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@ -439,11 +439,14 @@ func funcMinOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
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// === sum_over_time(Matrix parser.ValueTypeMatrix) Vector ===
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func funcSumOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
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return aggrOverTime(vals, enh, func(values []Point) float64 {
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var sum float64
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var sum, c float64
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for _, v := range values {
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sum += v.V
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sum, c = kahanSummationIter(v.V, sum, c)
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}
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return sum
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if math.IsInf(sum, 0) {
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return sum
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}
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return sum + c
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})
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}
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@ -675,23 +678,52 @@ func funcTimestamp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHe
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return enh.Out
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}
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func kahanSummation(samples []float64) float64 {
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sum, c := 0.0, 0.0
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for _, v := range samples {
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sum, c = kahanSummationIter(v, sum, c)
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}
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return sum + c
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}
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func kahanSummationIter(v, sum, c float64) (float64, float64) {
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t := sum + v
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// using Neumaier improvement, swap if next term larger than sum
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if math.Abs(sum) >= math.Abs(v) {
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c += (sum - t) + v
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} else {
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c += (v - t) + sum
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}
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sum = t
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return sum, c
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}
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// linearRegression performs a least-square linear regression analysis on the
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// provided SamplePairs. It returns the slope, and the intercept value at the
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// provided time.
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func linearRegression(samples []Point, interceptTime int64) (slope, intercept float64) {
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var (
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n float64
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sumX, sumY float64
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sumXY, sumX2 float64
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n float64
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sumX, cX float64
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sumY, cY float64
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sumXY, cXY float64
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sumX2, cX2 float64
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)
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for _, sample := range samples {
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x := float64(sample.T-interceptTime) / 1e3
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n += 1.0
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sumY += sample.V
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sumX += x
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sumXY += x * sample.V
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sumX2 += x * x
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x := float64(sample.T-interceptTime) / 1e3
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sumX, cX = kahanSummationIter(x, sumX, cX)
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sumY, cY = kahanSummationIter(sample.V, sumY, cY)
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sumXY, cXY = kahanSummationIter(x*sample.V, sumXY, cXY)
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sumX2, cX2 = kahanSummationIter(x*x, sumX2, cX2)
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}
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sumX = sumX + cX
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sumY = sumY + cY
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sumXY = sumXY + cXY
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sumX2 = sumX2 + cX2
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covXY := sumXY - sumX*sumY/n
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varX := sumX2 - sumX*sumX/n
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