@ai-on-browser/data-analysis-models
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Data analysis model package without any dependencies
330 lines (308 loc) • 7.32 kB
JavaScript
/**
* Bsae class for k-means like model
*/
class KMeansBase {
constructor() {
this._centroids = []
}
/**
* Centroids
* @type {Array<Array<number>>}
*/
get centroids() {
return this._centroids
}
/**
* Number of clusters.
* @type {number}
*/
get size() {
return this._centroids.length
}
_distance(a, b) {
let v = 0
for (let i = a.length - 1; i >= 0; i--) {
v += (a[i] - b[i]) ** 2
}
return Math.sqrt(v)
}
/**
* Add a new cluster.
* @param {Array<Array<number>>} datas Training data
* @returns {number[]} Added centroid
*/
add(datas) {
const cpoint = this._add(this._centroids, datas)
this._centroids.push(cpoint)
return cpoint
}
/**
* Clear all clusters.
*/
clear() {
this._centroids = []
}
/**
* Returns predicted categories.
* @param {Array<Array<number>>} datas Sample data
* @returns {number[]} Predicted values
*/
predict(datas) {
if (this._centroids.length === 0) {
throw new Error('Call fit before predict.')
}
return datas.map(value => {
let mind = Infinity
let mini = -1
for (let i = 0; i < this._centroids.length; i++) {
const d = this._distance(value, this._centroids[i])
if (d < mind) {
mind = d
mini = i
}
}
return mini
})
}
/**
* Fit model and returns total distance the centroid has moved.
* @param {Array<Array<number>>} datas Training data
* @returns {number} Total distance the centroid has moved
*/
fit(datas) {
if (this._centroids.length === 0 || datas.length === 0) {
return 0
}
const oldCentroids = this._centroids
this._centroids = this._move(this._centroids, datas)
const d = oldCentroids.reduce((s, c, i) => s + this._distance(c, this._centroids[i]), 0)
return d
}
}
/**
* k-means model
*/
export class KMeans extends KMeansBase {
_distance(a, b) {
return Math.sqrt(a.reduce((s, v, i) => s + (v - b[i]) ** 2, 0))
}
/**
* Returns a new centroid.
* @param {Array<Array<number>>} centroids Centroids
* @param {Array<Array<number>>} datas Training data
* @returns {number[]} Added centroid
*/
_add(centroids, datas) {
while (true) {
const p = datas[Math.floor(Math.random() * datas.length)]
if (
Math.min.apply(
null,
centroids.map(c => this._distance(p, c))
) > 1.0e-8
) {
return p.concat()
}
}
}
_mean(d) {
const n = d.length
const t = d[0].length
const m = Array(t).fill(0)
for (let i = 0; i < n; i++) {
for (let k = 0; k < t; k++) {
m[k] += d[i][k]
}
}
return m.map(v => v / n)
}
/**
* Returns moved centroid positions.
* @param {Array<Array<number>>} centroids Centroids
* @param {Array<Array<number>>} datas Training data
* @returns {Array<Array<number>>} Moved centroids
*/
_move(centroids, datas) {
let pred = this.predict(datas)
return centroids.map((c, k) => {
let catpoints = datas.filter((v, i) => pred[i] === k)
return this._mean(catpoints)
})
}
}
/**
* k-means++ model
*/
export class KMeanspp extends KMeans {
/**
* Returns a new centroid.
* @param {Array<Array<number>>} centroids Centroids
* @param {Array<Array<number>>} datas Training data
* @returns {number[]} Added centroid
*/
_add(centroids, datas) {
if (centroids.length === 0) {
return datas[Math.floor(Math.random() * datas.length)]
}
const d = datas.map(
d =>
Math.min.apply(
null,
centroids.map(c => this._distance(d, c))
) ** 2
)
const s = d.reduce((acc, v) => acc + v, 0)
let r = Math.random() * s
for (var i = 0; i < d.length; i++) {
if (r < d[i]) {
return datas[i]
}
r -= d[i]
}
}
}
/**
* k-medoids model
*/
export class KMedoids extends KMeans {
/**
* Returns moved centroid positions.
