1 | using System;
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2 | using System.Collections.Generic;
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3 | using System.Linq;
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4 | using System.Threading;
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5 | using HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression.Policies;
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6 | using HeuristicLab.Algorithms.DataAnalysis.MCTSSymbReg;
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7 | using HeuristicLab.Data;
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8 | using HeuristicLab.Optimization;
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9 | using HeuristicLab.Problems.DataAnalysis;
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10 | using HeuristicLab.Problems.Instances.DataAnalysis;
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11 | using HeuristicLab.Random;
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12 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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13 |
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14 | namespace HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression {
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15 | [TestClass()]
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16 | public class MctsSymbolicRegressionTest {
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17 | #region heuristics
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18 | [TestMethod]
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19 | [TestCategory("Algorithms.DataAnalysis")]
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20 | [TestProperty("Time", "short")]
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21 | public void TestSimple2dInteractions() {
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22 | {
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23 | // a, b ~ U(0, 1) should be trivial
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24 | var nRand = new MersenneTwister(1234);
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25 |
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26 | int n = 10000; // large sample so that we can use the thresholds below
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27 | var a = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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28 | var b = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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29 | var x = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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30 | var y = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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31 |
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32 | var z = a.Zip(b, (ai, bi) => ai * bi).ToArray();
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33 |
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34 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, z) > 0.05); // should be detected as relevant
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35 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) > 0.05); // a and b > 0 so these should be detected as well
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36 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) > 0.05);
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37 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) > 0.05);
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38 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) > 0.05);
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39 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z) < 0.05);
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40 | }
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41 | {
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42 | // a, b ~ U(1000, 2000) also trivial
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43 | var nRand = new UniformDistributedRandom(new MersenneTwister(1234), 1000, 2000);
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44 |
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45 | int n = 10000; // large sample so that we can use the thresholds below
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46 | var a = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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47 | var b = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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48 | var x = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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49 | var y = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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50 |
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51 | var z = a.Zip(b, (ai, bi) => ai * bi).ToArray();
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52 |
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53 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, z) > 0.05); // should be detected as relevant
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54 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) > 0.05);
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55 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) > 0.05);
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56 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) > 0.05);
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57 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) > 0.05);
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58 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z) < 0.05);
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59 | }
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60 | {
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61 | // a, b ~ U(-1, 1)
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62 | var nRand = new UniformDistributedRandom(new MersenneTwister(1234), -1, 1);
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63 |
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64 | int n = 10000; // large sample so that we can use the thresholds below
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65 | var a = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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66 | var b = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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67 | var x = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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68 | var y = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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69 |
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70 | var z = a.Zip(b, (ai, bi) => ai * bi).ToArray();
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71 |
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72 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, z) > 0.05); // should be detected as relevant
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73 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) < 0.05);
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74 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) < 0.05);
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75 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) < 0.05);
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76 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) < 0.05);
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77 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z) < 0.05);
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78 | }
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79 | {
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80 | // a, b ~ N(0, 1)
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81 | var nRand = new NormalDistributedRandom(new MersenneTwister(1234), 0, 1);
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82 |
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83 | int n = 10000; // large sample so that we can use the thresholds below
