@ai-on-browser/data-analysis-models
Version:
Data analysis model package without any dependencies
49 lines (47 loc) • 1.25 kB
JavaScript
/**
* Cubic interpolation
*/
export default class CubicInterpolation {
// http://paulbourke.net/miscellaneous/interpolation/
/**
* Fit model parameters.
* @param {number[]} x Training data
* @param {number[]} y Target values
*/
fit(x, y) {
const d = x.map((v, i) => [v, y[i]])
d.sort((a, b) => a[0] - b[0])
this._x = d.map(v => v[0])
this._y = d.map(v => v[1])
}
/**
* Returns predicted interpolated values.
* @param {number[]} target Target values
* @returns {number[]} Predicted values
*/
predict(target) {
const n = this._x.length
return target.map(t => {
if (t <= this._x[0]) {
return this._y[0]
} else if (t >= this._x[n - 1]) {
return this._y[n - 1]
}
for (let i = 1; i < n; i++) {
if (t <= this._x[i]) {
const p = (t - this._x[i - 1]) / (this._x[i] - this._x[i - 1])
const y0 = i > 1 ? this._y[i - 2] : 2 * this._y[i - 1] - this._y[i]
const y1 = this._y[i - 1]
const y2 = this._y[i]
const y3 = i < n - 1 ? this._y[i + 1] : 2 * this._y[i] + this._y[i - 1]
const a0 = y3 - y2 - y0 + y1
const a1 = y0 - y1 - a0
const a2 = y2 - y0
const a3 = y1
return a0 * p ** 3 + a1 * p ** 2 + a2 * p + a3
}
}
return this._y[n - 1]
})
}
}