* @param {Array<Array<number>>} centroids Centroids
* @param {Array<Array<number>>} datas Training data
* @returns {Array<Array<number>>} Moved centroids
*/
_move(centroids, datas) {
let pred = this.predict(datas)
return centroids.map((c, k) => {
let catpoints = datas.filter((v, i) => pred[i] === k)
if (catpoints.length > 0) {
let mind = Infinity
let mini = -1
for (let i = 0; i < catpoints.length; i++) {
let d = 0
for (let j = 0; j < catpoints.length; j++) {
d += this._distance(catpoints[i], catpoints[j])
}
if (d < mind) {
mind = d
mini = i
}
}
return catpoints[mini]
} else {
return c
}
})
}
}
/**
* k-medians model
*/
export class KMedians extends KMeans {
// https://en.wikipedia.org/wiki/K-medians_clustering
/**
* Returns moved centroid positions.
* @param {Array<Array<number>>} centroids Centroids
* @param {Array<Array<number>>} datas Training data
* @returns {Array<Array<number>>} Moved centroids
*/
_move(centroids, datas) {
const pred = this.predict(datas)
return centroids.map((c, k) => {
const catpoints = datas.filter((v, i) => pred[i] === k)
const dlen = catpoints.length
if (catpoints.length > 0) {
const cp = []
for (let i = 0; i < c.length; i++) {
const di = catpoints.map(v => v[i])
di.sort((a, b) => a - b)
if (dlen % 2 === 0) {
cp.push((di[dlen / 2] + di[dlen / 2 - 1]) / 2)
} else {
cp.push(di[(dlen - 1) / 2])
}
}
return cp
} else {
return c
}
})
}
}
/**
* semi-supervised k-means model
*/
export class SemiSupervisedKMeansModel extends KMeansBase {
// https://arxiv.org/abs/1307.0252
constructor() {
super()
}
/**
* Categories
* @type {*[]}
*/
get categories() {
return this._classes
}
_mean(d) {
const n = d.length
const t = d[0].length
const m = Array(t).fill(0)
for (let i = 0; i < n; i++) {
for (let k = 0; k < t; k++) {
m[k] += d[i][k]
}
}
return m.map(v => v / n)
}
/**
* Initialize model.
* @param {Array<Array<number>>} datas Training data
* @param {(* | null)[]} labels Target values
*/
init(datas, labels) {
this.clear()
this._classes = [...new Set(labels.filter(v => v != null))]
for (let k = 0; k < this._classes.length; k++) {
const labeledDatas = datas.filter((v, i) => labels[i] === this._classes[k])
this._centroids.push(this._mean(labeledDatas))
}
}
add() {}
/**
* Fit and returns total distance the centroid has moved.
* @param {Array<Array<number>>} datas Training data
* @param {(* | null)[]} labels Target values
* @returns {number} Total distance the centroid has moved
*/
fit(datas, labels) {
if (this._centroids.length === 0 || datas.length === 0) {
return 0
}
const oldCentroids = this._centroids
const pred = this.predict(datas)
for (let i = 0; i < labels.length; i++) {
const cidx = this._classes.indexOf(labels[i])
if (cidx >= 0) {
pred[i] = cidx
}
}
this._centroids = this._centroids.map((c, k) => {
const catpoints = datas.filter((v, i) => pred[i] === this._classes[k])
return this._mean(catpoints)
})
const d = oldCentroids.reduce((s, c, i) => s + this._distance(c, this._centroids[i]), 0)
return d
}
/**
* Returns predicted categories.
* @param {Array<Array<number>>} datas Sample data
* @returns {*[]} Predicted values
*/
predict(datas) {
if (this._centroids.length === 0) {
throw new Error('Call fit before predict.')
}
return super.predict(datas).map(v => this._classes[v])
}
}