/
mira.js
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/
mira.js
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/**
* Margin Infused Relaxed Algorithm
*/
export default class MIRA {
// Ultraconservative Online Algorithms for Multiclass Problems
// https://www.jmlr.org/papers/volume3/crammer03a/crammer03a.pdf
// https://en.wikipedia.org/wiki/Margin-infused_relaxed_algorithm
// Online (and Offline) on an Even Tighter Budget
// http://proceedings.mlr.press/r5/weston05a/weston05a.pdf
constructor() {
this._w = null
this._b = 0
}
/**
* Update model parameters with one data.
*
* @param {number[]} x Training data
* @param {1 | -1} y Target value
*/
update(x, y) {
const m = x.reduce((s, v, j) => s + v * this._w[j], this._b)
const v = (-y * m) / x.reduce((s, v) => s + v ** 2, 0)
const tau = Math.max(0, Math.min(1, v))
if (tau > 0) {
for (let i = 0; i < this._w.length; i++) {
this._w[i] += tau * y * x[i]
this._b += tau * y
}
}
}
/**
* 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(1)
}
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 m = data[i].reduce((s, v, j) => s + v * this._w[j], 0)
p.push(m + this._b <= 0 ? -1 : 1)
}
return p
}
}