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:material_ui/material_ui.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              : }