/// <summary>
/// Construct a new nearest neighbour iterator.
/// </summary>
/// <param name="pRoot">The root of the tree to begin searching from.</param>
/// <param name="tSearchPoint">The point in n-dimensional space to search.</param>
/// <param name="kDistance">The distance function used to evaluate the points.</param>
/// <param name="iMaxPoints">The max number of points which can be returned by this iterator. Capped to max in tree.</param>
/// <param name="fThreshold">Threshold to apply to the search space. Negative numbers indicate that no threshold is applied.</param>
public KDTreeNearestNeighbour(KDNode <T> pRoot, double[] tSearchPoint, DistanceFunctions kDistance, int iMaxPoints, double fThreshold)
{
// Check the dimensionality of the search point.
if (tSearchPoint.Length != pRoot.iDimensions)
{
throw new Exception("Dimensionality of search point and kd-tree are not the same.");
}
// Store the search point.
this.tSearchPoint = new double[tSearchPoint.Length];
Array.Copy(tSearchPoint, this.tSearchPoint, tSearchPoint.Length);
// Store the point count, distance function and tree root.
this.iPointsRemaining = Math.Min(iMaxPoints, pRoot.Size);
this.fThreshold = fThreshold;
this.kDistanceFunction = kDistance;
this.pRoot = pRoot;
this.iMaxPointsReturned = iMaxPoints;
_CurrentDistance = -1;
// Create an interval heap for the points we check.
this.pEvaluated = new IntervalHeap <T>();
// Create a min heap for the things we need to check.
this.pPending = new MinHeap <KDNode <T> >();
this.pPending.Insert(0, pRoot);
}
/// <summary>
/// Reset the iterator.
/// </summary>
public void Reset()
{
// Store the point count and the distance function.
this.iPointsRemaining = Math.Min(iMaxPointsReturned, pRoot.Size);
_CurrentDistance = -1;
// Create an interval heap for the points we check.
this.pEvaluated = new IntervalHeap <T>();
// Create a min heap for the things we need to check.
this.pPending = new MinHeap <KDNode <T> >();
this.pPending.Insert(0, pRoot);
}