refactor!: ♻️ rename KNearestNeighbor API to the KNN API

I have renamed the `KNearestNeighbor` API to the `KNN` API. I made this change to make the APIs of my K-D Tree library consistent with those of existing ones written in Go.

BREAKING CHANGE: With the rename of the `KNearestNeighbor` API to the `KNN` API in this commit, any code that uses the `KNearestNeighbor` API will no longer work with future releases of this library created after this commit. Affected codebases must migrate to the `KNN` API to be compatible with future releases of this library.

Author: rishitc

kdtree.go

@@ -402,13 +402,13 @@ func nearestNeighbor[T Comparable[T]](d int, v, nn *T, cd int, r *kdNode[T]) *T
 	return nn
 }
 
-func (t *KDTree[T]) KNearestNeighbor(value T, k int) []*T {
+func (t *KDTree[T]) KNN(value T, k int) []*T {
 	if t == nil || t.root == nil || t.size < k {
 		return nil
 	}
 
 	pqRes := NewBoundedPriorityQueue[T](k)
-	kNearestNeighbor(k, t.dimensions, &value, &pqRes, 0, t.root)
+	knn(k, t.dimensions, &value, &pqRes, 0, t.root)
 
 	res := make([]*T, 0, k)
 	for range k {
@@ -432,7 +432,7 @@ type nodeInfo[T Comparable[T]] struct {
 	dir  direction
 }
 
-func kNearestNeighbor[T Comparable[T]](k, d int, v *T, pq *BoundedPriorityQueue[T], cd int, r *kdNode[T]) {
+func knn[T Comparable[T]](k, d int, v *T, pq *BoundedPriorityQueue[T], cd int, r *kdNode[T]) {
 	if r == nil {
 		return
 	}
@@ -471,7 +471,7 @@ func kNearestNeighbor[T Comparable[T]](k, d int, v *T, pq *BoundedPriorityQueue[
 			} else {
 				next = cn.left
 			}
-			kNearestNeighbor(k, d, v, pq, (ncd+1)%d, next)
+			knn(k, d, v, pq, (ncd+1)%d, next)
 		}
 		ncd = (ncd - 1 + d) % d
 	}

tests/kdtree_2d_test.go

@@ -192,7 +192,7 @@ func tensor2DSortFunc(a, b *types.Tensor2D) int {
 	}
 }
 
-func Test2DKNearestNeighbor1(t *testing.T) {
+func Test2DKNN1(t *testing.T) {
 	const dimensions = 2
 	type input struct {
 		p types.Tensor2D
@@ -227,7 +227,7 @@ func Test2DKNearestNeighbor1(t *testing.T) {
 	}
 	for name, st := range testTable {
 		t.Run(name, func(t *testing.T) {
-			nns := tree.KNearestNeighbor(st.input.p, st.input.k)
+			nns := tree.KNN(st.input.p, st.input.k)
 			slices.SortFunc(nns, tensor2DSortFunc)
 			slices.SortFunc(st.expected, tensor2DSortFunc)
 			for i := range len(st.expected) {