/
kernel_kmeans.js
69 lines (65 loc) · 1.46 KB
/
kernel_kmeans.js
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/**
* Kernel k-means
*/
export default class KernelKMeans {
// http://ibisforest.org/index.php?%E3%82%AB%E3%83%BC%E3%83%8D%E3%83%ABk-means%E6%B3%95
// https://research.miidas.jp/2019/07/kernel-kmeans%E3%81%AEnumpy%E5%AE%9F%E8%A3%85/
/**
* @param {number} [k] Number of clusters
*/
constructor(k = 3) {
this._k = k
this._kernel = (a, b) => Math.exp(-(a.reduce((s, v, i) => s + (v - b[i]) ** 2, 0) ** 2))
}
_distance(x, c) {
const cx = this._x.filter((v, i) => this._labels[i] === c)
let v = this._kernel(x, x)
for (let i = 0; i < cx.length; i++) {
v -= (2 * this._kernel(x, cx[i])) / cx.length
}
for (let i = 0; i < cx.length; i++) {
v += this._kernel(cx[i], cx[i]) / cx.length ** 2
for (let j = 0; j < i; j++) {
v += (2 * this._kernel(cx[i], cx[j])) / cx.length ** 2
}
}
return v
}
/**
* Initialize model.
*
* @param {Array<Array<number>>} datas Training data
*/
init(datas) {
this._x = datas
this._labels = []
for (let i = 0; i < this._x.length; i++) {
this._labels[i] = Math.floor(Math.random() * this._k)
}
}
/**
* Returns predicted categories.
*
* @returns {number[]} Predicted values
*/
predict() {
return this._labels
}
/**
* Fit model.
*/
fit() {
this._labels = this._x.map(value => {
let min_d = Infinity
let min_i = -1
for (let i = 0; i < this._k; i++) {
const d = this._distance(value, i)
if (d < min_d) {
min_d = d
min_i = i
}
}
return min_i
})
}
}