Sync

Task/Monte-Carlo-methods/D/monte-carlo-methods-2.d

@@ -1,10 +1,9 @@
 import std.stdio, std.random, std.math, std.algorithm, std.range;
 
-bool isIn(int){ return hypot(uniform(0,1.0), uniform(0,1.0)) <= 1; }
-
-double pi(in int n) { return 4.0 * count!isIn(iota(n)) / n; }
+enum isIn = (int) => hypot(uniform(0, 1.0), uniform(0, 1.0)) <= 1;
+enum pi = (in int n)  => 4.0 * n.iota.count!isIn / n;
 
 void main() {
-    foreach (p; 1 .. 8)
+    foreach (immutable p; 1 .. 8)
         writefln("%10s: %07f", 10 ^^ p, pi(10 ^^ p));
 }

Task/Monte-Carlo-methods/Maxima/monte-carlo-methods.maxima

@@ -0,0 +1,10 @@
+load(distrib);
+approx_pi(n):= block(
+  [x: random_continuous_uniform(0, 1, n),
+   y: random_continuous_uniform(0, 1, n),
+   r, cin: 0],
+   r: x^2 + y^2,
+   for r0 in r do if r0<1 then cin: cin + 1,
+   4*cin/n);
+
+float(approx_pi(100));

Task/Monte-Carlo-methods/Perl-6/monte-carlo-methods.pl6

@@ -0,0 +1,9 @@
+my @random_distances := ([+] rand**2 xx 2) xx *;
+
+sub approximate_pi(Int $n) {
+    4 * @random_distances[^$n].grep(* < 1) / $n
+}
+
+say "Monte-Carlo π approximation:";
+say "$_ iterations:  ", approximate_pi $_
+    for 100, 1_000, 10_000;