Monte Carlo methods: Difference between revisions

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@@ Line 1,464: / Line 1,464: @@
 ===ES6===
 <lang JavaScript>(() => {
-    'use strict';
+    "use strict";
+    // --- APPROXIMATION OF PI BY A MONTE CARLO METHOD ---
     // monteCarloPi :: Int -> Float
     const monteCarloPi = n =>
-* range(1, n)
+* enumFromTo(1)(n).reduce(a => {
-        .reduce(a => {
             const [x, y] = [rnd(), rnd()];
-            return x * x + y * y < 1 ? a + 1 : a;
+            return (x * x) + (y * y) < 1 ? (
++ a
+            ) : a;
         }, 0) / n;
-    // GENERIC FUNCTIONS
+    // --------------------- GENERIC ---------------------
-    // range :: Int -> Int -> [Int]
+    // enumFromTo :: Int -> Int -> [Int]
-    const range = (m, n) =>
+    const enumFromTo = m =>
-        Array.from({
+        n => Array.from({
-            length: Math.floor(n - m) + 1
+            length: 1 + n - m
         }, (_, i) => m + i);
     // rnd :: () -> Float
@@ Line 1,487: / Line 1,492: @@
+    // ---------------------- TEST -----------------------
-    // TEST with from 1000 samples to 10E8 samples
+    // From 1000 samples to 10E7 samples
-    return range(3, 8)
+    return enumFromTo(3)(7).forEach(x => {
-        .map(x => monteCarloPi(Math.pow(10, x)));
+        const nSamples = 10 ** x;
+        console.log(
-    // e.g. -> [3.14, 3.1404, 3.13304, 3.142408, 3.1420304, 3.14156788]
+            `${nSamples} samples: ${monteCarloPi(nSamples)}`
-})();
+        );
-</lang>
+    });
+})();</lang>
+{{Out}} For example:
-{{Out}} (5 sample runs with increasing sample sizes)
+<pre>1000 samples: 3.064
-<lang JavaScript>[3.14, 3.1404, 3.13304, 3.142408, 3.1420304, 3.14156788]</lang>
+samples: 3.1416
+samples: 3.14756
+1000000 samples: 3.142536
+10000000 samples: 3.142808</pre>
 =={{header|jq}}==