frostfs-sdk-go/netmap/aggregator.go

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package netmap
import (
"sort"
)
type (
// aggregator can calculate some value across all netmap
// such as median, minimum or maximum.
aggregator interface {
Add(float64)
Compute() float64
}
// normalizer normalizes weight.
normalizer interface {
Normalize(w float64) float64
}
meanAgg struct {
mean float64
count int
}
minAgg struct {
min float64
minFound bool
}
meanIQRAgg struct {
k float64
arr []float64
}
reverseMinNorm struct {
min float64
}
sigmoidNorm struct {
scale float64
}
// weightFunc calculates n's weight.
weightFunc = func(NodeInfo) float64
)
var (
_ aggregator = (*meanAgg)(nil)
_ aggregator = (*minAgg)(nil)
_ aggregator = (*meanIQRAgg)(nil)
_ normalizer = (*reverseMinNorm)(nil)
_ normalizer = (*sigmoidNorm)(nil)
)
// newWeightFunc returns weightFunc which multiplies normalized
// capacity and price.
func newWeightFunc(capNorm, priceNorm normalizer) weightFunc {
return func(n NodeInfo) float64 {
return capNorm.Normalize(float64(n.capacity())) * priceNorm.Normalize(float64(n.Price()))
}
}
// newMeanAgg returns an aggregator which
// computes mean value by recalculating it on
// every addition.
func newMeanAgg() aggregator {
return new(meanAgg)
}
// newMinAgg returns an aggregator which
// computes min value.
func newMinAgg() aggregator {
return new(minAgg)
}
// newMeanIQRAgg returns an aggregator which
// computes mean value of values from IQR interval.
func newMeanIQRAgg() aggregator {
return new(meanIQRAgg)
}
// newReverseMinNorm returns a normalizer which
// normalize values in range of 0.0 to 1.0 to a minimum value.
func newReverseMinNorm(min float64) normalizer {
return &reverseMinNorm{min: min}
}
// newSigmoidNorm returns a normalizer which
// normalize values in range of 0.0 to 1.0 to a scaled sigmoid.
func newSigmoidNorm(scale float64) normalizer {
return &sigmoidNorm{scale: scale}
}
func (a *meanAgg) Add(n float64) {
c := a.count + 1
a.mean = a.mean*(float64(a.count)/float64(c)) + n/float64(c)
a.count++
}
func (a *meanAgg) Compute() float64 {
return a.mean
}
func (a *minAgg) Add(n float64) {
if !a.minFound {
a.min = n
a.minFound = true
return
}
if n < a.min {
a.min = n
}
}
func (a *minAgg) Compute() float64 {
return a.min
}
func (a *meanIQRAgg) Add(n float64) {
a.arr = append(a.arr, n)
}
func (a *meanIQRAgg) Compute() float64 {
l := len(a.arr)
if l == 0 {
return 0
}
sort.Slice(a.arr, func(i, j int) bool { return a.arr[i] < a.arr[j] })
var min, max float64
const minLn = 4
if l < minLn {
min, max = a.arr[0], a.arr[l-1]
} else {
start, end := l/minLn, l*3/minLn-1
iqr := a.k * (a.arr[end] - a.arr[start])
min, max = a.arr[start]-iqr, a.arr[end]+iqr
}
count := 0
sum := float64(0)
for _, e := range a.arr {
if e >= min && e <= max {
sum += e
count++
}
}
return sum / float64(count)
}
func (r *reverseMinNorm) Normalize(w float64) float64 {
return (r.min + 1) / (w + 1)
}
func (r *sigmoidNorm) Normalize(w float64) float64 {
if r.scale == 0 {
return 0
}
x := w / r.scale
return x / (1 + x)
}