/// <summary>
/// Build the index
/// </summary>
public virtual void Build(MetricDB db, int bsize, Random rand)
{
this.DB = db;
var n = this.DB.Count;
// randomized has very good performance, even compared with more "intelligent" strategies
var dseq = new DynamicSequentialOrdered ();
dseq.Build (db, rand);
this.NODES = new List<Node> (n / bsize + 1);
var L = new List<ItemPair> (n);
while (dseq.Count > 0) {
if (this.NODES.Count % 100 == 0) {
Console.WriteLine ("XXX {0}, bucketSize: {1}, remain {2}/{3}, db: {4}, date-time: {5}",
this, bsize, dseq.Count, db.Count, Path.GetFileName(db.Name), DateTime.Now);
}
var refID = dseq.GetAnyItem ();
dseq.Remove (refID);
L.Clear ();
dseq.ComputeDistances (this.DB[refID], L);
var near = new Result(bsize);
var far = new Result (1);
dseq.AppendKExtremes (near, far, L);
var node = new Node (refID);
this.NODES.Add (node);
dseq.Remove (near);
foreach (var p in near) {
node.Add(p.ObjID, p.Dist);
}
}
}
public virtual void Build(MetricDB DB, double alpha, int min_bs, int seed, bool do_far)
{
var idxDynamic = new DynamicSequentialOrdered ();
idxDynamic.Build (DB, RandomSets.GetRandomPermutation(DB.Count, new Random(seed)));
// this.Items = new ItemPair[DB.Count];
var pivs = new List<Pivot> (32);
var items = new List<ItemPair> (DB.Count);
int I = 0;
var extreme_items = new List<ItemPair>(idxDynamic.Count);
while (idxDynamic.Count > 0) {
var pidx = idxDynamic.GetAnyItem();
object piv = DB[pidx];
idxDynamic.Remove(pidx);
// this.Items[pidx] = new ItemPair(pidx, 0);
DynamicSequential.Stats stats;
Pivot piv_data;
double near_first = double.MaxValue;
double near_last = 0;
double far_first = double.MaxValue;
int num_near = 0;
int num_far = 0;
{
IResult near, far;
this.SearchExtremes(idxDynamic, extreme_items, piv, alpha, min_bs, out near, out far, out stats);
foreach (var pair in near) {
near_first = Math.Min (near_first, pair.Dist);
near_last = Math.Max (near_last, pair.Dist);
items.Add( new ItemPair { ObjID = pair.ObjID, Dist = pair.Dist} );
}
num_near = near.Count;
idxDynamic.Remove(near);
if (do_far) {
foreach (var pair in far) {
far_first = Math.Min (far_first, pair.Dist);
items.Add( new ItemPair {ObjID = pair.ObjID, Dist = pair.Dist} );
}
num_far = far.Count;
idxDynamic.Remove(far);
}
piv_data = new Pivot(pidx, stats.mean, stats.stddev, near_last, far_first, num_near, num_far);
pivs.Add(piv_data);
}
if (I % 10 == 0) {
Console.WriteLine ("");
Console.WriteLine (this.ToString());
Console.WriteLine("-- I {0}> remains: {1}, alpha: {2}, mean: {3}, stddev: {4}, pivot: {5}, min_bs: {6}, db: {7}, do_far: {8}",
I, idxDynamic.Count, alpha, stats.mean, stats.stddev, pidx, min_bs, DB.Name, do_far);
if (piv_data.num_near > 0) {
Console.WriteLine("-- (NORMVAL) first-near: {0}, last-near: {1}, near-count: {2}",
near_first / stats.max, piv_data.last_near / stats.max, piv_data.num_near);
}
if (piv_data.num_far > 0) {
Console.WriteLine("++ (NORMVAL) first-far: {0}, far-count: {1}",
piv_data.first_far / stats.max, piv_data.num_far);
}
}
++I;
//Console.WriteLine("Number of objects after: {0}",idxDynamic.DOCS.Count);
}
Console.WriteLine("Number of pivots per group: {0}", I);
this.Pivs = pivs.ToArray ();
this.Items = items.ToArray ();
}