Given a set of n points on plane, I want to preprocess these points somehow faster than O(n^2) (O(nlog(n)) preferably), and then be able to answer on queries of the followi
Build a KD-tree of the points, this should give you much better complexity than O(n), on average O(log(n)) I think.
You can use a 2D tree since the points are constrained to a plane.
Assuming that we have transformed the problem into 2D, we'll have something like this for the points:
struct Node {
Pos2 point;
enum {
X,
Y
} splitaxis;
Node* greater;
Node* less;
};
greater
and less
contains points with greater and lesser coordinates respectively along the splitaxis.
void
findPoints(Node* node, std::vector& result, const Pos2& origin, float radius) {
if (squareDist(origin - node->point) < radius * radius) {
result.push_back(node->point);
}
if (!node->greater) { //No children
return;
}
if (node->splitaxis == X) {
if (node->point.x - origin.x > radius) {
findPoints(node->greater, result, origin radius);
return;
}
if (node->point.x - origin.x < -radius) {
findPoints(node->less, result, origin radius);
return;
}
findPoints(node->greater, result, origin radius);
findPoints(node->less, result, origin radius);
} else {
//Same for Y
}
}
Then you call this function with the root of the KD-tree