Initial data commit

Task/Monte-Carlo-methods/JavaScript/monte-carlo-methods-1.js

@@ -0,0 +1,20 @@
+function mcpi(n) {
+    var x, y, m = 0;
+
+    for (var i = 0; i < n; i += 1) {
+        x = Math.random();
+        y = Math.random();
+
+        if (x * x + y * y < 1) {
+            m += 1;
+        }
+    }
+
+    return 4 * m / n;
+}
+
+console.log(mcpi(1000));
+console.log(mcpi(10000));
+console.log(mcpi(100000));
+console.log(mcpi(1000000));
+console.log(mcpi(10000000));

Task/Monte-Carlo-methods/JavaScript/monte-carlo-methods-2.js

@@ -0,0 +1,39 @@
+(() => {
+    "use strict";
+
+    // --- APPROXIMATION OF PI BY A MONTE CARLO METHOD ---
+
+    // monteCarloPi :: Int -> Float
+    const monteCarloPi = n =>
+        4 * enumFromTo(1)(n).reduce(a => {
+            const [x, y] = [rnd(), rnd()];
+
+            return (x ** 2) + (y ** 2) < 1 ? (
+                1 + a
+            ) : a;
+        }, 0) / n;
+
+
+    // --------------------- GENERIC ---------------------
+
+    // enumFromTo :: Int -> Int -> [Int]
+    const enumFromTo = m =>
+        n => Array.from({
+            length: 1 + n - m
+        }, (_, i) => m + i);
+
+
+    // rnd :: () -> Float
+    const rnd = Math.random;
+
+
+    // ---------------------- TEST -----------------------
+    // From 1000 samples to 10E7 samples
+    return enumFromTo(3)(7).forEach(x => {
+        const nSamples = 10 ** x;
+
+        console.log(
+            `${nSamples} samples: ${monteCarloPi(nSamples)}`
+        );
+    });
+})();