first commit
This commit is contained in:
commit
07bd6f6315
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import java.io.*;
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import java.util.ArrayList;
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public class DataFileReader {
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static ArrayList<ArrayList<Double>> readFile(String file){
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ArrayList<ArrayList<Double>> data = new ArrayList<ArrayList<Double>>();
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//Count: leere Zeilen werden nicht mitverwendet
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int count = 0;
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try(BufferedReader stream = new BufferedReader(new FileReader(file))){
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while(true){
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String line = stream.readLine();
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if(line == null){
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break;
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}
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if(!line.contains("#")){
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String[] parts = line.split("[ |\t]");
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data.add(new ArrayList<Double>());
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for(String s : parts){
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try{
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data.get(count).add(Double.parseDouble(s));
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}catch(NumberFormatException e){
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}
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}
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if(data.get(count).size() == 0){
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data.remove(count);
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}else{
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count++;
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}
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}
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}
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}
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catch(FileNotFoundException e){
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e.printStackTrace();
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System.out.println("file " + file + " not found");
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} catch (IOException e) {
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e.printStackTrace();
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}
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return data;
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}
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}
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@ -0,0 +1,67 @@
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import java.util.ArrayList;
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public class KMeans extends VarMin{
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public KMeans(int k){
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super(k);
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}
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@Override
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public void clustering(){
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initCluster();
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System.out.println(getCompactness());
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boolean change = true;
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while(change){
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change = false;
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for(int i = 0; i < data.size();i++) {
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int newCluster = getClosestCluster(data.get(i));
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int oldCluster = clusterMap.get(i);
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if(oldCluster != newCluster){
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clusterMap.set(i, newCluster);
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//update centroids
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ArrayList<Double> p = new ArrayList<>(data.get(i));
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ArrayList<Double> u1 = new ArrayList<>(centroids.get(oldCluster));
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ArrayList<Double> u2 = new ArrayList<>(centroids.get(newCluster));
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mulPoint(u1, (double)count[oldCluster]);
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mulPoint(u2, (double)count[newCluster]);
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addPoint(u2, p);
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mulPoint(p, -1.0);
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addPoint(u1, p);
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mulPoint(u1,(1.0 / (count[oldCluster] - 1)));
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mulPoint(u2,(1.0 / (count[newCluster] + 1)));
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centroids.set(oldCluster, u1);
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centroids.set(newCluster, u2);
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count[oldCluster]--;
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count[newCluster]++;
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//centroids.get(oldCluster) = (1.0 / (count[oldCluster] - 1)) * (count[oldCluster] * centroids.get(oldCluster) - data.get(i));
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//centroids.get(newCluster) = (1.0 / (count[newCluster] + 1)) * (count[newCluster] * centroids.get(newCluster) + data.get(i));
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//calcCluster();
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change = true;
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}
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}
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System.out.println(getCompactness());
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}
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}
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public static void main(String[] args) {
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int k = 3;
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if(args.length >= 1){
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k = Integer.parseInt(args[0]);
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}
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KMeans kmeans = new KMeans(k);
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ArrayList<ArrayList<Double>> data = DataFileReader.readFile("input.txt");
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kmeans.setData(data);
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kmeans.clustering();
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ArrayList<ArrayList<ArrayList<Double>>> cluster1 = kmeans.getCluster();
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System.out.println("Result:");
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printCluster(cluster1);
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writeToFile(cluster1, "cluster.txt");
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}
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}
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@ -0,0 +1,210 @@
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import java.io.BufferedWriter;
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import java.io.FileNotFoundException;
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import java.io.FileWriter;
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import java.io.IOException;
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import java.lang.reflect.Array;
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import java.util.ArrayList;
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public class VarMin {
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public ArrayList<ArrayList<Double>> data;
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public ArrayList<ArrayList<Double>> centroids;
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public ArrayList<Integer> clusterMap;
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int[] count;
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int k;
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int dim;
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public VarMin(int k){
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this.k = k;
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this.dim = 0;
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}
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//euklidische distanz
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public double distance(ArrayList<Double> p1, ArrayList<Double> p2){
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int count = Math.min(p1.size(), p2.size());
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double sum = 0;
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for(int i = 0; i < count;i++){
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sum += Math.pow(p1.get(i) - p2.get(i), 2);
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}
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return Math.sqrt(sum);
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}
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//p1 += p2
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public void addPoint(ArrayList<Double> p1, ArrayList<Double> p2){
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int count = Math.min(p1.size(), p2.size());
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for(int i = 0; i < count;i++){
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p1.set(i, p1.get(i) + p2.get(i));
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}
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}
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//p1 *= v
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public void mulPoint(ArrayList<Double> p1, Double v){
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for(int i = 0; i < p1.size();i++){
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p1.set(i, p1.get(i) * v);
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}
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}
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private void setParams(int k, int dim){
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this.k = k;
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this.dim = dim;
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centroids = new ArrayList<ArrayList<Double>>();
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for(int i = 0; i < k;i++){
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centroids.add(new ArrayList<Double>());
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for(int j = 0; j < dim;j++){
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centroids.get(i).add(0.0);
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}
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}
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clusterMap = new ArrayList<Integer>();
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count = new int[k];
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}
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public void setData(ArrayList<ArrayList<Double>> data){
