tidy up votes counter interface

lsh/model.go

@@ -73,13 +73,13 @@ func Fit(data []uint64, labels []int, hash Hash, opts ...bitknn.Option) *Model {
 }
 
 // Predict1 predicts the label for a single input using the LSH model.
-func (me *Model) Predict1(k int, x uint64, votes bitknn.Votes) int {
+func (me *Model) Predict1(k int, x uint64, votes bitknn.VoteCounter) int {
 	me.PreallocateHeap(k)
 	return me.Predict1Into(k, x, votes, me.HeapBucketDistances, me.HeapBucketIDs, me.HeapDistances, me.HeapIndices)
 }
 
 // Predicts the label of a single input point. Each call allocates three new slices of length [k]+1 for the neighbor heaps.
-func (me *Model) Predict1Alloc(k int, x uint64, votes bitknn.Votes) int {
+func (me *Model) Predict1Alloc(k int, x uint64, votes bitknn.VoteCounter) int {
 	bucketDistances := make([]int, k+1)
 	bucketIDs := make([]uint64, k+1)
 	distances := make([]int, k+1)
@@ -89,7 +89,7 @@ func (me *Model) Predict1Alloc(k int, x uint64, votes bitknn.Votes) int {
 }
 
 // Predict1Into predicts the label for a single input using the given slices (of length [k]+1 each) for the neighbor heaps.
-func (me *Model) Predict1Into(k int, x uint64, votes bitknn.Votes, bucketDistances []int, bucketIDs []uint64, distances []int, indices []int) int {
+func (me *Model) Predict1Into(k int, x uint64, votes bitknn.VoteCounter, bucketDistances []int, bucketIDs []uint64, distances []int, indices []int) int {
 	xp := me.Hash.Hash1(x)
 	k, n := Nearest(me.Data, me.BucketIDs, me.Buckets, k, xp, x, bucketDistances, bucketIDs, distances, indices)
 	me.Vote(k, distances, indices, votes)

lsh/model_bench_test.go

@@ -10,7 +10,6 @@ import (
 )
 
 func Benchmark_Model_Predict1(b *testing.B) {
-	votes := make([]float64, 256)
 	type bench struct {
 		hashes   []lsh.Hash
 		dataSize []int
@@ -35,7 +34,7 @@ func Benchmark_Model_Predict1(b *testing.B) {
 						model.PreallocateHeap(k)
 						b.ResetTimer()
 						for n := 0; n < b.N; n++ {
-							model.Predict1(k, query, bitknn.VoteSlice(votes))
+							model.Predict1(k, query, bitknn.DiscardVotes)
 						}
 					})
 				}

lsh/model_wide.go

@@ -66,13 +66,13 @@ func FitWide(data [][]uint64, labels []int, hash HashWide, opts ...bitknn.Option
 }
 
 // Predict1 predicts the label for a single input using the LSH model.
-func (me *WideModel) Predict1(k int, x []uint64, votes bitknn.Votes) int {
+func (me *WideModel) Predict1(k int, x []uint64, votes bitknn.VoteCounter) int {
 	me.PreallocateHeap(k)
 	return me.Predict1Into(k, x, votes, me.HeapBucketDistances, me.HeapBucketIDs, me.Narrow.HeapDistances, me.Narrow.HeapIndices)
 }
 
 // Predicts the label of a single input point. Each call allocates three new slices of length [k]+1 for the neighbor heaps.
-func (me *WideModel) Predict1Alloc(k int, x []uint64, votes bitknn.Votes) int {
+func (me *WideModel) Predict1Alloc(k int, x []uint64, votes bitknn.VoteCounter) int {
 	bucketDistances := make([]int, k+1)
 	bucketIDs := make([]uint64, k+1)
 	distances := make([]int, k+1)
@@ -82,7 +82,7 @@ func (me *WideModel) Predict1Alloc(k int, x []uint64, votes bitknn.Votes) int {
 }
 
 // Predict1Into predicts the label for a single input using the given slices (of length [k]+1 each) for the neighbor heaps.
-func (me *WideModel) Predict1Into(k int, x []uint64, votes bitknn.Votes, bucketDistances []int, bucketIDs []uint64, distances []int, indices []int) int {
+func (me *WideModel) Predict1Into(k int, x []uint64, votes bitknn.VoteCounter, bucketDistances []int, bucketIDs []uint64, distances []int, indices []int) int {
 	xp := me.Hash.Hash1Wide(x)
 	k0, _ := NearestWide(me.WideData, me.BucketIDs, me.Buckets, k, xp, x, bucketDistances, bucketIDs, distances, indices)
 	me.WideModel.Narrow.Vote(k0, distances, indices, votes)

model.go

@@ -43,26 +43,26 @@ func (me *Model) PreallocateHeap(k int) {
 }
 
 // Predicts the label of a single input point. Each call allocates two new slices of length K+1 for the neighbor heap.
-func (me *Model) Predict1Alloc(k int, x uint64, votes Votes) {
+func (me *Model) Predict1Alloc(k int, x uint64, votes VoteCounter) {
 	distances, indices := make([]int, k+1), make([]int, k+1)
 	me.Predict1Into(k, x, distances, indices, votes)
 }
 
