Start optimizations

TestHNSW/HNSWDemo/HNSWDemo.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -13,4 +13,10 @@
     <ProjectReference Include="..\..\ZeroLevel.HNSW\ZeroLevel.HNSW.csproj" />
   </ItemGroup>
 
+  <ItemGroup>
+    <None Update="t10k-images.idx3-ubyte">
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
 </Project>

TestHNSW/HNSWDemo/Program.cs

@@ -6,6 +6,7 @@ using System.IO;
 using System.Linq;
 using ZeroLevel.HNSW;
 using ZeroLevel.HNSW.Services;
+using ZeroLevel.HNSW.Services.OPT;
 
 namespace HNSWDemo
 {
@@ -13,6 +14,7 @@ namespace HNSWDemo
     {
         public class VectorsDirectCompare
         {
+            private const int HALF_LONG_BITS = 32;
             private readonly IList<float[]> _vectors;
             private readonly Func<float[], float[], float> _distance;
 
@@ -32,6 +34,70 @@ namespace HNSWDemo
                 }
                 return weights.OrderBy(p => p.Value).Take(k).Select(p => (p.Key, p.Value));
             }
+
+            public List<HashSet<int>> DetectClusters()
+            {
+                var links = new SortedList<long, float>();
+                for (int i = 0; i < _vectors.Count; i++)
+                {
+                    for (int j = i + 1; j < _vectors.Count; j++)
+                    {
+                        long k = (((long)(i)) << HALF_LONG_BITS) + j;
+                        links.Add(k, _distance(_vectors[i], _vectors[j]));
+                    }
+                }
+
+                // 1. Find R - bound between intra-cluster distances and out-of-cluster distances
+                var histogram = new Histogram(HistogramMode.SQRT, links.Values);
+                int threshold = histogram.OTSU();
+                var min = histogram.Bounds[threshold - 1];
+                var max = histogram.Bounds[threshold];
+                var R = (max + min) / 2;
+
+                // 2. Get links with distances less than R
+                var resultLinks = new SortedList<long, float>();
+                foreach (var pair in links)
+                {
+                    if (pair.Value < R)
+                    {
+                        resultLinks.Add(pair.Key, pair.Value);
+                    }
+                }
+
+                // 3. Extract clusters
+                List<HashSet<int>> clusters = new List<HashSet<int>>();
+                foreach (var pair in resultLinks)
+                {
+                    var k = pair.Key;
+                    var id1 = (int)(k >> HALF_LONG_BITS);
+                    var id2 = (int)(k - (((long)id1) << HALF_LONG_BITS));
+
+                    bool found = false;
+                    foreach (var c in clusters)
+                    {
+                        if (c.Contains(id1))
+                        {
+                            c.Add(id2);
+                            found = true;
+                            break;
+                        }
+                        else if (c.Contains(id2))
+                        {
+                            c.Add(id1);
+                            found = true;
+                            break;
+                        }
+                    }
+                    if (found == false)
+                    {
+                        var c = new HashSet<int>();
+                        c.Add(id1);
+                        c.Add(id2);
+                        clusters.Add(c);
+                    }
+                }
+                return clusters;
+            }
         }
 
         public enum Gender
@@ -91,7 +157,7 @@ namespace HNSWDemo
             {
                 var vector = new float[vectorSize];
                 DefaultRandomGenerator.Instance.NextFloats(vector);
-                //VectorUtils.NormalizeSIMD(vector);
+                VectorUtils.NormalizeSIMD(vector);
                 vectors.Add(vector);
             }
             return vectors;
@@ -100,11 +166,107 @@ namespace HNSWDemo
 
         static void Main(string[] args)
         {
-            AutoClusteringTest();
+            var samples = RandomVectors(128, 600);
+            var opt_world = new OptWorld<float[]>(NSWOptions<float[]>.Create(8, 15, 200, 200, Metrics.L2Euclidean, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
+            opt_world.AddItems(samples);
+            //AccuracityTest();
             Console.WriteLine("Completed");
             Console.ReadKey();
         }
 
+        static void BinaryHeapTest()
+        {
+            var heap = new BinaryHeap();
+            heap.Push(1, .03f);
+            heap.Push(2, .05f);
+            heap.Push(3, .01f);
+            heap.Push(4, 1.03f);
+            heap.Push(5, 2.03f);
+            heap.Push(6, .73f);
+
+            var n = heap.Nearest;
+            Console.WriteLine($"Nearest: [{n.Item1}] = {n.Item2}");
+            var f = heap.Farthest;
+            Console.WriteLine($"Farthest: [{f.Item1}] = {f.Item2}");
+
+            Console.WriteLine("From nearest to farthest");
+            while (heap.Count > 0)
+            {
+                var i = heap.PopNearest();
+                Console.WriteLine($"[{i.Item1}] = {i.Item2}");
+            }
+
+            heap.Push(1, .03f);
+            heap.Push(2, .05f);
+            heap.Push(3, .01f);
+            heap.Push(4, 1.03f);
+            heap.Push(5, 2.03f);
+            heap.Push(6, .73f);
+            Console.WriteLine("From farthest to nearest");
+            while (heap.Count > 0)
+            {
+                var i = heap.PopFarthest();
+                Console.WriteLine($"[{i.Item1}] = {i.Item2}");
+            }
+        }
+
+        static void TestOnMnist()
+        {
+            int imageCount, rowCount, colCount;
+            var buf = new byte[4];
+            var image = new byte[28 * 28];
+            var vectors = new List<float[]>();
+            using (var fs = new FileStream("t10k-images.idx3-ubyte", FileMode.Open, FileAccess.Read, FileShare.None))
+            {
+                // first 4 bytes is a magic number
+                fs.Read(buf, 0, 4);
+                // second 4 bytes is the number of images
+                fs.Read(buf, 0, 4);
+                imageCount = BitConverter.ToInt32(buf.Reverse().ToArray(), 0);
+                // third 4 bytes is the row count
+                fs.Read(buf, 0, 4);
+                rowCount = BitConverter.ToInt32(buf.Reverse().ToArray(), 0);
+                // fourth 4 bytes is the column count
+                fs.Read(buf, 0, 4);
+                colCount = BitConverter.ToInt32(buf.Reverse().ToArray(), 0);
+
+                for (int i = 0; i < imageCount; i++)
+                {
+                    fs.Read(image, 0, image.Length);
+                    vectors.Add(image.Select(b => (float)b).ToArray());
+                }
+            }
+
+            //var direct = new VectorsDirectCompare(vectors, Metrics.L2Euclidean);
+            
+            var options = NSWOptions<float[]>.Create(8, 16, 200, 200, Metrics.L2Euclidean, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple);
+            SmallWorld<float[]> world;
+            if (File.Exists("graph.bin"))
+            {
+                using (var fs = new FileStream("graph.bin", FileMode.Open, FileAccess.Read, FileShare.None))
+                {
+                    world = SmallWorld.CreateWorldFrom<float[]>(options, fs);
+                }
+            }
+            else
+            {
+                world = SmallWorld.CreateWorld<float[]>(options);
+                world.AddItems(vectors);
+                using (var fs = new FileStream("graph.bin", FileMode.Create, FileAccess.Write, FileShare.None))
+                {
+                    world.Serialize(fs);
+                }
+            }
+            
+            var clusters = AutomaticGraphClusterer.DetectClusters(world);
+            Console.WriteLine($"Found {clusters.Count} clusters");
+            for (int i = 0; i < clusters.Count; i++)
+            {
+                Console.WriteLine($"Cluster {i + 1} countains {clusters[i].Count} items");
+            }
+
+        }
+
         static void AutoClusteringTest()
         {
             var vectors = RandomVectors(128, 3000);
@@ -114,7 +276,7 @@ namespace HNSWDemo
             Console.WriteLine($"Found {clusters.Count} clusters");
             for (int i = 0; i < clusters.Count; i++)
             {
-                Console.WriteLine($"Cluster {i+1} countains {clusters[i].Count} items");
+                Console.WriteLine($"Cluster {i + 1} countains {clusters[i].Count} items");
             }
         }
 
