go.mod: vendor github.com/bsm/histogram

6 files changed, 510 insertions, 0 deletions

diff --git a/vendor/github.com/bsm/histogram/v3/.gitignore b/vendor/github.com/bsm/histogram/v3/.gitignore
new file mode 100644
index 000000000..48b8bf907
--- /dev/null
+++ b/vendor/github.com/bsm/histogram/v3/.gitignore
@@ -0,0 +1 @@
+vendor/
diff --git a/vendor/github.com/bsm/histogram/v3/LICENSE b/vendor/github.com/bsm/histogram/v3/LICENSE
new file mode 100644
index 000000000..1625b8474
--- /dev/null
+++ b/vendor/github.com/bsm/histogram/v3/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/vendor/github.com/bsm/histogram/v3/Makefile b/vendor/github.com/bsm/histogram/v3/Makefile
new file mode 100644
index 000000000..f9073368b
--- /dev/null
+++ b/vendor/github.com/bsm/histogram/v3/Makefile
@@ -0,0 +1,10 @@
+default: test
+
+test:
+	go test ./...
+
+bench:
+	go test ./... -run=NONE -bench=. -benchmem
+
+lint:
+	golangci-lint run
diff --git a/vendor/github.com/bsm/histogram/v3/README.md b/vendor/github.com/bsm/histogram/v3/README.md
new file mode 100644
index 000000000..e52c241fd
--- /dev/null
+++ b/vendor/github.com/bsm/histogram/v3/README.md
@@ -0,0 +1,14 @@
+# Histogram
+
+[![Build Status](https://travis-ci.org/bsm/histogram.svg)](https://travis-ci.org/bsm/histogram) [![GoDoc](https://godoc.org/github.com/bsm/histogram?status.svg)](https://godoc.org/github.com/bsm/histogram)
+
+Fast Go implementation of Ben-Haim's and Yom-Tov's streaming histogram algorithm, as described in their _A Streaming Parallel Decision Tree Algorithm_
+(2010, [PDF](http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf)) paper.
+
+## Documentation
+
+Please see the [API documentation](https://godoc.org/github.com/bsm/histogram) for package and API descriptions and examples.
+
+## Credits
+
+- Aaron Windsor - https://github.com/aaw/histosketch (released into the public domain).
diff --git a/vendor/github.com/bsm/histogram/v3/bins.go b/vendor/github.com/bsm/histogram/v3/bins.go
new file mode 100644
index 000000000..41e9e9252
--- /dev/null
+++ b/vendor/github.com/bsm/histogram/v3/bins.go
@@ -0,0 +1,18 @@
+package histogram
+
+import "math"
+
+type bin struct {
+	w float64 // weight
+	v float64 // value
+}
+
+func (b bin) Sum() float64 { return math.Abs(b.w) * b.v }
+
+// ----------------------------------------------------------
+
+type binSlice []bin
+
+func (s binSlice) Len() int           { return len(s) }
+func (s binSlice) Less(i, j int) bool { return s[i].v < s[j].v }
+func (s binSlice) Swap(i, j int)      { s[i], s[j] = s[j], s[i] }
diff --git a/vendor/github.com/bsm/histogram/v3/histogram.go b/vendor/github.com/bsm/histogram/v3/histogram.go
new file mode 100644
index 000000000..c7c68bf74
--- /dev/null
+++ b/vendor/github.com/bsm/histogram/v3/histogram.go
@@ -0,0 +1,266 @@
+package histogram
+
+import (
+	"math"
+	"sort"
+)
+
+// Histogram is a probabilistic, fixed-size data structure, able to
+// accommodate massive data streams while predicting distributions
+// and quantiles much more accurately than a sample-based approach.
+//
+// Please note that a historgram is not thread-safe. All operations
+// must be protected by a mutex if used across multiple goroutines.
+type Histogram struct {
+	bins   []bin
+	size   int
+	weight float64
+
+	min, max float64
+}
+
+// New creates a new histogram with a maximum size.
