@ai-on-browser/data-analysis-models
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Data analysis model package without any dependencies
121 lines (112 loc) • 2.99 kB
JavaScript
/**
* Selective sampling Perceptron
*/
export class SelectiveSamplingPerceptron {
// Online Learning: A Comprehensive Survey
// https://arxiv.org/abs/1802.02871
// Worst-case analysis of selective sampling for linear classification.
// https://www.jmlr.org/papers/volume7/cesa-bianchi06b/cesa-bianchi06b.pdf
/**
* @param {number} b Smooth parameter
* @param {number} rate Learning rate
*/
constructor(b, rate) {
this._b = b
this._r = rate
this._w = null
this._c = 0
}
/**
* Fit model.
* @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)
}
for (let i = 0; i < x.length; i++) {
const pt = x[i].reduce((s, v, j) => s + v * this._w[j], this._c)
const yh = pt <= 0 ? -1 : 1
const z = Math.random() < this._b / (this._b + Math.abs(pt))
if (z && y[i] !== yh) {
for (let j = 0; j < x[i].length; j++) {
this._w[j] += this._r * y[i] * x[i][j]
}
this._c += this._r * y[i]
}
}
}
/**
* Returns predicted values.
* @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, j) => s + v * this._w[j], this._c)
p.push(r <= 0 ? -1 : 1)
}
return p
}
}
/**
* Selective sampling Perceptron with adaptive parameter
*/
export class SelectiveSamplingAdaptivePerceptron {
// Online Learning: A Comprehensive Survey
// https://arxiv.org/abs/1802.02871
// Worst-case analysis of selective sampling for linear classification.
// https://www.jmlr.org/papers/volume7/cesa-bianchi06b/cesa-bianchi06b.pdf
/**
* @param {number} beta Smooth parameter
* @param {number} rate Learning rate
*/
constructor(beta, rate) {
this._beta = beta
this._r = rate
this._w = null
this._c = 0
this._k = 0
this._X = 0
}
/**
* Fit model.
* @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)
}
for (let i = 0; i < x.length; i++) {
const pt = x[i].reduce((s, v, j) => s + v * this._w[j], this._c)
const yh = pt <= 0 ? -1 : 1
const xd = Math.max(this._X, Math.sqrt(x[i].reduce((s, v) => s + v ** 2, 0)))
const b = this._beta * xd ** 2 * Math.sqrt(1 + this._k)
const z = Math.random() < b / (b + Math.abs(pt))
if (z && y[i] !== yh) {
for (let j = 0; j < x[i].length; j++) {
this._w[j] += this._r * y[i] * x[i][j]
}
this._c += this._r * y[i]
this._k += 1
this._X = xd
}
}
}
/**
* Returns predicted values.
* @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, j) => s + v * this._w[j], this._c)
p.push(r <= 0 ? -1 : 1)
}
return p
}
}