Vendoring with Glide and delete function handler

gateway/vendor/github.com/beorn7/perks/histogram/histogram.go

@@ -0,0 +1,108 @@
+// 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()
+}