void ANNbruteForce::annkSearch( // approx k near neighbor search
ANNpoint q, // query point
int k, // number of near neighbors to return
ANNidxArray nn_idx, // nearest neighbor indices (returned)
ANNdistArray dd, // dist to near neighbors (returned)
double eps) // error bound (ignored)
{
ANNmin_k mk(k); // construct a k-limited priority queue
int i;
if (k > n_pts) { // too many near neighbors?
annError("Requesting more near neighbors than data points", ANNabort);
}
// run every point through queue
for (i = 0; i < n_pts; i++) {
// compute distance to point
ANNdist sqDist = annDist(dim, pts[i], q);
if (ANN_ALLOW_SELF_MATCH || sqDist != 0)
mk.insert(sqDist, i);
}
for (i = 0; i < k; i++) { // extract the k closest points
dd[i] = mk.ith_smallest_key(i);
nn_idx[i] = mk.ith_smallest_info(i);
}
}
int ANNbruteForce::annkFRSearch( // approx fixed-radius kNN search
ANNpoint q, // query point
ANNdist sqRad, // squared radius
int k, // number of near neighbors to return
ANNidxArray nn_idx, // nearest neighbor array (returned)
ANNdistArray dd, // dist to near neighbors (returned)
double eps) // error bound
{
ANNmin_k mk(k); // construct a k-limited priority queue
int i;
int pts_in_range = 0; // number of points in query range
// run every point through queue
for (i = 0; i < n_pts; i++) {
// compute distance to point
ANNdist sqDist = annDist(dim, pts[i], q);
if (sqDist <= sqRad && // within radius bound
(ANN_ALLOW_SELF_MATCH || sqDist != 0)) { // ...and no self match
mk.insert(sqDist, i);
pts_in_range++;
}
}
for (i = 0; i < k; i++) { // extract the k closest points
if (dd != NULL)
dd[i] = mk.ith_smallest_key(i);
if (nn_idx != NULL)
nn_idx[i] = mk.ith_smallest_info(i);
}
return pts_in_range;
}