@@ -135,10 +297,10 @@ namespace HNSWDemo
 
         static void DrawHistogram(Histogram histogram, string filename)
         {
-           /* while (histogram.CountSignChanges() > 3)
+            /* while (histogram.CountSignChanges() > 3)
-            {
+             {
-                histogram.Smooth();
+                 histogram.Smooth();
-            }*/
+             }*/
             var wb = 1200 / histogram.Values.Length;
             var k = 600.0f / (float)histogram.Values.Max();
 
@@ -147,8 +309,7 @@ namespace HNSWDemo
 
             using (var bmp = new Bitmap(1200, 600))
             {
-                using (var g = Graphics.FromImage(bmp))
+                using (var g = Graphics.FromImage(bmp))                {
-                {
                     for (int i = 0; i < histogram.Values.Length; i++)
                     {
                         var height = (int)(histogram.Values[i] * k);
@@ -481,26 +642,37 @@ namespace HNSWDemo
         static void AccuracityTest()
         {
             int K = 200;
-            var count = 5000;
+            var count = 2000;
             var testCount = 1000;
             var dimensionality = 128;
             var totalHits = new List<int>();
             var timewatchesNP = new List<float>();
             var timewatchesHNSW = new List<float>();
+
+            var totalOptHits = new List<int>();
+            var timewatchesOptHNSW = new List<float>();
+
             var samples = RandomVectors(dimensionality, count);
 
             var sw = new Stopwatch();
 
-            var test = new VectorsDirectCompare(samples, CosineDistance.ForUnits);
+            var test = new VectorsDirectCompare(samples, Metrics.L2Euclidean);
-            var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(8, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
+            var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(8, 15, 200, 200, Metrics.L2Euclidean, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
+
+            var opt_world = new OptWorld<float[]>(NSWOptions<float[]>.Create(8, 15, 200, 200, Metrics.L2Euclidean, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
 
             sw.Start();
             var ids = world.AddItems(samples.ToArray());
             sw.Stop();
+            Console.WriteLine($"Insert {ids.Length} items: {sw.ElapsedMilliseconds} ms");
 
-            Console.WriteLine($"Insert {ids.Length} items on {sw.ElapsedMilliseconds} ms");
+            sw.Restart();
+            opt_world.AddItems(samples.ToArray());
+            sw.Stop();
+            Console.WriteLine($"Insert {ids.Length} items in OPT: {sw.ElapsedMilliseconds} ms");
             Console.WriteLine("Start test");
 
+
             var test_vectors = RandomVectors(dimensionality, testCount);
             foreach (var v in test_vectors)
             {
@@ -512,6 +684,7 @@ namespace HNSWDemo
                 sw.Restart();
                 var result = world.Search(v, K);
                 sw.Stop();
+
                 timewatchesHNSW.Add(sw.ElapsedMilliseconds);
                 var hits = 0;
                 foreach (var r in result)
@@ -522,15 +695,39 @@ namespace HNSWDemo
                     }
                 }
                 totalHits.Add(hits);
+
+
+                sw.Restart();
+                result = opt_world.Search(v, K);
+                sw.Stop();
+
+                timewatchesOptHNSW.Add(sw.ElapsedMilliseconds);
+                hits = 0;
+                foreach (var r in result)
+                {
+                    if (gt.ContainsKey(r.Item1))
+                    {
+                        hits++;
+                    }
+                }
+                totalOptHits.Add(hits);
             }
             Console.WriteLine($"MIN Accuracity: {totalHits.Min() * 100 / K}%");
             Console.WriteLine($"AVG Accuracity: {totalHits.Average() * 100 / K}%");
             Console.WriteLine($"MAX Accuracity: {totalHits.Max() * 100 / K}%");
 
+            Console.WriteLine($"MIN Opt Accuracity: {totalOptHits.Min() * 100 / K}%");
+            Console.WriteLine($"AVG Opt Accuracity: {totalOptHits.Average() * 100 / K}%");
+            Console.WriteLine($"MAX Opt Accuracity: {totalOptHits.Max() * 100 / K}%");
+
             Console.WriteLine($"MIN HNSW TIME: {timewatchesHNSW.Min()} ms");
             Console.WriteLine($"AVG HNSW TIME: {timewatchesHNSW.Average()} ms");
             Console.WriteLine($"MAX HNSW TIME: {timewatchesHNSW.Max()} ms");
 
+            Console.WriteLine($"MIN Opt HNSW TIME: {timewatchesOptHNSW.Min()} ms");
+            Console.WriteLine($"AVG Opt HNSW TIME: {timewatchesOptHNSW.Average()} ms");
+            Console.WriteLine($"MAX Opt HNSW TIME: {timewatchesOptHNSW.Max()} ms");
+
             Console.WriteLine($"MIN NP TIME: {timewatchesNP.Min()} ms");
             Console.WriteLine($"AVG NP TIME: {timewatchesNP.Average()} ms");
             Console.WriteLine($"MAX NP TIME: {timewatchesNP.Max()} ms");

ZeroLevel.HNSW/Model/Histogram.cs

@@ -21,7 +21,7 @@ namespace ZeroLevel.HNSW
         public float[] Bounds { get; }
         public int[] Values { get; }
 
