@Override public AssignCluster<double[]> copy() { return new AssignKMeans_F64(this); }
@Override public AssignCluster<double[]> copy() { return new AssignKMeans_F64(this); }
@Override public AssignCluster<double[]> getAssignment() { // creating a new list here to make serialization easier List<double[]> list = new ArrayList<double[]>(); list.addAll( bestClusters.toList() ); return new AssignKMeans_F64(list); }
@Override public AssignCluster<double[]> getAssignment() { // creating a new list here to make serialization easier List<double[]> list = new ArrayList<double[]>(); list.addAll( bestClusters.toList() ); return new AssignKMeans_F64(list); }
@Test public void serialize() { List<double[]> clusters = new ArrayList<double[]>(); clusters.add( new double[]{10,0,0}); clusters.add( new double[]{0,0,10}); AssignKMeans_F64 alg = new AssignKMeans_F64(clusters); byte[] encoded = save(alg); // AssignKMeans_F64 found = load( encoded ); }
@Test public void copy() { List<double[]> clusters = new ArrayList<double[]>(); clusters.add( new double[]{10,0,0}); clusters.add( new double[]{0,0,10}); AssignKMeans_F64 original = new AssignKMeans_F64(clusters); AssignKMeans_F64 copy = (AssignKMeans_F64)original.copy(); assertEquals(original.clusters.size(),copy.clusters.size()); for (int i = 0; i < original.clusters.size(); i++) { double[] o = original.clusters.get(i); double[] c = copy.clusters.get(i); assertTrue(o!=c); for (int j = 0; j < o.length; j++) { assertEquals(o[j],c[j],1e-8); } } } }
@Test public void assign() { List<double[]> clusters = new ArrayList<double[]>(); clusters.add( new double[]{10,0,0}); clusters.add( new double[]{0,0,10}); AssignKMeans_F64 alg = new AssignKMeans_F64(clusters); assertEquals(1,alg.assign(new double[]{0,0,9})); assertEquals(0, alg.assign(new double[]{12, 0, 0})); }
@Test public void assign_soft() { List<double[]> clusters = new ArrayList<double[]>(); clusters.add( new double[]{10,0,0}); clusters.add( new double[]{5,0,0}); AssignKMeans_F64 alg = new AssignKMeans_F64(clusters); double histogram[] = new double[2]; alg.assign(new double[]{10,0,0},histogram); assertEquals(1.0, histogram[0], 1e-8); assertEquals(0.0, histogram[1],1e-8); // see if much more weight is given to the second one alg.assign(new double[]{6, 0, 0}, histogram); assertTrue(histogram[0]*10 < histogram[1]); // this is actually a difficult case for using this type of distance metric // one cluster is much farther away and as a result the weight is equality split between the two closer points // which might not be desirable clusters.add( new double[]{5000,0,0}); histogram = new double[3]; alg.assign(new double[]{6,0,0},histogram); assertTrue(histogram[0]/30.0 > histogram[2]); assertEquals(histogram[0], histogram[1], 0.01); }