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84 | var a = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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85 | var b = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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86 | var x = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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87 | var y = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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88 |
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89 | var z = a.Zip(b, (ai, bi) => ai * bi).ToArray();
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90 |
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91 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, z) > 0.05); // should be detected as relevant
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92 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) < 0.05);
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93 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) < 0.05);
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94 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) < 0.05);
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95 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) < 0.05);
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96 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z) < 0.05);
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97 | }
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98 | {
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99 | var rand = new MersenneTwister(1234);
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100 | // a ~ N(100, 1), b ~ N(-100, 1)
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101 | var nRand = new NormalDistributedRandom(rand, 0, 1);
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102 | var aRand = new NormalDistributedRandom(rand, 100, 1);
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103 | var bRand = new NormalDistributedRandom(rand, -100, 1);
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104 |
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105 | int n = 10000; // large sample so that we can use the thresholds below
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106 | var a = Enumerable.Range(0, n).Select(_ => aRand.NextDouble()).ToArray();
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107 | var b = Enumerable.Range(0, n).Select(_ => bRand.NextDouble()).ToArray();
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108 | var x = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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109 | var y = Enumerable.Range(0, n).Select(_ => nRand.NextDouble()).ToArray();
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110 |
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111 | var z = a.Zip(b, (ai, bi) => ai * bi).ToArray();
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112 |
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113 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, z) > 0.05); // should be detected as relevant
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114 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) > 0.05); // a > 0
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115 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) > 0.05);
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116 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) > 0.05); // b < 0
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117 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) > 0.05);
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118 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z) < 0.05); // random variables are not correlated
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119 | }
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120 | }
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121 |
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122 | [TestMethod]
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123 | [TestCategory("Algorithms.DataAnalysis")]
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124 | [TestProperty("Time", "short")]
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125 | public void TestGeneral2dInteractions() {
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126 | {
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127 | // we should be able to reliably detect when a product of two variables is correlated with the target variable
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128 |
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129 | // the test samples x from a two dimensional normal distribution
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130 | // the covariance matrix for the normal distribution is randomly sampled
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131 | // this means x_1 and x_2 might be highly correlated
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132 | // the mean of the normal distribution is randomly sampled (most critical are probably zero-mean distributions)
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133 | // y is calculated as x_1*x_2
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134 |
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135 | var conditionNumber = 10000;
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136 | for (int iter = 0; iter < 100; iter++) {
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137 | double m0 = 0.0; double m1 = 0.0;
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138 | alglib.hqrndstate randState;
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139 | alglib.hqrndseed(1234, 31415, out randState);
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140 |
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141 | // uncomment if non-zero mean distributions should be tested
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142 | //alglib.hqrndnormal2(randState, out m0, out m1);
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143 |
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144 | double[,] cov_ab = new double[2, 2];
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145 | double[,] cov_xy = new double[2, 2];
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146 | alglib.matgen.spdmatrixrndcond(2, conditionNumber, ref cov_ab);
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147 | alglib.spdmatrixcholesky(ref cov_ab, 2, true);
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148 |
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149 | alglib.matgen.spdmatrixrndcond(2, conditionNumber, ref cov_xy);
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150 | alglib.spdmatrixcholesky(ref cov_xy, 2, true);
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151 |
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152 | // generate a, b by sampling from a 2dim multivariate normal distribution
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153 | // generate x, y by sampling from another 2dim multivariate normal distribution
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154 | // a,b and x,y might be correlated but x,y are not correlated to a,b
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155 | int N = 1000; // large sample size to make sure the test thresholds hold
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156 | double[] a = new double[N];
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157 | double[] b = new double[N];
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158 | double[] x = new double[N];
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159 | double[] y = new double[N];
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160 | double[] z = new double[N];
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161 |
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162 | for (int i = 0; i < N; i++) {
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163 | double r1, r2, r3, r4;
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164 | alglib.hqrndnormal2(randState, out r1, out r2);
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165 | alglib.hqrndnormal2(randState, out r3, out r4);
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166 |
