67 lines
1.9 KiB
Go
67 lines
1.9 KiB
Go
// Copyright 2015 The Go Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package stats
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import "math"
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// TODO: Implement histograms on top of scales.
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type Histogram interface {
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// Add adds a sample with value x to histogram h.
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Add(x float64)
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// Counts returns the number of samples less than the lowest
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// bin, a slice of the number of samples in each bin,
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// and the number of samples greater than the highest bin.
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Counts() (under uint, counts []uint, over uint)
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// BinToValue returns the value that would appear at the given
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// bin index.
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//
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// For integral values of bin, BinToValue returns the lower
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// bound of bin. That is, a sample value x will be in bin if
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// bin is integral and
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//
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// BinToValue(bin) <= x < BinToValue(bin + 1)
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//
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// For non-integral values of bin, BinToValue interpolates
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// between the lower and upper bounds of math.Floor(bin).
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//
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// BinToValue is undefined if bin > 1 + the number of bins.
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BinToValue(bin float64) float64
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}
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// HistogramQuantile returns the x such that n*q samples in hist are
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// <= x, assuming values are distibuted within each bin according to
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// hist's distribution.
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//
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// If the q'th sample falls below the lowest bin or above the highest
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// bin, returns NaN.
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func HistogramQuantile(hist Histogram, q float64) float64 {
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under, counts, over := hist.Counts()
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total := under + over
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for _, count := range counts {
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total += count
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}
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goal := uint(float64(total) * q)
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if goal <= under || goal > total-over {
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return math.NaN()
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}
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for bin, count := range counts {
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if count > goal {
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return hist.BinToValue(float64(bin) + float64(goal)/float64(count))
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}
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goal -= count
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}
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panic("goal count not reached")
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}
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// HistogramIQR returns the interquartile range of the samples in
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// hist.
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func HistogramIQR(hist Histogram) float64 {
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return HistogramQuantile(hist, 0.75) - HistogramQuantile(hist, 0.25)
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}
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