/** Generate a random neighbor which differs in only one medoid with current clusters. */
private double getRandomNeighbor(T[] data, T[] medoids, int[] y, double[] d) {
int n = data.length;
int index = Math.randomInt(k);
T medoid = null;
boolean dup;
do {
dup = false;
medoid = data[Math.randomInt(n)];
for (int i = 0; i < k; i++) {
if (medoid == medoids[i]) {
dup = true;
break;
}
}
} while (dup);
medoids[index] = medoid;
for (int i = 0; i < n; i++) {
double dist = distance.d(data[i], medoid);
if (d[i] > dist) {
y[i] = index;
d[i] = dist;
} else if (y[i] == index) {
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;
d[i] = dist;
}
}
}
}
}
return Math.sum(d);
}
/**
* 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;
}