 // Predicts the label of a single input point. Reuses two slices of length K+1 for the neighbor heap.
-func (me *Model) Predict1(k int, x uint64, votes Votes) {
+func (me *Model) Predict1(k int, x uint64, votes VoteCounter) {
 	me.HeapDistances = slice.OrAlloc(me.HeapDistances, k+1)
 	me.HeapIndices = slice.OrAlloc(me.HeapIndices, k+1)
 	me.Predict1Into(k, x, me.HeapDistances, me.HeapIndices, votes)
 }
 
 // Predicts the label of a single input point, using the given slices for the neighbor heap.
-func (me *Model) Predict1Into(k int, x uint64, distances []int, indices []int, votes Votes) {
+func (me *Model) Predict1Into(k int, x uint64, distances []int, indices []int, votes VoteCounter) {
 	k = Nearest(me.Data, k, x, distances, indices)
 	me.Vote(k, distances, indices, votes)
 }
 
 // Predicts the label of a single input point, using the given slices for the neighbor heap.
-func (me *Model) Vote(k int, distances []int, indices []int, votes Votes) {
+func (me *Model) Vote(k int, distances []int, indices []int, votes VoteCounter) {
 	votes.Clear()
 	switch me.DistanceWeighting {
 	case DistanceWeightingNone:
@@ -93,63 +93,63 @@ func (me *Model) Vote(k int, distances []int, indices []int, votes Votes) {
 	}
 }
 
-func (me *Model) votes1vc(k int, indices []int, votes Votes, f func(int) float64, distances []int) {
+func (me *Model) votes1vc(k int, indices []int, votes VoteCounter, f func(int) float64, distances []int) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, f(distances[i])*me.Values[index])
 	}
 }
 
-func (me *Model) votes1c(k int, indices []int, votes Votes, f func(int) float64, distances []int) {
+func (me *Model) votes1c(k int, indices []int, votes VoteCounter, f func(int) float64, distances []int) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, f(distances[i]))
 	}
 }
 
-func (me *Model) votes1vq(k int, indices []int, votes Votes, distances []int) {
+func (me *Model) votes1vq(k int, indices []int, votes VoteCounter, distances []int) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, DistanceWeightingFuncQuadratic(distances[i])*me.Values[index])
 	}
 }
 
-func (me *Model) votes1q(k int, indices []int, votes Votes, distances []int) {
+func (me *Model) votes1q(k int, indices []int, votes VoteCounter, distances []int) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, DistanceWeightingFuncQuadratic(distances[i]))
 	}
 }
 
-func (me *Model) votes1vl(k int, indices []int, votes Votes, distances []int) {
+func (me *Model) votes1vl(k int, indices []int, votes VoteCounter, distances []int) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, DistanceWeightingFuncLinear(distances[i])*me.Values[index])
 	}
 }
 
-func (me *Model) votes1l(k int, indices []int, votes Votes, distances []int) {
+func (me *Model) votes1l(k int, indices []int, votes VoteCounter, distances []int) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, DistanceWeightingFuncLinear(distances[i]))
 	}
 }
 
-func (me *Model) votes1v(k int, indices []int, votes Votes) {
+func (me *Model) votes1v(k int, indices []int, votes VoteCounter) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]
 		votes.Add(label, me.Values[index])
 	}
 }
 
-func (me *Model) votes1(k int, indices []int, votes Votes) {
+func (me *Model) votes1(k int, indices []int, votes VoteCounter) {
 	for i := range k {
 		index := indices[i]
 		label := me.Labels[index]

model_bench_test.go

@@ -10,7 +10,6 @@ import (
 )
 
 func Benchmark_Model_Predict1(b *testing.B) {
-	votes := make([]float64, 256)
 	type bench struct {
 		dataSize []int
 		k        []int
@@ -31,7 +30,7 @@ func Benchmark_Model_Predict1(b *testing.B) {
 					model.PreallocateHeap(k)
 					b.ResetTimer()
 					for n := 0; n < b.N; n++ {
-						model.Predict1(k, query, bitknn.VoteSlice(votes))
+						model.Predict1(k, query, bitknn.DiscardVotes)
 					}
 				})
 			}
@@ -59,9 +58,10 @@ func Benchmark_Model_Predict1V(b *testing.B) {
 					query := rand.Uint64()
 
