/
generalized_esd.js
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/
generalized_esd.js
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import Matrix from '../util/matrix.js'
const logGamma = z => {
// https://ja.wikipedia.org/wiki/%E3%82%AC%E3%83%B3%E3%83%9E%E9%96%A2%E6%95%B0
if (Number.isInteger(z) && z > 0) {
let x = 0
for (let i = 2; i < z; i++) {
x += Math.log(i)
}
return x
} else if (Number.isInteger(z - 0.5) && z > 0) {
const n = z - 0.5
let x = Math.log(Math.sqrt(Math.PI)) - Math.log(2) * n
for (let i = 2 * n - 1; i > 0; i -= 2) {
x += Math.log(i)
}
return x
}
throw ''
}
const beta = (p, q) => {
// https://www2.math.kyushu-u.ac.jp/~snii/AdvancedCalculus/7-1.pdf
// return gamma(p) * gamma(q) / gamma(p + q)
return Math.exp(logGamma(p) + logGamma(q) - logGamma(p + q))
}
const ibeta = (x, a, b) => {
// https://www.seijo.ac.jp/pdf/faeco/kenkyu/118/118-sekimoto.pdf
// https://github.com/LUXOPHIA/t-Distribution
if (x > (a + 1) / (a + b + 1)) {
return 1 - ibeta(1 - x, b, a)
}
let c0,
c1 = (-x * (a + b)) / (a + 1)
let u0,
u1 = 2,
u2 = 1 + c1 / u1
let v0,
v1 = 1,
v2 = 1 + c1 / v1
let g0 = 1,
g1 = (u1 / v1) * g0,
g2 = (u2 / v2) * g1
const e = 1.0e-12
for (let n = 1; n < 1000; n++) {
if (Math.abs(g2 - g0) < e) break
const n2a = n * 2 * a
c0 = (x * n * (b - n)) / (n2a * (n2a - 1))
c1 = (-x * (a + n) * (a + b + n)) / (n2a * (n2a + 1))
u0 = u2
u1 = 1 + c0 / u0
u2 = 1 + c1 / u1
v0 = v2
v1 = 1 + c0 / v0
v2 = 1 + c1 / v1
g0 = g2
g1 = (u1 / v1) * g0
g2 = (u2 / v2) * g1
}
return (x ** a * (1 - x) ** b * (g2 - 1)) / (a * beta(a, b))
}
const ct = (t, n) => {
// https://ja.wikipedia.org/wiki/T%E5%88%86%E5%B8%83#%E7%B4%AF%E7%A9%8D%E5%88%86%E5%B8%83%E9%96%A2%E6%95%B0
if (n === 1) {
return 1 / 2 + Math.atan(t) / Math.PI
} else if (n === 2) {
return (1 + t / Math.sqrt(2 + t ** 2)) / 2
}
const ttn = Math.sqrt(t ** 2 + n)
const x = (t + ttn) / (2 * ttn)
return ibeta(x, n / 2, n / 2)
}
const qt = (n, a) => {
if (Math.abs(a) > 1) {
throw "absolute of 'a' need less than or equals to 1."
}
if (Math.abs(a) === 1) {
return Math.sign(a) * Infinity
}
if (a > 0.5) {
return -qt(n, 1 - a)
}
a = 1 - a
let v = 1
let max = null,
min = 0
const e = 1.0e-5
let maxcount = 1.0e4
while (maxcount-- > 0) {
const c = ct(v, n)
if (isNaN(c)) {
return c
} else if (Math.abs(c - a) < e) {
return v
}
if (c < a) {
min = v
if (max === null) {
v *= 2
} else {
v = (v + max) / 2
}
} else {
max = v
v = (v + min) / 2
}
}
throw 'qt not converged.'
}
/**
* Generalized extreme studentized deviate
*/
export default class GeneralizedESD {
// https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm
/**
* @param {number} alpha Significance level
* @param {number} r Max number of outliers
*/
constructor(alpha, r) {
this._alpha = alpha
this._r = r
}
/**
* Returns a list of the data predicted as outliers or not.
*
* @param {Array<Array<number>>} data Training data
* @returns {boolean[]} Predicted values
*/
predict(data) {
const x = Matrix.fromArray(data)
const oidx = []
const r = []
const n = x.rows
for (let i = 1; i <= this._r; i++) {
const y = Matrix.sub(x, x.mean(0))
y.abs()
y.div(x.std(0))
const gs = y.max(1)
const gidx = gs.argmax(0).toScaler()
r.push(gs.at(gidx, 0))
x.remove(gidx)
oidx.push(gidx)
}
for (let i = oidx.length - 1; i >= 0; i--) {
for (let k = i + 1; k < oidx.length; k++) {
if (oidx[i] <= oidx[k]) oidx[k]++
}
}
for (let i = 1; i <= this._r; i++) {
const p = this._alpha / (2 * (n - i + 1))
const t = qt(n - i - 1, p)
const gc = ((n - i) * t) / Math.sqrt((n - i - 1 + t ** 2) * (n - i + 1))
if (r[i - 1] <= gc) {
oidx.splice(i - 1, this._r)
break
}
}
const outliers = Array(n).fill(false)
for (let i = 0; i < oidx.length; i++) {
outliers[oidx[i]] = true
}
return outliers
}
}