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 BenchmarkLargeArray(b *testing.B) {
var hashSize = 1000
testHash := hash.NewMurmur(int64(hashSize))
testArray := make([]int64, 1000)
for i := 0; i < b.N; i++ {
b.StopTimer()
for j := 0; j < len(testArray); j++ {
testArray[j] = int64(i * j)
}
b.StartTimer()
testHash.Hash(testArray)
}
}
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 TestMurmur(t *testing.T) {
var hashSize = 100
testHash := hash.NewMurmur(int64(hashSize))
resultCount := make([]int64, hashSize)
for i := 0; i < hashSize*100; i++ {
fakeArray := make([]int64, 1)
fakeArray[0] = int64(i)
resultCount[testHash.Hash(fakeArray)]++
}
var maxCount = hashSize * 13 / 10
var minCount = hashSize * 7 / 10
for index, indexCount := range resultCount {
if indexCount > int64(maxCount) || indexCount < int64(minCount) {
t.Errorf("X - Expected between %d - %d results, for index %d got %d", minCount, maxCount, index, indexCount)
}
}
t.Log("√ Murmur Hash test complete")
}
// 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 NewHash(hashMod int64) types.Hash {
return hash.NewMurmur(hashMod)
}