/// <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();
}
internal static void rsearch(HyperPoint lowk, HyperPoint uppk, KDNode t, int lev, int K, List <KDNode> v)
{
if (t == null)
{
return;
}
if (lowk.coord[lev] <= t.k.coord[lev])
{
rsearch(lowk, uppk, t.left, (lev + 1) % K, K, v);
}
int j;
for (j = 0; j < K && lowk.coord[j] <= t.k.coord[j] &&
uppk.coord[j] >= t.k.coord[j]; j++)
{
;
}
if (j == K)
{
v.Add(t);
}
if (uppk.coord[lev] > t.k.coord[lev])
{
rsearch(lowk, uppk, t.right, (lev + 1) % K, K, v);
}
}
internal static KDNode ins(HyperPoint key, Object val, KDNode t, int lev, int K)
{
if (t == null)
{
t = new KDNode(key, val);
}
else if (key.equals(t.k))
{
if (t.deleted)
{
t.deleted = false;
t.v = val;
}
else
{
if (t.duplicates == null)
{
t.duplicates = new List <object>();
}
t.duplicates.Add(val);
}
}
else if (key.coord[lev] > t.k.coord[lev])
{
t.right = ins(key, val, t.right, (lev + 1) % K, K);
}
else
{
t.left = ins(key, val, t.left, (lev + 1) % K, K);
}
return(t);
}
private KDNode(HyperPoint key, Object val)
{
k = key;
v = val;
left = null;
right = null;
deleted = false;
}
/// <summary>
///
/// </summary>
/// <param name="lowk"></param>
/// <param name="uppk"></param>
/// <returns></returns>
internal KDNode [] range(double [] lowk, double [] uppk)
{
if (lowk.Length != uppk.Length)
{
throw new ArgumentOutOfRangeException();
}
else if (lowk.Length != dimensions)
{
throw new ArgumentOutOfRangeException();
}
List <KDNode> list = new List <KDNode>();
KDNode.rsearch(new HyperPoint(lowk), new HyperPoint(uppk), root, 0, dimensions, list);
return(list.ToArray());
}
internal static KDNode srch(HyperPoint key, KDNode t, int K)
{
for (int lev = 0; t != null; lev = (lev + 1) % K)
{
if (!t.deleted && key.equals(t.k))
{
return(t);
}
else if (key.coord[lev] > t.k.coord[lev])
{
t = t.right;
}
else
{
t = t.left;
}
}
return(null);
}
/// <summary>
/// Insert a node in a KD-tree.
/// </summary>
/// <remarks>
/// Key is a vector of doubles used to locate the value in a cartesian space.
/// The value parameters should each be unique. Duplicate objects, or those that appear duplicates because the are indistinguishable will create exceptions.
/// value can be any object, but for efficiency should be a simple object for which efficient hashing algorithms are defined.
/// To speed up deletions a reverse index is maintained of values in a hashtable.</remarks>
/// <exception cref="ArgumentOutOfRangeException">If Key has wrong number of dimensions</exception>
/// <param name="key">key for KD-tree node</param>
/// <param name="value">External value associated with that key</param>
public void Insert(double [] key, Object value)
{
if (key.Length != dimensions)
{
throw new ArgumentOutOfRangeException();
}
else
{
try
{
root = KDNode.ins(new HyperPoint(key), value, root, 0, dimensions);
}
catch
{
duplicates++;
}
}
m_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;
}
}
/// <summary>
/// Constructor
/// </summary>
private KDTree()
{
dimensions = 0;
root = null;
}
