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romma.js
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romma.js
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/**
* Relaxed Online Maximum Margin Algorithm
*/
export class ROMMA {
// https://papers.nips.cc/paper/1999/file/515ab26c135e92ed8bf3a594d67e4ade-Paper.pdf
// The Relaxed Online Maximum Margin Algorithm.
// https://olpy.readthedocs.io/en/latest/_modules/olpy/classifiers/romma.html#ROMMA
constructor() {
this._w = null
this._w0 = 0
}
_mistake(m, y) {
return m * y <= 0
}
/**
* Update model parameters with one data.
*
* @param {number[]} x Training data
* @param {1 | -1} y Target value
*/
update(x, y) {
const m = this._w.reduce((s, v, d) => s + v * x[d], this._w0)
if (!this._mistake(m, y)) return
const wnorm = this._w.reduce((s, v) => s + v ** 2, this._w0 ** 2)
if (wnorm === 0) {
this._w = x.map(v => v * y)
this._w0 = y
return
}
const xwnorm = (x.reduce((s, v) => s + v ** 2, 0) + 1) * wnorm
const c = (xwnorm - y * m) / (xwnorm - m ** 2)
const d = (wnorm * (y - m)) / (xwnorm - m ** 2)
this._w = this._w.map((v, i) => v * c + x[i] * d)
this._w0 = this._w0 * c + d
}
/**
* Fit model parameters.
*
* @param {Array<Array<number>>} x Training data
* @param {Array<1 | -1>} y Target values
*/
fit(x, y) {
if (!this._w) {
this._w = Array(x[0].length).fill(0)
this._w0 = 0
}
for (let i = 0; i < x.length; i++) {
this.update(x[i], y[i])
}
}
/**
* Returns predicted datas.
*
* @param {Array<Array<number>>} data Sample data
* @returns {(1 | -1)[]} Predicted values
*/
predict(data) {
const p = []
for (let i = 0; i < data.length; i++) {
const r = data[i].reduce((s, v, d) => s + this._w[d] * v, this._w0)
p[i] = r <= 0 ? -1 : 1
}
return p
}
}
/**
* Aggressive Relaxed Online Maximum Margin Algorithm
*/
export class AggressiveROMMA extends ROMMA {
// https://papers.nips.cc/paper/1999/file/515ab26c135e92ed8bf3a594d67e4ade-Paper.pdf
// The Relaxed Online Maximum Margin Algorithm.
// https://olpy.readthedocs.io/en/latest/_modules/olpy/classifiers/aromma.html#aROMMA
_mistake(m, y) {
return m * y < 1
}
}