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