traefik/vendor/github.com/VividCortex/gohistogram/numerichistogram.go
2017-11-08 15:14:03 +01:00

160 lines
3.4 KiB
Go

package gohistogram
// Copyright (c) 2013 VividCortex, Inc. All rights reserved.
// Please see the LICENSE file for applicable license terms.
import (
"fmt"
)
type NumericHistogram struct {
bins []bin
maxbins int
total uint64
}
// NewHistogram returns a new NumericHistogram with a maximum of n bins.
//
// There is no "optimal" bin count, but somewhere between 20 and 80 bins
// should be sufficient.
func NewHistogram(n int) *NumericHistogram {
return &NumericHistogram{
bins: make([]bin, 0),
maxbins: n,
total: 0,
}
}
func (h *NumericHistogram) Add(n float64) {
defer h.trim()
h.total++
for i := range h.bins {
if h.bins[i].value == n {
h.bins[i].count++
return
}
if h.bins[i].value > n {
newbin := bin{value: n, count: 1}
head := append(make([]bin, 0), h.bins[0:i]...)
head = append(head, newbin)
tail := h.bins[i:]
h.bins = append(head, tail...)
return
}
}
h.bins = append(h.bins, bin{count: 1, value: n})
}
func (h *NumericHistogram) Quantile(q float64) float64 {
count := q * float64(h.total)
for i := range h.bins {
count -= float64(h.bins[i].count)
if count <= 0 {
return h.bins[i].value
}
}
return -1
}
// CDF returns the value of the cumulative distribution function
// at x
func (h *NumericHistogram) CDF(x float64) float64 {
count := 0.0
for i := range h.bins {
if h.bins[i].value <= x {
count += float64(h.bins[i].count)
}
}
return count / float64(h.total)
}
// Mean returns the sample mean of the distribution
func (h *NumericHistogram) Mean() float64 {
if h.total == 0 {
return 0
}
sum := 0.0
for i := range h.bins {
sum += h.bins[i].value * h.bins[i].count
}
return sum / float64(h.total)
}
// Variance returns the variance of the distribution
func (h *NumericHistogram) Variance() float64 {
if h.total == 0 {
return 0
}
sum := 0.0
mean := h.Mean()
for i := range h.bins {
sum += (h.bins[i].count * (h.bins[i].value - mean) * (h.bins[i].value - mean))
}
return sum / float64(h.total)
}
func (h *NumericHistogram) Count() float64 {
return float64(h.total)
}
// trim merges adjacent bins to decrease the bin count to the maximum value
func (h *NumericHistogram) trim() {
for len(h.bins) > h.maxbins {
// Find closest bins in terms of value
minDelta := 1e99
minDeltaIndex := 0
for i := range h.bins {
if i == 0 {
continue
}
if delta := h.bins[i].value - h.bins[i-1].value; delta < minDelta {
minDelta = delta
minDeltaIndex = i
}
}
// We need to merge bins minDeltaIndex-1 and minDeltaIndex
totalCount := h.bins[minDeltaIndex-1].count + h.bins[minDeltaIndex].count
mergedbin := bin{
value: (h.bins[minDeltaIndex-1].value*
h.bins[minDeltaIndex-1].count +
h.bins[minDeltaIndex].value*
h.bins[minDeltaIndex].count) /
totalCount, // weighted average
count: totalCount, // summed heights
}
head := append(make([]bin, 0), h.bins[0:minDeltaIndex-1]...)
tail := append([]bin{mergedbin}, h.bins[minDeltaIndex+1:]...)
h.bins = append(head, tail...)
}
}
// String returns a string reprentation of the histogram,
// which is useful for printing to a terminal.
func (h *NumericHistogram) String() (str string) {
str += fmt.Sprintln("Total:", h.total)
for i := range h.bins {
var bar string
for j := 0; j < int(float64(h.bins[i].count)/float64(h.total)*200); j++ {
bar += "."
}
str += fmt.Sprintln(h.bins[i].value, "\t", bar)
}
return
}