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# source:branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Problems.GrammaticalOptimization/SymbolicRegressionPoly10Problem.cs@11708

Last change on this file since 11708 was 11708, checked in by gkronber, 9 years ago

#2283: worked on bandits for grammatical optimization

File size: 2.5 KB
Line
1using System;
2using System.Collections.Generic;
3using System.Linq;
4using System.Security;
5using System.Security.AccessControl;
6using System.Text;
7
8namespace HeuristicLab.Problems.GrammaticalOptimization {
9  public class SymbolicRegressionPoly10Problem : IProblem {
10    private const string grammarString = @"
11G(E):
12E -> V | V+E | V-E | V*E | V/E | (E)
13V -> a .. j
14";
15
16
19
23
24    public SymbolicRegressionPoly10Problem() {
25      this.grammar = new Grammar(grammarString);
26      this.interpreter = new ExpressionInterpreter();
27
28      this.N = 500;
29      this.x = new double[N][];
30      this.y = new double[N];
31
32      GenerateData();
33    }
34
35    private void GenerateData() {
36      // generate data with fixed seed to make sure that data is always the same
37      var rand = new Random(31415);
38      for (int i = 0; i < N; i++) {
39        x[i] = new double[10];
40        for (int j = 0; j < 10; j++) {
41          x[i][j] = rand.NextDouble() * 2 - 1;
42        }
43        // poly-10 no noise
44        y[i] = x[i][0] * x[i][1] +
45               x[i][2] * x[i][3] +
46               x[i][4] * x[i][5] +
47               x[i][0] * x[i][6] * x[i][8] +
48               x[i][2] * x[i][5] * x[i][9];
49      }
50    }
51
52    public double GetBestKnownQuality(int maxLen) {
53      // for now only an upper bound is returned, ideally we have an R² of 1.0
54      // the optimal R² can only be reached for sentences of at least 23 symbols
55      return 1.0;
56    }
57
58    public IGrammar Grammar {
59      get { return grammar; }
60    }
61
62    public double Evaluate(string sentence) {
63      return RSq(y, Enumerable.Range(0, N).Select(i => interpreter.Interpret(sentence, x[i])).ToArray());
64    }
65
66    private double RSq(IEnumerable<double> xs, IEnumerable<double> ys) {
67      // two pass implementation, but we don't care
68      var meanX = xs.Average();
69      var meanY = ys.Average();
70
71      var s = 0.0;
72      var ssX = 0.0;
73      var ssY = 0.0;
74      foreach (var p in xs.Zip(ys, (x, y) => new { x, y })) {
75        s += (p.x - meanX) * (p.y - meanY);
76        ssX += Math.Pow(p.x - meanX, 2);
77        ssY += Math.Pow(p.y - meanY, 2);
78      }
79
80      if (s.IsAlmost(0)) return 0;
81      if (ssX.IsAlmost(0) || ssY.IsAlmost(0)) return 0;
82      return s * s / (ssX * ssY);
83    }
84  }
85}
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