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source: branches/HeuristicLab.Problems.GPDL/Examples/symbreg HEAL.txt @ 10086

Last change on this file since 10086 was 10086, checked in by gkronber, 9 years ago

#2026 worked on random search solver (now all examples are working)

File size: 4.0 KB
Line 
1PROBLEM SymbReg
2
3CODE <<
4  double[,] x;
5  double[] y;
6  string[] variableNames;
7  int[] rows;
8  Dictionary<string,int> nameToCol;
9 
10  double GetValue(double[,] data, string varName, int row) {
11    if(nameToCol == null) {
12      /* init mapping */
13      nameToCol = new Dictionary<string, int>();
14      for(int i=0; i<variableNames.Length; i++) {
15        nameToCol[variableNames[i]] = i;
16      }
17    }
18    return x[row, nameToCol[varName]];
19  }
20
21  double RSquared(IEnumerable<double> xs, IEnumerable<double> ys) {
22    // calculate Pearson's correlation in one pass over xs and ys
23    double sumx = 0.0;
24    double sumy = 0.0;
25    double sumxSq = 0.0;
26    double sumySq = 0.0;
27    double sumxy = 0.0;
28    int n = 0;
29    var xEnum = xs.GetEnumerator();
30    var yEnum = ys.GetEnumerator();
31    while(xEnum.MoveNext() & yEnum.MoveNext()) {
32      sumx += xEnum.Current;
33      sumy += yEnum.Current;
34      sumxSq += xEnum.Current * xEnum.Current;
35      sumySq += yEnum.Current * yEnum.Current;
36      sumxy += xEnum.Current * yEnum.Current;
37      n++;
38    }
39    System.Diagnostics.Debug.Assert(!(xEnum.MoveNext() | yEnum.MoveNext()));
40
41    double num;
42    double den;
43    double r = 0.0;
44    num = sumxy - ( ( sumx * sumy ) / n );
45    den = Math.Sqrt( ( sumxSq - ( sumx*sumx ) / n ) *
46                     ( sumySq - ( sumy*sumy ) / n ) );
47    if(den > 0){
48      r = num / den;
49    }
50    return r*r;
51  }
52>>
53
54INIT <<
55  // generate 500 case of poly-10 benchmark function
56  int n = 500;
57  variableNames = new string[] {"x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10" };
58  var rand = new System.Random();
59  x = new double[n, 10];
60  y = new double[n];
61  for(int row = 0; row < n; row++) {
62    for(int col = 0; col < 10; col++) {
63      x[row, col] = rand.NextDouble() * 2.0 - 1.0;
64    }
65    y[row] = x[row, 0] * x[row, 1] +
66             x[row, 2] * x[row, 3] +
67             x[row, 4] * x[row, 5] +
68             x[row, 0] * x[row, 6] + x[row, 8] +
69             x[row, 2] * x[row, 5] + x[row, 9];
70  }
71
72  rows = System.Linq.Enumerable.Range(0, n).ToArray();
73>>
74
75NONTERMINALS
76  Model<<int row, out double val>>.
77  RPB<<int row, out double val>>.
78  Addition<<int row, out double val>>.
79  Subtraction<<int row, out double val>>.
80  Multiplication<<int row, out double val>>.
81  Division<<int row, out double val>>.
82
83TERMINALS
84  Const<<out double val>>
85    CONSTRAINTS
86      val IN RANGE <<-100>> .. <<100>>
87  .
88  Var<<out string varName, out double weight>> 
89    CONSTRAINTS
90      varName IN SET <<variableNames>>
91      weight IN RANGE <<-100>> .. <<100>>
92  .
93
94RULES
95  Model<<int row, out double val>> =                       
96    RPB<<row, out val>> .
97   
98  RPB<<int row, out double val>> =                         LOCAL << string varName; double w; >>
99    Addition<<row, out val>>
100    | Subtraction<<row, out val>>
101    | Division<<row, out val>>
102    | Multiplication<<row, out val>>                       
103    | Var<<out varName, out w>>                            SEM << val = w * GetValue(x, varName, row); >>
104    | Const<<out val>>
105  .
106
107  Addition<<int row, out double val>> =                    LOCAL << double x1, x2; >>
108    RPB<<row, out x1>> RPB<<row, out x2>>                  SEM << val = x1 + x2; >>
109  .
110  Subtraction<<int row, out double val>> =                 LOCAL << double x1, x2; >>
111    RPB<<row, out x1>> RPB<<row, out x2>>                  SEM << val = x1 - x2; >>
112  .
113  Division<<int row, out double val>> =                    LOCAL << double x1, x2; >>
114    RPB<<row, out x1>> RPB<<row, out x2>>                  SEM << val = x1 / x2; >>
115  .
116  Multiplication<<int row, out double val>> =              LOCAL << double x1, x2; >>
117    RPB<<row, out x1>> RPB<<row, out x2>>                  SEM << val = x1 * x2; >>
118  .
119
120MAXIMIZE
121  <<
122    var predicted = rows.Select(r => {
123      double result;
124      Model(r, out result);                                /* we can call the root symbol directly */
125      return result;
126    });
127    return RSquared(predicted, y);
128  >>
129END SymbReg.
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