linx-simulator2/node_modules/stats-lite/stats.js
2019-09-18 11:11:16 +03:00

211 lines
5.0 KiB
JavaScript

"use strict";
module.exports.numbers = numbers
module.exports.sum = sum
module.exports.mean = mean
module.exports.median = median
module.exports.mode = mode
module.exports.variance = populationVariance
module.exports.sampleVariance = sampleVariance
module.exports.populationVariance = populationVariance
module.exports.stdev = populationStdev
module.exports.sampleStdev = sampleStdev
module.exports.populationStdev = populationStdev
module.exports.percentile = percentile
module.exports.histogram = histogram
var isNumber = require("isnumber")
function numbers(vals) {
var nums = []
if (vals == null)
return nums
for (var i = 0; i < vals.length; i++) {
if (isNumber(vals[i]))
nums.push(+vals[i])
}
return nums
}
function nsort(vals) {
return vals.sort(function numericSort(a, b) { return a - b })
}
function sum(vals) {
vals = numbers(vals)
var total = 0
for (var i = 0; i < vals.length; i++) {
total += vals[i]
}
return total
}
function mean(vals) {
vals = numbers(vals)
if (vals.length === 0) return NaN
return (sum(vals) / vals.length)
}
function median(vals) {
vals = numbers(vals)
if (vals.length === 0) return NaN
var half = (vals.length / 2) | 0
vals = nsort(vals)
if (vals.length % 2) {
// Odd length, true middle element
return vals[half]
}
else {
// Even length, average middle two elements
return (vals[half-1] + vals[half]) / 2.0
}
}
// Returns the mode of a unimodal dataset
// If the dataset is multi-modal, returns a Set containing the modes
function mode(vals) {
vals = numbers(vals)
if (vals.length === 0) return NaN
var mode = NaN
var dist = {}
for (var i = 0; i < vals.length; i++) {
var value = vals[i]
var me = dist[value] || 0
me++
dist[value] = me
}
var rank = numbers(Object.keys(dist).sort(function sortMembers(a, b) { return dist[b] - dist[a] }))
mode = rank[0]
if (dist[rank[1]] == dist[mode]) {
// multi-modal
if (rank.length == vals.length) {
// all values are modes
return vals
}
var modes = new Set([mode])
var modeCount = dist[mode]
for (var i = 1; i < rank.length; i++) {
if (dist[rank[i]] == modeCount) {
modes.add(rank[i])
}
else {
break
}
}
return modes
}
return mode
}
// This helper finds the mean of all the values, then squares the difference
// from the mean for each value and returns the resulting array. This is the
// core of the varience functions - the difference being dividing by N or N-1.
function valuesMinusMeanSquared(vals) {
vals = numbers(vals)
var avg = mean(vals)
var diffs = []
for (var i = 0; i < vals.length; i++) {
diffs.push(Math.pow((vals[i] - avg), 2))
}
return diffs
}
// Population Variance = average squared deviation from mean
function populationVariance(vals) {
return mean(valuesMinusMeanSquared(vals))
}
// Sample Variance
function sampleVariance(vals) {
var diffs = valuesMinusMeanSquared(vals)
if (diffs.length <= 1) return NaN
return sum(diffs) / (diffs.length - 1)
}
// Population Standard Deviation = sqrt of population variance
function populationStdev(vals) {
return Math.sqrt(populationVariance(vals))
}
// Sample Standard Deviation = sqrt of sample variance
function sampleStdev(vals) {
return Math.sqrt(sampleVariance(vals))
}
function percentile(vals, ptile) {
vals = numbers(vals)
if (vals.length === 0 || ptile == null || ptile < 0) return NaN
// Fudge anything over 100 to 1.0
if (ptile > 1) ptile = 1
vals = nsort(vals)
var i = (vals.length * ptile) - 0.5
if ((i | 0) === i) return vals[i]
// interpolated percentile -- using Estimation method
var int_part = i | 0
var fract = i - int_part
return (1 - fract) * vals[int_part] + fract * vals[Math.min(int_part + 1, vals.length - 1)]
}
function histogram (vals, bins) {
if (vals == null) {
return null
}
vals = nsort(numbers(vals))
if (vals.length === 0) {
return null
}
if (bins == null) {
// pick bins by simple method: Math.sqrt(n)
bins = Math.sqrt(vals.length)
}
bins = Math.round(bins)
if (bins < 1) {
bins = 1
}
var min = vals[0]
var max = vals[vals.length - 1]
if (min === max) {
// fudge for non-variant data
min = min - 0.5
max = max + 0.5
}
var range = (max - min)
// make the bins slightly larger by expanding the range about 10%
// this helps with dumb floating point stuff
var binWidth = (range + (range * 0.05)) / bins
var midpoint = (min + max) / 2
// even bin count, midpoint makes an edge
var leftEdge = midpoint - (binWidth * Math.floor(bins / 2))
if (bins % 2 !== 0) {
// odd bin count, center middle bin on midpoint
var leftEdge = (midpoint - (binWidth / 2)) - (binWidth * Math.floor(bins / 2))
}
var hist = {
values: Array(bins).fill(0),
bins: bins,
binWidth: binWidth,
binLimits: [leftEdge, leftEdge + (binWidth * bins)]
}
var binIndex = 0
for (var i = 0; i < vals.length; i++) {
while (vals[i] > (((binIndex + 1) * binWidth) + leftEdge)) {
binIndex++
}
hist.values[binIndex]++
}
return hist
}