internal static IEnumerable <List <TColor> > KmeansPlusPlus <TColor>(IReadOnlyList <TColor> colors, ISpace <TColor> space, int count, int?seed = null)
{
var random = seed.HasValue ? new Random(seed.Value) : new Random();
var centroids = new List <TColor> {
colors[random.Next(colors.Count)]
};
foreach (var c in SelectCentroids().Take(count - 1))
{
centroids.Add(c);
}
var preClusters = new List <TColor> [count];
while (true)
{
var clusters = Enumerable.Range(0, count).Select(_ => new List <TColor>()).ToArray();
foreach (var color in colors)
{
clusters[GetNearestCentroidIndex(color)].Add(color);
}
if (Enumerable.Range(0, count).All(i => preClusters[i] != null && clusters[i].SequenceEqual(preClusters[i])))
{
return(clusters.OrderByDescending(x => x.Count));
}
for (var i = 0; i < count; i++)
{
centroids[i] = space.GetCentroid(clusters[i]);
preClusters[i] = clusters[i];
}
}
int GetNearestCentroidIndex(TColor color)
{
var minDistance = double.MaxValue;
var minIndex = -1;
for (var i = 0; i < centroids.Count; i++)
{
var d = space.GetDistance(centroids[i], color);
if (d < minDistance)
{
minDistance = d;
minIndex = i;
}
}
return(minIndex);
}
IEnumerable <TColor> SelectCentroids()
{
var distances = colors.Select(x => centroids.Min(c => Math.Pow(space.GetDistance(x, c), 2))).ToArray();
var sum = distances.Sum();
while (true)
{
var init = random.NextDouble();
var d = 0.0;
var i = 0;
for (; i < colors.Count; i++)
{
d += distances[i] / sum;
if (d > init)
{
break;
}
}
yield return(colors[i]);
}
}
}