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167 | var r_ab = new double[] { r1, r2 };
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168 | var r_xy = new double[] { r3, r4 };
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169 | double[] s_ab = new double[2];
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170 | double[] s_xy = new double[2];
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171 | alglib.ablas.rmatrixmv(2, 2, cov_ab, 0, 0, 0, r_ab, 0, ref s_ab, 0);
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172 | alglib.ablas.rmatrixmv(2, 2, cov_xy, 0, 0, 0, r_xy, 0, ref s_xy, 0);
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173 |
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174 | a[i] = s_ab[0] + m0;
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175 | b[i] = s_ab[1] + m1;
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176 | x[i] = s_xy[0] + m0; // use same mean (just for the sake of it)
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177 | y[i] = s_xy[1] + m1;
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178 |
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179 | z[i] = a[i] * b[i];
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180 | }
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181 |
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182 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, z) > 0.05);
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183 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z) < 0.05);
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184 |
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185 | /* we might see correlations when only using one of the two relevant factors.
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186 | * this depends on the distribution / location of a and b
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187 | // for zero-mean distributions the following should all be quasi-zero
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188 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) < 0.05);
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189 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) < 0.05);
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190 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) < 0.05);
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191 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) < 0.05);
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192 | */
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193 | Console.WriteLine("a,b: {0:N3}\tx,y: {1:N3}\ta,x: {2:N3}\tb,x: {3:N3}\ta,y: {4:N3}\tb,y: {5:N3}\tcov(a,b): {6:N3}",
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194 | Heuristics.CorrelationForInteraction(a, b, z),
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195 | Heuristics.CorrelationForInteraction(x, y, z),
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196 | Heuristics.CorrelationForInteraction(a, x, z),
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197 | Heuristics.CorrelationForInteraction(b, x, z),
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198 | Heuristics.CorrelationForInteraction(a, y, z),
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199 | Heuristics.CorrelationForInteraction(b, y, z),
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200 | alglib.cov2(a, b)
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201 | );
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202 | }
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203 | }
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204 | }
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205 | [TestMethod]
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206 | [TestCategory("Algorithms.DataAnalysis")]
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207 | [TestProperty("Time", "short")]
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208 | public void TestGeneral3dInteractions() {
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209 | {
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210 | // same as TestGeneral2dInteractions but for terms with three variables
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211 |
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212 | var conditionNumber = 100;
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213 | for (int iter = 0; iter < 100; iter++) {
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214 | double m0 = 0.0; double m1 = 0.0; double m2 = 0.0;
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215 | alglib.hqrndstate randState;
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216 | alglib.hqrndseed(1234, 31415, out randState);
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217 |
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218 | // uncomment if non-zero mean distributions should be tested
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219 | //alglib.hqrndnormal2(randState, out m0, out m1);
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220 | //alglib.hqrndnormal2(randState, out m1, out m2);
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221 |
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222 | double[,] cov_abc = new double[3, 3];
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223 | double[,] cov_xyz = new double[3, 3];
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224 | alglib.matgen.spdmatrixrndcond(3, conditionNumber, ref cov_abc);
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225 | alglib.spdmatrixcholesky(ref cov_abc, 3, true);
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226 |
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227 | alglib.matgen.spdmatrixrndcond(3, conditionNumber, ref cov_xyz);
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228 | alglib.spdmatrixcholesky(ref cov_xyz, 3, true);
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229 |
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230 | int N = 1000; // large sample size to make sure the test thresholds hold
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231 | double[] a = new double[N];
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232 | double[] b = new double[N];
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233 | double[] c = new double[N];
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234 | double[] x = new double[N];
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235 | double[] y = new double[N];
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236 | double[] z = new double[N];
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237 | double[] t = new double[N];
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238 |
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239 | for (int i = 0; i < N; i++) {
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240 | double r1, r2, r3, r4, r5, r6;
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241 | alglib.hqrndnormal2(randState, out r1, out r2);
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242 | alglib.hqrndnormal2(randState, out r3, out r4);
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243 | alglib.hqrndnormal2(randState, out r5, out r6);
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244 |
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245 | var r_abc = new double[] { r1, r2, r3 };
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246 | var r_xyz = new double[] { r4, r5, r6 };
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247 | double[] s_abc = new double[3];
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248 | double[] s_xyz = new double[3];
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249 | alglib.ablas.rmatrixmv(3, 3, cov_abc, 0, 0, 0, r_abc, 0, ref s_abc, 0);
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250 | alglib.ablas.rmatrixmv(3, 3, cov_xyz, 0, 0, 0, r_xyz, 0, ref s_xyz, 0);
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251 |
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252 | a[i] = s_abc[0] + m0;
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253 | b[i] = s_abc[1] + m1;
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254 | c[i] = s_abc[2] + m2;
