@Override public int[][] performClustering(int[][] data) { return new IndexClusters(this.cluster(data).defaultHardAssigner().assign(data)).clusters(); } }
@Override public int[][] performClustering(byte[][] data) { ByteCentroidsResult res = this.cluster(data); return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(int[][] data) { IntCentroidsResult res = this.cluster(data); return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(long[][] data) { LongCentroidsResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(float[][] data) { FloatCentroidsResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(float[][] data) { FloatCentroidsResult res = this.cluster(data); return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(double[][] data) { DoubleCentroidsResult res = this.cluster(data); return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(long[][] data) { HierarchicalLongKMeansResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(byte[][] data) { ByteCentroidsResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(float[][] data) { HierarchicalFloatKMeansResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(double[][] data) { DoubleCentroidsResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(int[][] data) { return new IndexClusters(this.cluster(data).defaultHardAssigner().assign(data)).clusters(); } }
@Override public int[][] performClustering(short[][] data) { HierarchicalShortKMeansResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(short[][] data) { ShortCentroidsResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(long[][] data) { LongCentroidsResult res = this.cluster(data); return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(double[][] data) { HierarchicalDoubleKMeansResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(int[][] data) { HierarchicalIntKMeansResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(short[][] data) { ShortCentroidsResult res = this.cluster(data); return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(byte[][] data) { HierarchicalByteKMeansResult clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }
@Override public int[][] performClustering(T[] data) { final FeatureVectorCentroidsResult<T> clusters = this.cluster(data); return new IndexClusters(clusters.defaultHardAssigner().assign(data)).clusters(); }