/
histogram.js
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/
histogram.js
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import Matrix from '../util/matrix.js'
/**
* Histogram
*/
export default class Histogram {
/**
* @param {object} [config] Config
* @param {Array<Array<number>>} [config.range] Bin ranges
* @param {Array<[number, number]>} [config.domain] Domain of each dimension
* @param {number} [config.size] Bin size
* @param {number} [config.count] Bin count
* @param {'fd' | 'scott' | 'rice' | 'sturges' | 'doane' | 'sqrt'} [config.binMethod] Bin method
*/
constructor(config = {}) {
this._config = config
}
/**
* Returns histogram data.
*
* @param {Array<Array<number>>} datas Training data
* @returns {*[]} Predicted values. An array nested by the number of dimensions of the data
*/
fit(datas) {
let binRanges = this._config.range
if (!binRanges) {
let domain = this._config.domain
if (!domain) {
domain = datas[0].map(v => [v, v])
for (let i = 0; i < datas.length; i++) {
for (let d = 0; d < datas[i].length; d++) {
domain[d][0] = Math.min(datas[i][d], domain[d][0])
domain[d][1] = Math.max(datas[i][d], domain[d][1])
}
}
}
let size = this._config.size
let count = this._config.count
if (!size && !count) {
// https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html
const auto = this._config.binMethod || 'scott'
const x = Matrix.fromArray(datas)
const n = datas.length
if (auto === 'fd') {
const iqr = x.quantile(0.75, 0)
const q1 = x.quantile(0.25, 0)
iqr.sub(q1)
size = iqr.value.map(v => (2 * v) / Math.cbrt(n))
} else if (auto === 'scott') {
size = x.std(0).value.map(v => v * Math.cbrt((24 * Math.sqrt(Math.PI)) / n))
} else if (auto === 'rice') {
count = 2 * Math.cbrt(n)
} else if (auto === 'sturges') {
count = Math.log2(n) + 1
} else if (auto === 'doane') {
x.sub(x.mean(0))
x.div(x.std(0))
x.map(v => v ** 3)
count =
1 +
Math.log2(n) +
Math.log2(1 + Math.abs(x.mean()) / Math.sqrt((6 * (n - 1)) / ((n + 1) * (n + 3))))
} else if (auto === 'sqrt') {
count = Math.sqrt(n)
}
}
this._size = size
this._count = count
if (size) {
if (!Array.isArray(size)) {
size = Array(domain.length).fill(size)
}
binRanges = domain.map((r, k) => {
const [min, max] = r
const v = [min]
let i = 0
while (min + ++i * size[k] < max + size[k]) {
v.push(min + i * size[k])
}
return v
})
} else {
count = count || 10
if (!Array.isArray(count)) {
count = Array(domain.length).fill(count)
}
binRanges = domain.map((r, k) => {
const [min, max] = r
const d = (max - min) / count[k]
const v = [min]
for (let i = 1; i < count[k]; i++) {
v.push(min + i * d)
}
v.push(max)
return v
})
}
}
this._ranges = binRanges
const dense = []
const dat = []
let stack = [[dense, dat]]
for (let k = 0; k < this._ranges.length; k++) {
const nstack = []
const l = this._ranges[k].length
for (const [pdense, pdat] of stack) {
for (let i = 0; i < l - 1; i++) {
if (k === this._ranges.length - 1) {
pdense.push(0)
pdat.push([])
} else {
nstack.push([(pdense[i] = []), (pdat[i] = [])])
}
}
}
stack = nstack
}
for (const data of datas) {
let ds = dense
let dt = dat
for (let i = 0; i < data.length; i++) {
let k = 0
for (; k < this._ranges[i].length - 1; k++) {
if (data[i] <= this._ranges[i][k + 1]) {
break
}
}
if (i === data.length - 1) {
ds[k]++
dt[k].push(data)
} else {
ds = ds[k]
dt = dt[k]
}
}
}
this._separate_datas = dat
this._dense = dense
return dense
}
/**
* Returns predicted counted values.
*
* @param {Array<Array<number>>} datas Sample data
* @returns {number[]} Predicted values
*/
predict(datas) {
return datas.map(data => {
let ds = this._dense
for (let i = 0; i < data.length; i++) {
if (data[i] < this._ranges[i][0] || this._ranges[i][this._ranges[i].length - 1] < data[i]) {
return 0
}
let k = 0
for (; k < this._ranges[i].length - 1; k++) {
if (data[i] <= this._ranges[i][k + 1]) {
break
}
}
ds = ds[k]
}
return ds
})
}
}