monte carlo estimation of Pi

main.js

// a random number that includes both min and max
const trueRandom = (min = 0, max) => Math.random() * (max + Number.MIN_VALUE) + min;

// a class for tracking running average
const runningProportion = function(num = 1, den = 1) {
  // numerator
  this.num = num;
  // denominator
  this.den = den;

  this.add = (num, den) => {
    if (typeof num === 'number') this.num += num;
    if (typeof den === 'number') this.den += den;
    return this;
  }

  this.get = () => this.num / this.den;
}

// non blocking while loop so that we can get a ton of values without crashing the process

// convenient choice
const r = 0.5

// geometry
const distance = point => Math.sqrt(point.x**2 + point.y**2)

const inCircle = point => distance(point) < r

const randomPoint = () => ({ x: trueRandom(-r, r), y: trueRandom(-r, r) });
// The area of a circle of radius 0.5 is Pi / 4 and the area of a square of side length 1 is 1

// The assumption is that a random point in the square should have a probability of landing in the cirlce = area of circle / area of square = Pi/4 / 1 = Pi/4
// That assumption informs the following estimation of Pi:
// sample tons of random points inside the square
// track the ratio of points inside the circle to out, and use that to approximate Pi with the following equation: Pi / 4 = approximation => Pi = 4 * approximation
const monteCarlo = () => {
  const pro = new runningProportion();
  let i = 0;
  while(i < 1000000) {
    if(inCircle(randomPoint())) pro.add(1);
    else pro.add(0, 1);
    i++
  }
  return 4 * pro.get();
}

monteCarlo()