[TASK] Initial commit with basic product setup

Assets/Mapbox SDK/Mapbox/Unity/MeshGeneration/Data/KdTree/KDTree.cs

@@ -0,0 +1,62 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace KDTree
+{
+
+    /// <summary>
+    /// A KDTree class represents the root of a variable-dimension KD-Tree.
+    /// </summary>
+    /// <typeparam name="T">The generic data type we want this tree to contain.</typeparam>
+    /// <remarks>This is based on this: https://bitbucket.org/rednaxela/knn-benchmark/src/tip/ags/utils/dataStructures/trees/thirdGenKD/ </remarks>
+    public class KDTree<T> : KDNode<T>
+    {
+        /// <summary>
+        /// Create a new KD-Tree given a number of dimensions.
+        /// </summary>
+        /// <param name="iDimensions">The number of data sorting dimensions. i.e. 3 for a 3D point.</param>
+        public KDTree(int iDimensions)
+            : base(iDimensions, 24)
+        {
+        }
+
+        /// <summary>
+        /// Create a new KD-Tree given a number of dimensions and initial bucket capacity.
+        /// </summary>
+        /// <param name="iDimensions">The number of data sorting dimensions. i.e. 3 for a 3D point.</param>
+        /// <param name="iBucketCapacity">The default number of items that can be stored in each node.</param>
+        public KDTree(int iDimensions, int iBucketCapacity)
+            : base(iDimensions, iBucketCapacity)
+        {
+        }
+
+        /// <summary>
+        /// Get the nearest neighbours to a point in the kd tree using a square euclidean distance function.
+        /// </summary>
+        /// <param name="tSearchPoint">The point of interest.</param>
+        /// <param name="iMaxReturned">The maximum number of points which can be returned by the iterator.</param>
+        /// <param name="fDistance">A threshold distance to apply.  Optional.  Negative values mean that it is not applied.</param>
+        /// <returns>A new nearest neighbour iterator with the given parameters.</returns>
+        public NearestNeighbour<T> NearestNeighbors(double[] tSearchPoint, int iMaxReturned, double fDistance = -1)
+        {
+            DistanceFunctions distanceFunction = new SquareEuclideanDistanceFunction();
+            return NearestNeighbors(tSearchPoint, distanceFunction, iMaxReturned, fDistance);
+        }
+
+        /// <summary>
+        /// Get the nearest neighbours to a point in the kd tree using a user defined distance function.
+        /// </summary>
+        /// <param name="tSearchPoint">The point of interest.</param>
+        /// <param name="iMaxReturned">The maximum number of points which can be returned by the iterator.</param>
+        /// <param name="kDistanceFunction">The distance function to use.</param>
+        /// <param name="fDistance">A threshold distance to apply.  Optional.  Negative values mean that it is not applied.</param>
+        /// <returns>A new nearest neighbour iterator with the given parameters.</returns>
+        public NearestNeighbour<T> NearestNeighbors(double[] tSearchPoint, DistanceFunctions kDistanceFunction, int iMaxReturned, double fDistance)
+        {
+            return new NearestNeighbour<T>(this, tSearchPoint, kDistanceFunction, iMaxReturned, fDistance);
+        }
+    }
+}