/
ridge.js
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/
ridge.js
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import Matrix from '../util/matrix.js'
/**
* Ridge regressioin
*/
export class Ridge {
/**
* @param {number} [lambda] Regularization strength
*/
constructor(lambda = 0.1) {
this._w = null
this._lambda = lambda
}
/**
* Fit model.
*
* @param {Array<Array<number>>} x Training data
* @param {Array<Array<number>>} y Target values
*/
fit(x, y) {
x = Matrix.fromArray(x)
y = Matrix.fromArray(y)
const xtx = x.tDot(x)
for (let i = 0; i < xtx.rows; i++) {
xtx.addAt(i, i, this._lambda)
}
this._w = xtx.solve(x.t).dot(y)
}
/**
* Returns predicted values.
*
* @param {Array<Array<number>>} x Sample data
* @returns {Array<Array<number>>} Predicted values
*/
predict(x) {
x = Matrix.fromArray(x)
return x.dot(this._w).toArray()
}
/**
* Returns importances of the features.
*
* @returns {number[]} Importances
*/
importance() {
return this._w.value
}
}
/**
* Multiclass ridge regressioin
*/
export class MulticlassRidge {
/**
* @param {number} [lambda] Regularization strength
*/
constructor(lambda = 0.1) {
this._w = null
this._lambda = lambda
this._classes = []
}
/**
* Category list
*
* @type {*[]}
*/
get categories() {
return this._classes
}
/**
* Fit model.
*
* @param {Array<Array<number>>} x Training data
* @param {*[]} y Target values
*/
fit(x, y) {
x = Matrix.fromArray(x)
this._classes = [...new Set(y)]
const p = new Matrix(y.length, this._classes.length, -1)
for (let i = 0; i < y.length; i++) {
p.set(i, this._classes.indexOf(y[i]), 1)
}
const xtx = x.tDot(x)
for (let i = 0; i < xtx.rows; i++) {
xtx.addAt(i, i, this._lambda)
}
this._w = xtx.solve(x.t).dot(p)
}
/**
* Returns predicted values.
*
* @param {Array<Array<number>>} x Sample data
* @returns {*[]} Predicted values
*/
predict(x) {
x = Matrix.fromArray(x)
return x
.dot(this._w)
.argmax(1)
.value.map(i => this._classes[i])
}
/**
* Returns importances of the features.
*
* @returns {number[]} Importances
*/
importance() {
return this._w.toArray()
}
}
/**
* Kernel ridge regression
*/
export class KernelRidge {
/**
* @param {number} [lambda] Regularization strength
* @param {'gaussian' | { name: 'gaussian', s?: number } | function (number[], number[]): number} [kernel] Kernel name
*/
constructor(lambda = 0.1, kernel = 'gaussian') {
this._w = null
this._x = null
this._lambda = lambda
this._kernel = null
if (typeof kernel === 'function') {
this._kernel = (a, b) => kernel(a.value, b.value)
} else {
if (typeof kernel === 'string') {
kernel = { name: kernel }
}
const s = kernel.s ?? 1.0
this._kernel = (x, y, sigma = s) => {
const s = Matrix.sub(x, y).reduce((acc, v) => acc + v * v, 0)
return Math.exp(-s / sigma ** 2)
}
}
}
/**
* Fit model.
*
* @param {Array<Array<number>>} x Training data
* @param {Array<Array<number>>} y Target values
*/
fit(x, y) {
x = Matrix.fromArray(x)
y = Matrix.fromArray(y)
const K = new Matrix(x.rows, x.rows)
this._x = []
for (let i = 0; i < x.rows; i++) {
this._x.push(x.row(i))
K.set(i, i, this._kernel(this._x[i], this._x[i]) + this._lambda)
for (let j = 0; j < i; j++) {
const v = this._kernel(this._x[i], this._x[j])
K.set(i, j, v)
K.set(j, i, v)
}
}
this._w = K.solve(y)
}
/**
* Returns predicted values.
*
* @param {Array<Array<number>>} x Sample data
* @returns {Array<Array<number>>} Predicted values
*/
predict(x) {
x = Matrix.fromArray(x)
const K = new Matrix(x.rows, this._x.length)
for (let i = 0; i < x.rows; i++) {
const xi = x.row(i)
for (let j = 0; j < this._x.length; j++) {
const v = this._kernel(xi, this._x[j])
K.set(i, j, v)
}
}
return K.dot(this._w).toArray()
}
/**
* Returns importances of the features.
*
* @returns {number[]} Importances
*/
importance() {
return this._w.value
}
}