-        internal Histogram(HistogramMode mode, IList<float> data)
+        public Histogram(HistogramMode mode, IList<float> data)
         {
             Mode = mode;
             Min = data.Min();
@@ -171,6 +171,13 @@ namespace ZeroLevel.HNSW
                     threshold = k;
                 }
             }
+            /*
+            var local_max = Values[threshold];
+            for (int i = threshold + 1; i < Values.Length; i++)
+            {
+                
+            }
+            */
             return threshold;
         }
         #endregion

ZeroLevel.HNSW/Services/AutomaticGraphClusterer.cs

@@ -16,6 +16,7 @@ namespace ZeroLevel.HNSW.Services
             var max = histogram.Bounds[threshold];
             var R = (max + min) / 2;
 
+
             // 2. Get links with distances less than R
             var resultLinks = new SortedList<long, float>();
             foreach (var pair in links)

ZeroLevel.HNSW/Services/BinaryHeap.cs

@@ -1,80 +1,90 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
 
-namespace ZeroLevel.HNSW.Services
+namespace ZeroLevel.HNSW
 {
     /// <summary>
     /// Binary heap wrapper around the <see cref="IList{T}"/>
     /// It's a max-heap implementation i.e. the maximum element is always on top.
-    /// But the order of elements can be customized by providing <see cref="IComparer{T}"/> instance.
     /// </summary>
     /// <typeparam name="T">The type of the items in the source list.</typeparam>
-    public class BinaryHeap<T>
+    public class BinaryHeap :
+        IEnumerable<(int, float)>
     {
-        /// <summary>
+        private static BinaryHeap _empty = new BinaryHeap();
-        /// Initializes a new instance of the <see cref="BinaryHeap{T}"/> class.
-        /// </summary>
-        /// <param name="buffer">The buffer to store heap items.</param>
-        public BinaryHeap(IList<T> buffer)
-            : this(buffer, Comparer<T>.Default)
-        {
-        }
 
-        /// <summary>
+        public static BinaryHeap Empty => _empty;
-        /// Initializes a new instance of the <see cref="BinaryHeap{T}"/> class.
+
-        /// </summary>
+        private readonly List<(int, float)> _data;
-        /// <param name="buffer">The buffer to store heap items.</param>
+
-        /// <param name="comparer">The comparer which defines order of items.</param>
+        private bool _frozen = false;
-        public BinaryHeap(IList<T> buffer, IComparer<T> comparer)
+        public (int, float) Nearest => _data[_data.Count - 1];
+        public (int, float) Farthest => _data[0];
+
+        public (int, float) PopNearest()
         {
-            if (buffer == null)
+            if (this._data.Any())
             {
-                throw new ArgumentNullException(nameof(buffer));
+                var result = this._data[this._data.Count - 1];
+                this._data.RemoveAt(this._data.Count - 1);
+                return result;
             }
+            return (-1, -1);
+        }
 
-            this.Buffer = buffer;
+        public (int, float) PopFarthest()
-            this.Comparer = comparer;
+        {
-            for (int i = 1; i < this.Buffer.Count; ++i)
+            if (this._data.Any())
             {
-                this.SiftUp(i);
+                var result = this._data.First();
+                this._data[0] = this._data.Last();
+                this._data.RemoveAt(this._data.Count - 1);
+                this.SiftDown(0);
+                return result;
             }
+            return (-1, -1);
         }
 
-        /// <summary>
+        public int Count => _data.Count;
-        /// Gets the heap comparer.
+        public void Clear() => _data.Clear();
-        /// </summary>
-        public IComparer<T> Comparer { get; private set; }
 
         /// <summary>
-        /// Gets the buffer of the heap.
+        /// Initializes a new instance of the <see cref="BinaryHeap{T}"/> class.
         /// </summary>
-        public IList<T> Buffer { get; private set; }
+        /// <param name="buffer">The buffer to store heap items.</param>
+        public BinaryHeap(int k = -1, bool frozen = false)
+        {
+            _frozen = frozen;
+            if (k > 0)
+                _data = new List<(int, float)>(k);
+            else
+                _data = new List<(int, float)>();
+        }
 
         /// <summary>
         /// Pushes item to the heap.
         /// </summary>
         /// <param name="item">The item to push.</param>
-        public void Push(T item)
+        public void Push(int item, float distance)
         {
-            this.Buffer.Add(item);
+            this._data.Add((item, distance));
-            this.SiftUp(this.Buffer.Count - 1);
+            this.SiftUp(this._data.Count - 1);
         }
 
         /// <summary>
         /// Pops the item from the heap.
         /// </summary>
         /// <returns>The popped item.</returns>
-        public T Pop()
+        public (int, float) Pop()
         {
-            if (this.Buffer.Any())
+            if (this._data.Any())
             {
-                var result = this.Buffer.First();
+                var result = this._data.First();
 
-                this.Buffer[0] = this.Buffer.Last();
+                this._data[0] = this._data.Last();
-                this.Buffer.RemoveAt(this.Buffer.Count - 1);
+                this._data.RemoveAt(this._data.Count - 1);
                 this.SiftDown(0);
 
                 return result;
@@ -90,21 +100,19 @@ namespace ZeroLevel.HNSW.Services
         /// <param name="i">The position of item where heap property is violated.</param>
         private void SiftDown(int i)
         {
-            while (i < this.Buffer.Count)
+            while (i < this._data.Count)
             {
                 int l = (2 * i) + 1;
                 int r = l + 1;
-                if (l >= this.Buffer.Count)
+                if (l >= this._data.Count)
                 {
                     break;
                 }
-
+                int m = ((r < this._data.Count) && this._data[l].Item2 < this._data[r].Item2) ? r : l;
-                int m = r < this.Buffer.Count && this.Comparer.Compare(this.Buffer[l], this.Buffer[r]) < 0 ? r : l;
+                if (this._data[m].Item2 <= this._data[i].Item2)
-                if (this.Comparer.Compare(this.Buffer[m], this.Buffer[i]) <= 0)
                 {
                     break;
                 }
-
                 this.Swap(i, m);
                 i = m;
             }
@@ -120,11 +128,10 @@ namespace ZeroLevel.HNSW.Services
             while (i > 0)
             {
                 int p = (i - 1) / 2;
-                if (this.Comparer.Compare(this.Buffer[i], this.Buffer[p]) <= 0)
+                if (this._data[i].Item2 <= this._data[p].Item2)
                 {
                     break;
                 }
-
                 this.Swap(i, p);
                 i = p;
             }
@@ -137,9 +144,19 @@ namespace ZeroLevel.HNSW.Services
         /// <param name="j">The second index.</param>
         private void Swap(int i, int j)
         {
-            var temp = this.Buffer[i];
+            var temp = this._data[i];
-            this.Buffer[i] = this.Buffer[j];
+            this._data[i] = this._data[j];
-            this.Buffer[j] = temp;
+            this._data[j] = temp;
+        }
+
+        public IEnumerator<(int, float)> GetEnumerator()
+        {
+            return _data.GetEnumerator();
+        }
+
+        IEnumerator IEnumerable.GetEnumerator()
+        {
+            return _data.GetEnumerator();
         }
     }
 }

