96 lines
1.7 KiB
Go
96 lines
1.7 KiB
Go
package onlinestats
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// https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
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// http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm
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import (
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"math"
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"sort"
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)
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// From NR
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// KS performs a Kolmogorov-Smirnov test for the two datasets, and returns the
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// p-value for the null hypothesis that the two sets come from the same distribution.
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func KS(data1, data2 []float64) float64 {
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sort.Float64s(data1)
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sort.Float64s(data2)
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for math.IsNaN(data1[0]) {
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data1 = data1[1:]
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}
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for math.IsNaN(data2[0]) {
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data2 = data2[1:]
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}
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n1, n2 := len(data1), len(data2)
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en1, en2 := float64(n1), float64(n2)
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var d float64
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var fn1, fn2 float64
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j1, j2 := 0, 0
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for j1 < n1 && j2 < n2 {
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d1 := data1[j1]
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d2 := data2[j2]
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if d1 <= d2 {
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for j1 < n1 && d1 == data1[j1] {
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j1++
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fn1 = float64(j1) / en1
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}
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}
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if d2 <= d1 {
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for j2 < n2 && d2 == data2[j2] {
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j2++
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fn2 = float64(j2) / en2
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}
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}
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if dt := math.Abs(fn2 - fn1); dt > d {
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d = dt
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}
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}
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en := math.Sqrt((en1 * en2) / (en1 + en2))
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// R and Octave don't use this approximation that NR does
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//return qks((en + 0.12 + 0.11/en) * d)
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return qks(en * d)
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}
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func qks(z float64) float64 {
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if z < 0. {
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panic("bad z in qks")
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}
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if z == 0. {
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return 1.
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}
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if z < 1.18 {
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return 1. - pks(z)
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}
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x := math.Exp(-2. * (z * z))
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return 2. * (x - math.Pow(x, 4) + math.Pow(x, 9))
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}
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func pks(z float64) float64 {
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if z < 0. {
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panic("bad z in KSdist")
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}
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if z == 0. {
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return 0.
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}
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if z < 1.18 {
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y := math.Exp(-1.23370055013616983 / (z * z))
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return 2.25675833419102515 * math.Sqrt(-math.Log(y)) * (y + math.Pow(y, 9) + math.Pow(y, 25) + math.Pow(y, 49))
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}
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x := math.Exp(-2. * (z * z))
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return 1. - 2.*(x-math.Pow(x, 4)+math.Pow(x, 9))
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}
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