File: /home/a/l/l/alloesb/www/wp-content/plugins/customify/js/vendor/regression.js
/**
* regression-js v2.0.1
* https://github.com/Tom-Alexander/regression-js
*
* @license MIT
* https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE
*/
(function (global, factory) {
if (typeof define === "function" && define.amd) {
define(['module'], factory);
} else if (typeof exports !== "undefined") {
factory(module);
} else {
const mod = {
exports: {}
}
factory(mod);
global.regression = mod.exports;
}
})(this, function (module) {
'use strict';
function _defineProperty(obj, key, value) {
if (key in obj) {
Object.defineProperty(obj, key, {
value: value,
enumerable: true,
configurable: true,
writable: true
});
} else {
obj[key] = value;
}
return obj;
}
const _extends = Object.assign || function (target) {
for (let i = 1; i < arguments.length; i++) {
const source = arguments[i]
for (let key in source) {
if (Object.prototype.hasOwnProperty.call(source, key)) {
target[key] = source[key]
}
}
}
return target
}
function _toConsumableArray(arr) {
if (Array.isArray(arr)) {
let arr2 = Array(arr.length)
for (let i = 0; i < arr.length; i++) {
arr2[i] = arr[i];
}
return arr2;
} else {
return Array.from(arr);
}
}
const DEFAULT_OPTIONS = {order: 2, precision: 2, period: null}
/**
* Determine the coefficient of determination (r^2) of a fit from the observations
* and predictions.
*
* @param {Array<Array<number>>} data - Pairs of observed x-y values
* @param {Array<Array<number>>} results - Pairs of observed predicted x-y values
*
* @return {number} - The r^2 value, or NaN if one cannot be calculated.
*/
function determinationCoefficient(data, results) {
const predictions = []
const observations = []
data.forEach(function (d, i) {
if (d[1] !== null) {
observations.push(d);
predictions.push(results[i]);
}
});
const sum = observations.reduce(function (a, observation) {
return a + observation[1]
}, 0)
const mean = sum / observations.length
const ssyy = observations.reduce(function (a, observation) {
const difference = observation[1] - mean
return a + difference * difference
}, 0)
const sse = observations.reduce(function (accum, observation, index) {
const prediction = predictions[index]
const residual = observation[1] - prediction[1]
return accum + residual * residual
}, 0)
return 1 - sse / ssyy;
}
/**
* Determine the solution of a system of linear equations A * x = b using
* Gaussian elimination.
*
* @param {Array<Array<number>>} input - A 2-d matrix of data in row-major form [ A | b ]
* @param {number} order - How many degrees to solve for
*
* @return {Array<number>} - Vector of normalized solution coefficients matrix (x)
*/
function gaussianElimination(input, order) {
const matrix = input
const n = input.length - 1
const coefficients = [order]
for (let i = 0; i < n; i++) {
let maxrow = i
for (let j = i + 1; j < n; j++) {
if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][maxrow])) {
maxrow = j;
}
}
for (let k = i; k < n + 1; k++) {
const tmp = matrix[k][i]
matrix[k][i] = matrix[k][maxrow];
matrix[k][maxrow] = tmp;
}
for (let _j = i + 1; _j < n; _j++) {
for (let _k = n; _k >= i; _k--) {
matrix[_k][_j] -= matrix[_k][i] * matrix[i][_j] / matrix[i][i];
}
}
}
for (let _j2 = n - 1; _j2 >= 0; _j2--) {
let total = 0
for (let _k2 = _j2 + 1; _k2 < n; _k2++) {
total += matrix[_k2][_j2] * coefficients[_k2];
}
coefficients[_j2] = (matrix[n][_j2] - total) / matrix[_j2][_j2];
}
return coefficients;
}
/**
* Round a number to a precision, specified in number of decimal places
*
* @param {number} number - The number to round
* @param {number} precision - The number of decimal places to round to:
* > 0 means decimals, < 0 means powers of 10
*
*
* @return {number} - The number, rounded
*/
function round(number, precision) {
const factor = Math.pow(10, precision)
return Math.round(number * factor) / factor;
}
/**
* The set of all fitting methods
*
* @namespace
*/
const methods = {
linear: function linear (data, options) {
const sum = [0, 0, 0, 0, 0]
let len = 0
for (let n = 0; n < data.length; n++) {
if (data[n][1] !== null) {
len++
sum[0] += data[n][0]
sum[1] += data[n][1]
sum[2] += data[n][0] * data[n][0]
sum[3] += data[n][0] * data[n][1]
sum[4] += data[n][1] * data[n][1]
}
}
const run = len * sum[2] - sum[0] * sum[0]
const rise = len * sum[3] - sum[0] * sum[1]
const gradient = run === 0 ? 0 : round(rise / run, options.precision)
const intercept = round(sum[1] / len - gradient * sum[0] / len, options.precision)
const predict = function predict (x) {
return [round(x, options.precision), round(gradient * x + intercept, options.precision)]
}
const points = data.map(function (point) {
return predict(point[0])
})
return {
points: points,
predict: predict,
equation: [gradient, intercept],