ZeroLevel.HNSW/Services/CompactBiDirectionalLinksSet.cs

@@ -104,11 +104,23 @@ namespace ZeroLevel.HNSW
             _rwLock.EnterReadLock();
             try
             {
-                foreach (var (k, v) in Search(_set, id))
+                if (_set.Count == 1)
                 {
+                    var k = _set.Keys[0];
+                    var v = _set[k];
                     var id1 = (int)(k >> HALF_LONG_BITS);
                     var id2 = (int)(k - (((long)id1) << HALF_LONG_BITS));
-                    yield return (id1, id2, v);
+                    if (id1 == id) yield return (id, id2, v);
+                    else if (id2 == id) yield return (id1, id, v);
+                }
+                else if (_set.Count > 1)
+                {
+                    foreach (var (k, v) in Search(_set, id))
+                    {
+                        var id1 = (int)(k >> HALF_LONG_BITS);
+                        var id2 = (int)(k - (((long)id1) << HALF_LONG_BITS));
+                        yield return (id1, id2, v);
+                    }
                 }
             }
             finally

ZeroLevel.HNSW/Services/Layer.cs

@@ -53,6 +53,12 @@ namespace ZeroLevel.HNSW
                 int index = 0;
                 for (int ni = 1; ni < nearest.Length; ni++)
                 {
+                    // Если осталась ссылка узла на себя, удаляем ее в первую очередь
+                    if (nearest[ni].Item1 == nearest[ni].Item2)
+                    {
+                        index = ni;
+                        break;
+                    }
                     if (nearest[ni].Item3 > distance)
                     {
                         index = ni;
@@ -81,6 +87,23 @@ namespace ZeroLevel.HNSW
         }
 
         #region Implementation of https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf
+        internal int FingEntryPointAtLayer(Func<int, float> targetCosts)
+        {
+            var set = new HashSet<int>(_links.Items().Select(p => p.Item1));
+            int minId = -1;
+            float minDist = float.MaxValue;
+            foreach (var id in set)
+            {
+                var d = targetCosts(id);
+                if (d < minDist && Math.Abs(d) > float.Epsilon)
+                {
+                    minDist = d;
+                    minId = id;
+                }
+            }
+            return minId;
+        }
+
         /// <summary>
         /// Algorithm 2
         /// </summary>

