I have list of string items of any length, I need to "normalize" this list so that each item is part of a normal distribution, appending the weight to the string.
What is more effective and mathematical/statistical way to go about this other than what I have below?
func normalizeAppend(in []string, shuffle bool) []string {
var ret []string
if shuffle {
shuffleStrings(in)
}
l := len(in)
switch {
case remain(l, 3) == 0:
l3 := (l / 3)
var low, mid, high []string
for i, v := range in {
o := i + 1
switch {
case o <= l3:
low = append(low, v)
case o > l3 && o <= l3*2:
mid = append(mid, v)
case o >= l3*2:
high = append(high, v)
}
}
q1 := 1600 / len(low)
q2 := 6800 / len(mid)
q3 := 1600 / len(high)
for _, v := range low {
ret = append(ret, fmt.Sprintf("%s_%d", v, q1))
}
for _, v := range mid {
ret = append(ret, fmt.Sprintf("%s_%d", v, q2))
}
for _, v := range high {
ret = append(ret, fmt.Sprintf("%s_%d", v, q3))
}
case remain(l, 2) == 0 && l >= 4:
l4 := (l / 4)
var first, second, third, fourth []string
for i, v := range in {
o := i + 1
switch {
case o <= l4:
first = append(first, v)
case o > l4 && o <= l4*2:
second = append(second, v)
case o > l4*2 && o <= l4*3:
third = append(third, v)
case o > l4*3:
fourth = append(fourth, v)
}
}
q1 := 1600 / len(first)
q2 := 3400 / len(second)
q3 := 3400 / len(third)
q4 := 1600 / len(fourth)
for _, v := range first {
ret = append(ret, fmt.Sprintf("%s_%d", v, q1))
}
for _, v := range second {
ret = append(ret, fmt.Sprintf("%s_%d", v, q2))
}
for _, v := range third {
ret = append(ret, fmt.Sprintf("%s_%d", v, q3))
}
for _, v := range fourth {
ret = append(ret, fmt.Sprintf("%s_%d", v, q4))
}
default:
var first, second, third []string
q1 := (1 + math.Floor(float64(l)*.16))
q3 := (float64(l) - math.Floor(float64(l)*.16))
var o float64
for i, v := range in {
o = float64(i + 1)
switch {
case o <= q1:
first = append(first, v)
case o > q1 && o < q3:
second = append(second, v)
case o >= q3:
third = append(third, v)
}
}
lq1 := 1600 / len(first)
lq2 := 3400 / len(second)
lq3 := 1600 / len(third)
for _, v := range first {
ret = append(ret, fmt.Sprintf("%s_%d", v, lq1))
}
for _, v := range second {
ret = append(ret, fmt.Sprintf("%s_%d", v, lq2))
}
for _, v := range third {
ret = append(ret, fmt.Sprintf("%s_%d", v, lq3))
}
}
return ret
}
Some requested clarification:
I have a list of items that will chosen from the list many times one at a time by weighted selection, to start with I have a list with (implied) weights of 1:
[a_1, b_1, c_1, d_1, e_1, f_1, g_1, h_1, i_1, j_1, k_1]
I'm looking for a better way to make that list into something producing a more 'normal' distribution of weighting for selection:
[a_1, b_2, c_3, d_5, e_14, f_30, g_14, h_5, i_3, j_2, k_1]
or perhaps it is likely I need to change my methods to something more grounded statistically. Bottom line is I want to control selection from a list of items in many ways, one of which here is ensuring that items are returned in way approximating a normal curve.