mathjs/src/function/special/erf.js

import { deepMap } from '../../utils/collection'
import { sign } from '../../utils/number'
import { factory } from '../../utils/factory'

const name = 'erf'
const dependencies = [
  'typed'
]

export const createErf = /* #__PURE__ */ factory(name, dependencies, ({ typed }) => {
  /**
   * Compute the erf function of a value using a rational Chebyshev
   * approximations for different intervals of x.
   *
   * This is a translation of W. J. Cody's Fortran implementation from 1987
   * ( https://www.netlib.org/specfun/erf ). See the AMS publication
   * "Rational Chebyshev Approximations for the Error Function" by W. J. Cody
   * for an explanation of this process.
   *
   * For matrices, the function is evaluated element wise.
   *
   * Syntax:
   *
   *    math.erf(x)
   *
   * Examples:
   *
   *    math.erf(0.2)    // returns 0.22270258921047847
   *    math.erf(-0.5)   // returns -0.5204998778130465
   *    math.erf(4)      // returns 0.9999999845827421
   *
   * @param {number | Array | Matrix} x   A real number
   * @return {number | Array | Matrix}    The erf of `x`
   */
  return typed('name', {
    number: function (x) {
      const y = Math.abs(x)

      if (y >= MAX_NUM) {
        return sign(x)
      }
      if (y <= THRESH) {
        return sign(x) * erf1(y)
      }
      if (y <= 4.0) {
        return sign(x) * (1 - erfc2(y))
      }
      return sign(x) * (1 - erfc3(y))
    },

    'Array | Matrix': function (n) {
      return deepMap(n, this)
    }

    // TODO: For complex numbers, use the approximation for the Faddeeva function
    //  from "More Efficient Computation of the Complex Error Function" (AMS)

  })

  /**
   * Approximates the error function erf() for x <= 0.46875 using this function:
   *               n
   * erf(x) = x * sum (p_j * x^(2j)) / (q_j * x^(2j))
   *              j=0
   */
  function erf1 (y) {
    const ysq = y * y
    let xnum = P[0][4] * ysq
    let xden = ysq
    let i

    for (i = 0; i < 3; i += 1) {
      xnum = (xnum + P[0][i]) * ysq
      xden = (xden + Q[0][i]) * ysq
    }
    return y * (xnum + P[0][3]) / (xden + Q[0][3])
  }

  /**
   * Approximates the complement of the error function erfc() for
   * 0.46875 <= x <= 4.0 using this function:
   *                       n
   * erfc(x) = e^(-x^2) * sum (p_j * x^j) / (q_j * x^j)
   *                      j=0
   */
  function erfc2 (y) {
    let xnum = P[1][8] * y
    let xden = y
    let i

    for (i = 0; i < 7; i += 1) {
      xnum = (xnum + P[1][i]) * y
      xden = (xden + Q[1][i]) * y
    }
    const result = (xnum + P[1][7]) / (xden + Q[1][7])
    const ysq = parseInt(y * 16) / 16
    const del = (y - ysq) * (y + ysq)
    return Math.exp(-ysq * ysq) * Math.exp(-del) * result
  }

  /**
   * Approximates the complement of the error function erfc() for x > 4.0 using
   * this function:
   *
   * erfc(x) = (e^(-x^2) / x) * [ 1/sqrt(pi) +
   *               n
   *    1/(x^2) * sum (p_j * x^(-2j)) / (q_j * x^(-2j)) ]
   *              j=0
   */
  function erfc3 (y) {
    let ysq = 1 / (y * y)
    let xnum = P[2][5] * ysq
    let xden = ysq
    let i

    for (i = 0; i < 4; i += 1) {
      xnum = (xnum + P[2][i]) * ysq
      xden = (xden + Q[2][i]) * ysq
    }
    let result = ysq * (xnum + P[2][4]) / (xden + Q[2][4])
    result = (SQRPI - result) / y
    ysq = parseInt(y * 16) / 16
    const del = (y - ysq) * (y + ysq)
    return Math.exp(-ysq * ysq) * Math.exp(-del) * result
  }
})

/**
 * Upper bound for the first approximation interval, 0 <= x <= THRESH
 * @constant
 */
const THRESH = 0.46875

/**
 * Constant used by W. J. Cody's Fortran77 implementation to denote sqrt(pi)
 * @constant
 */
const SQRPI = 5.6418958354775628695e-1

/**
 * Coefficients for each term of the numerator sum (p_j) for each approximation
 * interval (see W. J. Cody's paper for more details)
 * @constant
 */
const P = [[
  3.16112374387056560e00, 1.13864154151050156e02,
  3.77485237685302021e02, 3.20937758913846947e03,
  1.85777706184603153e-1
], [
  5.64188496988670089e-1, 8.88314979438837594e00,
  6.61191906371416295e01, 2.98635138197400131e02,
  8.81952221241769090e02, 1.71204761263407058e03,
  2.05107837782607147e03, 1.23033935479799725e03,
  2.15311535474403846e-8
], [
  3.05326634961232344e-1, 3.60344899949804439e-1,
  1.25781726111229246e-1, 1.60837851487422766e-2,
  6.58749161529837803e-4, 1.63153871373020978e-2
]]

/**
 * Coefficients for each term of the denominator sum (q_j) for each approximation
 * interval (see W. J. Cody's paper for more details)
 * @constant
 */
const Q = [[
  2.36012909523441209e01, 2.44024637934444173e02,
  1.28261652607737228e03, 2.84423683343917062e03
], [
  1.57449261107098347e01, 1.17693950891312499e02,
  5.37181101862009858e02, 1.62138957456669019e03,
  3.29079923573345963e03, 4.36261909014324716e03,
  3.43936767414372164e03, 1.23033935480374942e03
], [
  2.56852019228982242e00, 1.87295284992346047e00,
  5.27905102951428412e-1, 6.05183413124413191e-2,
  2.33520497626869185e-3
]]

/**
 * Maximum/minimum safe numbers to input to erf() (in ES6+, this number is
 * Number.[MAX|MIN]_SAFE_INTEGER). erf() for all numbers beyond this limit will
 * return 1
 */
const MAX_NUM = Math.pow(2, 53)