@ai-on-browser/data-analysis-models
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Data analysis model package without any dependencies
85 lines (77 loc) • 2.52 kB
JavaScript
const kernels = {
gaussian: x => Math.exp((-x * x) / 2) / Math.sqrt(2 * Math.PI),
rectangular: x => (Math.abs(x) <= 1 ? 0.5 : 0),
triangular: x => (Math.abs(x) <= 1 ? 1 - Math.abs(x) : 0),
epanechnikov: x => (Math.abs(x) <= 1 ? (3 * (1 - x ** 2)) / 4 : 0),
biweight: x => (Math.abs(x) <= 1 ? (15 / 16) * (1 - x ** 2) ** 2 : 0),
triweight: x => (Math.abs(x) <= 1 ? (35 / 32) * (1 - x ** 2) ** 3 : 0),
}
/**
* Kernel density estimator
*/
export default class KernelDensityEstimator {
// https://ja.wikipedia.org/wiki/%E3%82%AB%E3%83%BC%E3%83%8D%E3%83%AB%E5%AF%86%E5%BA%A6%E6%8E%A8%E5%AE%9A
// http://ibis.t.u-tokyo.ac.jp/suzuki/lecture/2015/dataanalysis/L9.pdf
/**
* @param {number} [h] Smoothing parameter for the kernel
* @param {'gaussian' | 'rectangular' | 'triangular' | 'epanechnikov' | 'biweight' | 'triweight' | { name: 'gaussian' } | { name: 'rectangular' } | { name: 'triangular' } | { name: 'epanechnikov' } | { name: 'biweight' } | { name: 'triweight' } | function (number): number} [kernel] Kernel name
*/
constructor(h = 0, kernel = 'gaussian') {
this._h = h
if (typeof kernel === 'function') {
this._kernel = kernel
} else {
if (typeof kernel === 'string') {
kernel = { name: kernel }
}
this._kernel = kernels[kernel.name]
}
}
/**
* Fit model.
* @param {Array<Array<number>>} x Training data
*/
fit(x) {
this._x = x
if (this._h > 0) {
return
}
// Silverman's method
const n = x.length
const k = x.map(d => Math.sqrt(d.reduce((s, v) => s + v ** 2, 0)))
const mean = k.reduce((s, v) => s + v, 0) / n
const std = Math.sqrt(k.reduce((s, v) => s + (v - mean) ** 2, 0) / n)
k.sort((a, b) => a - b)
const q = p => {
const np = (n - 1) * p
const np_l = Math.floor(np)
const np_h = Math.ceil(np)
return k[np_l] + (np - np_l) * (k[np_h] - k[np_l])
}
const sgm = Math.min(std, (q(0.75) - q(0.25)) / 1.34)
this._h = (1.06 * sgm) / n ** 0.2
}
/**
* Returns probabilities of the datas.
* @param {Array<Array<number>>} x Sample data
* @returns {number[]} Predicted values
*/
probability(x) {
const n = this._x.length
return x.map(d => {
let s = 0
for (let i = 0; i < n; i++) {
s += this._kernel(Math.sqrt(d.reduce((a, v, j) => a + (v - this._x[i][j]) ** 2, 0)) / this._h)
}
return s / (n * this._h)
})
}
/**
* Returns probabilities of the datas.
* @param {Array<Array<number>>} x Sample data
* @returns {number[]} Predicted values
*/
predict(x) {
return this.probability(x)
}
}