public KdTree(ModelLoadContext ctx)
{
bool hasSeed = ctx.Reader.ReadByte() == 1;
if (hasSeed)
{
var seed = ctx.Reader.ReadInt32();
_rnd = new Random(seed);
_seed = seed;
}
else
{
_rnd = new Random();
_seed = null;
}
dimension = ctx.Reader.ReadInt32();
int i = ctx.Reader.ReadInt32();
_distance = (NearestNeighborsDistance)i;
root = ReadNode(ctx);
byte b = ctx.Reader.ReadByte();
if (b != 168)
{
throw Contracts.Except("Detected inconsistency in deserializing.");
}
SetDistanceFunction();
}
public static void SaveNode(ModelSaveContext ctx, IKdTreeNode node)
{
ctx.Writer.Write((byte)(node != null ? (node as KdTreeLeaf == null ? 1 : 2) : 0));
if (node != null)
{
node.Save(ctx);
}
ctx.Writer.Write((byte)169);
}
public KdTreeNode(IPointIdFloat point, IKdTreeNode left, IKdTreeNode right, int depth)
{
this.point = point;
this.left = left;
this.right = right;
this.depth = depth;
size = 1 + left.size + right.size;
if (depth > MaxDepth)
{
throw Contracts.Except("The k-d tree depth is too high (> {0}). This probably means the datasets is too sparse to be effective. You should reduce the number of dimensions (PCA for example).", MaxDepth);
}
}
public void Add(IPointIdFloat p)
{
if (root == null)
{
var points = new IPointIdFloat[] { p };
ValidatePointsArray(points);
root = CreateTree(points.ToList(), _rnd);
}
else
{
ValidatePoint(p);
root.Add(p);
}
}
public KdTree(IEnumerable <IPointIdFloat> points, int dimension = -1, int?seed = null,
NearestNeighborsDistance distance = NearestNeighborsDistance.L2)
{
_seed = seed;
_rnd = seed.HasValue ? new Random(seed.Value) : new Random();
_distance = distance;
this.dimension = dimension;
if (points.Any())
{
ValidatePointsArray(points);
root = CreateTree(points.ToList(), _rnd);
}
else
{
root = null;
}
SetDistanceFunction();
}
internal static IKdTreeNode CreateTree(IList <IPointIdFloat> points, Random rnd, int depth = 0)
{
switch (points.Count())
{
case 0:
return(new KdTreeLeaf(depth));
case 1:
return(new KdTreeNode(points.First(), new KdTreeLeaf(depth + 1), new KdTreeLeaf(depth + 1), depth));
default:
IList <IPointIdFloat> leftPoints = null;
IList <IPointIdFloat> rightPoints = null;
IPointIdFloat median = MedianPoint(points, ref leftPoints, ref rightPoints, depth, rnd);
IKdTreeNode left = CreateTree(leftPoints, rnd, depth + 1);
IKdTreeNode right = CreateTree(rightPoints, rnd, depth + 1);
return(new KdTreeNode(median, left, right, depth));
}
}