ZeroLevel.HNSW/Services/OPT/OptLayer.cs

@@ -0,0 +1,491 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using ZeroLevel.Services.Serialization;
+
+namespace ZeroLevel.HNSW.Services.OPT
+{
+    /// <summary>
+    /// NSW graph
+    /// </summary>
+    internal sealed class OptLayer<TItem>
+        : IBinarySerializable
+    {
+        private readonly NSWOptions<TItem> _options;
+        private readonly VectorSet<TItem> _vectors;
+        private readonly CompactBiDirectionalLinksSet _links;
+        internal SortedList<long, float> Links => _links.Links;
+
+        /// <summary>
+        /// There are links е the layer
+        /// </summary>
+        internal bool HasLinks => (_links.Count > 0);
+
+        /// <summary>
+        /// HNSW layer
+        /// </summary>
+        /// <param name="options">HNSW graph options</param>
+        /// <param name="vectors">General vector set</param>
+        internal OptLayer(NSWOptions<TItem> options, VectorSet<TItem> vectors)
+        {
+            _options = options;
+            _vectors = vectors;
+            _links = new CompactBiDirectionalLinksSet();
+        }
+
+        /// <summary>
+        /// Adding new bidirectional link
+        /// </summary>
+        /// <param name="q">New node</param>
+        /// <param name="p">The node with which the connection will be made</param>
+        /// <param name="qpDistance"></param>
+        /// <param name="isMapLayer"></param>
+        internal void AddBidirectionallConnections(int q, int p, float qpDistance, bool isMapLayer)
+        {
+            // поиск в ширину ближайших узлов к найденному
+            var nearest = _links.FindLinksForId(p).ToArray();
+            // если у найденного узла максимальное количество связей
+            // if │eConn│ > Mmax // shrink connections of e
+            if (nearest.Length >= (isMapLayer ? _options.M * 2 : _options.M))
+            {
+                // ищем связь с самой большой дистанцией
+                float distance = nearest[0].Item3;
+                int index = 0;
+                for (int ni = 1; ni < nearest.Length; ni++)
+                {
+                    // Если осталась ссылка узла на себя, удаляем ее в первую очередь
+                    if (nearest[ni].Item1 == nearest[ni].Item2)
+                    {
+                        index = ni;
+                        break;
+                    }
+                    if (nearest[ni].Item3 > distance)
+                    {
+                        index = ni;
+                        distance = nearest[ni].Item3;
+                    }
+                }
+                // делаем перелинковку вставляя новый узел между найденными
+                var id1 = nearest[index].Item1;
+                var id2 = nearest[index].Item2;
+                _links.Relink(id1, id2, q, qpDistance, _options.Distance(_vectors[id2], _vectors[q]));
+            }
+            else
+            {
+                if (nearest.Length == 1 && nearest[0].Item1 == nearest[0].Item2)
+                {
+                    // убираем связи на самих себя
+                    var id1 = nearest[0].Item1;
+                    var id2 = nearest[0].Item2;
+                    _links.Relink(id1, id2, q, qpDistance, _options.Distance(_vectors[id2], _vectors[q]));
+                }
+                else
+                {
+                    // добавляем связь нового узла к найденному
+                    _links.Add(q, p, qpDistance);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Adding a node with a connection to itself
+        /// </summary>
+        /// <param name="q"></param>
+        internal void Append(int q)
+        {
+            _links.Add(q, q, 0);
+        }
+
+        #region Implementation of https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf
+        /// <summary>
+        /// Algorithm 2
+        /// </summary>
+        /// <param name="q">query element</param>
+        /// <param name="ep">enter points ep</param>
+        /// <returns>Output: ef closest neighbors to q</returns>
+        internal void KNearestAtLayer(int entryPointId, Func<int, float> targetCosts, BinaryHeap W, int ef)
+        {
+            /*
+             * v ← ep // set of visited elements
+             * C ← ep // set of candidates
+             * W ← ep // dynamic list of found nearest neighbors
+             * while │C│ > 0
+             *   c ← extract nearest element from C to q
+             *   f ← get furthest element from W to q
+             *   if distance(c, q) > distance(f, q)
+             *     break // all elements in W are evaluated
+             *   for each e ∈ neighbourhood(c) at layer lc // update C and W
+             *     if e ∉ v
+             *       v ← v ⋃ e
+             *       f ← get furthest element from W to q
+             *       if distance(e, q) < distance(f, q) or │W│ < ef
+             *         C ← C ⋃ e
+             *         W ← W ⋃ e
+             *         if │W│ > ef
+             *           remove furthest element from W to q
+             * return W
+             */
+            var v = new VisitedBitSet(_vectors.Count, _options.M);
+            // v ← ep // set of visited elements
+            v.Add(entryPointId);
+
+            var d = targetCosts(entryPointId);
+            // C ← ep // set of candidates
+            var C = new BinaryHeap();
+            C.Push(entryPointId, d);
+            // W ← ep // dynamic list of found nearest neighbors
+            W.Push(entryPointId, d);
+
+            // run bfs
+            while (C.Count > 0)
+            {
+                // get next candidate to check and expand
+                var toExpand = C.PopNearest();
+                var farthestResult = W.Farthest;
+                if (toExpand.Item2 > farthestResult.Item2)
+                {
+                    // the closest candidate is farther than farthest result
+                    break;
+                }
+
+                // expand candidate
+                var neighboursIds = GetNeighbors(toExpand.Item1).ToArray();
+                for (int i = 0; i < neighboursIds.Length; ++i)
+                {
+                    int neighbourId = neighboursIds[i];
+                    if (!v.Contains(neighbourId))
+                    {
+                        // enqueue perspective neighbours to expansion list
+                        farthestResult = W.Farthest;
+
+                        var neighbourDistance = targetCosts(neighbourId);
+                        if (W.Count < ef || neighbourDistance < farthestResult.Item2)
+                        {
+                            C.Push(neighbourId, neighbourDistance);
+                            W.Push(neighbourId, neighbourDistance);
+                            if (W.Count > ef)
+                            {
+                                W.PopFarthest();
+                            }
+                        }
+                        v.Add(neighbourId);
+                    }
+                }
+            }
+            C.Clear();
+            v.Clear();
+        }
+
+        /// <summary>
+        /// Algorithm 2
+        /// </summary>
+        /// <param name="q">query element</param>
+        /// <param name="ep">enter points ep</param>
+        /// <returns>Output: ef closest neighbors to q</returns>
+        internal void KNearestAtLayer(int entryPointId, Func<int, float> targetCosts, BinaryHeap W, int ef, SearchContext context)
+        {
+            /*
+             * v ← ep // set of visited elements
+             * C ← ep // set of candidates
+             * W ← ep // dynamic list of found nearest neighbors
+             * while │C│ > 0
+             *   c ← extract nearest element from C to q
+             *   f ← get furthest element from W to q
+             *   if distance(c, q) > distance(f, q)
+             *     break // all elements in W are evaluated
+             *   for each e ∈ neighbourhood(c) at layer lc // update C and W
+             *     if e ∉ v
+             *       v ← v ⋃ e
+             *       f ← get furthest element from W to q
+             *       if distance(e, q) < distance(f, q) or │W│ < ef
+             *         C ← C ⋃ e
+             *         W ← W ⋃ e
+             *         if │W│ > ef
+             *           remove furthest element from W to q
+             * return W
+             */
+            var v = new VisitedBitSet(_vectors.Count, _options.M);
+            // v ← ep // set of visited elements
+            v.Add(entryPointId);
+            // C ← ep // set of candidates
+            var C = new BinaryHeap();
+            var d = targetCosts(entryPointId);
+            C.Push(entryPointId, d);
+            // W ← ep // dynamic list of found nearest neighbors
+            if (context.IsActiveNode(entryPointId))
+            {
+                W.Push(entryPointId, d);
+            }
+            // run bfs
+            while (C.Count > 0)
+            {
+                // get next candidate to check and expand
+                var toExpand = C.PopNearest();
+                if (W.Count > 0)
+                {
+                    if (toExpand.Item2 > W.Farthest.Item2)
+                    {
+                        // the closest candidate is farther than farthest result
+                        break;
+                    }
+                }
+
+                // expand candidate
+                var neighboursIds = GetNeighbors(toExpand.Item1).ToArray();
+                for (int i = 0; i < neighboursIds.Length; ++i)
+                {
+                    int neighbourId = neighboursIds[i];
