Remove unused KMeans clustering (half broken implementation anyway)

src/main/java/eva2/tools/Cluster.java

@@ -1,33 +0,0 @@
-package eva2.tools;
-
-/**
- * This class represents a cluster object in the R^N.
- */
-public class Cluster {
-    /**
-     * Number of samples in the cluster.
-     */
-    public int samplesInCluster;
-    /**
-     * Center of the cluster.
-     */
-    public double[] center;
-    /**
-     * nearest sample (double[]) to the center of the cluster.
-     */
-    public double[] nearestSample;
-
-    /**
-     * This class represents a cluster of
-     * sample points.
-     *
-     * @param center           center
-     * @param SamplesInCluster Number of samples in cluster
-     * @param nearestSample    Nearest sample to cluster center.
-     */
-    public Cluster(double[] center, int SamplesInCluster, double[] nearestSample) {
-        samplesInCluster = SamplesInCluster;
-        this.center = center;
-        this.nearestSample = nearestSample;
-    }
-}

src/main/java/eva2/tools/KMEANSJAVA.java

@@ -1,154 +0,0 @@
-package eva2.tools;
-
-import eva2.tools.math.RNG;
-
-import java.util.Comparator;
-
-/**
- *
- */
-public class KMEANSJAVA {
-    protected double[][] c;
-    protected int[] indices;
-
-    /**
-     *
-     */
-    public double[][] getC() {
-        return c;
-    }
-
-    /**
-     *
-     */
-    public int[] getIDX() {
-        return indices;
-    }
-
-    /**
-     *
-     */
-    private double dist(double[] x1, double[] x2) {
-        double ret = 0;
-        for (int i = 0; i < x1.length; i++) {
-            ret += (x1[i] - x2[i]) * (x1[i] - x2[i]);
-        }
-        return Math.sqrt(ret);
-    }
-
-    /**
-     *
-     */
-    public KMEANSJAVA(double[][] samples, int K, int iterations) {
-        if (K > samples.length) {
-            K = samples.length;
-        }
-        int counter = 0;
-        c = new double[K][];
-        for (int i = 0; i < K; i++) {
-            c[i] = samples[i].clone();
-        }
-        indices = new int[samples.length];
-        while (counter++ < iterations) {
-            // determine indices start
-            for (int i = 0; i < indices.length; i++) {
-                int index_nc = 0; // index of nearest cluster
-                double mindist = 999999999;
-                for (int j = 0; j < c.length; j++) {
-                    if (mindist > dist(samples[i], c[j])) {
-                        mindist = dist(samples[i], c[j]);
-                        index_nc = j;
-                    }
-                }
-                indices[i] = index_nc;
-            }
-            // determine indices end !
-            // determine the new centers
-            for (int indexofc = 0; indexofc < c.length; indexofc++) {
-                double[] newcenter = new double[samples[0].length];
-                int treffer = 0;
-                for (int indice : indices) { //System.out.println("j="+j);
-                    if (indice == indexofc) {
-                        treffer++;
-                        for (int d = 0; d < newcenter.length; d++) {
-                            newcenter[d] += c[indice][d];
-                            //newcenter[d] = newcenter[d] + samples[j][d];
-                        }
-                    }
-                }
-                for (int d = 0; d < newcenter.length; d++) {
-                    newcenter[d] /= treffer;
-                }
-                c[indexofc] = newcenter;
-            }
-            // determine the new centers
-        }
-    }
-
-    /**
-     * Just a test function.
-     */
-    public static void main(String[] args) {
-        int k = 3;
-        int samples = 10;
-        int d = 2;
-        double[][] test = new double[samples][d];
-        for (int i = 0; i < samples; i++) {
-            for (int j = 0; j < d; j++) {
-                test[i][j] = RNG.randomDouble(0, 10);
-            }
-        }
-        KMEANSJAVA app = new KMEANSJAVA(test, k, 5);
-        double[][] c = app.getC();
-        int[] idx = app.getIDX();
-        System.out.println("c");
-        for (int i = 0; i < c.length; i++) {
-            for (int j = 0; j < c[i].length; j++) {
-                System.out.print(c[i][j] + " ");
-            }
-            System.out.println("");
-        }
-        System.out.println("test");
-        for (double[] aTest : test) {
-            for (int j = 0; j < aTest.length; j++) {
-                System.out.print(aTest[j] + " ");
-            }
-            System.out.println("");
-        }
-    }
-}
-
-/**
- *
- */
-class ClusterComp implements Comparator {
-    /**
-     *
-     */
-    public ClusterComp() {
-    }
-
-    /**
-     *
-     */
-    @Override
-    public int compare(Object p1, Object p2) {
-        int x1 = ((Cluster) p1).samplesInCluster;
-        int x2 = ((Cluster) p2).samplesInCluster;
-        if (x1 > x2) {
-            return -1;
-        }
-        if (x1 <= x2) {
-            return 1;
-        }
-        return 0;
-    }
-
-    /**
-     *
-     */
-    @Override
-    public boolean equals(Object x) {
-        return false;
-    }
-}