/// <summary>
/// Look for 'n' neighbours to a given point. Each neighbour must be unique..
/// </summary>
/// <param name="inputVector">vector to find neighbours of</param>
/// <param name="neighbourCount">number of neighbours to find</param>
/// <param name="distances">array of distances</param>
public void UniqueSearch(double[] inputVector, int neighbourCount, ref List <KDTreePoint> distances) //
{
distances.Clear();
if (neighbourCount < 0 || neighbourCount > m_count)
{
throw new ArgumentOutOfRangeException("Number of neighbors cannot" +
" be negative or greater than number of nodes");
}
if (inputVector.Length != dimensions)
{
throw new ArgumentOutOfRangeException();
}
Object [] nbrs = new Object [neighbourCount];
NearestNeighborList nnl = new NearestNeighborList(neighbourCount);
// initial call is with infinite hyper-rectangle and max distance
HyperRect hr = HyperRect.infiniteHyperRect(inputVector.Length);
double max_dist_sqd = Double.MaxValue;
HyperPoint keyp = new HyperPoint(inputVector);
KDNode.nnbr(root, keyp, hr, max_dist_sqd, 0, dimensions, nnl);
while (distances.Count < neighbourCount)
{
KDNode kd = (KDNode)nnl.removeHighest();
double dist = HyperPoint.eucdist(keyp, kd.k);
distances.Add(new KDTreePoint(kd.v, dist * dist));
}
distances.Sort();
}
/// <summary>
/// find the neighbours within a distance of a point
/// </summary>
/// <remarks>Note that the distance array contains the squares of the distances found.</remarks>
/// <param name="inputVector">Vector to find neighbours of</param>
/// <param name="maxDistance">distance to search over</param>
/// <param name="neighbourCount">number of neighbours found within distance</param>
/// <param name="distances">array of squared distances of neighbours from the input vector</param>
/// <param name="maxNeighbourCount">maximum permissable size of arrays</param>
public void SearchByDistance(double[] inputVector, double maxDistance, out int neighbourCount, ref List <KDTreePoint> distances, int maxNeighbourCount)
{
double[] lower = new double[inputVector.Length];
double[] upper = new double[inputVector.Length];
for (int n = 0; n < inputVector.Length; n++)
{
lower[n] = inputVector[n] - maxDistance;
upper[n] = inputVector[n] + maxDistance;
}
KDNode[] list = this.range(lower, upper);
foreach (KDNode node in list)
{
double dist = HyperPoint.eucdist(new HyperPoint(inputVector), node.k);
if (dist <= maxDistance)
{
distances.Add(new KDTreePoint(node.v, dist * dist));
}
}
distances.Sort();
neighbourCount = distances.Count;
}
internal static void nnbr(KDNode kd, HyperPoint target, HyperRect hr, double max_dist_sqd, int lev, int K, NearestNeighborList nnl)
{
// 1. if kd is empty then set dist-sqd to infinity and exit.
if (kd == null)
{
return;
}
// 2. s := split field of kd
int s = lev % K;
// 3. pivot := dom-elt field of kd
HyperPoint pivot = kd.k;
double pivot_to_target = HyperPoint.sqrdist(pivot, target);
// 4. Cut hr into to sub-hyperrectangles left-hr and right-hr.
// The cut plane is through pivot and perpendicular to the s
// dimension.
HyperRect left_hr = hr; // optimize by not cloning
HyperRect right_hr = (HyperRect)hr.clone();
left_hr.max.coord[s] = pivot.coord[s];
right_hr.min.coord[s] = pivot.coord[s];
// 5. target-in-left := target_s <= pivot_s
bool target_in_left = target.coord[s] < pivot.coord[s];
KDNode nearer_kd;
HyperRect nearer_hr;
KDNode further_kd;
HyperRect further_hr;
// 6. if target-in-left then
// 6.1. nearer-kd := left field of kd and nearer-hr := left-hr
// 6.2. further-kd := right field of kd and further-hr := right-hr
if (target_in_left)
{
nearer_kd = kd.left;
nearer_hr = left_hr;
further_kd = kd.right;
further_hr = right_hr;
}
//
// 7. if not target-in-left then
// 7.1. nearer-kd := right field of kd and nearer-hr := right-hr
// 7.2. further-kd := left field of kd and further-hr := left-hr
else
{
nearer_kd = kd.right;
nearer_hr = right_hr;
further_kd = kd.left;
further_hr = left_hr;
}
// 8. Recursively call Nearest Neighbor with paramters
// (nearer-kd, target, nearer-hr, max-dist-sqd), storing the
// results in nearest and dist-sqd
nnbr(nearer_kd, target, nearer_hr, max_dist_sqd, lev + 1, K, nnl);
KDNode nearest = (KDNode)nnl.getHighest();
double dist_sqd;
if (!nnl.isCapacityReached())
{
dist_sqd = Double.MaxValue;
}
else
{
dist_sqd = nnl.getMaxPriority();
}
// 9. max-dist-sqd := minimum of max-dist-sqd and dist-sqd
max_dist_sqd = Math.Min(max_dist_sqd, dist_sqd);
// 10. A nearer point could only lie in further-kd if there were some
// part of further-hr within distance sqrt(max-dist-sqd) of
// target. If this is the case then
HyperPoint closest = further_hr.closest(target);
if (HyperPoint.eucdist(closest, target) < Math.Sqrt(max_dist_sqd))
{
// 10.1 if (pivot-target)^2 < dist-sqd then
if (pivot_to_target < dist_sqd)
{
// 10.1.1 nearest := (pivot, range-elt field of kd)
nearest = kd;
// 10.1.2 dist-sqd = (pivot-target)^2
dist_sqd = pivot_to_target;
// add to nnl
if (!kd.deleted)
{
nnl.insert(kd, dist_sqd);
}
// 10.1.3 max-dist-sqd = dist-sqd
// max_dist_sqd = dist_sqd;
if (nnl.isCapacityReached())
{
max_dist_sqd = nnl.getMaxPriority();
}
else
{
max_dist_sqd = Double.MaxValue;
}
}
// 10.2 Recursively call Nearest Neighbor with parameters
// (further-kd, target, further-hr, max-dist_sqd),
// storing results in temp-nearest and temp-dist-sqd
nnbr(further_kd, target, further_hr, max_dist_sqd, lev + 1, K, nnl);
KDNode temp_nearest = (KDNode)nnl.getHighest();
double temp_dist_sqd = nnl.getMaxPriority();
// 10.3 If tmp-dist-sqd < dist-sqd then
if (temp_dist_sqd < dist_sqd)
{
// 10.3.1 nearest := temp_nearest and dist_sqd := temp_dist_sqd
nearest = temp_nearest;
dist_sqd = temp_dist_sqd;
}
}
// SDL: otherwise, current point is nearest
else if (pivot_to_target < max_dist_sqd)
{
nearest = kd;
dist_sqd = pivot_to_target;
}
}