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255 | x[i] = s_xyz[0] + m0; // use same mean (just for the sake of it)
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256 | y[i] = s_xyz[1] + m1;
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257 | z[i] = s_xyz[2] + m2;
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258 |
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259 | t[i] = a[i] * b[i] * c[i];
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260 | }
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261 |
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262 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, b, c, z) > 0.05);
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263 | Assert.IsTrue(Heuristics.CorrelationForInteraction(x, y, z, z) < 0.05);
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264 |
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265 | /* we might see correlations when only using one of the two relevant factors.
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266 | * this depends on the distribution / location of a and b
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267 | // for zero-mean distributions the following should all be quasi-zero
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268 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, x, z) < 0.05);
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269 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, x, z) < 0.05);
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270 | Assert.IsTrue(Heuristics.CorrelationForInteraction(a, y, z) < 0.05);
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271 | Assert.IsTrue(Heuristics.CorrelationForInteraction(b, y, z) < 0.05);
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272 | */
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273 | Console.WriteLine("a,b: {0:N3}\tx,y: {1:N3}\ta,x: {2:N3}\tb,x: {3:N3}\ta,y: {4:N3}\tb,y: {5:N3}\tcov(a,b): {6:N3}",
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274 | Heuristics.CorrelationForInteraction(a, b, z),
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275 | Heuristics.CorrelationForInteraction(x, y, z),
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276 | Heuristics.CorrelationForInteraction(a, x, z),
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277 | Heuristics.CorrelationForInteraction(b, x, z),
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278 | Heuristics.CorrelationForInteraction(a, y, z),
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279 | Heuristics.CorrelationForInteraction(b, y, z),
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280 | alglib.cov2(a, b)
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281 | );
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282 | }
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283 | }
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284 | }
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285 | #endregion
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286 |
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287 |
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288 | #region expression hashing
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289 | [TestMethod]
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290 | [TestCategory("Algorithms.DataAnalysis")]
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291 | [TestProperty("Time", "short")]
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292 | public void ExprHashTest() {
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293 | int nParams;
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294 | byte[] code;
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295 |
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296 | {
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297 | // addition of variables
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298 | var codeGen = new CodeGenerator();
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299 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
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300 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
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301 | codeGen.Emit1(OpCodes.Add);
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302 | codeGen.Emit1(OpCodes.Exit);
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303 | codeGen.GetCode(out code, out nParams);
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304 | var h1 = ExprHash.GetHash(code, nParams);
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305 |
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306 | codeGen = new CodeGenerator();
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307 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
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308 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
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309 | codeGen.Emit1(OpCodes.Add);
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310 | codeGen.Emit1(OpCodes.Exit);
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311 | codeGen.GetCode(out code, out nParams);
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312 | var h2 = ExprHash.GetHash(code, nParams);
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313 |
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314 | Assert.AreEqual(h1, h2);
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315 | }
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316 |
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317 | {
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318 | // multiplication of variables
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319 | var codeGen = new CodeGenerator();
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320 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
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321 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
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322 | codeGen.Emit1(OpCodes.Mul);
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323 | codeGen.Emit1(OpCodes.Exit);
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324 | codeGen.GetCode(out code, out nParams);
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325 | var h1 = ExprHash.GetHash(code, nParams);
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326 |
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327 | codeGen = new CodeGenerator();
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328 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
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329 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
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330 | codeGen.Emit1(OpCodes.Mul);
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331 | codeGen.Emit1(OpCodes.Exit);
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332 | codeGen.GetCode(out code, out nParams);
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333 | var h2 = ExprHash.GetHash(code, nParams);
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334 |
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335 | Assert.AreEqual(h1, h2);
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336 | }
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337 |
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338 | {
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339 | // distributivity
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340 | var codeGen = new CodeGenerator();
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341 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
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342 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
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343 | codeGen.Emit1(OpCodes.Add);
|
---|
344 | codeGen.Emit2(OpCodes.LoadVar, 3);
|
---|
345 | codeGen.Emit1(OpCodes.Mul);
|
---|
346 | codeGen.Emit1(OpCodes.Exit);
|
---|
347 | codeGen.GetCode(out code, out nParams);
|
---|
348 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
349 |
|
---|
350 | codeGen = new CodeGenerator();
|
---|
351 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
