func TestSimpleLeastDistanceVsKmeans(t *testing.T) {
//Create fake data
var numClusters = 5
var numRows = 400
var dimensionality = 1000
data := make([][]float64, numRows, numRows)
for i := 0; i < numRows; i++ {
row := make([]float64, dimensionality, dimensionality)
for j := 0; j < dimensionality; j++ {
row[j] = rand.Float64()
}
data[i] = row
}
start := time.Now()
//Test RPHash with Fake Object
RPHashObject := reader.NewSimpleArray(data, numClusters)
simpleObject := simple.NewSimple(RPHashObject)
simpleObject.Run()
if len(RPHashObject.GetCentroids()) != numClusters {
t.Errorf("Requested %v centriods. But RPHashSimple returned %v.", numClusters, len(RPHashObject.GetCentroids()))
}
rpHashResult := RPHashObject.GetCentroids()
fmt.Println("RPHash: ", time.Since(start))
//Find clusters using KMeans
start = time.Now()
clusterer := clusterer.NewKMeansSimple(numClusters, data)
clusterer.Run()
kMeansResult := clusterer.GetCentroids()
fmt.Println("kMeans: ", time.Since(start))
var kMeansAssignment = 0
var rpHashAssignment = 0
var matchingAssignmentCount = 0
var kMeansTotalDist = float64(0)
var rpHashTotalDist = float64(0)
for _, vector := range data {
rpHashAssignment, _ = utils.FindNearestDistance(vector, rpHashResult)
kMeansAssignment, _ = utils.FindNearestDistance(vector, kMeansResult)
kMeansTotalDist += utils.Distance(vector, kMeansResult[kMeansAssignment])
rpHashTotalDist += utils.Distance(vector, rpHashResult[rpHashAssignment])
//t.Log(rpHashAssignments[i], kMeansAssignments[i]);
if rpHashAssignment == kMeansAssignment {
matchingAssignmentCount += 1
}
}
t.Log("RPHash:", rpHashTotalDist)
t.Log("KMeans:", kMeansTotalDist)
t.Log("Ratio: ", kMeansTotalDist/rpHashTotalDist)
}
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")
}
//Add a new data point to the stream
func (this *KMeansStream) addDataPointWeighted(data []float64, weight int64) {
if len(data) != this.dimensionality {
return
// panic("The input data does not have the correct dimenstionality")
}
minIndex := 0
minDist := 0.0
for i, centriod := range this.candidateClusters {
currDist := utils.Distance(data, centriod.Centroid())
if i == 0 || minDist > currDist {
minDist = currDist
minIndex = i
}
}
minDistSquared := minDist * minDist
if len(this.candidateClusters) < this.k || rand.Float64() < float64(weight)*(minDistSquared/this.frequency) {
this.candidateClusters = append(this.candidateClusters, *itemset.NewCentroidWeighted(data, weight))
} else {
this.candidateClusters[minIndex].UpdateVector(data)
}
if len(this.candidateClusters) > this.maxCandidateClusters {
this.reduceCandidateClusters()
}
}