assert(index >=0 && index < n);
centers[0] = dsindices[index];
+ // Computing distance^2 will have the advantage of even higher probability further to pick new centers
+ // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article)
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
+ closestDistSq[i] *= closestDistSq[i];
currentPot += closestDistSq[i];
}
// Compute the new potential
double newPot = 0;
- for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] );
+ for (int i = 0; i < n; i++) {
+ DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
+ newPot += std::min( dist*dist, closestDistSq[i] );
+ }
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
// Add the appropriate center
centers[centerCount] = dsindices[bestNewIndex];
currentPot = bestNewPot;
- for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] );
+ for (int i = 0; i < n; i++) {
+ DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols);
+ closestDistSq[i] = std::min( dist*dist, closestDistSq[i] );
+ }
}
centers_length = centerCount;