+                    if (!v.Contains(neighbourId))
+                    {
+                        // enqueue perspective neighbours to expansion list
+                        var neighbourDistance = targetCosts(neighbourId);
+                        if (context.IsActiveNode(neighbourId))
+                        {
+                            if (W.Count < ef || (W.Count > 0 && neighbourDistance < W.Farthest.Item2))
+                            {
+                                W.Push(neighbourId, neighbourDistance);
+                                if (W.Count > ef)
+                                {
+                                    W.PopFarthest();
+                                }
+                            }
+                        }
+                        if (W.Count < ef)
+                        {
+                            C.Push(neighbourId, neighbourDistance);
+                        }
+                        v.Add(neighbourId);
+                    }
+                }
+            }
+            C.Clear();
+            v.Clear();
+        }
+
+        /// <summary>
+        /// Algorithm 2, modified for LookAlike
+        /// </summary>
+        /// <param name="q">query element</param>
+        /// <param name="ep">enter points ep</param>
+        /// <returns>Output: ef closest neighbors to q</returns>
+        internal void KNearestAtLayer(BinaryHeap W, int ef, SearchContext context)
+        {
+            /*
+             * v ← ep // set of visited elements
+             * C ← ep // set of candidates
+             * W ← ep // dynamic list of found nearest neighbors
+             * while │C│ > 0
+             *   c ← extract nearest element from C to q
+             *   f ← get furthest element from W to q
+             *   if distance(c, q) > distance(f, q)
+             *     break // all elements in W are evaluated
+             *   for each e ∈ neighbourhood(c) at layer lc // update C and W
+             *     if e ∉ v
+             *       v ← v ⋃ e
+             *       f ← get furthest element from W to q
+             *       if distance(e, q) < distance(f, q) or │W│ < ef
+             *         C ← C ⋃ e
+             *         W ← W ⋃ e
+             *         if │W│ > ef
+             *           remove furthest element from W to q
+             * return W
+             */
+            // v ← ep // set of visited elements
+            var v = new VisitedBitSet(_vectors.Count, _options.M);
+            // C ← ep // set of candidates
+            var C = new BinaryHeap();
+            foreach (var ep in context.EntryPoints)
+            {
+                var neighboursIds = GetNeighbors(ep).ToArray();
+                for (int i = 0; i < neighboursIds.Length; ++i)
+                {
+                    C.Push(ep, _links.Distance(ep, neighboursIds[i]));
+                }
+                v.Add(ep);
+            }
+            // W ← ep // dynamic list of found nearest neighbors
+            // run bfs
+            while (C.Count > 0)
+            {
+                // get next candidate to check and expand
+                var toExpand = C.PopNearest();
+                if (W.Count > 0)
+                {
+                    if (toExpand.Item2 > W.Farthest.Item2)
+                    {
+                        // the closest candidate is farther than farthest result
+                        break;
+                    }
+                }
+                if (context.IsActiveNode(toExpand.Item1))
+                {
+                    if (W.Count < ef || W.Count == 0 || (W.Count > 0 && toExpand.Item2 < W.Farthest.Item2))
+                    {
+                        W.Push(toExpand.Item1, toExpand.Item2);
+                        if (W.Count > ef)
+                        {
+                            W.PopFarthest();
+                        }
+                    }
+                }
+            }
+            if (W.Count > ef)
+            {
+                while (W.Count > ef)
+                {
+                    W.PopFarthest();
+                }
+                return;
+            }
+            else
+            {
+                foreach (var c in W)
+                {
+                    C.Push(c.Item1, c.Item2);
+                }
+            }
+            while (C.Count > 0)
+            {
+                // get next candidate to check and expand
+                var toExpand = C.PopNearest();
+                // expand candidate
+                var neighboursIds = GetNeighbors(toExpand.Item1).ToArray();
+                for (int i = 0; i < neighboursIds.Length; ++i)
+                {
+                    int neighbourId = neighboursIds[i];
+                    if (!v.Contains(neighbourId))
+                    {
+                        // enqueue perspective neighbours to expansion list
+                        var neighbourDistance = _links.Distance(toExpand.Item1, neighbourId);
+                        if (context.IsActiveNode(neighbourId))
+                        {
+                            if (W.Count < ef || (W.Count > 0 && neighbourDistance < W.Farthest.Item2))
+                            {
+                                W.Push(neighbourId, neighbourDistance);
+                                if (W.Count > ef)
+                                {
+                                    W.PopFarthest();
+                                }
+                            }
+                        }
+                        if (W.Count < ef)
+                        {
+                            C.Push(neighbourId, neighbourDistance);
+                        }
+                        v.Add(neighbourId);
+                    }
+                }
+            }
+            C.Clear();
+            v.Clear();
+        }
+
+        /// <summary>
+        /// Algorithm 3
+        /// </summary>
+        internal BinaryHeap SELECT_NEIGHBORS_SIMPLE(BinaryHeap W, int M)
+        {
+            var bestN = M;
+            if (W.Count > bestN)
+            {
+                while (W.Count > bestN)
+                {
+                    W.PopFarthest();
+                }
+            }
+            return W;
+        }
+
+
+
+        /// <summary>
+        /// Algorithm 4
+        /// </summary>
+        /// <param name="q">base element</param>
+        /// <param name="C">candidate elements</param>
+        /// <param name="extendCandidates">flag indicating whether or not to extend candidate list</param>
+        /// <param name="keepPrunedConnections">flag indicating whether or not to add discarded elements</param>
+        /// <returns>Output: M elements selected by the heuristic</returns>
+        internal BinaryHeap SELECT_NEIGHBORS_HEURISTIC(Func<int, float> distance, BinaryHeap W, int M)
+        {
+            // R ← ∅
+            var R = new BinaryHeap();
+            // W ← C // working queue for the candidates
+            // if extendCandidates // extend candidates by their neighbors
+            if (_options.ExpandBestSelection)
+            {
+                var extendBuffer = new HashSet<int>();
+                // for each e ∈ C
+                foreach (var e in W)
+                {
+                    var neighbors = GetNeighbors(e.Item1);
+                    // for each e_adj ∈ neighbourhood(e) at layer lc
+                    foreach (var e_adj in neighbors)
+                    {
+                        // if eadj ∉ W
+                        if (extendBuffer.Contains(e_adj) == false)
+                        {
+                            extendBuffer.Add(e_adj);
+                        }
+                    }
+                }
+                // W ← W ⋃ eadj
+                foreach (var id in extendBuffer)
+                {
+                    W.Push(id, distance(id));
+                }
+            }
+
+            //  Wd ← ∅ // queue for the discarded candidates
+            var Wd = new BinaryHeap();
+            // while │W│ > 0 and │R│< M
+            while (W.Count > 0 && R.Count < M)
+            {
+                // e ← extract nearest element from W to q
+                var (e, ed) = W.PopNearest();
+                var (fe, fd) = R.PopFarthest();
+
+                // if e is closer to q compared to any element from R
+                if (R.Count == 0 ||
+                    ed < fd)
+                {
+                    // R ← R ⋃ e
+                    R.Push(e, ed);
+                }
+                else
+                {
+                    // Wd ← Wd ⋃ e
+                    Wd.Push(e, ed);
+                }
+            }
+            // if keepPrunedConnections // add some of the discarded // connections from Wd
+            if (_options.KeepPrunedConnections)
+            {
+                // while │Wd│> 0 and │R│< M
+                while (Wd.Count > 0 && R.Count < M)
+                {
+                    // R ← R ⋃ extract nearest element from Wd to q
+                    var nearest = Wd.PopNearest();
+                    R.Push(nearest.Item1, nearest.Item2);
+                }
+            }
+            //  return R
+            return R;
+        }
+        #endregion
+
+        private IEnumerable<int> GetNeighbors(int id) => _links.FindLinksForId(id).Select(d => d.Item2);
+
+        public void Serialize(IBinaryWriter writer)
+        {
+            _links.Serialize(writer);
+        }
+
+        public void Deserialize(IBinaryReader reader)
+        {
+            _links.Deserialize(reader);
+        }
+
+        internal Histogram GetHistogram(HistogramMode mode) => _links.CalculateHistogram(mode);
+    }
+}

