@ai-on-browser/data-analysis-models
Version:
Data analysis model package without any dependencies
122 lines (110 loc) • 2.88 kB
JavaScript
const f = (n, xr, xi, s, q, d) => {
const m = n / 2
const th0 = (2 * Math.PI) / n
if (n > 1) {
for (let p = 0; p < m; p++) {
const wpr = Math.cos(p * th0)
const wpi = -Math.sin(p * th0)
const ar = xr[p + q]
const ai = xi[p + q]
const br = xr[p + q + m]
const bi = xi[p + q + m]
xr[p + q] = ar + br
xi[p + q] = ai + bi
xr[p + q + m] = (ar - br) * wpr - (ai - bi) * wpi
xi[p + q + m] = (ai - bi) * wpr + (ar - br) * wpi
}
f(n / 2, xr, xi, 2 * s, q, d)
f(n / 2, xr, xi, 2 * s, q + m, d + s)
} else if (q > d) {
;[xr[q], xr[d]] = [xr[d], xr[q]]
;[xi[q], xi[d]] = [xi[d], xi[q]]
}
}
const fft = (real, imag = null) => {
// http://wwwa.pikara.ne.jp/okojisan/stockham/cooley-tukey.html
const n = real.length
if (!Number.isInteger(Math.log2(n))) {
throw 'Invalid value length.'
}
if (!imag) {
imag = Array(n).fill(0)
}
f(n, real, imag, 1, 0, 0)
return [real, imag]
}
const ifft = (real, imag) => {
imag = imag.map(v => -v)
fft(real, imag)
real = real.map(v => v / real.length)
imag = imag.map(v => -v / real.length)
return [real, imag]
}
const dft = (real, imag = null) => {
// https://www.kazetest.com/vcmemo/dft/dft.htm
const n = real.length
if (!imag) {
imag = Array(n).fill(0)
}
const ar = Array(n).fill(0)
const ai = Array(n).fill(0)
const t = (-2 * Math.PI) / n
for (let i = 0; i < n; i++) {
for (let j = 0; j < n; j++) {
ar[i] += real[j] * Math.cos(j * i * t) - imag[j] * Math.sin(j * i * t)
ai[i] += real[j] * Math.sin(j * i * t) + imag[j] * Math.cos(j * i * t)
}
}
return [ar, ai]
}
const idft = (real, imag) => {
imag = imag.map(v => -v)
let [ar, ai] = dft(real, imag)
ar = ar.map(v => v / real.length)
ai = ai.map(v => -v / real.length)
return [ar, ai]
}
const ft = (real, imag = null) => {
const n = real.length
return Number.isInteger(Math.log2(n)) ? fft(real, imag) : dft(real, imag)
}
const ift = (real, imag) => {
const n = real.length
return Number.isInteger(Math.log2(n)) ? ifft(real, imag) : idft(real, imag)
}
/**
* Butterworth filter
*/
export default class ButterworthFilter {
// https://en.wikipedia.org/wiki/Butterworth_filter
/**
* @param {number} [n] Order
* @param {number} [c] Cutoff rate
*/
constructor(n = 2, c = 0.5) {
this._c = c
this._n = n
}
_cutoff(i, c, xr, xi) {
const d = Math.sqrt(1 + (i / c) ** (2 * this._n))
return [xr / d, xi / d]
}
/**
* Returns predicted datas.
* @param {number[]} x Training data
* @returns {number[]} Predicted values
*/
predict(x) {
const [fr, fi] = ft(x)
const m = x.length / 2
const c = Math.floor(m * (1 - this._c))
for (let i = 1; i <= m; i++) {
;[fr[i], fi[i]] = this._cutoff(i, c, fr[i], fi[i])
if (i !== m) {
;[fr[x.length - i], fi[x.length - i]] = this._cutoff(i, c, fr[x.length - i], fi[x.length - i])
}
}
const [rr] = ift(fr, fi)
return rr
}
}