352 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 3);
|
---|
353 | codeGen.Emit1(OpCodes.Mul);
|
---|
354 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
355 | codeGen.Emit2(OpCodes.LoadVar, 3);
|
---|
356 | codeGen.Emit1(OpCodes.Mul);
|
---|
357 | codeGen.Emit1(OpCodes.Add);
|
---|
358 | codeGen.Emit1(OpCodes.Exit);
|
---|
359 | codeGen.GetCode(out code, out nParams);
|
---|
360 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
361 |
|
---|
362 | Assert.AreEqual(h1, h2);
|
---|
363 | }
|
---|
364 |
|
---|
365 |
|
---|
366 | {
|
---|
367 | // div
|
---|
368 | var codeGen = new CodeGenerator();
|
---|
369 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
370 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
371 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
372 | codeGen.Emit1(OpCodes.Inv);
|
---|
373 | codeGen.Emit1(OpCodes.Exit);
|
---|
374 | codeGen.GetCode(out code, out nParams);
|
---|
375 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
376 |
|
---|
377 | codeGen = new CodeGenerator();
|
---|
378 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
379 | codeGen.Emit1(OpCodes.Inv);
|
---|
380 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
381 | codeGen.Emit1(OpCodes.Inv);
|
---|
382 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
383 | codeGen.Emit1(OpCodes.Exit);
|
---|
384 | codeGen.GetCode(out code, out nParams);
|
---|
385 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
386 |
|
---|
387 | Assert.AreEqual(h1, h2);
|
---|
388 | }
|
---|
389 | {
|
---|
390 | // exp
|
---|
391 | var codeGen = new CodeGenerator();
|
---|
392 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
393 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
394 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Add);
|
---|
395 | codeGen.Emit1(OpCodes.Exp);
|
---|
396 | codeGen.Emit1(OpCodes.Exit);
|
---|
397 | codeGen.GetCode(out code, out nParams);
|
---|
398 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
399 |
|
---|
400 | codeGen = new CodeGenerator();
|
---|
401 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
402 | codeGen.Emit1(OpCodes.Exp);
|
---|
403 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
404 | codeGen.Emit1(OpCodes.Exp);
|
---|
405 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
406 | codeGen.GetCode(out code, out nParams);
|
---|
407 | codeGen.Emit1(OpCodes.Exit);
|
---|
408 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
409 |
|
---|
410 | Assert.AreEqual(h1, h2);
|
---|
411 | }
|
---|
412 | {
|
---|
413 | // log
|
---|
414 | var codeGen = new CodeGenerator();
|
---|
415 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
416 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
417 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
418 | codeGen.Emit1(OpCodes.Log);
|
---|
419 | codeGen.Emit1(OpCodes.Exit);
|
---|
420 | codeGen.GetCode(out code, out nParams);
|
---|
421 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
422 |
|
---|
423 | codeGen = new CodeGenerator();
|
---|
424 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
425 | codeGen.Emit1(OpCodes.Log);
|
---|
426 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 2);
|
---|
427 | codeGen.Emit1(OpCodes.Log);
|
---|
428 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Add);
|
---|
429 | codeGen.Emit1(OpCodes.Exit);
|
---|
430 | codeGen.GetCode(out code, out nParams);
|
---|
431 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
432 |
|
---|
433 | Assert.AreEqual(h1, h2);
|
---|
434 | }
|
---|
435 |
|
---|
436 | {
|
---|
437 | // x1 + x1 is equivalent to x1
|
---|
438 | var codeGen = new CodeGenerator();
|
---|
439 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
440 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
441 | codeGen.Emit1(OpCodes.Add);
|
---|
442 | codeGen.Emit1(OpCodes.Exit);
|
---|
443 | codeGen.GetCode(out code, out nParams);
|
---|
444 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
445 |
|
---|
446 | codeGen = new CodeGenerator();
|
---|
447 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
448 | codeGen.Emit1(OpCodes.Exit);
|
---|
449 | codeGen.GetCode(out code, out nParams);
|
---|
450 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
451 |
|
---|
452 | Assert.AreEqual(h1, h2);
|
---|
453 | }
|
---|
454 | {
|
---|
455 | // c1*x1 + c2*x1 is equivalent to c3*x1
|
---|
456 | var codeGen = new CodeGenerator();
|
---|
457 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadParamN);
|
---|
458 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
459 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
460 |
|
---|
461 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadParamN);
|
---|
462 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
463 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
464 |
|
---|
465 | codeGen.Emit1(OpCodes.Add);
|
---|
466 | codeGen.Emit1(OpCodes.Exit);
|
---|
467 | codeGen.GetCode(out code, out nParams);
|
---|
468 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
469 |
|
---|
470 | codeGen = new CodeGenerator();
|
---|
471 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadParamN);
|
---|
472 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
473 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
474 | codeGen.Emit1(OpCodes.Exit);
|
---|
475 | codeGen.GetCode(out code, out nParams);
|
---|
476 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
477 |
|
---|
478 | Assert.AreEqual(h1, h2);
|
---|
479 | }
|
---|
480 |
|
---|
481 | {
|
---|
482 | var codeGen = new CodeGenerator();
|
---|
483 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadConst0);
|
---|
484 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadConst1);
|
---|
485 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadParamN);
|
---|
486 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
487 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
488 |
|
---|
489 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadParamN);
|
---|
490 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
491 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
492 |
|
---|
493 | codeGen.Emit1(OpCodes.Add);
|
---|
494 |
|
---|
495 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
496 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Add);
|
---|
497 |
|
---|
498 | codeGen.Emit1(OpCodes.Exit);
|
---|
499 | codeGen.GetCode(out code, out nParams);
|
---|
500 | var h1 = ExprHash.GetHash(code, nParams);
|
---|
501 |
|
---|
502 | codeGen = new CodeGenerator();
|
---|
503 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadConst0);
|
---|
504 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadConst1);
|
---|
505 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.LoadParamN);
|
---|
506 | codeGen.Emit2(MctsSymbolicRegression.OpCodes.LoadVar, 1);
|
---|
507 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
508 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Mul);
|
---|
509 | codeGen.Emit1(MctsSymbolicRegression.OpCodes.Add);
|
---|
510 | codeGen.Emit1(OpCodes.Exit);
|
---|
511 | codeGen.GetCode(out code, out nParams);
|
---|
512 | var h2 = ExprHash.GetHash(code, nParams);
|
---|
513 |
|
---|
514 | Assert.AreEqual(h1, h2);
|
---|
515 | }
|
---|
516 | }
|
---|
517 | #endregion
|
---|
518 |
|
---|
519 | #region number of solutions
|
---|
520 | // the algorithm should visits each solution only once
|
---|
521 | [TestMethod]
|
---|
522 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
523 | [TestProperty("Time", "short")]
|
---|
524 | public void MctsSymbRegNumberOfSolutionsOneVariable() {
|
---|
525 | // this problem has only one variable
|
---|
526 | var provider = new NguyenInstanceProvider();
|
---|
527 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F1 ")));
|
---|
528 | {
|
---|
529 | // possible solutions with max one variable reference:
|
---|
530 | // x
|
---|
531 | // log(x)
|
---|
532 | // exp(x)
|
---|
533 | // 1/x
|
---|
534 | TestMctsNumberOfSolutions(regProblem, 1, 4);
|
---|
535 | }
|
---|
536 | {
|
---|
537 | // possible solutions with max two variable references:
|
---|
538 | // TODO: equal terms should not be allowed (see ConstraintHandler)
|
---|
539 | // x
|
---|
540 | // log(x)
|
---|
541 | // exp(x)
|
---|
542 | // 1/x
|
---|
543 | // -- 4
|
---|
544 | // x * x
|
---|
545 | // x * log(x)
|
---|
546 | // x * exp(x)
|
---|
547 | // x * 1/x
|
---|
548 | // x + x ?
