by centroid-based clustering algorithms.
CentroidCluster(Clusterable) - Constructor for class org.hipparchus.clustering.CentroidCluster: Build a cluster centered at a specified point.
centroidOf(Cluster<T>) - Method in class org.hipparchus.clustering.evaluation.ClusterEvaluator: Computes the centroid for a cluster.
ChebyshevDistance - Class in org.hipparchus.clustering.distance: Calculates the L_∞ (max of abs) distance between two points.
ChebyshevDistance() - Constructor for class org.hipparchus.clustering.distance.ChebyshevDistance: Empty constructor.
cluster(Collection<T>) - Method in class org.hipparchus.clustering.Clusterer: Perform a cluster analysis on the given set of Clusterable instances.
cluster(Collection<T>) - Method in class org.hipparchus.clustering.DBSCANClusterer: Performs DBSCAN cluster analysis.
cluster(Collection<T>) - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Performs Fuzzy K-Means cluster analysis.
cluster(Collection<T>) - Method in class org.hipparchus.clustering.KMeansPlusPlusClusterer: Runs the K-means++ clustering algorithm.
cluster(Collection<T>) - Method in class org.hipparchus.clustering.MultiKMeansPlusPlusClusterer: Runs the K-means++ clustering algorithm.
Cluster<T extends Clusterable> - Class in org.hipparchus.clustering: Cluster holding a set of Clusterable points.
Cluster() - Constructor for class org.hipparchus.clustering.Cluster: Build a cluster centered at a specified point.
Clusterable - Interface in org.hipparchus.clustering: Interface for n-dimensional points that can be clustered together.
Clusterer<T extends Clusterable> - Class in org.hipparchus.clustering: Base class for clustering algorithms.
Clusterer(DistanceMeasure) - Constructor for class org.hipparchus.clustering.Clusterer: Build a new clusterer with the given DistanceMeasure.
ClusterEvaluator<T extends Clusterable> - Class in org.hipparchus.clustering.evaluation: Base class for cluster evaluation methods.
ClusterEvaluator() - Constructor for class org.hipparchus.clustering.evaluation.ClusterEvaluator: Creates a new cluster evaluator with an EuclideanDistance as distance measure.
ClusterEvaluator(DistanceMeasure) - Constructor for class org.hipparchus.clustering.evaluation.ClusterEvaluator: Creates a new cluster evaluator with the given distance measure.
compute(double[], double[]) - Method in class org.hipparchus.clustering.distance.CanberraDistance: Compute the distance between two n-dimensional vectors.
compute(double[], double[]) - Method in class org.hipparchus.clustering.distance.ChebyshevDistance: Compute the distance between two n-dimensional vectors.
compute(double[], double[]) - Method in interface org.hipparchus.clustering.distance.DistanceMeasure: Compute the distance between two n-dimensional vectors.
compute(double[], double[]) - Method in class org.hipparchus.clustering.distance.EarthMoversDistance: Compute the distance between two n-dimensional vectors.
compute(double[], double[]) - Method in class org.hipparchus.clustering.distance.EuclideanDistance: Compute the distance between two n-dimensional vectors.
compute(double[], double[]) - Method in class org.hipparchus.clustering.distance.ManhattanDistance: Compute the distance between two n-dimensional vectors.

D

DBSCANClusterer<T extends Clusterable> - Class in org.hipparchus.clustering: DBSCAN (density-based spatial clustering of applications with noise) algorithm.
DBSCANClusterer(double, int) - Constructor for class org.hipparchus.clustering.DBSCANClusterer: Creates a new instance of a DBSCANClusterer.
DBSCANClusterer(double, int, DistanceMeasure) - Constructor for class org.hipparchus.clustering.DBSCANClusterer: Creates a new instance of a DBSCANClusterer.
distance(Clusterable, Clusterable) - Method in class org.hipparchus.clustering.Clusterer: Calculates the distance between two Clusterable instances with the configured DistanceMeasure.
distance(Clusterable, Clusterable) - Method in class org.hipparchus.clustering.evaluation.ClusterEvaluator: Calculates the distance between two Clusterable instances with the configured DistanceMeasure.
DistanceMeasure - Interface in org.hipparchus.clustering.distance: Interface for distance measures of n-dimensional vectors.
DoublePoint - Class in org.hipparchus.clustering: A simple implementation of Clusterable for points with double coordinates.
DoublePoint(double[]) - Constructor for class org.hipparchus.clustering.DoublePoint: Build an instance wrapping an double array.
DoublePoint(int[]) - Constructor for class org.hipparchus.clustering.DoublePoint: Build an instance wrapping an integer array.

