|
|
|
|
using System;
|
|
|
|
|
using System.Collections.Generic;
|
|
|
|
|
using System.Diagnostics;
|
|
|
|
|
using System.Linq;
|
|
|
|
|
using ZeroLevel.HNSW;
|
|
|
|
|
|
|
|
|
|
namespace HNSWDemo
|
|
|
|
|
{
|
|
|
|
|
class Program
|
|
|
|
|
{
|
|
|
|
|
public class VectorsDirectCompare
|
|
|
|
|
{
|
|
|
|
|
private readonly IList<float[]> _vectors;
|
|
|
|
|
private readonly Func<float[], float[], float> _distance;
|
|
|
|
|
|
|
|
|
|
public VectorsDirectCompare(List<float[]> vectors, Func<float[], float[], float> distance)
|
|
|
|
|
{
|
|
|
|
|
_vectors = vectors;
|
|
|
|
|
_distance = distance;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
public IEnumerable<(int, float)> KNearest(float[] v, int k)
|
|
|
|
|
{
|
|
|
|
|
var weights = new Dictionary<int, float>();
|
|
|
|
|
for (int i = 0; i < _vectors.Count; i++)
|
|
|
|
|
{
|
|
|
|
|
var d = _distance(v, _vectors[i]);
|
|
|
|
|
weights[i] = d;
|
|
|
|
|
}
|
|
|
|
|
return weights.OrderBy(p => p.Value).Take(k).Select(p => (p.Key, p.Value));
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
public enum Gender
|
|
|
|
|
{
|
|
|
|
|
Unknown, Male, Feemale
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
public class Person
|
|
|
|
|
{
|
|
|
|
|
public Gender Gender { get; set; }
|
|
|
|
|
public int Age { get; set; }
|
|
|
|
|
public long Number { get; set; }
|
|
|
|
|
|
|
|
|
|
private static (float[], Person) Generate(int vector_size)
|
|
|
|
|
{
|
|
|
|
|
var rnd = new Random((int)Environment.TickCount);
|
|
|
|
|
var vector = new float[vector_size];
|
|
|
|
|
DefaultRandomGenerator.Instance.NextFloats(vector);
|
|
|
|
|
VectorUtils.NormalizeSIMD(vector);
|
|
|
|
|
var p = new Person();
|
|
|
|
|
p.Age = rnd.Next(15, 80);
|
|
|
|
|
var gr = rnd.Next(0, 3);
|
|
|
|
|
p.Gender = (gr == 0) ? Gender.Male : (gr == 1) ? Gender.Feemale : Gender.Unknown;
|
|
|
|
|
p.Number = CreateNumber(rnd);
|
|
|
|
|
return (vector, p);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
public static List<(float[], Person)> GenerateRandom(int vectorSize, int vectorsCount)
|
|
|
|
|
{
|
|
|
|
|
var vectors = new List<(float[], Person)>();
|
|
|
|
|
for (int i = 0; i < vectorsCount; i++)
|
|
|
|
|
{
|
|
|
|
|
vectors.Add(Generate(vectorSize));
|
|
|
|
|
}
|
|
|
|
|
return vectors;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
static HashSet<long> _exists = new HashSet<long>();
|
|
|
|
|
private static long CreateNumber(Random rnd)
|
|
|
|
|
{
|
|
|
|
|
long start_number;
|
|
|
|
|
do
|
|
|
|
|
{
|
|
|
|
|
start_number = 79600000000L;
|
|
|
|
|
start_number = start_number + rnd.Next(4, 8) * 10000000;
|
|
|
|
|
start_number += rnd.Next(0, 1000000);
|
|
|
|
|
}
|
|
|
|
|
while (_exists.Add(start_number) == false);
|
|
|
|
|
return start_number;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
private static List<float[]> RandomVectors(int vectorSize, int vectorsCount)
|
|
|
|
|
{
|
|
|
|
|
var vectors = new List<float[]>();
|
|
|
|
|
for (int i = 0; i < vectorsCount; i++)
|
|
|
|
|
{
|
|
|
|
|
var vector = new float[vectorSize];
|
|
|
|
|
DefaultRandomGenerator.Instance.NextFloats(vector);
|
|
|
|
|
VectorUtils.NormalizeSIMD(vector);
|
|
|
|
|
vectors.Add(vector);
|
|
|
|
|
}
|
|
|
|
|
return vectors;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
private static Dictionary<int, Person> _database = new Dictionary<int, Person>();
|
|
|
|
|
|
|
|
|
|
static void Main(string[] args)
|
|
|
|
|
{
|
|
|
|
|
FilterTest();
|
|
|
|
|
Console.ReadKey();
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
static void FilterTest()
|
|
|
|
|
{
|
|
|
|
|
var count = 5000;
|
|
|
|
|
var testCount = 1000;
|
|
|
|
|
var dimensionality = 128;
|
|
|
|
|