+func New(sz int) *Histogram {
+	h := new(Histogram)
+	h.Reset(sz)
+	return h
+}
+
+// Reset resets the struct to its initial state with
+// a specific size.
+func (h *Histogram) Reset(sz int) {
+	if sz < cap(h.bins) {
+		h.bins = h.bins[:0]
+	} else {
+		h.bins = make([]bin, 0, sz+1)
+	}
+
+	h.size = sz
+	h.min = math.NaN()
+	h.max = math.NaN()
+	h.weight = 0
+}
+
+// Copy copies h to x and returns x. If x is passed as nil
+// a new Histogram will be inited.
+func (h *Histogram) Copy(x *Histogram) *Histogram {
+	if x == nil {
+		x = new(Histogram)
+	}
+	if sz := h.size; sz < cap(x.bins) {
+		x.bins = x.bins[:len(h.bins)]
+	} else {
+		x.bins = make([]bin, len(h.bins), sz+1)
+	}
+	copy(x.bins, h.bins)
+
+	x.size = h.size
+	x.min = h.min
+	x.max = h.max
+	x.weight = h.weight
+	return x
+}
+
+// Count returns the observed weight truncated to the next integer.
+func (h *Histogram) Count() int { return int(h.weight) }
+
+// Weight returns the observed weight (usually, the number of items seen).
+func (h *Histogram) Weight() float64 { return h.weight }
+
+// Min returns the smallest observed value.
+// Returns NaN if Count is zero.
+func (h *Histogram) Min() float64 {
+	if h.weight == 0 {
+		return math.NaN()
+	}
+	return h.min
+}
+
+// Max returns the largest observed value.
+// Returns NaN if Count is zero.
+func (h *Histogram) Max() float64 {
+	if h.weight == 0 {
+		return math.NaN()
+	}
+	return h.max
+}
+
+// Sum returns the (approximate) sum of all observed values.
+// Returns NaN if Count is zero.
+func (h *Histogram) Sum() float64 {
+	if h.weight == 0 {
+		return math.NaN()
+	}
+
+	var sum float64
+	for _, b := range h.bins {
+		sum += b.Sum()
+	}
+	return sum
+}
+
+// Mean returns the (approximate) average observed value.
+// Returns NaN if Count is zero.
+func (h *Histogram) Mean() float64 {
+	if h.weight == 0 {
+		return math.NaN()
+	}
+	return h.Sum() / h.weight
+}
+
+// Variance returns the (approximate) sample variance of the distribution.
+// Returns NaN if Count is zero.
+func (h *Histogram) Variance() float64 {
+	if h.weight <= 1 {
+		return math.NaN()
+	}
+
+	var vv float64
+	mean := h.Mean()
+	for _, b := range h.bins {
+		delta := mean - b.v
+		vv += delta * delta * b.w
+	}
+	return vv / (h.weight - 1)
+}
+
+// Quantile returns the (approximate) quantile of the distribution.
+// Accepted values for q are between 0.0 and 1.0.
+// Returns NaN if Count is zero or bad inputs.
+func (h *Histogram) Quantile(q float64) float64 {
+	if h.weight == 0 || q < 0.0 || q > 1.0 {
+		return math.NaN()
+	} else if q == 0.0 {
+		return h.min
+	} else if q == 1.0 {
+		return h.max
+	}
+
+	delta := q * h.weight
+	pos := 0
+	for w0 := 0.0; pos < len(h.bins); pos++ {
+		w1 := math.Abs(h.bins[pos].w) / 2.0
+		if delta-w1-w0 < 0 {
+			break
+		}
+		delta -= (w1 + w0)
+		w0 = w1
+	}
+
+	switch pos {
+	case 0: // lower bound
+		return h.solve(bin{v: h.min, w: 0}, h.bins[pos], delta)
+	case len(h.bins): // upper bound
+		return h.solve(h.bins[pos-1], bin{v: h.max, w: 0}, delta)
+	default:
+		return h.solve(h.bins[pos-1], h.bins[pos], delta)
+	}
+}
+
+// Add is the same as AddWeight(v, 1)
+func (h *Histogram) Add(v float64) { h.AddWeight(v, 1) }
+
+// AddN is the same as AddWeight(v, float64(n))
+func (h *Histogram) AddN(v float64, n int) { h.AddWeight(v, float64(n)) }
+
+// AddWeight adds observations of v with the weight w to the distribution.
