private int[] compute(double[][] points)
{
// first, select initial points
double[][] seeds = createSeeds(points, 2 * Bandwidth);
var maxcandidates = new ConcurrentStack <double[]>();
// construct map of the data
tree = KDTree.FromData <int>(points, distance);
// now, for each initial point
if (UseParallelProcessing)
{
Parallel.For(0, seeds.Length, (index) =>
iterate(seeds, maxcandidates, index));
}
else
{
for (int index = 0; index < seeds.Length; index++)
{
iterate(seeds, maxcandidates, index);
}
}
// suppress non-maximum points
double[][] maximum = cut ? maxcandidates.ToArray() : supress(seeds);
// create a decision map using seeds
int[] seedLabels = classifySeeds(seeds, maximum);
tree = KDTree.FromData(seeds, seedLabels, distance);
// create the cluster structure
clusters = new MeanShiftClusterCollection(tree, maximum);
// label each point
return(clusters.Nearest(points));
}
/// <summary>
/// Initializes a new instance of the <see cref="MeanShiftCluster"/> class.
/// </summary>
///
/// <param name="owner">The owner.</param>
/// <param name="index">The cluster index.</param>
///
public MeanShiftCluster(MeanShiftClusterCollection owner, int index)
{
this.owner = owner;
this.index = index;
}