E

EarthMoversDistance - Class in org.hipparchus.clustering.distance: Calculates the Earh Mover's distance (also known as Wasserstein metric) between two distributions.
EarthMoversDistance() - Constructor for class org.hipparchus.clustering.distance.EarthMoversDistance: Empty constructor.
EMPTY_CLUSTER_IN_K_MEANS - Enum constant in enum org.hipparchus.clustering.LocalizedClusteringFormats: EMPTY_CLUSTER_IN_K_MEANS.
equals(Object) - Method in class org.hipparchus.clustering.DoublePoint
ERROR - Enum constant in enum org.hipparchus.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy: Generate an error.
EuclideanDistance - Class in org.hipparchus.clustering.distance: Calculates the L₂ (Euclidean) distance between two points.
EuclideanDistance() - Constructor for class org.hipparchus.clustering.distance.EuclideanDistance: Empty constructor.

F

FARTHEST_POINT - Enum constant in enum org.hipparchus.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy: Create a cluster around the point farthest from its centroid.
FuzzyKMeansClusterer<T extends Clusterable> - Class in org.hipparchus.clustering: Fuzzy K-Means clustering algorithm.
FuzzyKMeansClusterer(int, double) - Constructor for class org.hipparchus.clustering.FuzzyKMeansClusterer: Creates a new instance of a FuzzyKMeansClusterer.
FuzzyKMeansClusterer(int, double, int, DistanceMeasure) - Constructor for class org.hipparchus.clustering.FuzzyKMeansClusterer: Creates a new instance of a FuzzyKMeansClusterer.
FuzzyKMeansClusterer(int, double, int, DistanceMeasure, double, RandomGenerator) - Constructor for class org.hipparchus.clustering.FuzzyKMeansClusterer: Creates a new instance of a FuzzyKMeansClusterer.

G

getCenter() - Method in class org.hipparchus.clustering.CentroidCluster: Get the point chosen to be the center of this cluster.
getClusterer() - Method in class org.hipparchus.clustering.MultiKMeansPlusPlusClusterer: Returns the embedded k-means clusterer used by this instance.
getClusterEvaluator() - Method in class org.hipparchus.clustering.MultiKMeansPlusPlusClusterer: Returns the ClusterEvaluator used to determine the "best" clustering.
getClusters() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns the list of clusters resulting from the last call to FuzzyKMeansClusterer.cluster(Collection).
getDataPoints() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns an unmodifiable list of the data points used in the last call to FuzzyKMeansClusterer.cluster(Collection).
getDistanceMeasure() - Method in class org.hipparchus.clustering.Clusterer: Returns the DistanceMeasure instance used by this clusterer.
getEmptyClusterStrategy() - Method in class org.hipparchus.clustering.KMeansPlusPlusClusterer: Returns the KMeansPlusPlusClusterer.EmptyClusterStrategy used by this instance.
getEps() - Method in class org.hipparchus.clustering.DBSCANClusterer: Returns the maximum radius of the neighborhood to be considered.
getEpsilon() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns the convergence criteria used by this instance.
getFuzziness() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns the fuzziness factor used by this instance.
getK() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Return the number of clusters this instance will use.
getK() - Method in class org.hipparchus.clustering.KMeansPlusPlusClusterer: Return the number of clusters this instance will use.
getLocalizedString(Locale) - Method in enum org.hipparchus.clustering.LocalizedClusteringFormats
getMaxIterations() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns the maximum number of iterations this instance will use.
getMaxIterations() - Method in class org.hipparchus.clustering.KMeansPlusPlusClusterer: Returns the maximum number of iterations this instance will use.
getMembershipMatrix() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns the nxk membership matrix, where n is the number of data points and k the number of clusters.
getMinPts() - Method in class org.hipparchus.clustering.DBSCANClusterer: Returns the minimum number of points needed for a cluster.
getNumTrials() - Method in class org.hipparchus.clustering.MultiKMeansPlusPlusClusterer: Returns the number of trials this instance will do.
getObjectiveFunctionValue() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Get the value of the objective function.
getPoint() - Method in interface org.hipparchus.clustering.Clusterable: Gets the n-dimensional point.
getPoint() - Method in class org.hipparchus.clustering.DoublePoint: Gets the n-dimensional point.
getPoints() - Method in class org.hipparchus.clustering.Cluster: Get the points contained in the cluster.
getRandomGenerator() - Method in class org.hipparchus.clustering.FuzzyKMeansClusterer: Returns the random generator this instance will use.
getRandomGenerator() - Method in class org.hipparchus.clustering.KMeansPlusPlusClusterer: Returns the random generator this instance will use.
getSourceString() - Method in enum org.hipparchus.clustering.LocalizedClusteringFormats

H

hashCode() - Method in class org.hipparchus.clustering.DoublePoint

I

isBetterScore(double, double) - Method in class org.hipparchus.clustering.evaluation.ClusterEvaluator: Returns whether the first evaluation score is considered to be better than the second one by this evaluator.

