# Accelerate the speed of selection in Random

Tossing 4 points on a circle at a time, and calculate the chance of all 4 points on the upper part of the circle. This is very simple by math:

$$\left( \frac{1}{2} \right)^4 = 0.0625$$

But I want do a simulation in Mathematica.

po = RandomVariate[UniformDistribution[{{0, 1}, {0, 2 Pi}}], 40000000];
f[m_, n_] := {m Cos[n], m Sin[n]};
Apply[f, po, {1}] // Partition[#, 4] & //
Select[#, AllTrue[Last /* GreaterEqualThan]] & //
Length // AbsoluteTiming


it takes around 140 seconds for this code to finish.

Is there a way to speed this up?

• Do not post unsearchable images of equations. Instead typeset using MathJax. – David G. Stork Jul 15 '20 at 4:18
• @DavidG.Stork I only know a few about Tex. Could you edit it for me? – kile Jul 15 '20 at 4:26

We get additional speed-up combining RandomPoint with UnitStep,Total and Min or Count:

n = 10^6;

SeedRandom;

RepeatedTiming[
Total[Min /@ UnitStep[RandomPoint[Disk[], {n/4, 4}][[All, All, 2]]]]]

{0.099, 15733}

SeedRandom;

RepeatedTiming[
Count @ Total[UnitStep[RandomPoint[Disk[], {n/4, 4}][[All, All, 2]]], {2}]]

{0.11, 15733}


versus the method from Bob Hanlon's answer:

SeedRandom;

RepeatedTiming[
RandomPoint[Disk[], n] // Partition[#, 4] & //
Select[#, AllTrue[Last /* GreaterEqualThan]] & // Length]

{0.8295, 15733}


It is more efficient to use RandomPoint and RandomPoint distributes the points uniformly over the region.

Clear["Global*"]

f[m_, n_] := {m Cos[n], m Sin[n]};

n = 80000;


To accurately compare the two methods, the point generation needs to be included in the timing.

RepeatedTiming[
po = RandomVariate[
UniformDistribution[{{0, 1}, {0, 2 Pi}}], n];
(Apply[f, po, {1}] // Partition[#, 4] & //
Select[#, AllTrue[Last /* GreaterEqualThan]] & // Length)/(n/4) // N]

(* {0.160, 0.06455} *)


Note that the points cluster near the origin.

ListPlot[f @@@ po, AspectRatio -> 1] RepeatedTiming[
((pp = RandomPoint[Disk[], n]) // Partition[#, 4] & //
Select[#, AllTrue[Last /* GreaterEqualThan]] & // Length)/(n/4) // N]

(* {0.0423, 0.0619} *)

ListPlot[pp, AspectRatio -> 1] • f[m_, n_] := {Sqrt[m] Cos[n], Sqrt[m] Sin[n]} fixes the distribution problem and it only slows it down by a small fraction but RandomPoint is still faster. When generating $10^6$ points in a vectorized fashion, I found that it took 0.020 seconds versus 0.0256 with RandomPoint, so then they are about equivalent. – C. E. Jul 15 '20 at 8:35
• @C.E. why can adding Sqrt` solve that problem? Can you explain? – kile Jul 16 '20 at 12:45
• @kile Have a look here. – C. E. Jul 16 '20 at 16:29