Compute PI using montecarlo approach, i.e. using a random generator. / published by https://github.com/dacr/code-examples-manager #3cbc2540-3f21-4114-8218-7e461a470bb6/c12e49a86ce6c9111c2b00ce8f9f8f9834f880d0

testing-pi-calculus.sc

// summary : Compute PI using montecarlo approach, i.e. using a random generator.
// keywords : scala, scalatest, pi, random, algorithm, montecarlo, math, tailrec, @testable
// publish : gist
// authors : David Crosson
// license : Apache License Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0.txt)
// id : 3cbc2540-3f21-4114-8218-7e461a470bb6
// created-on : 2020-05-31T19:54:52Z
// managed-by : https://github.com/dacr/code-examples-manager
// run-with : scala-cli $file

// ---------------------
//> using scala "3.4.2"
//> using dep "org.scalatest::scalatest:3.2.16"
// ---------------------

import org.scalatest.*, flatspec.*, matchers.*

def monteCarloPI(iterations:Long=10000000L):Double = {
  @annotation.tailrec
  def worker(in:Long, out:Long, remainingIterations:Long):Double = {
    if (remainingIterations==0) 4d*in/(in+out)
    else {
      val x = Math.random()
      val y = Math.random()
      if (x * x + y * y > 1) worker(in, out + 1, remainingIterations - 1)
      else worker(in + 1, out, remainingIterations - 1)
    }
  }
  worker(0,0,iterations)
}


object pitest extends AnyFlatSpec with should.Matchers {
  "pi monte carlo calculus" should "return a precise enough pi value" in {
    monteCarloPI() shouldBe 3.14158d +- 0.001d
  }
}

pitest.execute()