From 2c11d2f3cc68bb3bce09c314dcb5dbdd8459e143 Mon Sep 17 00:00:00 2001
From: Bruno BELANYI <bruno@belanyi.fr>
Date: Fri, 16 Aug 2024 08:42:10 +0100
Subject: [PATCH] posts: kd-tree-revisited: add implementation

---
 .../2024-08-11-kd-tree-revisited/index.md     | 72 +++++++++++++++++++
 1 file changed, 72 insertions(+)

diff --git a/content/posts/2024-08-11-kd-tree-revisited/index.md b/content/posts/2024-08-11-kd-tree-revisited/index.md
index 5e5227f..be15f48 100644
--- a/content/posts/2024-08-11-kd-tree-revisited/index.md
+++ b/content/posts/2024-08-11-kd-tree-revisited/index.md
@@ -38,3 +38,75 @@ us a minimal bound on the distance of the points stored on the other side.
 This can be easily computed from the `axis` and `mid` values which are stored in
 the inner nodes: to project the node on the plane we simply replace its
 coordinate for this axis by `mid`.
+
+## Simplified search
+
+With that out of the way, let's now see how `closest` can be implemented without
+needing to track the tree's `AABB` at the root:
+
+```python
+# Wrapper type for closest points, ordered by `distance`
+@dataclasses.dataclass(order=True)
+class ClosestPoint[T](NamedTuple):
+    point: Point = field(compare=False)
+    value: T = field(compare=False)
+    distance: float
+
+class KdTree[T]:
+    def closest(self, point: Point, n: int = 1) -> list[ClosestPoint[T]]:
+        assert n > 0
+        res = MaxHeap()
+        # Instead of passing an `AABB`, we give an initial projection point,
+        # the query origin itself (since we haven't visited any split node yet)
+        self._root.closest(point, res, n, point)
+        return sorted(res)
+
+class KdNode[T]:
+    def closest(
+        self,
+        point: Point,
+        out: MaxHeap[ClosestPoint[T]],
+        n: int,
+        projection: Point,
+    ) -> None:
+        # Same implementation
+        self.inner.closest(point, out, n, bounds)
+
+class KdLeafNode[T]:
+    def closest(
+        self,
+        point: Point,
+        out: MaxHeap[ClosestPoint[T]],
+        n: int,
+        projection: Point,
+    ) -> None:
+        # Same implementation
+        for p, val in self.points.items():
+            item = ClosestPoint(p, val, dist(p, point))
+            if len(out) < n:
+                out.push(item)
+            elif out.peek().distance > item.distance:
+                out.pushpop(item)
+
+class KdSplitNode[T]:
+    def closest(
+        self,
+        point: Point,
+        out: list[ClosestPoint[T]],
+        n: int,
+        projection: Point,
+    ) -> None:
+        index = self._index(point)
+        self.children[index].closest(point, out, n, projection)
+        # Project onto the splitting plane, for a minimum distance to its points
+        projection = projection.replace(self.axis, self.mid)
+        # If we're at capacity and can't possibly find any closer points, exit
+        if len(out) == n and dist(point, projection) > out.peek().distance:
+            return
+        # Otherwise recurse on the other side to check for nearer neighbours
+        self.children[1 - index].closest(point, out, n, projection)
+```
+
+As you can see, the main difference is in `KdSplitNode`'s implementation, where
+we can quickly compute the minimum distance between the search's origin and all
+potential points in that subspace.