doc

lsh/nearest_wide.go

@@ -7,7 +7,7 @@ import (
 	"github.com/keilerkonzept/bitknn/internal/slice"
 )
 
-// Nearest, but for wide data.
+// [Nearest], but for wide data.
 func NearestWide(data [][]uint64, bucketIDs []uint64, buckets map[uint64]slice.IndexRange, k int, xh uint64, x []uint64, bucketDistances []int, heapBucketIDs []uint64, distances []int, indices []int) (int, int) {
 	dataHeap := heap.MakeMax[int](distances, indices)
 	exactBucket := buckets[xh]

model_wide.go

@@ -29,7 +29,7 @@ func (me *WideModel) Find(k int, x []uint64) ([]int, []int) {
 	return me.FindInto(k, x, me.Narrow.HeapDistances, me.Narrow.HeapIndices)
 }
 
-// FindV is [Find], but vectorizable (currently only on ARM64 with NEON instructions).
+// FindV is [WideModel.Find], but vectorizable (currently only on ARM64 with NEON instructions).
 // The provided [batch] slice must have length >=k and is used to pre-compute batches of distances.
 func (me *WideModel) FindV(k int, x []uint64, batch []uint32) ([]int, []int) {
 	me.PreallocateHeap(k)
@@ -45,7 +45,7 @@ func (me *WideModel) FindInto(k int, x []uint64, distances []int, indices []int)
 	return distances[:k], indices[:k]
 }
 
-// FindIntoV is [FindInto], but vectorizable (currently only on ARM64 with NEON instructions).
+// FindIntoV is [WideModel.FindInto], but vectorizable (currently only on ARM64 with NEON instructions).
 // The provided [batch] slice must have length >=k and is used to pre-compute batches of distances.
 func (me *WideModel) FindIntoV(k int, x []uint64, batch []uint32, distances []int, indices []int) ([]int, []int) {
 	k = NearestWideV(me.WideData, k, x, batch, distances, indices)
@@ -67,14 +67,14 @@ func (me *WideModel) PredictInto(k int, x []uint64, distances []int, indices []i
 	return k
 }
 
-// PredictV is [Predict], but vectorizable (currently only on ARM64 with NEON instructions).
+// PredictV is [WideModel.Predict], but vectorizable (currently only on ARM64 with NEON instructions).
 // The provided [batch] slice must have length >=k and is used to pre-compute batches of distances.
 func (me *WideModel) PredictV(k int, x []uint64, batch []uint32, votes VoteCounter) int {
 	me.PreallocateHeap(k)
 	return me.PredictIntoV(k, x, batch, me.Narrow.HeapDistances, me.Narrow.HeapIndices, votes)
 }
 
-// PredictIntoV is [PredictInto], but vectorizable (currently only on ARM64 with NEON instructions).
+// PredictIntoV is [WideModel.PredictInto], but vectorizable (currently only on ARM64 with NEON instructions).
 // The provided [batch] slice must have length >=k and is used to pre-compute batches of distances.
 func (me *WideModel) PredictIntoV(k int, x []uint64, batch []uint32, distances []int, indices []int, votes VoteCounter) int {
 	k = NearestWideV(me.WideData, k, x, batch, distances, indices)

nearest_wide.go

@@ -7,7 +7,7 @@ import (
 	"github.com/keilerkonzept/bitknn/internal/neon"
 )
 
-// [bitknn.Nearest], but for wide data.
+// [Nearest], but for wide data.
 func NearestWide(data [][]uint64, k int, x []uint64, distances, indices []int) int {
 	heap := heap.MakeMax(distances, indices)
 	distance0 := &distances[0]