func (k *knn) Estimate(data gotypes.Value) float64 {
result := 0.0
for i := 0; i < k.data.Rows(); i++ {
v, _ := gotypes.WrapArray(k.data.Row(i), gotypes.ValueType)
result += gaussian(v, data, k.h)
}
return result / float64(k.data.Rows())
}
// KMeans clustering
// TODO: Change to not only be numeric
func KMeans(data []classify.Data, K int, metric Metric, means [][]float64) [][]float64 {
features := len(data[0].Value())
if means == nil {
/* Todo: randomly initialize k means */
means = make([][]float64, K)
for i := range means {
means[i] = make([]float64, features)
val := data[rand.Intn(len(data))]
for j, v := range val.Value() {
means[i][j] = v.Real()
}
}
}
counts := make([]int, K)
newMeans := make([][]float64, K)
for i := range newMeans {
newMeans[i] = make([]float64, features)
}
maxElemPerThread := 10000 // 1K
iterations := gomath.MaxInt(1, (len(data)+maxElemPerThread-1)/maxElemPerThread)
elemPerThread := (len(data) + iterations - 1) / iterations
change := true
for change {
change = false
var lock sync.Mutex
parallels.Foreach(func(i int) bool {
// List of stuff
nMs := make([][]float64, K)
cs := make([]int, K)
for i := range nMs {
nMs[i] = make([]float64, features)
}
start := i * elemPerThread
end := gomath.MinInt(start+elemPerThread, len(data))
for start < end {
d := data[start]
closest := 0
wrap, err := gotypes.WrapArray(means[0], gotypes.Array)
if err != nil {
panic(err)
}
dist := metric(d.Value(), wrap.Array())
for k := 1; k < K; k++ {
wrap, err = gotypes.WrapArray(means[k], gotypes.Array)
if err != nil {
panic(err)
}
nDist := metric(d.Value(), wrap.Array())
if nDist < dist {
dist = nDist
closest = k
}
}
// Save
cs[closest]++
for i := 0; i < features; i++ {
t := d.Value()[i].Real()
nMs[closest][i] += t
}
start++
}
lock.Lock()
defer lock.Unlock()
for i := 0; i < K; i++ {
for j := 0; j < features; j++ {
newMeans[i][j] += nMs[i][j]
}
counts[i] += cs[i]
}
return false
}, iterations)
// Update means
for i := range newMeans {
for j := range newMeans[i] {
newMeans[i][j] /= float64(counts[i])
}
if !same(newMeans[i], means[i]) {
change = true
}
for j := range newMeans[i] {
means[i][j] = newMeans[i][j]
newMeans[i][j] = 0
}
counts[i] = 0
}
}
return means
}