Uses of Class
at.tuwien.ifs.somtoolbox.visualization.clustering.LabelCoordinates

Packages that use LabelCoordinates
at.tuwien.ifs.somtoolbox.visualization.clustering Contains classes implementing clustering methods on the SOM. 
at.tuwien.ifs.somtoolbox.visualization.comparison   
 

Uses of LabelCoordinates in at.tuwien.ifs.somtoolbox.visualization.clustering
 

Methods in at.tuwien.ifs.somtoolbox.visualization.clustering with parameters of type LabelCoordinates
 double LabelCoordinates.distance(LabelCoordinates other)
          Calculate euclidean distance between this point and the other point
 

Uses of LabelCoordinates in at.tuwien.ifs.somtoolbox.visualization.comparison
 

Fields in at.tuwien.ifs.somtoolbox.visualization.comparison declared as LabelCoordinates
private  LabelCoordinates[] SOMComparison.coords1
           
private  LabelCoordinates[] SOMComparison.coords2
           
 

Methods in at.tuwien.ifs.somtoolbox.visualization.comparison that return LabelCoordinates
private  LabelCoordinates SOMComparison.getClusterMeanPoint(int[][] assignment, int y, int x, GrowingSOM gsom)
          Try to find a mean-point for a cluster
static LabelCoordinates[] SOMComparison.getLabelCoordinates(GrowingSOM gsom)
           
 

Methods in at.tuwien.ifs.somtoolbox.visualization.comparison with parameters of type LabelCoordinates
static double[][] SOMComparison.calculcateIntraSOMClusterDistanceMatrix(LabelCoordinates[] coords, int[][] secSOMClusterAssignment, int clusterNo, double[][] distances)
          Calculate the cluster distance matrix for all mapped vectors from the information where the inputs are mapped
static double[][] SOMComparison.calculcateIntraSOMDistanceMatrix(LabelCoordinates[] coords)
          Calculate the distance matrix for all mapped vectors from the information where the inputs are mapped
 int[] SOMComparison.clusterEquivalent(int[][] assignment1, int[][] assignment2, LabelCoordinates[] coords1, LabelCoordinates[] coords2, int numberOfClusters, double[] percentages)
           
 int[] SOMComparison.clusterEquivalent(int[][] assignment1, int[][] assignment2, LabelCoordinates[] coords1, LabelCoordinates[] coords2, int numberOfClusters, double[] percentages)