/// <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="distance_function">The distance function used to evaluate the points.</param>
/// <param name="max_result_count">The max number of points which can be returned by this iterator. Capped to max in tree.</param>
/// <param name="threshold">Threshold to apply to the search space. Negative numbers indicate that no threshold is applied.</param>
public NearestNeighbourEnumerator(DistanceType distance_zero, KDNode <DomainType, DistanceType, LabelType> pRoot, DomainType[] tSearchPoint, IFunctionDistance <DomainType[], DistanceType> distance_function, int max_result_count, DistanceType threshold, bool use_threshold)
{
// Check the dimensionality of the search point.
if (tSearchPoint.Length != pRoot.d_dimension_count)
{
throw new Exception("Dimensionality of search point and kd-tree are not the same.");
}
//this.algebra_domain = algebra;
// Store the search point.
this.search_point = new DomainType[tSearchPoint.Length];
Array.Copy(tSearchPoint, this.search_point, tSearchPoint.Length);
// Store the point count, distance function and tree root.
this.iPointsRemaining = Math.Min(max_result_count, pRoot.Size);
this.fThreshold = threshold;
this.d_use_threshold = use_threshold;
this.distance_function = distance_function;
this.pRoot = pRoot;
this.iMaxPointsReturned = max_result_count;
this._CurrentDistance = distance_zero;
this.d_current_distance_set = false;
this.distance_zero = distance_zero;
// Create an interval heap for the points we check.
this.pEvaluated = new IntervalHeap <LabelType, DistanceType>();
// Create a min heap for the things we need to check.
this.pPending = new MinHeap <KDNode <DomainType, DistanceType, LabelType>, DistanceType>();
this.pPending.Insert(distance_zero, 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 = distance_zero;
d_current_distance_set = false;
// Create an interval heap for the points we check.
this.pEvaluated = new IntervalHeap <LabelType, DistanceType>();
// Create a min heap for the things we need to check.
this.pPending = new MinHeap <KDNode <DomainType, DistanceType, LabelType>, DistanceType>();
this.pPending.Insert(distance_zero, pRoot);
}
