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public Build ( MetricDB db, IList |
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| db | MetricDB | |
| sample | IList |
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| return | void | |
/// <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 EPListMeanPivots(MetricDB DB, int seed, int num_pivs)
{
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);
int next_piv = rand.Next (0, n);
for (int i = 0; i < num_pivs; ++i) {
var varX = 0.0;
double min_diff = double.MaxValue;
this.ComputeDistRow (next_piv, idxseq, rand, pivs, tmp_items);
for (int objID = 0; objID < this.Items.Length; ++objID) {
var u = this.Items [objID];
var diff = Math.Abs (u.Dist - pivs [u.ObjID].mean);
if (diff < min_diff) {
min_diff = diff;
next_piv = objID;
}
varX += diff * diff / n;
}
++i;
Console.WriteLine ("XXXXXX i: {0}, variance: {1}", i, varX);
}
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 EPListOptimizedA(MetricDB DB, int num_indexes, Random rand, double max_error_factor = 0.001)
{
this.Items = null;
var pivs = new List<EPivot> (32);
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;
//var pivsel = new PivotSelectorSSS (DB, 0.4, rand);
var pivsel = new PivotSelectorRandom (n, rand);
double avg_prev_cost = n;
// anything larger than 1.x can be considered a valid starting error_factor
double error_factor = n;
var avg_window = 16;
var iterID = 1;
max_error_factor += 1;
while (max_error_factor <= error_factor) {
double avg_curr_cost = 0;
for (int i = 0; i < avg_window; ++i, ++iterID) {
this.ComputeDistRow (pivsel.NextPivot(), idxseq, rand, pivs, tmp_items);
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;
}
var curr_cost = this.expected_cost(qrad, varX, varY, n, iterID, num_indexes);
avg_curr_cost += curr_cost;
}
avg_curr_cost /= avg_window;
error_factor = avg_prev_cost / avg_curr_cost;
Console.WriteLine("XXXXXXXXXXXXXXXXXXXX {0}, iterID: {1}, DB: {2} ", this, iterID, DB.Name);
Console.WriteLine("XXX DB: {0}", DB.Name);
Console.WriteLine("XXX avg_curr_cost: {0}, avg_prev_cost: {1}, error_factor: {2}",
avg_curr_cost, avg_prev_cost, error_factor);
avg_prev_cost = avg_curr_cost;
}
this.Pivs = pivs.ToArray ();
Console.WriteLine("Number of pivots per group: {0}", this.Pivs.Length);
}
public EPListRandomPivots(MetricDB DB, int num_pivots, Random rand)
{
this.Items = null;
var already_pivot = new HashSet<int> ();
var pivs = new List<EPivot> (32);
var idxseq = new DynamicSequentialOrdered ();
idxseq.Build (DB, RandomSets.GetIdentity (DB.Count));
var tmp_items = new List<ItemPair> (DB.Count);
for (int i = 0; i < num_pivots; ++i) {
this.ComputeDistRow (idxseq, rand, already_pivot, pivs, tmp_items);
// double sum = 0;
// for (int objID = 0; objID < this.Items.Length; ++objID) {
// var u = this.Items[objID];
// sum += Math.Abs( u.dist - pivs[u.objID].mean );
// }
if (i % 10 == 0) {
Console.WriteLine("XXXXXX {0}, iteration: {1}/{2}, DB: {3}", this, i, num_pivots, DB.Name);
}
}
this.Pivs = pivs.ToArray ();
Console.WriteLine("Number of pivots per group: {0}", this.Pivs.Length);
}
public int[] GetMetricShell(object q)
{
var seq = new List<int> ();
var idx = new DynamicSequentialOrdered ();
// optimize the following:
idx.Build (this.R, RandomSets.GetIdentity (this.R.Count));
List<ItemPair> cache = new List<ItemPair>(this.R.Count);
// Console.WriteLine ("START GetMetricShell");
while (idx.Count > 0) {
cache.Clear();
DynamicSequential.Stats stats;
int min_objID, max_objID;
idx.ComputeDistances(q, cache, out stats, out min_objID, out max_objID);
for (int i = 0; i < cache.Count; ++i) {
var obj_min = this.DB [min_objID];
var obj_cur = this.DB [cache[i].ObjID];
if (cache[i].Dist >= this.DB.Dist(obj_min, obj_cur)) {
idx.Remove (cache[i].ObjID);
}
}
//Console.WriteLine ("min: {0}, min_dist: {1}, refs_size: {2}", min_objID, stats.min, idx.Count);
seq.Add (min_objID);
}
return seq.ToArray ();
}
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 ();
}
public EPListOptimizedB(MetricDB DB, int num_indexes, Random rand)
{
this.Items = null;
var pivs = new List<EPivot> (32);
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, 64, 128, out mean, out varY, out qrad);
//double prev_cost = -1;
//double curr_cost = n;
var pivsel = new PivotSelectorRandom (n, rand);
double weight_prev = 0.99;
double weight_curr = 1.0 - weight_prev;
double max_error = 0.01;
double error = 1;
double prev_cost = 1.0;
double min_cost = 1;
// anything larger than 1.x can be considered a valid starting error_factor
var iterID = 0;
var window = 1;
while (true) {
//++iterID;
double curr_cost = 0;
for (int i = 0; i < window; ++i, ++iterID) {
this.ComputeDistRow (pivsel.NextPivot(), idxseq, rand, pivs, tmp_items);
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;
}
curr_cost += this.expected_cost(qrad, varX, varY, n, iterID, num_indexes);
}
curr_cost = (curr_cost / window) / n;
curr_cost = weight_prev * prev_cost + weight_curr * curr_cost;
if (curr_cost < min_cost) {
min_cost = curr_cost;
} else {
break;
}
if (iterID % 10 == 0) {
Console.WriteLine("XXXXXXXXXXXXXXXXXXXX {0}, db: {1}", this, Path.GetFileName(DB.Name));
Console.WriteLine("XXX prev-cost: {0:0.000}, curr-cost: {1:0.000}, min-cost: {6}, error: {2:0.00000}, max-error: {3:0.00000}, pivs: {4}, groups: {5}",
prev_cost, curr_cost, error, max_error, iterID, num_indexes, min_cost);
}
error = prev_cost - curr_cost;
prev_cost = curr_cost;
}
this.Pivs = pivs.ToArray ();
Console.WriteLine("Number of pivots per group: {0}", this.Pivs.Length);
}