+func (h *Histogram) AddWeight(v, w float64) {
+	if w <= 0 {
+		return
+	}
+	if h.weight == 0 || v < h.min {
+		h.min = v
+	}
+	if h.weight == 0 || v > h.max {
+		h.max = v
+	}
+
+	h.insert(v, w)
+	h.weight += w
+
+	h.prune()
+}
+
+// Merge sets h to the union x ∪ y.
+func (h *Histogram) Merge(x, y *Histogram) {
+	h.bins = append(h.bins[:0], x.bins...)
+	h.bins = append(h.bins, y.bins...)
+	sort.Sort(binSlice(h.bins))
+	h.prune()
+}
+
+// MergeWith sets h to the union h ∪ x.
+func (h *Histogram) MergeWith(x *Histogram) { h.Merge(h, x) }
+
+// NumBins returns bin (bucket) count.
+func (h *Histogram) NumBins() int { return len(h.bins) }
+
+// Bin returns bin (bucket) data.
+// Requested index must be 0 <= i < NumBins() or it will panic.
+func (h *Histogram) Bin(i int) (value, weight float64) {
+	b := h.bins[i]
+	return b.v, b.w
+}
+
+func (h *Histogram) solve(b1, b2 bin, delta float64) float64 {
+	w1, w2 := b1.w, b2.w
+
+	// return if both bins are exact (unmerged)
+	if w1 > 0 && w2 > 0 {
+		return b2.v
+	}
+
+	// normalise
+	w1, w2 = math.Abs(w1), math.Abs(w2)
+
+	// calculate multiplier
+	var z float64
+	if w1 == w2 {
+		z = delta / w1
+	} else {
+		a := 2 * (w2 - w1)
+		b := 2 * w1
+		z = (math.Sqrt(b*b+4*a*delta) - b) / a
+	}
+	return b1.v + (b2.v-b1.v)*z
+}
+
+func (h *Histogram) insert(v, w float64) {
+	pos := h.search(v)
+	if pos < len(h.bins) && h.bins[pos].v == v {
+		h.bins[pos].w += math.Copysign(w, h.bins[pos].w)
+		return
+	}
+
+	maxi := len(h.bins)
+	h.bins = h.bins[:len(h.bins)+1]
+	if pos != maxi {
+		copy(h.bins[pos+1:], h.bins[pos:])
+	}
+	h.bins[pos].w = w
+	h.bins[pos].v = v
+}
+
+func (h *Histogram) prune() {
+	for len(h.bins) > h.size {
+		delta := math.MaxFloat64
+		pos := 0
+		for i := 0; i < len(h.bins)-1; i++ {
+			b1, b2 := h.bins[i], h.bins[i+1]
+			if x := b2.v - b1.v; x < delta {
+				pos, delta = i, x
+			}
+		}
+
+		b1, b2 := h.bins[pos], h.bins[pos+1]
+		w := math.Abs(b1.w) + math.Abs(b2.w)
+		v := (b1.Sum() + b2.Sum()) / w
+		h.bins[pos+1].w = -w
+		h.bins[pos+1].v = v
+		h.bins = h.bins[:pos+copy(h.bins[pos:], h.bins[pos+1:])]
+	}
+}
+
+func (h *Histogram) search(v float64) int {
+	return sort.Search(len(h.bins), func(i int) bool { return h.bins[i].v >= v })
+}