/
squared_loss_mi.js
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/
squared_loss_mi.js
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import Matrix from '../util/matrix.js'
/**
* Squared-loss Mutual information change point detection
*/
export default class SquaredLossMICPD {
// http://www.ms.k.u-tokyo.ac.jp/sugi/2015/JRSJ-jp.pdf
/**
* @param {object} model Density ratio estimation model
* @param {function (Array<Array<number>>, Array<Array<number>>): void} model.fit Fit model
* @param {function (Array<Array<number>>): number[]} model.predict Returns predicted values
* @param {number} w Window size
* @param {number} [take] Take number
* @param {number} [lag] Lag
*/
constructor(model, w, take, lag) {
this._model = model
this._window = w
this._take = take || Math.max(1, Math.floor(w / 2))
this._lag = lag || Math.max(1, Math.floor(this._take / 2))
}
/**
* Returns anomaly degrees.
*
* @param {Array<Array<number>>} datas Sample data
* @returns {number[]} Predicted values
*/
predict(datas) {
const x = []
for (let i = 0; i < datas.length - this._window + 1; i++) {
x.push(datas.slice(i, i + this._window).flat())
}
const pred = []
for (let i = 0; i < x.length - this._take - this._lag + 1; i++) {
const h = Matrix.fromArray(x.slice(i, i + this._take))
const t = Matrix.fromArray(x.slice(i + this._lag, i + this._take + this._lag))
let c = 0
this._model.fit(h, t)
let dr = this._model.predict(t)
for (let i = 0; i < dr.length; i++) {
c += (dr[i] - 1) ** 2 / dr.length
}
this._model.fit(t, h)
dr = this._model.predict(h)
for (let i = 0; i < dr.length; i++) {
c += (dr[i] - 1) ** 2 / dr.length
}
pred.push(c)
}
return pred
}
}