Uses of Class
org.ojalgo.data.cluster.Point
Packages that use Point
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Uses of Point in org.ojalgo.data.cluster
Methods in org.ojalgo.data.cluster that return PointModifier and TypeMethodDescription(package private) Point
PointDistanceCache.centroid
(Collection<Point> cluster) static Point
Point.mean
(Collection<Point> points) Point.Factory.newPoint
(float... coordinates) static Point
Point.of
(int id, float... coordinates) Methods in org.ojalgo.data.cluster that return types with arguments of type PointModifier and TypeMethodDescriptionPoint.cluster
(Collection<Point> input) Essentially works like this: Calculate, and store, distances between all the points (to enable statistical analysis, and speed up the following steps) Perform statistical analysis of the distances to determine a suitable distance threshold for greedy clustering Perform greedy clustering to get an initial set of centroids Filter out centroids/clusters corresponding to extremely small clusters (This determines the 'k') Perform k-means clustering to refine the clusters and centroidsConverts a list of objects to a list of points using the provided converter to derive the coordinates.PointDistanceCache.initialiser
(Collection<Point> input) static ClusteringAlgorithm
<Point> Point.newGreedyClusterer
(double distanceThreshold) Greedy algorithm.static ClusteringAlgorithm
<Point> Point.newKMeansClusterer
(int k) Standard k-means clusteringMethods in org.ojalgo.data.cluster with parameters of type PointModifier and TypeMethodDescriptionint
double
The sum of the squared differences between the coordinates of this and the other point.(package private) double
Method parameters in org.ojalgo.data.cluster with type arguments of type PointModifier and TypeMethodDescription(package private) Point
PointDistanceCache.centroid
(Collection<Point> cluster) Point.cluster
(Collection<Point> input) Essentially works like this: Calculate, and store, distances between all the points (to enable statistical analysis, and speed up the following steps) Perform statistical analysis of the distances to determine a suitable distance threshold for greedy clustering Perform greedy clustering to get an initial set of centroids Filter out centroids/clusters corresponding to extremely small clusters (This determines the 'k') Perform k-means clustering to refine the clusters and centroidsPointDistanceCache.initialiser
(Collection<Point> input) static Point
Point.mean
(Collection<Point> points) (package private) void
PointDistanceCache.setup
(Collection<Point> input, ToDoubleBiFunction<Point, Point> distanceCalculator) (package private) void
PointDistanceCache.setup
(Collection<Point> input, ToDoubleBiFunction<Point, Point> distanceCalculator) (package private) void
PointDistanceCache.setup
(Collection<Point> input, ToDoubleBiFunction<Point, Point> distanceCalculator)