55 lines
1.4 KiB
Go
55 lines
1.4 KiB
Go
package onlinestats
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import (
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"math"
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)
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// http://www.drdobbs.com/tools/discontiguous-exponential-averaging/184410671
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type DEA struct {
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sumOfWeights float64
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sumOfData float64
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sumOfSquaredData float64
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previousTime float64
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alpha float64
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newDataWeightUpperBound float64
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}
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func NewDEA(alpha float64, maxDt float64) *DEA {
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return &DEA{
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alpha: alpha,
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newDataWeightUpperBound: 1 - math.Pow(alpha, float64(maxDt)),
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}
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}
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func (ew *DEA) Update(newData float64, t float64) {
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weightReductionFactor := math.Pow(ew.alpha, float64(t-ew.previousTime))
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newDataWeight := minf(1-weightReductionFactor, ew.newDataWeightUpperBound)
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ew.sumOfWeights = weightReductionFactor*ew.sumOfWeights + newDataWeight
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ew.sumOfData = weightReductionFactor*ew.sumOfData + newDataWeight*newData
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ew.sumOfSquaredData = weightReductionFactor*ew.sumOfSquaredData + (newDataWeight * (newData * newData))
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ew.previousTime = t
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}
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func (ew *DEA) CompletenessFraction(t float64) float64 {
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return math.Pow(ew.alpha, float64(t-ew.previousTime)*ew.sumOfWeights)
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}
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func (ew *DEA) Mean() float64 {
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return (ew.sumOfData / ew.sumOfWeights)
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}
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func (ew *DEA) Var() float64 {
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m := ew.Mean()
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return (ew.sumOfSquaredData/ew.sumOfWeights - m*m)
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}
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func (ew *DEA) Stddev() float64 {
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return math.Sqrt(ew.Var())
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}
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func minf(a, b float64) float64 {
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if a < b {
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return a
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}
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return b
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}
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