Add simple k-d-tree

init.lua

@@ -78,6 +78,7 @@ for _, component in ipairs{
     "quaternion",
     "minetest",
     "trie",
+    "kdtree",
     "heap",
     "ranked_set",
     "b3d"

kdtree.lua

@@ -0,0 +1,54 @@
+local metatable = {__index = getfenv(1)}
+
+distance = modlib.vector.distance
+
+--: vectors first vector is used to infer the dimension
+--: distance (vector, other_vector) -> number, default: modlib.vector.distance
+function new(vectors, distance)
+    assert(#vectors > 0, "vector list must not be empty")
+    local dimension = #vectors[1]
+    local function builder(vectors, axis)
+		if #vectors == 1 then return { value = vectors[1] } end
+		table.sort(vectors, function(a, b) return a[axis] > b[axis] end)
+		local median = math.floor(#vectors / 2)
+		local next_axis = ((axis + 1) % dimension) + 1
+		return setmetatable({
+			axis = axis,
+			pivot = vectors[median],
+			left = builder({ unpack(vectors, 1, median) }, next_axis),
+			right = builder({ unpack(vectors, median + 1) }, next_axis)
+		}, metatable)
+	end
+    local self = builder(vectors, 1)
+    self.distance = distance
+    return self
+end
+
+function get_nearest_neighbor(self, vector)
+    local min_distance = math.huge
+    local nearest_neighbor
+    local distance_func = self.distance
+    local axis = tree.axis
+    local function visit(tree)
+        if tree.value ~= nil then
+            local distance = distance_func(tree.value, vector)
+            if distance < min_distance then
+                min_distance = distance
+                nearest_neighbor = tree.value
+            end
+            return
+        else
+            local this_side, other_side = tree.left, tree.right
+            if vector[axis] < tree.pivot[axis] then this_side, other_side = other_side, this_side end
+            visit(this_side)
+            if tree.pivot then
+                local dist = math.abs(tree.pivot[axis] - color[axis])
+                if dist <= min_distance then visit(other_side) end
+            end
+        end
+    end
+    visit(self)
+    return nearest_neighbor, min_distance
+end
+
+-- TODO insertion & deletion + rebalancing