|
---|
549 | // x + log(x)
|
---|
550 | // x + exp(x)
|
---|
551 | // x + 1/x
|
---|
552 | // -- 8
|
---|
553 | // log(x) * log(x)
|
---|
554 | // log(x) * exp(x)
|
---|
555 | // log(x) * 1/x
|
---|
556 | // log(x) + log(x) ?
|
---|
557 | // log(x) + exp(x) ?
|
---|
558 | // log(x) + 1/x
|
---|
559 | // -- 6
|
---|
560 | // exp(x) * exp(x)
|
---|
561 | // exp(x) * 1/x
|
---|
562 | // exp(x) + exp(x) ?
|
---|
563 | // exp(x) + 1/x
|
---|
564 | // -- 4
|
---|
565 | // 1/x * 1/x
|
---|
566 | // 1/x + 1/x ?
|
---|
567 | // -- 2
|
---|
568 | // log(x+x) ?
|
---|
569 | // log(x*x)
|
---|
570 | // exp(x*x)
|
---|
571 | // 1/(x+x) ?
|
---|
572 | // 1/(x*x)
|
---|
573 | // -- 5
|
---|
574 |
|
---|
575 |
|
---|
576 | TestMctsNumberOfSolutions(regProblem, 2, 29);
|
---|
577 | }
|
---|
578 | {
|
---|
579 | // possible solutions with max three variable references:
|
---|
580 | // without log and inv
|
---|
581 | // x
|
---|
582 | // exp(x)
|
---|
583 | // -- 2
|
---|
584 | // x * x
|
---|
585 | // x + x ?
|
---|
586 | // x * exp(x)
|
---|
587 | // x + exp(x)
|
---|
588 | // exp(x) * exp(x)
|
---|
589 | // exp(x) + exp(x) ?
|
---|
590 | // exp(x*x)
|
---|
591 | // -- 7
|
---|
592 | // x * x * x
|
---|
593 | // x + x * x
|
---|
594 | // x + x + x ?
|
---|
595 | // x * x * exp(x)
|
---|
596 | // x + x * exp(x)
|
---|
597 | // x + x + exp(x) ?
|
---|
598 | // exp(x) + x*x
|
---|
599 | // exp(x) + x*exp(x)
|
---|
600 | // x + exp(x) * exp(x)
|
---|
601 | // x + exp(x) + exp(x) ?
|
---|
602 | // x * exp(x) * exp(x)
|
---|
603 | // x * exp(x*x)
|
---|
604 | // x + exp(x*x)
|
---|
605 | // -- 13
|
---|
606 |
|
---|
607 | // exp(x) * exp(x) * exp(x)
|
---|
608 | // exp(x) + exp(x) * exp(x)
|
---|
609 | // exp(x) + exp(x) + exp(x) ?
|
---|
610 | // -- 3
|
---|
611 |
|
---|
612 | // exp(x) * exp(x*x)
|
---|
613 | // exp(x) + exp(x*x)
|
---|
614 | // -- 2
|
---|
615 | // exp(x*x*x)
|
---|
616 | // -- 1
|
---|
617 | TestMctsNumberOfSolutions(regProblem, 3, 2 + 7 + 13 + 3 + 2 + 1, allowLog: false, allowInv: false);
|
---|
618 | }
|
---|
619 | {
|
---|
620 | // possible solutions with max 4 variable references:
|
---|
621 | // without exp, log and inv
|
---|
622 | // x
|
---|
623 | // x*x
|
---|
624 | // x+x ?
|
---|
625 | // x*x*x
|
---|
626 | // x+x*x
|
---|
627 | // x+x+x ?
|
---|
628 | // x*x*x*x
|
---|
629 | // x+x*x*x
|
---|
630 | // x*x+x*x ?
|
---|
631 | // x+x+x*x ?
|
---|
632 | // x+x+x+x ?
|
---|
633 |
|
---|
634 | TestMctsNumberOfSolutions(regProblem, 4, 11, allowLog: false, allowInv: false, allowExp: false);
|
---|
635 | }
|
---|
636 | {
|
---|
637 | // possible solutions with max 5 variable references:
|
---|
638 | // without exp, log and inv
|
---|
639 | // x
|
---|
640 | // xx
|
---|
641 | // x+x ?
|
---|
642 | // xxx
|
---|
643 | // x+xx
|
---|
644 | // x+x+x ?
|
---|
645 | // xxxx
|
---|
646 | // x+xxx
|
---|
647 | // xx+xx ?
|
---|
648 | // x+x+xx ?
|
---|
649 | // x+x+x+x ?
|
---|
650 | // xxxxx
|
---|
651 | // x+xxxx
|
---|
652 | // xx+xxx
|
---|
653 | // x+x+xxx ?
|
---|
654 | // x+xx+xx ?