r2: round(determinationCoefficient(data, points), options.precision),
string: intercept === 0 ? 'y = ' + gradient + 'x' : 'y = ' + gradient + 'x + ' + intercept
}
},
exponential: function exponential (data, options) {
const sum = [0, 0, 0, 0, 0, 0]
for (var n = 0; n < data.length; n++) {
if (data[n][1] !== null) {
sum[0] += data[n][0]
sum[1] += data[n][1]
sum[2] += data[n][0] * data[n][0] * data[n][1]
sum[3] += data[n][1] * Math.log(data[n][1])
sum[4] += data[n][0] * data[n][1] * Math.log(data[n][1])
sum[5] += data[n][0] * data[n][1]
}
}
const denominator = sum[1] * sum[2] - sum[5] * sum[5]
const a = Math.exp((sum[2] * sum[3] - sum[5] * sum[4]) / denominator)
const b = (sum[1] * sum[4] - sum[5] * sum[3]) / denominator
const coeffA = round(a, options.precision)
const coeffB = round(b, options.precision)
const predict = function predict (x) {
return [round(x, options.precision), round(coeffA * Math.exp(coeffB * x), options.precision)]
}
const points = data.map(function (point) {
return predict(point[0])
})
return {
points: points,
predict: predict,
equation: [coeffA, coeffB],
string: 'y = ' + coeffA + 'e^(' + coeffB + 'x)',
r2: round(determinationCoefficient(data, points), options.precision)
}
},
logarithmic: function logarithmic (data, options) {
const sum = [0, 0, 0, 0]
const len = data.length
for (let n = 0; n < len; n++) {
if (data[n][1] !== null) {
sum[0] += Math.log(data[n][0])
sum[1] += data[n][1] * Math.log(data[n][0])
sum[2] += data[n][1]
sum[3] += Math.pow(Math.log(data[n][0]), 2)
}
}
const a = (len * sum[1] - sum[2] * sum[0]) / (len * sum[3] - sum[0] * sum[0])
const coeffB = round(a, options.precision)
const coeffA = round((sum[2] - coeffB * sum[0]) / len, options.precision)
const predict = function predict (x) {
return [round(x, options.precision), round(round(coeffA + coeffB * Math.log(x), options.precision), options.precision)]
}
const points = data.map(function (point) {
return predict(point[0])
})
return {
points: points,
predict: predict,
equation: [coeffA, coeffB],
string: 'y = ' + coeffA + ' + ' + coeffB + ' ln(x)',
r2: round(determinationCoefficient(data, points), options.precision)
}
},
power: function power (data, options) {
const sum = [0, 0, 0, 0, 0]
const len = data.length
for (let n = 0; n < len; n++) {
if (data[n][1] !== null) {
sum[0] += Math.log(data[n][0])
sum[1] += Math.log(data[n][1]) * Math.log(data[n][0])
sum[2] += Math.log(data[n][1])
sum[3] += Math.pow(Math.log(data[n][0]), 2)
}
}
const b = (len * sum[1] - sum[0] * sum[2]) / (len * sum[3] - Math.pow(sum[0], 2))
const a = (sum[2] - b * sum[0]) / len
const coeffA = round(Math.exp(a), options.precision)
const coeffB = round(b, options.precision)
const predict = function predict (x) {
return [round(x, options.precision), round(round(coeffA * Math.pow(x, coeffB), options.precision), options.precision)]
}
const points = data.map(function (point) {
return predict(point[0])
})
return {
points: points,
predict: predict,
equation: [coeffA, coeffB],
string: 'y = ' + coeffA + 'x^' + coeffB,
r2: round(determinationCoefficient(data, points), options.precision)
}
},
polynomial: function polynomial (data, options) {
const lhs = []
const rhs = []
let a = 0
let b = 0
const len = data.length
const k = options.order + 1
for (let i = 0; i < k; i++) {
for (let l = 0; l < len; l++) {
if (data[l][1] !== null) {
a += Math.pow(data[l][0], i) * data[l][1]
}
}
lhs.push(a)
a = 0
const c = []
for (let j = 0; j < k; j++) {
for (let _l = 0; _l < len; _l++) {
if (data[_l][1] !== null) {
b += Math.pow(data[_l][0], i + j)
}
}
c.push(b)
b = 0
}
rhs.push(c)
}
rhs.push(lhs)
const coefficients = gaussianElimination(rhs, k).map(function (v) {
return round(v, options.precision)
})
const predict = function predict (x) {
return [round(x, options.precision), round(coefficients.reduce(function (sum, coeff, power) {
return sum + coeff * Math.pow(x, power)
}, 0), options.precision)]
}
const points = data.map(function (point) {
return predict(point[0])
})
let string = 'y = '
for (let _i = coefficients.length - 1; _i >= 0; _i--) {
if (_i > 1) {
string += coefficients[_i] + 'x^' + _i + ' + '
} else if (_i === 1) {
string += coefficients[_i] + 'x + '
} else {
string += coefficients[_i]
}
}
return {
string: string,
points: points,
predict: predict,
equation: [].concat(_toConsumableArray(coefficients)).reverse(),
r2: round(determinationCoefficient(data, points), options.precision)
}
}
}
function createWrapper() {
const reduce = function reduce (accumulator, name) {
return _extends({
_round: round
}, accumulator, _defineProperty({}, name, function (data, supplied) {
return methods[name](data, _extends({}, DEFAULT_OPTIONS, supplied))
}))
}
return Object.keys(methods).reduce(reduce, {});
}
module.exports = createWrapper();
});