//KMeans
#region
/// <summary>
/// Search the nearest cluster
/// </summary>
/// <param name="cluster">List of cluster considered</param>
public void SearchCluster(SyncronisedList <UICluster> cluster, out Cluster finded)
{
// If parameters is equal to null
if (cluster == null || cluster.Count == 0)
{
throw new InvalidOperationException("There's no cluster");
}
double distance = double.MaxValue;
Cluster min = cluster[0].Element;
// Find the real nearest cluster
for (int index = 0; index < cluster.Count; index++)
{
double tmp = Math.Sqrt(Math.Pow(cluster[index].Element.X - X, 2) + Math.Pow(cluster[index].Element.Y - Y, 2));
if (tmp < distance)
{
min = cluster[index].Element;
distance = tmp;
}
}
//Saves the cluster nearest
if (Member != min)
{
finded = min;
}
else
{
finded = null;
}
}
public Cluster(double x, double y, UICluster element)
{
X = x;
Y = y;
Points = new SyncronisedList <Point>();
Element = element;
}
internal Scope(SyncronisedList <U> owner, bool write)
{
_owner = owner;
_write = write;
if (_write)
{
_owner._threadLock.EnterWriteLock();
}
else
{
_owner._threadLock.EnterReadLock();
}
}
public void SearchCluster(SyncronisedList <UICluster> cluster, SyncronisedList <UIPoint> points)
{
Cluster finded = null;
if (Element.Typology == Point.Type.KMEANS)
{
Element.SearchCluster(cluster, out finded);
}
else if (Element.Typology == Point.Type.KNN)
{
Element.SearchType(points.ToArray(), out finded);
}
if (finded != null)
{
if (Element.Member != null)
{
Element.Member.Points.Remove(Element);
}
Element.Member = finded;
Element.Member.Points.Add(Element);
}
}
public Cluster()
{
Points = new SyncronisedList <Point>();
}
