typo

lsh/model.go

@@ -1,6 +1,6 @@
 // Package lsh implements Locality-Sensitive Hashing (LSH) for efficient approximate nearest neighbor search in Hamming space.
 //
-// This package also provides several hash functions for use with binary feature vectors (`uint64`), as well as `Wide` variants of the hash functions that work with the `[]uint64`s and the [ModelWide] model:
+// This package also provides several hash functions for use with binary feature vectors (`uint64`), as well as `Wide` variants of the hash functions that work with the `[]uint64`s and the [WideModel] model:
 //
 //   - [MinHash]: A hashing scheme for similarity search based on common bits.
 //   - [Blur]: Hashes values based on thresholding the number of bits in common with predefined bitmasks.

lsh/model_wide_test.go

@@ -11,7 +11,7 @@ import (
 	"pgregory.net/rapid"
 )
 
-func Test_ModelWide_64bit_Equal_To_Narrow(t *testing.T) {
+func Test_WideModel_64bit_Equal_To_Narrow(t *testing.T) {
 	id := func(a uint64) uint64 { return a }
 	rapid.Check(t, func(t *rapid.T) {
 		k := rapid.IntRange(3, 1001).Draw(t, "k")

model_wide_bench_test.go

@@ -8,7 +8,7 @@ import (
 	"github.com/keilerkonzept/bitknn/internal/testrandom"
 )
 
-func Benchmark_ModelWide_Predict1(b *testing.B) {
+func Benchmark_WideModel_Predict1(b *testing.B) {
 	votes := make([]float64, 256)
 	type bench struct {
 		dim      []int