K

KMeansPlusPlusClusterer<T extends Clusterable> - Class in org.hipparchus.clustering: Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
KMeansPlusPlusClusterer(int) - Constructor for class org.hipparchus.clustering.KMeansPlusPlusClusterer: Build a clusterer.
KMeansPlusPlusClusterer(int, int) - Constructor for class org.hipparchus.clustering.KMeansPlusPlusClusterer: Build a clusterer.
KMeansPlusPlusClusterer(int, int, DistanceMeasure) - Constructor for class org.hipparchus.clustering.KMeansPlusPlusClusterer: Build a clusterer.
KMeansPlusPlusClusterer(int, int, DistanceMeasure, RandomGenerator) - Constructor for class org.hipparchus.clustering.KMeansPlusPlusClusterer: Build a clusterer.
KMeansPlusPlusClusterer(int, int, DistanceMeasure, RandomGenerator, KMeansPlusPlusClusterer.EmptyClusterStrategy) - Constructor for class org.hipparchus.clustering.KMeansPlusPlusClusterer: Build a clusterer.
KMeansPlusPlusClusterer.EmptyClusterStrategy - Enum in org.hipparchus.clustering: Strategies to use for replacing an empty cluster.

L

LARGEST_POINTS_NUMBER - Enum constant in enum org.hipparchus.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy: Split the cluster with largest number of points.
LARGEST_VARIANCE - Enum constant in enum org.hipparchus.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy: Split the cluster with largest distance variance.
LocalizedClusteringFormats - Enum in org.hipparchus.clustering: Enumeration for localized messages formats used in exceptions messages.

M

ManhattanDistance - Class in org.hipparchus.clustering.distance: Calculates the L₁ (sum of abs) distance between two points.
ManhattanDistance() - Constructor for class org.hipparchus.clustering.distance.ManhattanDistance: Empty constructor.
MultiKMeansPlusPlusClusterer<T extends Clusterable> - Class in org.hipparchus.clustering: A wrapper around a k-means++ clustering algorithm which performs multiple trials and returns the best solution.
MultiKMeansPlusPlusClusterer(KMeansPlusPlusClusterer<T>, int) - Constructor for class org.hipparchus.clustering.MultiKMeansPlusPlusClusterer: Build a clusterer.
MultiKMeansPlusPlusClusterer(KMeansPlusPlusClusterer<T>, int, ClusterEvaluator<T>) - Constructor for class org.hipparchus.clustering.MultiKMeansPlusPlusClusterer: Build a clusterer.

O

org.hipparchus.clustering - package org.hipparchus.clustering: Clustering algorithms.
org.hipparchus.clustering.distance - package org.hipparchus.clustering.distance: Common distance measures.
org.hipparchus.clustering.evaluation - package org.hipparchus.clustering.evaluation: Cluster evaluation methods.

S

score(List<? extends Cluster<T>>) - Method in class org.hipparchus.clustering.evaluation.ClusterEvaluator: Computes the evaluation score for the given list of clusters.
score(List<? extends Cluster<T>>) - Method in class org.hipparchus.clustering.evaluation.SumOfClusterVariances: Computes the evaluation score for the given list of clusters.
SumOfClusterVariances<T extends Clusterable> - Class in org.hipparchus.clustering.evaluation: Computes the sum of intra-cluster distance variances according to the formula: \] score = \sum\limits_{i=1}^n \sigma_i^2 \] where n is the number of clusters and \( \sigma_i^2 \) is the variance of intra-cluster distances of cluster \( c_i \).
SumOfClusterVariances(DistanceMeasure) - Constructor for class org.hipparchus.clustering.evaluation.SumOfClusterVariances: Simple constructor.

T

toString() - Method in class org.hipparchus.clustering.DoublePoint

V

valueOf(String) - Static method in enum org.hipparchus.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.clustering.LocalizedClusteringFormats: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.hipparchus.clustering.KMeansPlusPlusClusterer.EmptyClusterStrategy: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.clustering.LocalizedClusteringFormats: Returns an array containing the constants of this enum type, in the order they are declared.

A C D E F G H I K L M O S T V
All Classes and Interfaces|All Packages|Serialized Form