 					model.PreallocateHeap(k)
+					voteSlice := bitknn.VoteSlice(votes)
 					b.ResetTimer()
 					for n := 0; n < b.N; n++ {
-						model.Predict1(k, query, bitknn.VoteSlice(votes))
+						model.Predict1(k, query, &voteSlice)
 					}
 				})
 			}
@@ -89,10 +89,11 @@ func Benchmark_Model_Predict1D(b *testing.B) {
 						model.DistanceWeighting = d
 						model.DistanceWeightingFunc = func(d int) float64 { return 1 / float64(1+d) }
 						query := rand.Uint64()
+						voteSlice := bitknn.VoteSlice(votes)
 
 						b.ResetTimer()
 						for n := 0; n < b.N; n++ {
-							model.Predict1(k, query, bitknn.VoteSlice(votes))
+							model.Predict1(k, query, &voteSlice)
 						}
 					})
 				}
@@ -122,10 +123,11 @@ func Benchmark_Model_Predict1DV(b *testing.B) {
 						model.DistanceWeighting = d
 						model.DistanceWeightingFunc = func(d int) float64 { return 1 / float64(1+d) }
 						query := rand.Uint64()
+						voteSlice := bitknn.VoteSlice(votes)
 
 						b.ResetTimer()
 						for n := 0; n < b.N; n++ {
-							model.Predict1(k, query, bitknn.VoteSlice(votes))
+							model.Predict1(k, query, &voteSlice)
 						}
 					})
 				}

model_wide.go

@@ -23,14 +23,14 @@ func (me *WideModel) PreallocateHeap(k int) {
 
 // Predicts the label of a single input point. Reuses two slices of length K+1 for the neighbor heap.
 // Returns the number of neighbors found.
-func (me *WideModel) Predict1(k int, x []uint64, votes Votes) int {
+func (me *WideModel) Predict1(k int, x []uint64, votes VoteCounter) int {
 	me.Narrow.PreallocateHeap(k)
 	return me.Predict1Into(k, x, me.Narrow.HeapDistances, me.Narrow.HeapIndices, votes)
 }
 
 // Predicts the label of a single input point, using the given slices for the neighbor heap.
 // Returns the number of neighbors found.
-func (me *WideModel) Predict1Into(k int, x []uint64, distances []int, indices []int, votes Votes) int {
+func (me *WideModel) Predict1Into(k int, x []uint64, distances []int, indices []int, votes VoteCounter) int {
 	k = NearestWide(me.WideData, k, x, distances, indices)
 	me.Narrow.Vote(k, distances, indices, votes)
 	return k

model_wide_bench_test.go

@@ -9,7 +9,6 @@ import (
 )
 
 func Benchmark_WideModel_Predict1(b *testing.B) {
-	votes := make([]float64, 256)
 	type bench struct {
 		dim      []int
 		dataSize []int
@@ -32,7 +31,7 @@ func Benchmark_WideModel_Predict1(b *testing.B) {
 						model.PreallocateHeap(k)
 						b.ResetTimer()
 						for n := 0; n < b.N; n++ {
-							model.Predict1(k, query, bitknn.VoteSlice(votes))
+							model.Predict1(k, query, bitknn.DiscardVotes)
 						}
 					})
 				}

votes.go

@@ -2,8 +2,8 @@ package bitknn
 
 import "slices"
 
-// Votes is a k-NN vote counter interface.
-type Votes interface {
+// VoteCounter is a k-NN vote counter interface.
+type VoteCounter interface {
 	// Clear removes all votes.
 	Clear()
 
@@ -21,6 +21,17 @@ type Votes interface {
 	Add(label int, delta float64)
 }
 
+type discardVotes int
+
+// DiscardVotes is a no-op vote counter.
+const DiscardVotes = discardVotes(0)
+
+func (me discardVotes) Clear()                       {}
+func (me discardVotes) ArgMax() int                  { return 0 }
+func (me discardVotes) Max() float64                 { return 0 }
+func (me discardVotes) Get(label int) float64        { return 0 }
+func (me discardVotes) Add(label int, delta float64) {}
+
 // VoteSlice is a dense vote counter that stores votes in a slice.
 // It is efficient for small sets of class labels.
 type VoteSlice []float64

votes_test.go

@@ -10,6 +10,35 @@ import (
 
 const eps = 1e-9
 
+func TestVoteDiscard(t *testing.T) {
+	discard := bitknn.DiscardVotes
+	{
+		pre := discard
+		discard.Add(0, 1)
+		post := discard
+		if pre != post {
+			t.Fail()
+		}
+	}
+	if discard.Get(0) != 0 {
+		t.Fatal()
+	}
+	if discard.ArgMax() != 0 {
+		t.Fatal()
+	}
+	if discard.Max() != 0 {
+		t.Fatal()
+	}
+	{
+		pre := discard
+		discard.Clear()
+		post := discard
+		if pre != post {
+			t.Fail()
+		}
+	}
+}
+
 func TestVoteSlice_Clear(t *testing.T) {
 	rapid.Check(t, func(t *rapid.T) {
 		length := rapid.IntRange(0, 100).Draw(t, "length")