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 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();
}
public virtual void Read(ModelLoadContext ctx, IHost host)
{
k = ctx.Reader.ReadInt32();
algo = (NearestNeighborsAlgorithm)ctx.Reader.ReadInt32();
weighting = (NearestNeighborsWeights)ctx.Reader.ReadInt32();
distance = (NearestNeighborsDistance)ctx.Reader.ReadInt32();
numThreads = ctx.Reader.ReadInt32();
if (numThreads == -1)
{
numThreads = null;
}
seed = ctx.Reader.ReadInt32();
if (seed == -1)
{
seed = null;
}
colId = ctx.Reader.ReadString();
if (string.IsNullOrEmpty(colId))
{
colId = null;
}
}
public KdTree(NearestNeighborsDistance distance, int?seed, params IPointIdFloat[] points) :
this(points.ToList(), distance : distance, seed : seed)
{
}
