# source:branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.Bandits/TruncatedNormalBandit.cs@11710

Last change on this file since 11710 was 11710, checked in by gkronber, 8 years ago

#2283: more bandit policies and tests

File size: 1.4 KB
Line
1using System;
2using System.Collections.Generic;
3using System.Linq;
4using System.Text;
6
7namespace HeuristicLab.Algorithms.Bandits {
8  public class TruncatedNormalBandit {
9    public int NumArms { get; private set; }
10    public double OptimalExpectedReward { get; private set; } // reward of the best arm, for calculating regret
13    public TruncatedNormalBandit(Random random, int nArms) {
14      this.random = random;
15      this.NumArms = nArms;
16      // expected reward of arms is iid and uniformly distributed
17      expReward = new double[nArms];
18      OptimalExpectedReward = double.NegativeInfinity;
19      for (int i = 0; i < nArms; i++) {
20        expReward[i] = random.NextDouble();
21        if (expReward[i] > OptimalExpectedReward) OptimalExpectedReward = expReward[i];
22      }
23    }
24
25    // pulling an arm results in a truncated normally distributed reward
26    // with mean expReward[i] and std.dev 0.1
27    public double Pull(int arm) {
28      double x = 0;
29      do {
30        var z = Transform(random.NextDouble(), random.NextDouble());
31        x = z * 0.1 + expReward[arm];
32      }
33      while (x < 0 || x > 1);
34      return x;
35    }
36
37    // box muller transform
38    private double Transform(double u1, double u2) {
39      return Math.Sqrt(-2 * Math.Log(u1)) * Math.Cos(2 * Math.PI * u2);
40    }
41  }
42}
Note: See TracBrowser for help on using the repository browser.