} // of AnnexCluster()
public static UnitCluster MergeClusters(UnitCluster p_C1, UnitCluster p_C2)
{
if (p_C2.ListPoints.Count > 0)
{
p_C1.AnnexCluster(p_C2);
}
return(p_C1);
} // of MergeClusters()
public static List <UnitCluster> RunKmeans <T>(List <T> objList, double distance) where T : CacheObject
{
List <UnitCluster> LC_ = new List <UnitCluster>();
if (objList.Count == 0)
{
return(LC_);
}
List <CacheObject> l_ListUnits = new List <CacheObject>(objList.ToArray());
if (l_ListUnits.Count == 0)
{
return(LC_);
}
// for starters, take a point to create one cluster
CacheUnit l_P1 = (CacheUnit)l_ListUnits[0];
l_ListUnits.RemoveAt(0);
// so far, we have a one-point cluster
LC_.Add(new UnitCluster(distance, l_P1));
#region Main Loop
// the algorithm is inside this loop
List <UnitCluster> l_ListAttainableClusters;
UnitCluster l_c;
foreach (CacheUnit p in l_ListUnits)
{
l_ListAttainableClusters = new List <UnitCluster>();
l_ListAttainableClusters = LC_.FindAll(x => x.IsPointReachable(p.PointPosition));
LC_.RemoveAll(x => x.IsPointReachable(p.PointPosition));
l_c = new UnitCluster(distance, p);
// merge point's "reachable" clusters
if (l_ListAttainableClusters.Count > 0)
{
l_c.AnnexCluster(l_ListAttainableClusters.Aggregate((c, x) =>
c = MergeClusters(x, c)));
}
LC_.Add(l_c);
//Logger.DBLog.InfoFormat("Cluster Found: Total Points {0} with Centeroid {1}", l_c.ListPoints.Count, l_c.Centeroid.ToString());
l_ListAttainableClusters = null;
l_c = null;
} // of loop over candidate points
//LC_=LC_.OrderByDescending(o => o.ListPoints.Count).ToList();
#endregion
return(LC_);
}