|
---|
655 | // x+x+x+xx ?
|
---|
656 | // x+x+x+x+x ?
|
---|
657 | TestMctsNumberOfSolutions(regProblem, 5, 18, allowLog: false, allowInv: false, allowExp: false);
|
---|
658 | }
|
---|
659 | }
|
---|
660 |
|
---|
661 | [TestMethod]
|
---|
662 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
663 | [TestProperty("Time", "short")]
|
---|
664 | public void MctsSymbRegNumberOfSolutionsTwoVariables() {
|
---|
665 | // this problem has only two input variables
|
---|
666 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider();
|
---|
667 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F9 ")));
|
---|
668 | {
|
---|
669 | // possible solutions with max one variable reference:
|
---|
670 | // x
|
---|
671 | // log(x)
|
---|
672 | // exp(x)
|
---|
673 | // 1/x
|
---|
674 | // y
|
---|
675 | // log(y)
|
---|
676 | // exp(y)
|
---|
677 | // 1/y
|
---|
678 | TestMctsNumberOfSolutions(regProblem, 1, 8);
|
---|
679 | }
|
---|
680 | {
|
---|
681 | // possible solutions with max one variable reference:
|
---|
682 | // without log and inv
|
---|
683 |
|
---|
684 | // x
|
---|
685 | // exp(x)
|
---|
686 | // y
|
---|
687 | // exp(y)
|
---|
688 | TestMctsNumberOfSolutions(regProblem, 1, 4, allowLog: false, allowInv: false);
|
---|
689 | }
|
---|
690 | {
|
---|
691 | // possible solutions with max two variable references:
|
---|
692 | // without log and inv
|
---|
693 |
|
---|
694 | // x
|
---|
695 | // y
|
---|
696 | // exp(x)
|
---|
697 | // exp(y)
|
---|
698 | // -- 4
|
---|
699 | // x (*|+) x
|
---|
700 | // x (*|+) exp(x)
|
---|
701 | // x (*|+) y
|
---|
702 | // x (*|+) exp(y)
|
---|
703 | // -- 8
|
---|
704 | // exp(x) (*|+) exp(x)
|
---|
705 | // exp(x) (*|+) exp(y)
|
---|
706 | // -- 4
|
---|
707 | // y (*|+) y
|
---|
708 | // y (*|+) exp(x)
|
---|
709 | // y (*|+) exp(y)
|
---|
710 | // -- 6
|
---|
711 | // exp(y) (*|+) exp(y)
|
---|
712 | // -- 2
|
---|
713 | //
|
---|
714 | // exp(x*x)
|
---|
715 | // exp(x*y)
|
---|
716 | // exp(y*y)
|
---|
717 | // -- 3
|
---|
718 |
|
---|
719 | TestMctsNumberOfSolutions(regProblem, 2, 4 + 8 + 4 + 6 + 2 + 3, allowLog: false, allowInv: false);
|
---|
720 | }
|
---|
721 |
|
---|
722 | {
|
---|
723 | // possible solutions with max two variable references:
|
---|
724 | // without exp and sum
|
---|
725 | // x
|
---|
726 | // y
|
---|
727 | // log(x)
|
---|
728 | // log(y)
|
---|
729 | // inv(x)
|
---|
730 | // inv(y)
|
---|
731 | // -- 6
|
---|
732 | // x * x
|
---|
733 | // x * y
|
---|
734 | // x * log(x)
|
---|
735 | // x * log(y)
|
---|
736 | // x * inv(x)
|
---|
737 | // x * inv(y)
|
---|
738 | // -- 6
|
---|
739 | // log(x) * log(x)
|
---|
740 | // log(x) * log(y)
|
---|
741 | // log(x) * inv(x)
|
---|
742 | // log(x) * inv(y)
|
---|
743 | // -- 4
|
---|
744 | // inv(x) * inv(x)
|
---|
745 | // inv(x) * inv(y)
|
---|
746 | // -- 2
|
---|
747 | // y * y
|
---|
748 | // y * log(x)
|
---|
749 | // y * log(y)
|
---|
750 | // y * inv(x)
|
---|
751 | // y * inv(y)
|
---|
752 | // -- 5
|
---|
753 | // log(y) * log(y)
|
---|
754 | // log(y) * inv(x)
|
---|
755 | // log(y) * inv(y)
|
---|
756 | // -- 3
|
---|
757 | // inv(y) * inv(y)
|
---|
758 | // -- 1
|
---|
759 | // log(x*x)
|
---|
760 | // log(x*y)
|
---|
761 | // log(y*y)
|
---|
762 |
|
---|
763 | // inv(x*x)
|
---|
764 | // inv(x*y)
|
---|
765 | // inv(y*y)
|
---|
766 | // -- 6
|
---|
767 | // log(x+x)
|
---|
768 | // log(x+y)
|
---|
769 | // log(y+y)
|
---|
770 |
|
---|
771 | // inv(x+x)
|
---|
772 | // inv(x+y)
|
---|
773 | // inv(y+y)
|
---|
774 | // -- 6
|
---|
775 | TestMctsNumberOfSolutions(regProblem, 2, 6 + 6 + 4 + 2 + 5 + 3 + 1 + 6 + 6, allowExp: false, allowSum: false);
|
---|
776 | }
|
---|
777 | }
|
---|
778 | #endregion
|
---|
779 |
|
---|
780 |
|
---|
781 | #region test structure search (no constants)
|
---|
782 | [TestMethod]
|
---|
