func NewSimpleArray(inData [][]float64, k int) *SimpleArray {
randomSeed := rand.Int63()
data := utils.NewIterator(inData)
numDataPoints := len(inData)
dimension := 2
// As the number of rotations increases, the distance increases.
// Increases the noise.
numberOfRotations := 6
numberOfSearches := 1
numberOfProjections := 2
numberOfBlurs := 2
if data != nil {
// Get the first vector in the data set's length.
dimension = len(inData[0])
}
hashModulus := int64(math.MaxInt64)
// Set the target dimension much lower.
targetDimension := int(math.Floor(float64(dimension / 2)))
decoder := decoder.NewSpherical(targetDimension, numberOfRotations, numberOfSearches)
centroids := [][]float64{}
topIDs := []int64{}
return &SimpleArray{
numDataPoints: numDataPoints,
data: data,
dimension: dimension,
numberOfProjections: numberOfProjections,
randomSeed: randomSeed,
hashModulus: hashModulus,
k: k,
numberOfBlurs: numberOfBlurs,
decoder: decoder,
centroids: centroids,
topIDs: topIDs,
}
}
func TestSpherical(t *testing.T) {
var dimension, k, l, iterations int = 64, 6, 4, 10000
sphere := decoder.NewSpherical(dimension, k, l)
var collisions int = 0
var distavg float64 = 0.0
for j := 0; j < iterations; j++ {
p1, p2 := make([]float64, dimension), make([]float64, dimension)
for k := 0; k < dimension; k++ {
p1[k] = rand.Float64()*2 - 1
p2[k] = rand.Float64()*2 - 1
}
/* Get the distance of each vector from eachother. */
distavg += utils.Distance(p1, p2)
mh := hash.NewMurmur(1<<63 - 1)
/* Decode from 24-dimensions -> 1-dimensional integer */
hp1, hp2 := sphere.Hash(utils.Normalize(p1)), sphere.Hash(utils.Normalize(p2))
/* Blurring the integers into a smaller space. */
hash1, hash2 := mh.Hash(hp1), mh.Hash(hp2)
if hash1 == hash2 {
collisions++
}
}
if collisions > (iterations / 100) {
t.Errorf("More than 1 percent of the iterations resulted in collisions. %v collisions in %v iterations.",
collisions, iterations)
}
t.Log("Average Distance: ", distavg/float64(iterations))
t.Log("Percent collisions : ", float64(collisions)/float64(iterations))
t.Log("√ Spherical Decoder test complete")
}
func TestLSHStream(t *testing.T) {
var seed int64 = 0
var d, k, l int = 64, 6, 4
data := []float64{1.0, 0.0, 2.0, 7.0, 4.0, 0.0, 8.0, 3.0, 2.0, 1.0}
var inDimensions, outDimentions int = 10, 2
hash := hash.NewMurmur(1<<63 - 1)
decoder := decoder.NewSpherical(d, k, l)
projector := projector.NewDBFriendly(inDimensions, outDimentions, seed)
lsh := lsh.NewLSH(hash, decoder, projector)
lsh.LSHHashStream(data, 1)
t.Log("√ LSH Stream test complete")
}
func BenchmarkStreamLSH(b *testing.B) {
var seed int64 = 0
var d, k, l int = 64, 6, 4
data := []float64{1.0, 0.0, 2.0, 7.0, 4.0, 0.0, 8.0, 3.0, 2.0, 1.0}
var inDimensions, outDimentions int = 10, 2
hash := hash.NewMurmur(1<<63 - 1)
decoder := decoder.NewSpherical(d, k, l)
projector := projector.NewDBFriendly(inDimensions, outDimentions, seed)
for i := 0; i < b.N; i++ {
lsh := lsh.NewLSH(hash, decoder, projector)
b.StopTimer()
lsh.LSHHashStream(data, 1)
b.StartTimer()
}
}
func BenchmarkSpherical(b *testing.B) {
b.StopTimer()
randomSeed := rand.New(rand.NewSource(time.Now().UnixNano()))
var d, k, l int = 64, 6, 4
sphere := decoder.NewSpherical(d, k, l)
p1, p2 := make([]float64, d), make([]float64, d)
for i := 0; i < b.N; i++ {
for j := 0; j < d; j++ {
p1[j], p2[j] = randomSeed.NormFloat64(), randomSeed.NormFloat64()
}
b.StartTimer()
hp1, hp2 := sphere.Hash(utils.Normalize(p1)), sphere.Hash(utils.Normalize(p2))
b.StopTimer()
if hp1 == nil || hp2 == nil {
b.Error("Spherical hashes are null")
}
}
}
func NewStreamObject(dimension, k int) *StreamObject {
var centroids [][]float64
var topIDs []int64
numberOfRotations := 2
numberOfSearches := 2
targetDimension := 24
decoder := decoder.NewSpherical(targetDimension, numberOfRotations, numberOfSearches)
data := utils.NewIterator([][]float64{})
return &StreamObject{
decoder: decoder,
data: data,
dimension: dimension,
randomSeed: int64(0),
hashModulus: int64(int(0 >> 1)),
numberOfProjections: 1,
numberOfBlurs: 1,
k: k,
topIDs: topIDs,
centroids: centroids,
numDataPoints: 0,
}
}
// The datapoints are seeded in so that the first two data points are near eachother in euclidian geometery and the 3rd and 4th datapoint are
// near eachother in euclidian geometery. So the result1Cluster1 and result2Cluster1 should be closer together than the other two points.
