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Zero/ZeroLevel.HNSW/Services/AutomaticGraphClusterer.cs

151 lines
4.2 KiB

using System;
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using System.Collections;
using System.Collections.Generic;
using System.Linq;
namespace ZeroLevel.HNSW.Services
{
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public class Cluster
: IEnumerable<int>
{
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private HashSet<int> _elements = new HashSet<int>();
public int Count => _elements.Count;
public bool Contains(int id) => _elements.Contains(id);
public bool Add(int id) => _elements.Add(id);
public IEnumerator<int> GetEnumerator()
{
return _elements.GetEnumerator();
}
IEnumerator IEnumerable.GetEnumerator()
{
return _elements.GetEnumerator();
}
public void Merge(Cluster cluster)
{
foreach (var e in cluster)
{
this._elements.Add(e);
}
}
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public float MaxDistance(Func<int, int, float> distance, Cluster other)
{
var max = float.MinValue;
foreach (var e in this._elements)
{
foreach (var o in other)
{
var d = distance(e, o);
if (d > max)
{
max = d;
}
}
}
return max;
}
public float MinDistance(Func<int, int, float> distance, Cluster other)
{
var min = float.MaxValue;
foreach (var e in this._elements)
{
foreach (var o in other)
{
var d = distance(e, o);
if (d < min)
{
min = d;
}
}
}
return min;
}
public float AvgDistance(Func<int, int, float> distance, Cluster other)
{
var dist = new List<float>();
foreach (var e in this._elements)
{
foreach (var o in other)
{
dist.Add(distance(e, o));
}
}
return dist.Average();
}
}
public static class AutomaticGraphClusterer
{
private class Link
{
public int Id1;
public int Id2;
public float Distance;
}
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public static List<Cluster> DetectClusters<T>(SmallWorld<T> world)
{
var distance = world.DistanceFunction;
var links = world.GetLinks().SelectMany(pair => pair.Value.Select(id => new Link { Id1 = pair.Key, Id2 = id, Distance = distance(pair.Key, id) })).ToList();
// 1. Find R - bound between intra-cluster distances and out-of-cluster distances
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var histogram = new Histogram(HistogramMode.LOG, links.Select(l => l.Distance));
int threshold = histogram.CuttOff();
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 List<Link>();
foreach (var l in links)
{
if (l.Distance < R)
{
resultLinks.Add(l);
}
}
// 3. Extract clusters
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List<Cluster> clusters = new List<Cluster>();
foreach (var l in resultLinks)
{
var id1 = l.Id1;
var id2 = l.Id2;
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)
{
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var c = new Cluster();
c.Add(id1);
c.Add(id2);
clusters.Add(c);
}
}
return clusters;
}
}
}

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