Welford Variance
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const sum = xs => xs.reduce((a, b) => a + b, 0)
const stats = data => data.reduce(
({count, sum, mean, vari, min, max}, x) => {
const k = 1 / (count + 1)
const mean_ = mean + k * (x - mean)
const ssr_ = (count - 1) * vari + k * count * (x - mean) * (x - mean)
return {
count: count + 1
, sum: sum + x
, mean: mean_
, vari: ssr_ / Math.max(1, count)
, min: isNaN(min) || x < min ? x : min
, max: isNaN(max) || x > max ? x : max
}
}
, {count: 0, sum: 0, mean: 0, vari: 0, min: NaN, max: NaN}
)
const stats_classic = data => {
const dsum = sum(data)
const dmean = dsum / data.length
const sum_sq_dist = sum(data.map(d => (d - dmean) * (d - dmean)))
return {
count: data.length,
sum: dsum,
mean: dmean,
vari: sum_sq_dist / data.length,
tvari: sum_sq_dist / (data.length - 1)
}
}
console.log(
stats([3,4,2,2,4,3,2,2,-3,4,5])
)
console.log(
stats_classic([3,4,2,2,4,3,2,2,-3,4,5])
)
const fromPairs = pairs => pairs.reduce((acc, [k, v]) => { acc[k] = v; return acc } , {})
const statsp = props => data => data.reduce(
(acc, a) => fromPairs(props.map(p => {
const {count, sum, mean, vari, min, max} = acc[p]
const x = a[p]
const k = 1 / (count + 1)
const mean_ = mean + k * (x - mean)
const ssr_ = (count - 1) * vari + k * count * (x - mean) * (x - mean)
return [p, {
count: count + 1
, sum: sum + x
, mean: mean_
, vari: ssr_ / Math.max(1, count)
, min: isNaN(min) || x < min ? x : min
, max: isNaN(max) || x > max ? x : max
}]
}))
, fromPairs(props.map(p => [p, {count: 0, sum: 0, mean: 0, vari: 0, min: NaN, max: NaN}]))
)
const props = ['views']
console.log(
statsp(props)([3,4,2,2,4,3,2,2,4,5].map(views => ({views})))
)
JavaScript
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