73 lines
2.1 KiB
Go
73 lines
2.1 KiB
Go
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package teststat
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import (
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"math"
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"math/rand"
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"github.com/go-kit/kit/metrics"
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)
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// PopulateNormalHistogram makes a series of normal random observations into the
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// histogram. The number of observations is determined by Count. The randomness
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// is determined by Mean, Stdev, and the seed parameter.
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//
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// This is a low-level function, exported only for metrics that don't perform
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// dynamic quantile computation, like a Prometheus Histogram (c.f. Summary). In
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// most cases, you don't need to use this function, and can use TestHistogram
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// instead.
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func PopulateNormalHistogram(h metrics.Histogram, seed int) {
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r := rand.New(rand.NewSource(int64(seed)))
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for i := 0; i < Count; i++ {
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sample := r.NormFloat64()*float64(Stdev) + float64(Mean)
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if sample < 0 {
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sample = 0
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}
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h.Observe(sample)
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}
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}
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func normalQuantiles() (p50, p90, p95, p99 float64) {
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return nvq(50), nvq(90), nvq(95), nvq(99)
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}
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func nvq(quantile int) float64 {
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// https://en.wikipedia.org/wiki/Normal_distribution#Quantile_function
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return float64(Mean) + float64(Stdev)*math.Sqrt2*erfinv(2*(float64(quantile)/100)-1)
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}
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func erfinv(y float64) float64 {
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// https://stackoverflow.com/questions/5971830/need-code-for-inverse-error-function
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if y < -1.0 || y > 1.0 {
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panic("invalid input")
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}
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var (
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a = [4]float64{0.886226899, -1.645349621, 0.914624893, -0.140543331}
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b = [4]float64{-2.118377725, 1.442710462, -0.329097515, 0.012229801}
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c = [4]float64{-1.970840454, -1.624906493, 3.429567803, 1.641345311}
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d = [2]float64{3.543889200, 1.637067800}
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)
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const y0 = 0.7
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var x, z float64
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if math.Abs(y) == 1.0 {
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x = -y * math.Log(0.0)
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} else if y < -y0 {
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z = math.Sqrt(-math.Log((1.0 + y) / 2.0))
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x = -(((c[3]*z+c[2])*z+c[1])*z + c[0]) / ((d[1]*z+d[0])*z + 1.0)
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} else {
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if y < y0 {
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z = y * y
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x = y * (((a[3]*z+a[2])*z+a[1])*z + a[0]) / ((((b[3]*z+b[3])*z+b[1])*z+b[0])*z + 1.0)
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} else {
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z = math.Sqrt(-math.Log((1.0 - y) / 2.0))
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x = (((c[3]*z+c[2])*z+c[1])*z + c[0]) / ((d[1]*z+d[0])*z + 1.0)
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}
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x = x - (math.Erf(x)-y)/(2.0/math.SqrtPi*math.Exp(-x*x))
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x = x - (math.Erf(x)-y)/(2.0/math.SqrtPi*math.Exp(-x*x))
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}
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return x
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}
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