public ANNISetup(int n, int expectedK)
{
this.Selector = new PivotSelectorRandom (n);
this.StepWidth = (int)Math.Sqrt (n) + 1;
this.NumberQueries = 32; // (int)Math.Log (n, 2) + 1;
this.ExpectedK = expectedK;
}
public ANNISetup(PivotSelector sel, int expectedK, double alphaStop, int step, int numQueries)
{
this.Selector = sel;
this.ExpectedK = expectedK;
this.AlphaStop = alphaStop;
this.StepWidth = step;
this.NumberQueries = numQueries;
}
public EPListRandomPivotsPriorized(MetricDB DB, int seed, int num_pivs)
{
var n = DB.Count;
this.Items = new ItemPair[n];
var pivs = new List<EPivot> (32);
var rand = new Random (seed);
var pivsel = new PivotSelector (n, rand);
var piv = pivsel.NextPivot ();
var pivOBJ = DB [piv];
for (int objID = 0; objID < n; ++objID) {
var d = DB.Dist(pivOBJ, DB[objID]);
this.Items[objID] = new ItemPair(0, d);
}
double mean, variance;
this.Statistics (out mean, out variance);
pivs.Add(new EPivot(piv, Math.Sqrt(variance), mean, 0, 0, 0, 0));
var item_cmp = new Comparison<ItemPair>((x,y) => {
var diff_x = Math.Abs (x.dist - pivs[x.objID].mean);
var diff_y = Math.Abs (y.dist - pivs[y.objID].mean);
return diff_x.CompareTo(diff_y);
});
var queue = new SkipList2<int> (0.5, (x,y) => item_cmp (this.Items [x], this.Items [y]));
for (int objID = 0; objID < n; ++objID) {
queue.Add(objID, null);
}
var max_review = 2 * n / num_pivs;
var list = new List<int> ();
for (int i = 0; i < num_pivs; ++i) {
Console.WriteLine("XXXXXX BEGIN {0} i: {1}", this, i);
piv = pivsel.NextPivot();
double piv_mean, piv_variance, qrad;
PivotSelector.EstimatePivotStatistics(DB, rand, DB[piv], 256, out piv_mean, out piv_variance, out qrad);
var pivID = pivs.Count;
pivs.Add(new EPivot(piv, Math.Sqrt(piv_variance), mean, 0, 0, 0, 0));
list.Clear();
for (int s = 0; s < max_review; ++s) {
var objID = queue.RemoveFirst();
var d = DB.Dist(DB[objID], pivOBJ);
var new_item = new ItemPair(pivID, d);
if (item_cmp(new_item, this.Items[objID]) > 0) {
this.Items[objID] = new_item;
}
list.Add (objID);
}
foreach (var objID in list) {
queue.Add(objID, null);
}
Console.WriteLine("XXXXXX END {0} i: {1}", this, i);
}
this.Pivs = pivs.ToArray ();
Console.WriteLine("Number of pivots per group: {0}", this.Pivs.Length);
}
public EPListOptimized(MetricDB DB, int seed, int num_indexes, int max_iters, double error_factor)
{
Console.WriteLine ("XXX {0}, num_indexes: {1}, max_iters: {2}, error_factor: {3}", this, num_indexes, max_iters, error_factor);
this.Items = null;
var pivs = new List<EPivot> (32);
var rand = new Random (seed);
var n = DB.Count;
var idxseq = new DynamicSequentialOrdered ();
idxseq.Build (DB, RandomSets.GetIdentity (DB.Count));
var tmp_items = new List<ItemPair> (DB.Count);
double qrad;
double varY;
double mean;
PivotSelector.EstimateQueryStatistics (DB, rand, 128, 128, out mean, out varY, out qrad);
double prev_cost = -1;
double curr_cost = n;
double derivative;
var pivsel = new PivotSelector (n, rand);
int nextpiv = pivsel.NextPivot();
int i = 0;
do {
// Console.WriteLine("A {0} => {1}, {2}", this, i, seed);
//double min_diff = double.MaxValue;
this.ComputeDistRow (nextpiv, idxseq, rand, pivs, tmp_items);
// Console.WriteLine("B {0} => {1}, {2}", this, i, seed);
double varX = 0;
for (int objID = 0; objID < this.Items.Length; ++objID) {
var u = this.Items[objID];
var diff = Math.Abs( u.dist - pivs[u.objID].mean );
varX += diff * diff / n;
// if (diff < min_diff) {
// min_diff = diff;
// next_piv = objID;
// }
}
nextpiv = pivsel.NextPivot();
// Console.WriteLine("C {0} => {1}, {2}", this, i, seed);
++i;
prev_cost = curr_cost;
curr_cost = this.expected_cost(qrad, varX, varY, n, i, num_indexes, error_factor);
derivative = curr_cost - prev_cost;
// Console.WriteLine ("DEBUG: stddev: {0}", stats.stddev);
if (i % 10 == 1) {
Console.Write("XXXXXX {0}, seed: {1}, iteration: {2}, DB: {3}, ", this, seed, i, DB.Name);
Console.WriteLine("qcurr_cost: {0}, prev_cost: {1}, varX: {2}, varY: {3}, qrad: {4}",
curr_cost, prev_cost, varX, varY, qrad);
}
} while (derivative < 0 && i < max_iters);
this.Pivs = pivs.ToArray ();
Console.WriteLine("Number of pivots per group: {0}", this.Pivs.Length);
}
public void PartialBuild(MetricDB db, PivotSelector pivsel)
{
this.DB = db;
int n = this.DB.Count;
this.ACT = new List<int>(256); // just a starting capacity
this.CT = new int[n];
this.DT = new double[n];
var pivID = 0;
var nextPivot = pivsel.NextPivot ();
this.ACT.Add (nextPivot);
this.DT [nextPivot] = 0.0;
this.CT [nextPivot] = -1;
var piv = this.DB [nextPivot];
for (int docID = 0; docID < n; ++docID) {
this.CT [docID] = pivID;
this.DT [docID] = this.DB.Dist (this.DB [docID], piv);
}
}
public void PartialBuild(MetricDB db, PivotSelector pivsel)
{
this.DB = db;
int n = this.DB.Count;
this.clusters.Clear();
Node node = new Node ();
var pivID = pivsel.NextPivot ();
node.objID = pivID;
this.clusters.Add (node);
// take distances from all points to the center
var piv = this.DB [pivID];
for (int docID = 0; docID < n; ++docID) {
if (pivID == docID) {
continue;
}
var d = this.DB.Dist (this.DB [docID], piv);
node.Add (docID, d);
}
}
