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rkof.js
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rkof.js
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/**
* Robust Kernel-based Outlier Factor
*/
export default class RKOF {
// RKOF: Robust Kernel-Based Local Outlier Detection
// http://www.nlpr.ia.ac.cn/2011papers/gjhy/gh116.pdf
/**
* @param {number} k Number of neighborhoods
* @param {number} h Smoothing parameter
* @param {number} alpha Sensitivity parameter
* @param {'gaussian' | 'epanechnikov' | 'volcano' | { name: 'gaussian' } | { name: 'epanechnikov' } | { name: 'volcano', beta?: number } | function (number[]): number} [kernel] Kernel name
*/
constructor(k, h, alpha, kernel = 'gaussian') {
this._k = k
this._h = h
this._alpha = alpha
this._s = 1
if (typeof kernel === 'function') {
this._kernel = kernel
} else {
if (typeof kernel === 'string') {
kernel = { name: kernel }
}
if (kernel.name === 'gaussian') {
this._kernel = x =>
Math.exp(-x.reduce((s, v) => s + v ** 2, 0) / 2) / Math.sqrt(2 * Math.PI) ** x.length
} else if (kernel.name === 'epanechnikov') {
this._kernel = x => {
const s2 = x.reduce((s, v) => s + v ** 2, 0)
return s2 > 1 ? 0 : (3 / 4) ** x.length * (1 - s2)
}
} else if (kernel.name === 'volcano') {
this._beta = kernel.beta ?? 1
this._kernel = x => {
const s2 = x.reduce((s, v) => s + v ** 2, 0)
return s2 <= 1 ? this._beta : this._beta * Math.exp(-s2 + 1)
}
}
}
}
_distance(a, b) {
return Math.sqrt(a.reduce((s, v, i) => s + (v - b[i]) ** 2, 0))
}
/**
* Returns anomaly degrees.
*
* @param {Array<Array<number>>} datas Training data
* @returns {number[]} Predicted values
*/
predict(datas) {
const n = datas.length
const d = []
for (let i = 0; i < n; i++) {
d[i] = []
d[i][i] = { d: 0, i }
for (let j = 0; j < i; j++) {
const dist = this._distance(datas[i], datas[j])
d[i][j] = { d: dist, i: j }
d[j][i] = { d: dist, i }
}
}
const kdist = []
let logg = 0
for (let i = 0; i < n; i++) {
d[i].sort((a, b) => a.d - b.d)
kdist[i] = d[i][this._k + 1].d
logg += Math.log(1 / kdist[i])
}
logg /= n
const g = Math.exp(logg)
const C = this._h * g ** this._alpha
const kde = []
const mink = []
for (let i = 0; i < n; i++) {
kde[i] = 0
mink[i] = Infinity
for (let k = 1; k <= this._k; k++) {
const dv = C * kdist[d[i][k].i] ** this._alpha
const x = datas[d[i][k].i].map((v, d) => (datas[i][d] - v) / dv)
kde[i] += this._kernel(x) / dv ** 2
mink[i] = Math.min(mink[i], kdist[d[i][k].i])
}
kde[i] /= this._k
}
const wde = []
for (let i = 0; i < n; i++) {
wde[i] = 0
let ws = 0
for (let k = 1; k <= this._k; k++) {
const w = Math.exp(-((kdist[d[i][k].i] / mink[i] - 1) ** 2) / (2 * this._s ** 2))
wde[i] += w * kde[d[i][k].i]
ws += w
}
wde[i] /= ws
}
const rkof = []
for (let i = 0; i < n; i++) {
if (kde[i] === 0) {
rkof[i] = Infinity
} else {
rkof[i] = wde[i] / kde[i]
}
}
return rkof
}
}