How to use

AssignKMeans_F64

in

org.ddogleg.clustering.kmeans

@Override
public AssignCluster<double[]> copy() {
  return new AssignKMeans_F64(this);
}

@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 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_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);
}

@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 );
  }