public static List <DataPoint> KMeansAlgorithm(List <DataPoint> points, int k)
{
if (k < 1)
{
throw new Exception("K must be greater than 0");
}
Debug.Log("Initial set:");
foreach (DataPoint p in points)
{
Debug.Log(" " + p);
}
Debug.Log("");
double minX = double.MaxValue, minY = double.MaxValue;
double maxX = double.MinValue, maxY = double.MinValue;
//searching for boundaries of coordinates
for (int i = 0; i < points.Count(); i++)
{
if (points[i].X < minX)
{
minX = points[i].X;
}
if (points[i].X > maxX)
{
maxX = points[i].X;
}
if (points[i].Y < minY)
{
minY = points[i].Y;
}
if (points[i].Y > maxY)
{
maxY = points[i].Y;
}
}
//Debug.Log("Borders: xє[{0},{1}] ; yє[{2},{3}]", minX, maxX, minY, maxY);
//initializing k centroids randomly
DataPoint[] centroids = new DataPoint[k];
System.Random random = new System.Random();
double centrX, centrY = 0;
Debug.Log("Centroids ");
for (int i = 0; i < k; i++)
{
centrX = random.NextDouble() * (maxX - minX) + minX;
centrY = random.NextDouble() * (maxY - minY) + minY;
centroids[i] = new DataPoint(centrX, centrY);
Debug.Log(centroids[i] + " ");
}
Debug.Log("");
bool centroidsChanged = true;
int counter = 0;
//while centroids are changing their positions
while (centroidsChanged)
{
//binding each point to the nearest centroid
for (int i = 0; i < points.Count(); i++)
{
double minDist = int.MaxValue;
double dist = 0;
for (int c = 0; c < k; c++)
{
dist = Math.Sqrt(Math.Pow(points[i].X - centroids[c].X, 2) + Math.Pow(points[i].Y - centroids[c].Y, 2));
if (dist < minDist)
{
points[i].Cluster = c;
minDist = dist;
}
}
}
int[] clusterSize = new int[k];
double[] xSum = new double[k];
double[] ySum = new double[k];
string[] clusterPoints = new string[k];
//calculating mean point for each cluster
for (int i = 0; i < points.Count(); i++)
{
int currCluster = points[i].Cluster;
clusterSize[currCluster]++;
xSum[currCluster] += points[i].X;
ySum[currCluster] += points[i].Y;
if (clusterPoints[points[i].Cluster] == null)
{
clusterPoints[points[i].Cluster] = "";
}
clusterPoints[points[i].Cluster] += " " + points[i].ToString();
}
//printing clusters
for (int i = 0; i < k; i++)
{
Debug.Log("Cluster " + i + " : " + clusterPoints[i]);
}
int centroidsChangedCounter = 0;
//setting new mean points as centroids
Debug.Log("Centroids ");
for (int i = 0; i < k; i++)
{
DataPoint dp = new DataPoint(xSum[i] / clusterSize[i], ySum[i] / clusterSize[i]);
if (double.IsNaN(dp.X) || double.IsNaN(dp.Y))
{
throw new Exception("K value is too large for this set");
}
else if (dp != centroids[i])
{
centroids[i] = dp;
centroidsChangedCounter++;
}
Debug.Log(centroids[i] + " ");
}
Debug.Log("");
//if new centroids are the same as old - stop algorithm
if (centroidsChangedCounter == 0)
{
centroidsChanged = false;
}
counter++;
}
Debug.Log("RESULT:");
string[] resultClusters = new string[k];
for (int i = 0; i < points.Count(); i++)
{
resultClusters[points[i].Cluster] += " " + points[i].ToString();
}
for (int i = 0; i < k; i++)
{
Debug.Log("Cluster " + i + " :" + resultClusters[i]);
}
return(points);
}
private static int CompareByClusterIndex(DataPoint a, DataPoint b)
{
return(a.ClusterIndex.CompareTo(b.ClusterIndex));
}
