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