for (int i = 0; i < n; i++) {
closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[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_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
+ for (int i = 0; i < n; i++) {
+ DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[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] = indices[bestNewIndex];
currentPot = bestNewPot;
- for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
+ for (int i = 0; i < n; i++) {
+ DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
+ closestDistSq[i] = std::min( dist*dist, closestDistSq[i] );
+ }
}
centers_length = centerCount;