fnv.New64a test fix and README update

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2121 Vitali Fedulov (fedulov.vitali@gmail.com)
+Copyright (c) 2021 Vitali Fedulov (fedulov.vitali@gmail.com)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -4,10 +4,11 @@ Search nearest neighbour vectors in n-dimensional space with hashes. There are n
 
 Each vestor is discretized into a set of hashes, as described [here](https://vitali-fedulov.github.io/similar.pictures/algorithm-for-hashing-high-dimensional-float-vectors.html) (also as [PDF](https://github.com/vitali-fedulov/research/blob/main/Algorithm%20for%20hashing%20float%20vectors.pdf)).
 
-Usage sequence:
-1) CubeSet or CentralCube, depending which one is used for a database record and which one for a query.
-2) HashSet and DecimalHash to get corresponding hash set and central hash from results of (1). If DecimalHash is not suitable because of very large number of buckets or dimensions, use FNV1aHash to get both the hash set and the central hash).
+## How to use
 
-[Example](https://github.com/vitali-fedulov/images3/blob/master/hashes.go) of usage for image comparison.
+1) Provided a float vector []float64, use `CubeSet` and `CentralCube` functions to generate hypercube coordinates []int. The difference between the two functions is that one corresponds to hash-table record and the other to a query or vice versa, depending on performance/memory preference.
+2) `HashSet` and `DecimalHash`/`FNV1aHash` are used to get corresponding hash set and central hash from the hypercube coordinates above. There are 2 alternative hash functions: DecimalHash and FNV1aHash. DecimalHash does not have collisions, but is not suitable for cases with large number of buckets or dimensions. FNV1aHash is applicable for all cases.
 
-[Go doc](https://pkg.go.dev/github.com/vitali-fedulov/hyper) for code documentation.
+[Example](https://github.com/vitali-fedulov/imagehash/blob/master/hashes.go) for similar image search/clustering.
+
+[Go doc](https://pkg.go.dev/github.com/vitali-fedulov/hyper) for full code documentation.

cubes.go

@@ -18,7 +18,7 @@ type Params struct {
 // CubeSet returns a set of hypercubes, which represent
 // fuzzy discretization of one n-dimensional vector,
 // as described in
-// https://vitali-fedulov.github.io/algorithm-for-hashing-high-dimensional-float-vectors.html
+// https://vitali-fedulov.github.io/similar.pictures/algorithm-for-hashing-high-dimensional-float-vectors.html
 // One hupercube is defined by bucket numbers in each dimension.
 // min and max are minimum and maximum possible values of
 // the vector components. The assumption is that min and max

hashes_test.go

@@ -17,7 +17,7 @@ func TestDecimalHash(t *testing.T) {
 func TestFNV1aHash(t *testing.T) {
 	cube := Cube{5, 59, 255, 9, 7, 12, 22, 31}
 	hash := cube.FNV1aHash()
-	want := uint64(1659788114117494335)
+	want := uint64(6267598672213710911)
 	if hash != want {
 		t.Errorf(`Got %v, want %v.`, hash, want)
 	}
@@ -31,10 +31,10 @@ func TestHashSet(t *testing.T) {
 		{1, 0, 8, 3, 0, 0, 9}}
 	hashSet := cubes.HashSet((Cube).FNV1aHash)
 	want := []uint64{
-		6172277127052188606,
-		3265650857171344968,
-		13730239218993256724,
-		6843127655045710906}
+		9211138565158515574,
+		6304441926533466432,
+		5296875461196147964,
+		13706017245957046114}
 	if !reflect.DeepEqual(hashSet, want) {
 		t.Errorf(`Got %v, want %v.`, hashSet, want)
 	}