### Question:
TupleKDTree { public static TupleKDTree forTupleListBalanced(final TupleList tuples, final DistanceMetric distanceMetric) { final TupleKDTree kd = new TupleKDTree(tuples, distanceMetric); final int tupleCount = tuples.getTupleCount(); final int tupleLen = tuples.getTupleLength(); final int[] tupleIndices = new int[tupleCount]; for (int i = 0; i < tupleCount; i++) { tupleIndices[i] = i; } final IntComparator[] comparators = new IntComparator[tupleLen]; for (int dim = 0; dim < tupleLen; dim++) { comparators[dim] = new TupleIndexComparator(tuples, dim); } generateBalanced(kd, tupleIndices, 0, tupleIndices.length - 1, 0, comparators); return kd; } TupleKDTree(final TupleList tuples, final DistanceMetric distanceMetric); KDNode getRoot(); TupleList getTupleList(); DistanceMetric getDistanceMetric(); int getDimensions(); static TupleKDTree forTupleList(final TupleList tuples,
        final DistanceMetric distanceMetric); static TupleKDTree forTupleListBalanced(final TupleList tuples,
        final DistanceMetric distanceMetric); void insert(final int ndx); boolean contains(final int ndx); int search(final double[] coords); int nearestNeighbor(final int ndx); int nearest(final double[] coords); int[] nearest(final int ndx, final int num); int[] nearest(final double[] coords, final int num); int[] closeTo(final int ndx, final double maxDistance); int[] closeTo(final double[] coords, final double maxDistance); int[] inside(final HyperRect rect);  }### Answer:
@Test public void testForTupleListBalanced() { int tupleCount = 100; int numClusters = 10; int tupleLength = 10; int nnCount = 5; TupleList tuples = generateTestTuples(tupleCount, tupleLength, numClusters, 123L); DistanceMetric distMetric = new EuclideanDistanceMetric(); TupleKDTree kdTree = TupleKDTree.forTupleListBalanced(tuples, distMetric); Random random = new Random(123L); int ndx = random.nextInt(tupleCount); List<TupleKDTree.DistanceEntry> distEntries = sortedDistanceEntries(ndx, tuples, distMetric); System.out.printf("Using dumb searching, the %d nearest neighbors are:\n", nnCount); final int lim = Math.min(nnCount, distEntries.size()); int[] dumbNns = new int[lim]; for (int i=0; i<lim; i++) { TupleKDTree.DistanceEntry entry = distEntries.get(i); dumbNns[i] = entry.getIndex(); } int[] nns = kdTree.nearest(ndx, nnCount); for (int i=0; i<lim; i++) { assertTrue(dumbNns[i] == nns[i]); } double[] buf1 = new double[tupleLength]; double[] buf2 = new double[tupleLength]; tuples.getTuple(ndx, buf1); tuples.getTuple(nns[nns.length - 1], buf2); double md = distMetric.distance(buf1, buf2); int[] closeto = kdTree.closeTo(ndx, md); System.out.println(" md = " + md); System.out.println(" ndx = " + ndx); System.out.println(" nns = " + toStr(nns)); System.out.println("closeTo = " + toStr(closeto)); TupleKDTree.KDNode node = kdTree.getRoot(); System.out.println("root balance factor = " + node.balanceFactor()); }