ZeroLevel.HNSW/Services/OPT/OptWorld.cs

@@ -0,0 +1,347 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Threading;
+using ZeroLevel.Services.Serialization;
+
+namespace ZeroLevel.HNSW.Services.OPT
+{
+    public class OptWorld<TItem>
+    {
+        private readonly NSWOptions<TItem> _options;
+        private VectorSet<TItem> _vectors;
+        private OptLayer<TItem>[] _layers;
+        private int EntryPoint = 0;
+        private int MaxLayer = 0;
+        private readonly ProbabilityLayerNumberGenerator _layerLevelGenerator;
+        private ReaderWriterLockSlim _lockGraph = new ReaderWriterLockSlim();
+        internal SortedList<long, float> GetNSWLinks() => _layers[0].Links;
+
+        public OptWorld(NSWOptions<TItem> options)
+        {
+            _options = options;
+            _vectors = new VectorSet<TItem>();
+            _layers = new OptLayer<TItem>[_options.LayersCount];
+            _layerLevelGenerator = new ProbabilityLayerNumberGenerator(_options.LayersCount, _options.M);
+            for (int i = 0; i < _options.LayersCount; i++)
+            {
+                _layers[i] = new OptLayer<TItem>(_options, _vectors);
+            }
+        }
+
+        internal OptWorld(NSWOptions<TItem> options, Stream stream)
+        {
+            _options = options;
+            Deserialize(stream);
+        }
+
+        /// <summary>
+        /// Search in the graph K for vectors closest to a given vector
+        /// </summary>
+        /// <param name="vector">Given vector</param>
+        /// <param name="k">Count of elements for search</param>
+        /// <param name="activeNodes"></param>
+        /// <returns></returns>
+        public IEnumerable<(int, TItem, float)> Search(TItem vector, int k)
+        {
+            foreach (var pair in KNearest(vector, k))
+            {
+                yield return (pair.Item1, _vectors[pair.Item1], pair.Item2);
+            }
+        }
+
+        public IEnumerable<(int, TItem, float)> Search(TItem vector, int k, SearchContext context)
+        {
+            if (context == null)
+            {
+                foreach (var pair in KNearest(vector, k))
+                {
+                    yield return (pair.Item1, _vectors[pair.Item1], pair.Item2);
+                }
+            }
+            else
+            {
+                foreach (var pair in KNearest(vector, k, context))
+                {
+                    yield return (pair.Item1, _vectors[pair.Item1], pair.Item2);
+                }
+            }
+        }
+
+        public IEnumerable<(int, TItem, float)> Search(int k, SearchContext context)
+        {
+            if (context == null)
+            {
+                throw new ArgumentNullException(nameof(context));
+            }
+            else
+            {
+                foreach (var pair in KNearest(k, context))
+                {
+                    yield return (pair.Item1, _vectors[pair.Item1], pair.Item2);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Adding vectors batch
+        /// </summary>
+        /// <param name="vectors">Vectors</param>
+        /// <returns>Vector identifiers in a graph</returns>
+        public int[] AddItems(IEnumerable<TItem> vectors)
+        {
+            _lockGraph.EnterWriteLock();
+            try
+            {
+                var ids = _vectors.Append(vectors);
+                for (int i = 0; i < ids.Length; i++)
+                {
+                    INSERT(ids[i]);
+                }
+                return ids;
+            }
+            finally
+            {
+                _lockGraph.ExitWriteLock();
+            }
+        }
+
+        #region https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf
+        /// <summary>
+        /// Algorithm 1
+        /// </summary>
+        private void INSERT(int q)
+        {
+            var distance = new Func<int, float>(candidate => _options.Distance(_vectors[q], _vectors[candidate]));
+            // W ← ∅ // list for the currently found nearest elements
+            var W = new BinaryHeap();
+            // ep ← get enter point for hnsw
+            //var ep = _layers[MaxLayer].FingEntryPointAtLayer(distance);
+            //if(ep == -1) ep = EntryPoint;
+            var ep = EntryPoint;
+            var epDist = distance(ep);
+            // L ← level of ep // top layer for hnsw
+            var L = MaxLayer;
+            // l ← ⌊-ln(unif(0..1))∙mL⌋ // new element’s level            
+            int l = _layerLevelGenerator.GetRandomLayer();
+            // for lc ← L … l+1
+            // Проход с верхнего уровня до уровня где появляется элемент, для нахождения точки входа
+            for (int lc = L; lc > l; --lc)
+            {
+                // W ← SEARCH-LAYER(q, ep, ef = 1, lc)
+                _layers[lc].KNearestAtLayer(ep, distance, W, 1);
+                // ep ← get the nearest element from W to q
+                var nearest = W.Nearest;
+                ep = nearest.Item1;
+                epDist = nearest.Item2;
+                W.Clear();
+            }
+            //for lc ← min(L, l) … 0
+            // connecting new node to the small world
+            for (int lc = Math.Min(L, l); lc >= 0; --lc)
+            {
+                if (_layers[lc].HasLinks == false)
+                {
+                    _layers[lc].Append(q);
+                }
+                else
+                {
+                    // W ← SEARCH - LAYER(q, ep, efConstruction, lc)
+                    _layers[lc].KNearestAtLayer(ep, distance, W, _options.EFConstruction);
+
+                    // ep ← W
+                    var nearest = W.Nearest;
+                    ep = nearest.Item1;
+                    epDist = nearest.Item2;
+
+                    // neighbors ← SELECT-NEIGHBORS(q, W, M, lc) // alg. 3 or alg. 4
+                    var neighbors = SelectBestForConnecting(lc, distance, W);
+                    // add bidirectionall connectionts from neighbors to q at layer lc
+                    // for each e ∈ neighbors // shrink connections if needed
+                    foreach (var e in neighbors)
+                    {
+                        // eConn ← neighbourhood(e) at layer lc
+                        _layers[lc].AddBidirectionallConnections(q, e.Item1, e.Item2, lc == 0);
+                        // if distance from newNode to newNeighbour is better than to bestPeer => update bestPeer
+                        if (e.Item2 < epDist)
+                        {
+                            ep = e.Item1;
+                            epDist = e.Item2;
+                        }
+                    }
+                    W.Clear();
+                }
+            }
+            // if l > L
+            if (l > L)
+            {
+                // set enter point for hnsw to q
+                L = l;
+                MaxLayer = l;
+                EntryPoint = ep;
+            }
+        }
+
+        /// <summary>
+        /// Get maximum allowed connections for the given level.
+        /// </summary>
+        /// <remarks>
+        /// Article: Section 4.1:
+        /// "Selection of the Mmax0 (the maximum number of connections that an element can have in the zero layer) also
+        /// has a strong influence on the search performance, especially in case of high quality(high recall) search.
+        /// Simulations show that setting Mmax0 to M(this corresponds to kNN graphs on each layer if the neighbors
+        /// selection heuristic is not used) leads to a very strong performance penalty at high recall.
+        /// Simulations also suggest that 2∙M is a good choice for Mmax0;
+        /// setting the parameter higher leads to performance degradation and excessive memory usage."
+        /// </remarks>
+        /// <param name="layer">The level of the layer.</param>
+        /// <returns>The maximum number of connections.</returns>
+        private int GetM(int layer)
+        {
+            return layer == 0 ? 2 * _options.M : _options.M;
+        }
+
+        private BinaryHeap SelectBestForConnecting(int layer, Func<int, float> distance, BinaryHeap candidates)
+        {
+            if (_options.SelectionHeuristic == NeighbourSelectionHeuristic.SelectSimple)
+                return _layers[layer].SELECT_NEIGHBORS_SIMPLE(candidates, GetM(layer));
+            return _layers[layer].SELECT_NEIGHBORS_HEURISTIC(distance, candidates, GetM(layer));
+        }
+
+        /// <summary>
+        /// Algorithm 5
+        /// </summary>
+        private BinaryHeap KNearest(TItem q, int k)
+        {
+            _lockGraph.EnterReadLock();
+            try
+            {
+                if (_vectors.Count == 0)
+                {
+                    return BinaryHeap.Empty;
+                }
+                var distance = new Func<int, float>(candidate => _options.Distance(q, _vectors[candidate]));
+
+                // W ← ∅ // set for the current nearest elements
+                var W = new BinaryHeap(k + 1);
+                // ep ← get enter point for hnsw
+                var ep = EntryPoint;
+                // L ← level of ep // top layer for hnsw
+                var L = MaxLayer;
+                // for lc ← L … 1
+                for (int layer = L; layer > 0; --layer)
+                {
+                    // W ← SEARCH-LAYER(q, ep, ef = 1, lc)
+                    _layers[layer].KNearestAtLayer(ep, distance, W, 1);
+                    // ep ← get nearest element from W to q
+                    ep = W.Nearest.Item1;
+                    W.Clear();
+                }
+                // W ← SEARCH-LAYER(q, ep, ef, lc =0)
+                _layers[0].KNearestAtLayer(ep, distance, W, k);
+                // return K nearest elements from W to q
+                return W;
+            }
+            finally
+            {
+                _lockGraph.ExitReadLock();
+            }
+        }
+        private BinaryHeap KNearest(TItem q, int k, SearchContext context)
+        {
+            _lockGraph.EnterReadLock();
+            try
+            {
+                if (_vectors.Count == 0)
+                {
+                    return BinaryHeap.Empty;
+                }
+                var distance = new Func<int, float>(candidate => _options.Distance(q, _vectors[candidate]));
+
+                // W ← ∅ // set for the current nearest elements
+                var W = new BinaryHeap(k + 1);
+                // ep ← get enter point for hnsw
+                var ep = EntryPoint;
+                // L ← level of ep // top layer for hnsw
+                var L = MaxLayer;
+                // for lc ← L … 1
+                for (int layer = L; layer > 0; --layer)
+                {
+                    // W ← SEARCH-LAYER(q, ep, ef = 1, lc)
+                    _layers[layer].KNearestAtLayer(ep, distance, W, 1);
+                    // ep ← get nearest element from W to q
+                    ep = W.Nearest.Item1;
+                    W.Clear();
+                }
+                // W ← SEARCH-LAYER(q, ep, ef, lc =0)
+                _layers[0].KNearestAtLayer(ep, distance, W, k, context);
+                // return K nearest elements from W to q
+                return W;
+            }
+            finally
+            {
+                _lockGraph.ExitReadLock();
+            }
+        }
+
+        private BinaryHeap KNearest(int k, SearchContext context)
+        {
+            _lockGraph.EnterReadLock();
+            try
+            {
+                if (_vectors.Count == 0)
+                {
+                    return BinaryHeap.Empty;
+                }
+                var distance = new Func<int, int, float>((id1, id2) => _options.Distance(_vectors[id1], _vectors[id2]));
+                // W ← ∅ // set for the current nearest elements
+                var W = new BinaryHeap(k + 1);
+                // W ← SEARCH-LAYER(q, ep, ef, lc =0)
+                _layers[0].KNearestAtLayer(W, k, context);
+                // return K nearest elements from W to q
+                return W;
+            }
+            finally
+            {
+                _lockGraph.ExitReadLock();
+            }
+        }
+        #endregion
+
+        public void Serialize(Stream stream)
+        {
+            using (var writer = new MemoryStreamWriter(stream))
+            {
+                writer.WriteInt32(EntryPoint);
+                writer.WriteInt32(MaxLayer);
+                _vectors.Serialize(writer);
+                writer.WriteInt32(_layers.Length);
+                foreach (var l in _layers)
+                {
+                    l.Serialize(writer);
+                }
+            }
+        }
+
+        public void Deserialize(Stream stream)
+        {
+            using (var reader = new MemoryStreamReader(stream))
+            {
+                this.EntryPoint = reader.ReadInt32();
+                this.MaxLayer = reader.ReadInt32();
+                _vectors = new VectorSet<TItem>();
+                _vectors.Deserialize(reader);
+                var countLayers = reader.ReadInt32();
+                _layers = new OptLayer<TItem>[countLayers];
+                for (int i = 0; i < countLayers; i++)
+                {
+                    _layers[i] = new OptLayer<TItem>(_options, _vectors);
+                    _layers[i].Deserialize(reader);
+                }
+            }
+        }
+
+        public Histogram GetHistogram(HistogramMode mode = HistogramMode.SQRT)
+            => _layers[0].GetHistogram(mode);
+    }
+}

