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KMean

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Esiur Project 2022-12-01 17:11:44 +03:00
parent 9775b2a878
commit 2325bbcb34
1 changed files with 48 additions and 2 deletions
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50
Esiur.Analysis/Statistics/StatisticalFunctions.cs
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@ -1,5 +1,6 @@
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
@ -35,9 +36,9 @@ namespace Esiur.Analysis.Statistics
public static double RMS(this double[] x)
{
var r = Math.Sqrt(x.Sum(x =>(float) x * (float) x) / x.Length);
var r = Math.Sqrt(x.Sum(x => (float)x * (float)x) / x.Length);
//if (double.IsNaN(r) || double.IsInfinity(r))
// Console.WriteLine();
// Console.WriteLine();
return r;
}
@ -46,5 +47,50 @@ namespace Esiur.Analysis.Statistics
{
return Covariance(x, y) / (StdDiv(x) * StdDiv(y));
}
public static double Distance(this PointF from, PointF to)
{
return Math.Sqrt(Math.Pow(from.X - to.X, 2) + Math.Pow(from.Y - to.Y, 2));
}
class KClass
{
public int Id;
public PointF Center;
public override string ToString()
{
return $"C{Id} <{Center.X}, {Center.Y}>";
}
}
public static Dictionary<string, PointF> KMean(PointF[] seeds, PointF[] points)
{
// calculate distance
var classes = new Dictionary<KClass, List<PointF>>();
for (var i = 0; i < seeds.Length; i++)
classes.Add(new KClass() { Id = i+1, Center = seeds[i] }, new List<PointF>());
while (true)
{
foreach (var point in points)
{
var cls = classes.Keys.OrderBy(x => x.Center.Distance(point)).First();
classes[cls].Add(point);
}
// update center
foreach(var kv in classes)
{
kv.Key.Center = new PointF() { X = kv.Value.Average(p => p.X), Y = kv.Value.Average(p => p.Y) };
kv.Value.Clear();
}
}
}
}
}