Line data    Source code

       1             : // Copyright 2023 The terCAD team. All rights reserved.
       2             : // Use of this source code is governed by a CC BY-NC-ND 4.0 license that can be found in the LICENSE file.
       3             : 
       4             : import 'dart:math';
       5             : 
       6             : import 'package:flutter/material.dart';
       7             : 
       8             : class MonteCarloSimulation {
       9             :   final Random random = Random();
      10             :   final int cycles;
      11             :   final double coercion;
      12             : 
      13           0 :   MonteCarloSimulation({this.cycles = 30, this.coercion = 1});
      14             : 
      15           0 :   List<Offset> generate(List<Offset> scope, double step, double max) {
      16           0 :     final List<List<double>> distribution = [];
      17           0 :     for (int i = 0; i < scope.length; i++) {
      18           0 :       final state = mcNormal(scope[i].dy, coercion, cycles);
      19           0 :       for (int j = 0; j < state.length; j++) {
      20           0 :         if (j >= distribution.length) {
      21           0 :           distribution.add([]);
      22             :         }
      23           0 :         distribution[j].add(state[j]);
      24             :       }
      25             :     }
      26           0 :     double posX = scope.last.dx + step;
      27           0 :     List<Offset> result = [];
      28             :     int idx = 0;
      29           0 :     while (posX <= max) {
      30           0 :       result.add(Offset(posX, distribution[idx][distribution[idx].length * random.nextDouble() ~/ 1]));
      31           0 :       posX += step;
      32           0 :       idx++;
      33             :     }
      34             :     return result;
      35             :   }
      36             : 
      37           0 :   List<double> mcNormal(double mean, double stdDev, int samples) {
      38           0 :     List<double> results = [];
      39           0 :     for (int i = 0; i < samples; i++) {
      40           0 :       results.add(_normalRandom(mean, stdDev));
      41             :     }
      42             :     return results;
      43             :   }
      44             : 
      45           0 :   double _normalRandom(double mean, double stdDev) {
      46           0 :     double u1 = random.nextDouble();
      47           0 :     double u2 = random.nextDouble();
      48           0 :     double z0 = sqrt(-2.0 * log(u1)) * cos(2 * pi * u2);
      49           0 :     return mean + stdDev * z0;
      50             :   }
      51             : }