K-Means

package SOM;
import java.util.ArrayList;


public class KMeans {
	public static int[][] Lloyd(MapLayer map,int cluster){
		/*
		 * Initialisierung: Wähle k zufällige Mittelwerte (Means) aus dem Datensatz.
			Zuordnung: Jedes Datenobjekt wird demjenigen Cluster zugeordnet, bei dem die Cluster-Varianz am wenigsten erhöht wird.
			Aktualisieren: Berechne die Mittelpunkte der Cluster neu
			
			Weightvectors are used as means
			
			For better results, start at distinct bfc's
		 */
		int index=0;
		int[] dim=map.GetDim();
		int[][] ClusterMap=new int[dim[0]][dim[1]];
		double[] cluster_wsum=new double[cluster];
		int weight_dim=map.GetNeuron(0, 0).GetInpWeightsArray().length;
		double[][] cluster_w=new double[cluster][weight_dim];
		double temp=0;
		double[] tempvec;
		double min;
		boolean change=true;
		double[][] Means=new double[cluster][weight_dim];
		Neuron[][] N=map.GetNeurons();
		
		//Initialize Cluster Map
		for(int i=0;i<dim[0];i++){
			for(int j=0;j<dim[1];j++){
				ClusterMap[i][j]=cluster*2;
			}
		}
		//Initializing with random means
		for(int i=0;i<cluster;i++){
			int randx=(int)Math.round(Math.random()*(dim[0]-1));
			int randy=(int)Math.round(Math.random()*(dim[1]-1));
			for(int j=0;j<weight_dim;j++){
				Means[i][j]=N[randx][randy].GetInpWeightsArray()[j];
			}
		}
		int iter=0;
		while(change && iter<1000){
			iter++;
			change=false;
			//assign each neuron  to a cluster 
			for(int x=0;x<dim[0];x++){
				for(int y=0;y<dim[1];y++){
					temp=TB.distance(N[x][y].GetInpWeightsArray(),TB.To1D(Means,weight_dim,0));
					min=temp;
					index=0;
					for(int i=1;i<cluster;i++){
	
						temp=TB.distance(N[x][y].GetInpWeightsArray(),TB.To1D(Means,weight_dim,i));
						if(temp<min){
							min=temp;
							index=i;
						}
						
					}
					if(ClusterMap[x][y]!=index){
						change=true;
					}
					ClusterMap[x][y]=index;
				}
			}
			//Reset neuron count and cluster sum for each cluster
			for(int i=0;i<cluster;i++){
				cluster_wsum[i]=0;
				for(int j=0;j<weight_dim;j++){
					cluster_w[i][j]=0;
					
				}
			}
			//Calculate Cluster Means anew

			for(int x=0;x<dim[0];x++){
				for(int y=0;y<dim[1];y++){
					/*
					index=ClusterMap[x][y];
					for(int j=0;j<weight_dim;j++){
						cluster_wsum[index]+=N[x][y].GetInpWeightsArray()[j];
						cluster_w[index][j]+=N[x][y].GetInpWeightsArray()[j];
					}
					*/
					//Tryout:
					/*
					 * add the abs value of the weight vectors
					 */
					index=ClusterMap[x][y];
					for(int j=0;j<weight_dim;j++){
						cluster_w[index][j]+=N[x][y].GetInpWeightsArray()[j];
					}
					cluster_wsum[index]+=TB.abs(N[x][y].GetInpWeightsArray());
					
				}
			}
			for(int i=0;i<cluster;i++){
				for(int j=0;j<weight_dim;j++){
					if(cluster_wsum[i]==0){
						System.out.println("No element found for cluster "+i);
						break;
					}
					Means[i][j]=(1/cluster_wsum[i])*cluster_w[i][j];
				}
				//ShowArray(To1D(Means,weight_dim,i));
			}

		}
		if(iter%100==0){System.out.println("Iteration: " + iter);}
		System.out.println(iter);
		return ClusterMap;
	}
}