73 lines
1.5 KiB
Go
73 lines
1.5 KiB
Go
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package onlinestats
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import "math"
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// WindExp maintains a window of values, followed by a exponentially-weighted
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// region for the items that have left the window. This is similar to the
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// Average Loss Interval from "Equation-Based Congestion Control for Unicast
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// Applications" ( http://www.icir.org/tfrc/tcp-friendly.pdf ), but with lower
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// update cost. The advantage is that this maintains more local history, but
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// doesn't have the large changes when items leave the window.
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type WindExp struct {
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n int
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w *Windowed
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exp *ExpWeight
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}
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// NewWindExp returns WindExp with the specified window capacity and exponential alpha
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func NewWindExp(capacity int, alpha float64) *WindExp {
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return &WindExp{
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w: NewWindowed(capacity),
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exp: NewExpWeight(alpha),
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}
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}
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func (we *WindExp) Push(n float64) {
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old := we.w.Push(n)
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if we.n >= len(we.w.data) {
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we.exp.Push(old)
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}
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we.n++
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}
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func (we *WindExp) Len() int {
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return we.n
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}
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func (we *WindExp) Mean() float64 {
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// The math says this should be correct
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if we.n <= len(we.w.data) {
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return we.w.Mean()
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}
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return (we.w.sum + we.exp.Mean()) / float64(we.w.Len()+1)
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}
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func (we *WindExp) Var() float64 {
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// http://www.emathzone.com/tutorials/basic-statistics/combined-variance.html
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cmean := we.Mean()
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n1 := float64(we.w.Len())
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s1 := we.w.Var()
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x1xc := we.w.Mean() - cmean
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n2 := float64(we.exp.Len())
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s2 := we.exp.Var()
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x2xc := we.exp.Mean() - cmean
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cvar := (n1*(s1+x1xc*x1xc) + n2*(s2+x2xc*x2xc)) / (n1 + n2)
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return cvar
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}
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func (we *WindExp) Stddev() float64 {
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return math.Sqrt(we.Var())
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}
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