ZeroLevel.HNSW/SmallWorld.cs

@@ -11,7 +11,7 @@ namespace ZeroLevel.HNSW
     public class SmallWorld<TItem>
     {
         private readonly NSWOptions<TItem> _options;
-        private readonly VectorSet<TItem> _vectors;
+        private VectorSet<TItem> _vectors;
         private Layer<TItem>[] _layers;
         private int EntryPoint = 0;
         private int MaxLayer = 0;
@@ -118,6 +118,8 @@ namespace ZeroLevel.HNSW
             // W ← ∅ // list for the currently found nearest elements
             IDictionary<int, float> W = new Dictionary<int, float>();
             // ep ← get enter point for hnsw
+            //var ep = _layers[MaxLayer].FingEntryPointAtLayer(distance);
+            //if(ep == -1) ep = EntryPoint;
             var ep = EntryPoint;
             var epDist = distance(ep);
             // L ← level of ep // top layer for hnsw
@@ -334,6 +336,7 @@ namespace ZeroLevel.HNSW
             {
                 this.EntryPoint = reader.ReadInt32();
                 this.MaxLayer = reader.ReadInt32();
+                _vectors = new VectorSet<TItem>();
                 _vectors.Deserialize(reader);
                 var countLayers = reader.ReadInt32();
                 _layers = new Layer<TItem>[countLayers];

ZeroLevel.HNSW/ZeroLevel.HNSW.csproj

@@ -1,7 +1,8 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <TargetFramework>net5.0</TargetFramework>
+    <Platforms>AnyCPU;x64</Platforms>
   </PropertyGroup>
 
   <ItemGroup>