Data commit

Task/Monte-Carlo-methods/Dart/monte-carlo-methods.dart

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+import 'dart:async';
+import 'dart:html';
+import 'dart:math' show Random;
+
+// We changed 5 lines of code to make this sample nicer on
+// the web (so that the execution waits for animation frame,
+// the number gets updated in the DOM, and the program ends
+// after 500 iterations).
+
+main() async {
+  print('Compute π using the Monte Carlo method.');
+  var output = querySelector("#output");
+  await for (var estimate in computePi().take(500)) {
+    print('π ≅ $estimate');
+    output.text = estimate.toStringAsFixed(5);
+    await window.animationFrame;
+  }
+}
+
+/// Generates a stream of increasingly accurate estimates of π.
+Stream<double> computePi({int batch: 100000}) async* {
+  var total = 0;
+  var count = 0;
+  while (true) {
+    var points = generateRandom().take(batch);
+    var inside = points.where((p) => p.isInsideUnitCircle);
+    total += batch;
+    count += inside.length;
+    var ratio = count / total;
+    // Area of a circle is A = π⋅r², therefore π = A/r².
+    // So, when given random points with x ∈ <0,1>,
+    // y ∈ <0,1>, the ratio of those inside a unit circle
+    // should approach π / 4. Therefore, the value of π
+    // should be:
+    yield ratio * 4;
+  }
+}
+
+Iterable<Point> generateRandom([int seed]) sync* {
+  final random = new Random(seed);
+  while (true) {
+    yield new Point(random.nextDouble(), random.nextDouble());
+  }
+}
+
+class Point {
+  final double x, y;
+  const Point(this.x, this.y);
+  bool get isInsideUnitCircle => x * x + y * y <= 1;
+}