diff options
Diffstat (limited to 'ms/ms.go')
| -rw-r--r-- | ms/ms.go | 51 |
1 files changed, 41 insertions, 10 deletions
@@ -16,35 +16,48 @@ package ms import ( "math" + "sort" "github.com/network-quality/goresponsiveness/saturating" "github.com/network-quality/goresponsiveness/utilities" "golang.org/x/exp/constraints" ) -type MathematicalSeries[T constraints.Float | constraints.Integer] struct { +type MathematicalSeries[T constraints.Float | constraints.Integer] interface { + AddElement(T) + CalculateAverage() float64 + AllSequentialIncreasesLessThan(float64) (bool, float64) + StandardDeviation() (bool, T) + IsNormallyDistributed() bool + Size() int + Values() []T + Percentile(int) T +} + +type CappedMathematicalSeries[T constraints.Float | constraints.Integer] struct { elements_count int elements []T index int divisor *saturating.SaturatingInt } -func NewMathematicalSeries[T constraints.Float | constraints.Integer](instants_count int) *MathematicalSeries[T] { - return &MathematicalSeries[T]{ +func NewCappedMathematicalSeries[T constraints.Float | constraints.Integer](instants_count int) MathematicalSeries[T] { + return &CappedMathematicalSeries[T]{ elements: make([]T, instants_count), elements_count: instants_count, divisor: saturating.NewSaturatingInt(instants_count), + index: 0, } } -func (ma *MathematicalSeries[T]) AddElement(measurement T) { +func (ma *CappedMathematicalSeries[T]) AddElement(measurement T) { ma.elements[ma.index] = measurement ma.divisor.Add(1) // Invariant: ma.index always points to the oldest measurement ma.index = (ma.index + 1) % ma.elements_count } -func (ma *MathematicalSeries[T]) CalculateAverage() float64 { +func (ma *CappedMathematicalSeries[T]) CalculateAverage() float64 { total := T(0) for i := 0; i < ma.elements_count; i++ { total += ma.elements[i] @@ -52,7 +65,7 @@ func (ma *MathematicalSeries[T]) CalculateAverage() float64 { return float64(total) / float64(ma.divisor.Value()) } -func (ma *MathematicalSeries[T]) AllSequentialIncreasesLessThan(limit float64) (_ bool, maximumSequentialIncrease float64) { +func (ma *CappedMathematicalSeries[T]) AllSequentialIncreasesLessThan(limit float64) (_ bool, maximumSequentialIncrease float64) { // If we have not yet accumulated a complete set of intervals, // this is false. @@ -80,7 +93,7 @@ func (ma *MathematicalSeries[T]) AllSequentialIncreasesLessThan(limit float64) ( /* * N.B.: Overflow is possible -- use at your discretion! */ -func (ma *MathematicalSeries[T]) StandardDeviation() (bool, T) { +func (ma *CappedMathematicalSeries[T]) StandardDeviation() (bool, T) { // If we have not yet accumulated a complete set of intervals, // we are always false. @@ -120,7 +133,7 @@ func (ma *MathematicalSeries[T]) StandardDeviation() (bool, T) { return true, sd } -func (ma *MathematicalSeries[T]) IsNormallyDistributed() bool { +func (ma *CappedMathematicalSeries[T]) IsNormallyDistributed() bool { valid, stddev := ma.StandardDeviation() // If there are not enough values in our series to generate a standard // deviation, then we cannot do this calculation either. @@ -139,10 +152,28 @@ func (ma *MathematicalSeries[T]) IsNormallyDistributed() bool { return within/float64(ma.divisor.Value()) >= 0.68 } -func (ma *MathematicalSeries[T]) Values() []T { +func (ma *CappedMathematicalSeries[T]) Values() []T { return ma.elements } -func (ma *MathematicalSeries[T]) Size() int { +func (ma *CappedMathematicalSeries[T]) Size() int { return len(ma.elements) } + +func (ma *CappedMathematicalSeries[T]) Percentile(p int) (result T) { + result = T(0) + if p < 0 || p > 100 { + return + } + + // Because we need to sort the list to perform the percentile calculation, + // we have to make a copy of the list so that we don't disturb + // the time-relative ordering of the elements. + + kopy := make([]T, len(ma.elements)) + copy(kopy, ma.elements) + sort.Slice(kopy, func(l int, r int) bool { return kopy[l] < kopy[r] }) + pindex := int64((float64(p) / float64(100)) * float64(ma.elements_count)) + result = kopy[pindex] + return +} |