var samples = Person.GenerateRandom(dimensionality, count);
|
|
|
|
|
|
|
|
|
|
var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
|
|
|
|
|
|
|
|
|
|
var ids = world.AddItems(samples.Select(i => i.Item1).ToArray());
|
|
|
|
|
for (int bi = 0; bi < samples.Count; bi++)
|
|
|
|
|
{
|
|
|
|
|
_database.Add(ids[bi], samples[bi].Item2);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
Console.WriteLine("Start test");
|
|
|
|
|
int K = 200;
|
|
|
|
|
var vectors = RandomVectors(dimensionality, testCount);
|
|
|
|
|
|
|
|
|
|
var activeNodes = _database.Where(pair => pair.Value.Age > 20 && pair.Value.Age < 50 && pair.Value.Gender == Gender.Feemale).Select(pair => pair.Key).ToHashSet();
|
|
|
|
|
|
|
|
|
|
var hits = 0;
|
|
|
|
|
var miss = 0;
|
|
|
|
|
foreach (var v in vectors)
|
|
|
|
|
{
|
|
|
|
|
var result = world.Search(v, K, activeNodes);
|
|
|
|
|
foreach (var r in result)
|
|
|
|
|
{
|
|
|
|
|
var record = _database[r.Item1];
|
|
|
|
|
if (record.Gender == Gender.Feemale && record.Age > 20 && record.Age < 50)
|
|
|
|
|
{
|
|
|
|
|
hits++;
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
{
|
|
|
|
|
miss++;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
Console.WriteLine($"SUCCESS: {hits}");
|
|
|
|
|
Console.WriteLine($"ERROR: {miss}");
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
static void AccuracityTest()
|
|
|
|
|
{
|
|
|
|
|
int K = 200;
|
|
|
|
|
var count = 5000;
|
|
|
|
|
var testCount = 1000;
|
|
|
|
|
var dimensionality = 128;
|
|
|
|
|
var totalHits = new List<int>();
|
|
|
|
|
var timewatchesNP = new List<float>();
|
|
|
|
|
var timewatchesHNSW = new List<float>();
|
|
|
|
|
var samples = RandomVectors(dimensionality, count);
|
|
|
|
|
|
|
|
|
|
var sw = new Stopwatch();
|
|
|
|
|
|
|
|
|
|
var test = new VectorsDirectCompare(samples, CosineDistance.ForUnits);
|
|
|
|
|
var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 15, 200, 200, CosineDistance.ForUnits, true, true, selectionHeuristic: NeighbourSelectionHeuristic.SelectSimple));
|
|
|
|
|
|
|
|
|
|
sw.Start();
|
|
|
|
|
var ids = world.AddItems(samples.ToArray());
|
|
|
|
|
sw.Stop();
|
|
|
|
|
|
|
|
|
|
Console.WriteLine($"Insert {ids.Length} items on {sw.ElapsedMilliseconds} ms");
|
|
|
|
|
Console.WriteLine("Start test");
|
|
|
|
|
|
|
|
|
|
var test_vectors = RandomVectors(dimensionality, testCount);
|
|
|
|
|
foreach (var v in test_vectors)
|
|
|
|
|
{
|
|
|
|
|
sw.Restart();
|
|
|
|
|
var gt = test.KNearest(v, K).ToDictionary(p => p.Item1, p => p.Item2);
|
|
|
|
|
sw.Stop();
|
|
|
|
|
timewatchesNP.Add(sw.ElapsedMilliseconds);
|
|
|
|
|
|
|
|
|
|
sw.Restart();
|
|
|
|
|
var result = world.Search(v, K);
|
|
|
|
|
sw.Stop();
|
|
|
|
|
timewatchesHNSW.Add(sw.ElapsedMilliseconds);
|
|
|
|
|
var hits = 0;
|
|
|
|
|
foreach (var r in result)
|
|
|
|
|
{
|
|
|
|
|
if (gt.ContainsKey(r.Item1))
|
|
|
|
|
{
|
|
|
|
|
hits++;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
totalHits.Add(hits);
|
|
|
|
|
}
|
|
|
|
|
Console.WriteLine($"MIN Accuracity: {totalHits.Min() * 100 / K}%");
|
|
|
|
|
Console.WriteLine($"AVG Accuracity: {totalHits.Average() * 100 / K}%");
|
|
|
|
|
Console.WriteLine($"MAX Accuracity: {totalHits.Max() * 100 / K}%");
|
|
|
|
|
|
|
|
|
|
Console.WriteLine($"MIN HNSW TIME: {timewatchesHNSW.Min()} ms");
|
|
|
|
|
Console.WriteLine($"AVG HNSW TIME: {timewatchesHNSW.Average()} ms");
|
|
|
|
|
Console.WriteLine($"MAX HNSW TIME: {timewatchesHNSW.Max()} ms");
|
|
|
|
|
|
|
|
|
|
Console.WriteLine($"MIN NP TIME: {timewatchesNP.Min()} ms");
|
|
|
|
|
Console.WriteLine($"AVG NP TIME: {timewatchesNP.Average()} ms");
|
|
|
|
|
Console.WriteLine($"MAX NP TIME: {timewatchesNP.Max()} ms");
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|