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dim = data.get(0).size();
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this.data = data;
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setParams(k,dim);
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for(int i = 0; i < data.size();i++){
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clusterMap.add(0);
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}
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}
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public int getClosestCluster(ArrayList<Double> p){
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double minDist = Double.MAX_VALUE;
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int minIndex = 0;
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for(int c = 0; c < k;c++){
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double dist = distance(p, centroids.get(c));
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if(dist < minDist) {
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minDist = dist;
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minIndex = c;
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}
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}
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return minIndex;
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}
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public void initCluster(){
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int pointsPerCluster = data.size() / k;
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for(int i = 0; i < data.size();i++){
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int c = i / pointsPerCluster;
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addPoint(centroids.get(c), data.get(i));
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clusterMap.set(i, c);
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}
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for(int c = 0; c < k;c++){
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mulPoint(centroids.get(c), 1.0 / pointsPerCluster);
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count[c] = pointsPerCluster;
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}
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}
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public void calcCluster(){
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for(int c = 0; c < k;c++){
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mulPoint(centroids.get(c), 0.0);
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}
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for(int c = 0; c < k;c++){
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count[c] = 0;
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}
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for(int i = 0; i < data.size();i++){
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int c = clusterMap.get(i);
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addPoint(centroids.get(c), data.get(i));
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count[c]++;
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}
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for(int c = 0; c < k;c++){
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if(count[c] == 0){
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mulPoint(centroids.get(c),0.0);
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}else{
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mulPoint(centroids.get(c), 1.0 / count[c]);
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}
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}
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}
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public void clustering(){
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initCluster();
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System.out.println(getCompactness());
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//iteration
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boolean change = true;
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while(change){
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change = false;
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for(int i = 0; i < data.size();i++) {
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int newCluster = getClosestCluster(data.get(i));
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if(clusterMap.get(i) != newCluster){
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change = true;
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}
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clusterMap.set(i, newCluster);
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}
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calcCluster();
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System.out.println(getCompactness());
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}
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}
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public double getCompactness(){
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double sum = 0;
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for(int i = 0; i < data.size();i++){
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sum += distance(data.get(i), centroids.get(clusterMap.get(i)));
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}
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return sum;
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}
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public ArrayList<ArrayList<ArrayList<Double>>> getCluster(){
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ArrayList<ArrayList<ArrayList<Double>>> cluster = new ArrayList<>();
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for(int i = 0; i < k;i++){
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cluster.add(new ArrayList<>());
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}
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for(int i = 0; i < data.size();i++) {
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int c = clusterMap.get(i);
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cluster.get(c).add(data.get(i));
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}
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return cluster;
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}
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public static void printCluster(ArrayList<ArrayList<ArrayList<Double>>> cluster){
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int i = 0;
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for(ArrayList<ArrayList<Double>> c : cluster){
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System.out.print("Cluster" + ++i + ": ");
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for(ArrayList<Double> p : c){
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System.out.print("(");
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for (int j = 0; j < p.size();j++) {
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System.out.print(p.get(j));
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if(j < p.size() - 1){
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System.out.print(",");
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}
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}
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System.out.print("); ");
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}
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System.out.println();
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}
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}
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public static void writeToFile(ArrayList<ArrayList<ArrayList<Double>>> cluster, String file){
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try(BufferedWriter stream = new BufferedWriter(new FileWriter(file))) {
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for(ArrayList<ArrayList<Double>> c : cluster){
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for(ArrayList<Double> p : c){
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for (int j = 0; j < p.size();j++) {
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stream.write(p.get(j).toString() + " ");
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}
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stream.write("\n");
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}
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stream.write("\n");
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}
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} catch (FileNotFoundException e) {
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e.printStackTrace();
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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public static void main(String[] args) {
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int k = 3;
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if(args.length >= 1){
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k = Integer.parseInt(args[0]);
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}
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VarMin varmin = new VarMin(k);
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ArrayList<ArrayList<Double>> data = DataFileReader.readFile("input.txt");
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varmin.setData(data);
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varmin.clustering();
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ArrayList<ArrayList<ArrayList<Double>>> cluster1 = varmin.getCluster();
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System.out.println("Result:");
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printCluster(cluster1);
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writeToFile(cluster1, "cluster.txt");
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}
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}
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@ -0,0 +1,28 @@
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import matplotlib.pyplot as plt
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import numpy as np
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a = open("cluster.txt").read()
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a = a.split("\n\n")
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data = []
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for b in a:
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cluster = []
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for c in b.split("\n"):
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d = c.split(" ")
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vec = []
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for e in d:
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if e != "":
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vec.append(float(e))
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if len(vec) != 0:
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cluster.append(vec)
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if len(cluster) != 0:
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data.append(cluster)
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for c in data:
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xs = [x[0] for x in c]
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ys = [x[1] for x in c]
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plt.subplot()
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plt.plot(xs, ys, "o")
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plt.show()
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