@ai-on-browser/data-analysis-models
Version:
Data analysis model package without any dependencies
82 lines (78 loc) • 1.99 kB
JavaScript
/**
* Standardization
*/
export default class Standardization {
/**
* @param {number} [ddof] Delta Degrees of Freedom
*/
constructor(ddof = 0) {
this._ddof = ddof
}
/**
* Fit model.
* @param {number[] | Array<Array<number>>} x Training data
*/
fit(x) {
if (Array.isArray(x[0])) {
this._m = Array(x[0].length).fill(0)
for (let i = 0; i < x.length; i++) {
for (let k = 0; k < x[i].length; k++) {
this._m[k] += x[i][k]
}
}
this._m = this._m.map(v => v / x.length)
this._s = Array(x[0].length).fill(0)
for (let i = 0; i < x.length; i++) {
for (let k = 0; k < x[i].length; k++) {
this._s[k] += (x[i][k] - this._m[k]) ** 2
}
}
this._s = this._s.map(v => Math.sqrt(v / (x.length - this._ddof)))
} else {
this._m = x.reduce((s, v) => s + v, 0) / x.length
this._s = Math.sqrt(x.reduce((s, v) => s + (v - this._m) ** 2, 0) / (x.length - this._ddof))
}
}
/**
* Returns transformed values.
* @param {number[] | Array<Array<number>>} x Sample data
* @returns {number[] | Array<Array<number>>} Predicted values
*/
predict(x) {
return x.map(r => {
if (Array.isArray(r)) {
if (Array.isArray(this._m)) {
return r.map((v, i) => (v - this._m[i]) / this._s[i])
} else {
return r.map(v => (v - this._m) / this._s)
}
}
if (Array.isArray(this._m)) {
return (r - this._m[0]) / this._s[0]
} else {
return (r - this._m) / this._s
}
})
}
/**
* Returns inverse transformed values.
* @param {number[] | Array<Array<number>>} z Sample data
* @returns {number[] | Array<Array<number>>} Predicted values
*/
inverse(z) {
return z.map(r => {
if (Array.isArray(r)) {
if (Array.isArray(this._m)) {
return r.map((v, i) => v * this._s[i] + this._m[i])
} else {
return r.map(v => v * this._s + this._m)
}
}
if (Array.isArray(this._m)) {
return r * this._s[0] + this._m[0]
} else {
return r * this._s + this._m
}
})
}
}