/// <summary>
/// Find how accurate NearestFromRange is on average when searching for the neighbors of every point (or a large sample of points).
/// This finds the true K-nearest neighbors of each reference point (using Nearest)
/// and the approximate K-nearest neighbors using the Hilbert index,
/// then computes how accurate the Hilbert index was.
/// </summary>
/// <param name="k">Number of nearest neighbors sought.</param>
/// <param name="rangeLength">Number of points in the Hilbert index to sample.
/// The higher the number, the poorer the performance but higher the accuracy.
/// </param>
/// <param name="sampleSize">If zero, then analyze all points.
/// Otherwise, base the accuracy on a sample of the points.</param>
/// <returns>A value from zero to 1.0, where 1.0 means perfectly accurate.</returns>
public double Accuracy(int k, int rangeLength, int sampleSize = 0)
{
if (sampleSize == 0)
{
sampleSize = Count;
}
var avg = UnsortedPoints.Take(sampleSize).Select(p => Accuracy(p, k, rangeLength)).Average();
return(avg);
}
private void InitIndexing()
{
Index = new Dictionary <UnsignedPoint, Position>();
foreach (var pointWithIndex in UnsortedPoints.Select((p, i) => new { Point = p, OriginalPosition = i }))
{
Index[pointWithIndex.Point] = new Position(pointWithIndex.OriginalPosition, -1);
}
foreach (var pointWithIndex in SortedPoints.Select((p, i) => new { Point = p, SortedPosition = i }))
{
Index[pointWithIndex.Point].Sorted = pointWithIndex.SortedPosition;
}
IdsToPoints = new Dictionary <int, UnsignedPoint>();
foreach (var p in UnsortedPoints)
{
IdsToPoints[p.UniqueId] = p;
}
}
