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// Package histogram provides a Go implementation of BigML's histogram package
// for Clojure/Java. It is currently experimental.
package histogram
import (
"container/heap"
"math"
"sort"
)
type Bin struct {
Count int
Sum float64
}
func (b *Bin) Update(x *Bin) {
b.Count += x.Count
b.Sum += x.Sum
}
func (b *Bin) Mean() float64 {
return b.Sum / float64(b.Count)
}
type Bins []*Bin
func (bs Bins) Len() int { return len(bs) }
func (bs Bins) Less(i, j int) bool { return bs[i].Mean() < bs[j].Mean() }
func (bs Bins) Swap(i, j int) { bs[i], bs[j] = bs[j], bs[i] }
func (bs *Bins) Push(x interface{}) {
*bs = append(*bs, x.(*Bin))
}
func (bs *Bins) Pop() interface{} {
return bs.remove(len(*bs) - 1)
}
func (bs *Bins) remove(n int) *Bin {
if n < 0 || len(*bs) < n {
return nil
}
x := (*bs)[n]
*bs = append((*bs)[:n], (*bs)[n+1:]...)
return x
}
type Histogram struct {
res *reservoir
}
func New(maxBins int) *Histogram {
return &Histogram{res: newReservoir(maxBins)}
}
func (h *Histogram) Insert(f float64) {
h.res.insert(&Bin{1, f})
h.res.compress()
}
func (h *Histogram) Bins() Bins {
return h.res.bins
}
type reservoir struct {
n int
maxBins int
bins Bins
}
func newReservoir(maxBins int) *reservoir {
return &reservoir{maxBins: maxBins}
}
func (r *reservoir) insert(bin *Bin) {
r.n += bin.Count
i := sort.Search(len(r.bins), func(i int) bool {
return r.bins[i].Mean() >= bin.Mean()
})
if i < 0 || i == r.bins.Len() {
// TODO(blake): Maybe use an .insert(i, bin) instead of
// performing the extra work of a heap.Push.
heap.Push(&r.bins, bin)
return
}
r.bins[i].Update(bin)
}
func (r *reservoir) compress() {
for r.bins.Len() > r.maxBins {
minGapIndex := -1
minGap := math.MaxFloat64
for i := 0; i < r.bins.Len()-1; i++ {
gap := gapWeight(r.bins[i], r.bins[i+1])
if minGap > gap {
minGap = gap
minGapIndex = i
}
}
prev := r.bins[minGapIndex]
next := r.bins.remove(minGapIndex + 1)
prev.Update(next)
}
}
func gapWeight(prev, next *Bin) float64 {
return next.Mean() - prev.Mean()
}
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