address stylistic nits
@@ -387,7 +387,7 @@ func funcAvgOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
continue
}
}
- mean, c = kahanSummationIter(v.V/count-mean/count, mean, c)
+ mean, c = kahanSumInc(v.V/count-mean/count, mean, c)
}
if math.IsInf(mean, 0) {
@@ -445,7 +445,7 @@ func funcSumOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
return aggrOverTime(vals, enh, func(values []Point) float64 {
var sum, c float64
for _, v := range values {
- sum, c = kahanSummationIter(v.V, sum, c)
+ sum, c = kahanSumInc(v.V, sum, c)
}
if math.IsInf(sum, 0) {
return sum
@@ -682,25 +682,24 @@ func funcTimestamp(vals []parser.Value, args parser.Expressions, enh *EvalNodeHe
return enh.Out
}
-func kahanSummation(samples []float64) float64 {
- sum, c := 0.0, 0.0
+func kahanSum(samples []float64) float64 {
+ var sum, c float64
for _, v := range samples {
- sum, c = kahanSummationIter(v, sum, c)
+ sum, c = kahanSumInc(v, sum, c)
}
return sum + c
}
-func kahanSummationIter(v, sum, c float64) (float64, float64) {
- t := sum + v
- // using Neumaier improvement, swap if next term larger than sum
- if math.Abs(sum) >= math.Abs(v) {
- c += (sum - t) + v
+func kahanSumInc(inc, sum, c float64) (newSum, newC float64) {
+ t := sum + inc
+ // Using Neumaier improvement, swap if next term larger than sum.
+ if math.Abs(sum) >= math.Abs(inc) {
+ c += (sum - t) + inc
} else {
- c += (v - t) + sum
+ c += (inc - t) + sum
}
- sum = t
- return sum, c
+ return t, c
}
// linearRegression performs a least-square linear regression analysis on the
@@ -717,10 +716,10 @@ func linearRegression(samples []Point, interceptTime int64) (slope, intercept fl
for _, sample := range samples {
n += 1.0
x := float64(sample.T-interceptTime) / 1e3
- sumX, cX = kahanSummationIter(x, sumX, cX)
- sumY, cY = kahanSummationIter(sample.V, sumY, cY)
- sumXY, cXY = kahanSummationIter(x*sample.V, sumXY, cXY)
- sumX2, cX2 = kahanSummationIter(x*x, sumX2, cX2)
+ sumX, cX = kahanSumInc(x, sumX, cX)
+ sumY, cY = kahanSumInc(sample.V, sumY, cY)
+ sumXY, cXY = kahanSumInc(x*sample.V, sumXY, cXY)
+ sumX2, cX2 = kahanSumInc(x*x, sumX2, cX2)
}
sumX = sumX + cX