// The same is true for the points in cluster two vs either point in cluster one.
func TestLSHSimple(t *testing.T) {
var seed int64 = 0
// We want to limit the dimension reduction because it causes a lot of noise.
var inDimensions, outDimentions, numberOfClusters, numberOfSearches int = 10, 5, 3, 1
dataPoint1Cluster1 := []float64{1.0, 0.0, 2.0, 7.0, 4.0, 0.0, 8.0, 3.0, 2.0, 1.0}
dataPoint2Cluster1 := []float64{2.0, 3.0, 2.0, 6.0, 5.5, 2.0, 8.0, 3.1, 2.0, 0.0}
dataPoint1Cluster2 := []float64{100.0, -120.0, 6.0, 18.0, 209.0, 0.0, -2.0, 1036.0, 15.0, 123.0}
dataPoint2Cluster2 := []float64{99.0, -119.0, 2.0, 18.0, 208.5, 0.0, -3.0, 1048.0, 13.0, 122.0}
hash := hash.NewMurmur(1<<63 - 1)
decoder := decoder.NewSpherical(inDimensions, numberOfClusters, numberOfSearches)
projector := projector.NewDBFriendly(inDimensions, outDimentions, seed)
lsh := lsh.NewLSH(hash, decoder, projector)
result1Cluster1 := lsh.LSHHashSimple(dataPoint1Cluster1)
result2Cluster1 := lsh.LSHHashSimple(dataPoint2Cluster1)
result1Cluster2 := lsh.LSHHashSimple(dataPoint1Cluster2)
result2Cluster2 := lsh.LSHHashSimple(dataPoint2Cluster2)
// Assert that results are still localy sensetive based on the original euclidian geometry
if math.Abs(float64(result1Cluster1-result2Cluster1)) > math.Abs(float64(result1Cluster1-result1Cluster2)) {
t.Errorf("\nThe first datapoint in cluster two is closer to the first data point in cluster one than the second data point in cluster one"+
"\ndatapoint cluster one datapoint one: %d, \ndatapoint cluster one datapoint two: %d, \ndatapoint cluster two datapoint one: %d",
result1Cluster1, result2Cluster1, result1Cluster2)
}
if math.Abs(float64(result1Cluster1-result2Cluster1)) > math.Abs(float64(result1Cluster1-result2Cluster2)) {
t.Errorf("\nThe second datapoint in cluster two is closer to the first data point in cluster one than the second data point in cluster one"+
"\nCluster one datapoint one: %d, \nCluster one datapoint two: %d, \nCluster two datapoint two: %d",
result1Cluster1, result2Cluster1, result2Cluster2)
}
if math.Abs(float64(result1Cluster2-result2Cluster2)) > math.Abs(float64(result1Cluster1-result1Cluster2)) {
t.Errorf("\nThe first datapoint in cluster one is closer to the first data point in cluster two than the second data point in cluster two"+
"\nCluster one datapoint one: %d, \nCluster two datapoint one: %d, \nCluster two datapoint two: %d",
result1Cluster1, result1Cluster2, result2Cluster2)
}
t.Log("√ LSH Simple test complete")
}
func NewDecoder(dimension, rotations, numberOfSearches int) types.Decoder {
return decoder.NewSpherical(dimension, rotations, numberOfSearches)
}
