Check box-muller transform

Box-Muller.matlab

clear;
alpha = rand(1, 1000);
beta = rand(1, 1000);
val1 = sqrt(-2 * log(alpha)) .* sin(2 * pi * beta);
val2 = sqrt(-2 * log(alpha)) .* cos(2 * pi * beta);
hist([val1,val2], 20);

gaussian_random_ central_limit_theorem.js

"use strict";

function gaussianRandom(mean, std) {
  if (mean === undefined || std === undefined) {
    throw "Gaussian random needs 2 arguments (mean, standard deviation)";
  }
  return randByCentralLimitTheorem() * std + mean;
}

// 中心極限定理を使って標準正規分布N(0,1)を得る
function randByCentralLimitTheorem() {
  var v = 0;
  for (var i = 0; i < 12; i++) {
    v += Math.random();
  }
  return v - 6;
}

// Module export
module.exports = gaussianRandom;

gaussian_random_boxmuller.js

"use strict";

function gaussianRandom(mean, std) {
  if (mean === undefined || std === undefined) {
    throw "Gaussian random needs 2 arguments (mean, standard deviation)";
  }
  return randByBoxMullerTransform() * std + mean;
}

// Box Muller法を使って標準正規分布N(0,1)を得る
var randByBoxMullerTransform = (function() {
  var vals = [];

  function calc() {
    var alpha = Math.random(),
        beta = Math.random();
    return [
      Math.sqrt(-2 * Math.log(alpha)) * Math.sin(2 * Math.PI * beta),
      Math.sqrt(-2 * Math.log(alpha)) * Math.cos(2 * Math.PI * beta)
    ];
  }

  return function() {
    vals = vals.length == 0 ? calc() : vals;
    return vals.pop();
  }
})();


// Module export
module.exports = gaussianRandom;