How to use

Distance

in

smile.math.distance

/**
 * Cluster a new instance.
 * @param x a new instance.
 * @return the cluster label, which is the index of nearest medoid.
 */
@Override
public int predict(T x) {
  double minDist = Double.MAX_VALUE;
  int bestCluster = 0;
  for (int i = 0; i < k; i++) {
    double dist = distance.d(x, medoids[i]);
    if (dist < minDist) {
      minDist = dist;
      bestCluster = i;
    }
  }
  return bestCluster;
}

  @Override
  public void range(T q, double radius, List<Neighbor<T,T>> neighbors) {
    if (radius <= 0.0) {
      throw new IllegalArgumentException("Invalid radius: " + radius);
    }

    for (int i = 0; i < data.length; i++) {
      if (q == data[i] && identicalExcluded) {
        continue;
      }

      double d = distance.d(q, data[i]);

      if (d <= radius) {
        neighbors.add(new SimpleNeighbor<>(data[i], i, d));
      }
    }
  }
}

@Override
public Neighbor<T,T> nearest(T q) {
  T neighbor = null;
  int index = -1;
  double dist = Double.MAX_VALUE;
  for (int i = 0; i < data.length; i++) {
    if (q == data[i] && identicalExcluded) {
      continue;
    }
    double d = distance.d(q, data[i]);
    if (d < dist) {
      neighbor = data[i];
      index = i;
      dist = d;
    }
  }
  return new SimpleNeighbor<>(neighbor, index, dist);
}

double cost = distance.d(x1[i-1], x2[j-1]);

double dist = distance.d(data[i], medoid);
if (d[i] > dist) {
  y[i] = index;
  for (int j = 0; j < k; j++) {
    if (j != index) {
      dist = distance.d(data[i], medoids[j]);
      if (d[i] > dist) {
        y[i] = j;

  double dist = distance.d(data[i], medoid);
  if (dist < d[i]) {
    d[i] = dist;
double dist = distance.d(data[i], medoid);
if (dist < d[i]) {
  d[i] = dist;

double dist = distance.d(q, data[i]);
Neighbor<T,T> datum = heap.peek();
if (dist < datum.distance) {