1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
|
/*
* This file is part of Go Responsiveness.
*
* Go Responsiveness is free software: you can redistribute it and/or modify it under
* the terms of the GNU General Public License as published by the Free Software Foundation,
* either version 2 of the License, or (at your option) any later version.
* Go Responsiveness is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
* PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along
* with Go Responsiveness. If not, see <https://www.gnu.org/licenses/>.
*/
package ms
import (
"math"
"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 {
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]{
elements: make([]T, instants_count),
elements_count: instants_count,
divisor: saturating.NewSaturatingInt(instants_count),
}
}
func (ma *MathematicalSeries[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 {
total := T(0)
for i := 0; i < ma.elements_count; i++ {
total += ma.elements[i]
}
return float64(total) / float64(ma.divisor.Value())
}
func (ma *MathematicalSeries[T]) AllSequentialIncreasesLessThan(limit float64) (_ bool, maximumSequentialIncrease float64) {
// If we have not yet accumulated a complete set of intervals,
// this is false.
if ma.divisor.Value() != ma.elements_count {
return false, 0
}
// Invariant: ma.index always points to the oldest (see AddMeasurement
// above)
oldestIndex := ma.index
previous := ma.elements[oldestIndex]
maximumSequentialIncrease = 0
for i := 1; i < ma.elements_count; i++ {
currentIndex := (oldestIndex + i) % ma.elements_count
current := ma.elements[currentIndex]
percentChange := utilities.SignedPercentDifference(current, previous)
previous = current
if percentChange > limit {
return false, percentChange
}
}
return true, maximumSequentialIncrease
}
func (ma *MathematicalSeries[T]) StandardDeviationLessThan(limit T) (bool, T) {
// If we have not yet accumulated a complete set of intervals,
// we are always false.
if ma.divisor.Value() != ma.elements_count {
return false, T(0)
}
// From https://www.mathsisfun.com/data/standard-deviation-calculator.html
// Yes, for real!
// Calculate the average of the numbers ...
average := ma.CalculateAverage()
// Calculate the square of each of the elements' differences from the mean.
differences_squared := make([]float64, ma.elements_count)
for index, value := range ma.elements {
differences_squared[index] = math.Pow(float64(value-T(average)), 2)
}
// The variance is the average of the squared differences.
// So, we need to ...
// Accumulate all those squared differences.
sds := float64(0)
for _, dss := range differences_squared {
sds += dss
}
// And then divide that total by the number of elements
variance := sds / float64(ma.divisor.Value())
// Finally, the standard deviation is the square root
// of the variance.
sd := T(math.Sqrt(variance))
return T(sd) < limit, sd
}
|