783 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
784 | [TestProperty("Time", "short")]
|
---|
785 | public void MctsSymbReg_NoConstants_Nguyen1() {
|
---|
786 | // x³ + x² + x
|
---|
787 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
788 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F1 ")));
|
---|
789 | TestMctsWithoutConstants(regProblem, nVarRefs: 10, allowExp: false, allowLog: false, allowInv: false);
|
---|
790 | }
|
---|
791 | [TestMethod]
|
---|
792 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
793 | [TestProperty("Time", "short")]
|
---|
794 | public void MctsSymbReg_NoConstants_Nguyen2() {
|
---|
795 | // x^4 + x³ + x² + x
|
---|
796 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
797 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F2 ")));
|
---|
798 | TestMctsWithoutConstants(regProblem, allowExp: false, allowLog: false, allowInv: false);
|
---|
799 | }
|
---|
800 | [TestMethod]
|
---|
801 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
802 | [TestProperty("Time", "short")]
|
---|
803 | public void MctsSymbReg_NoConstants_Nguyen3() {
|
---|
804 | // x^5 + x^4 + x³ + x² + x
|
---|
805 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
806 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F3 ")));
|
---|
807 | TestMctsWithoutConstants(regProblem, nVarRefs: 15, iterations: 1000000, allowExp: false, allowLog: false, allowInv: false);
|
---|
808 | }
|
---|
809 | [TestMethod]
|
---|
810 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
811 | [TestProperty("Time", "short")]
|
---|
812 | public void MctsSymbReg_NoConstants_Nguyen4() {
|
---|
813 | // x^6 + x^5 + x^4 + x³ + x² + x
|
---|
814 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
815 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F4 ")));
|
---|
816 | TestMctsWithoutConstants(regProblem, nVarRefs: 25, iterations: 1000000, allowExp: false, allowLog: false, allowInv: false);
|
---|
817 | }
|
---|
818 |
|
---|
819 | [TestMethod]
|
---|
820 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
821 | [TestProperty("Time", "short")]
|
---|
822 | public void MctsSymbReg_NoConstants_Nguyen7() {
|
---|
823 | // log(x + 1) + log(x² + 1)
|
---|
824 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
825 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F7 ")));
|
---|
826 | TestMctsWithoutConstants(regProblem, nVarRefs: 10, iterations: 100000, allowExp: false, allowLog: true, allowInv: false);
|
---|
827 | }
|
---|
828 |
|
---|
829 | [TestMethod]
|
---|
830 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
831 | [TestProperty("Time", "short")]
|
---|
832 | public void MctsSymbReg_NoConstants_Poly10_Part1() {
|
---|
833 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
834 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
835 |
|
---|
836 | // Y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10
|
---|
837 | // Y' = X1*X2 + X3*X4 + X5*X6
|
---|
838 | // simplify problem by changing target
|
---|
839 | var ds = ((Dataset)regProblem.Dataset).ToModifiable();
|
---|
840 | var ys = ds.GetDoubleValues("Y").ToArray();
|
---|
841 | var x1 = ds.GetDoubleValues("X1").ToArray();
|
---|
842 | var x2 = ds.GetDoubleValues("X2").ToArray();
|
---|
843 | var x3 = ds.GetDoubleValues("X3").ToArray();
|
---|
844 | var x4 = ds.GetDoubleValues("X4").ToArray();
|
---|
845 | var x5 = ds.GetDoubleValues("X5").ToArray();
|
---|
846 | var x6 = ds.GetDoubleValues("X6").ToArray();
|
---|
847 | var x7 = ds.GetDoubleValues("X7").ToArray();
|
---|
848 | var x8 = ds.GetDoubleValues("X8").ToArray();
|
---|
849 | var x9 = ds.GetDoubleValues("X9").ToArray();
|
---|
850 | var x10 = ds.GetDoubleValues("X10").ToArray();
|
---|
851 | for (int i = 0; i < ys.Length; i++) {
|
---|
852 | ys[i] -= x1[i] * x7[i] * x9[i];
|
---|
853 | ys[i] -= x3[i] * x6[i] * x10[i];
|
---|
854 | }
|
---|
855 | ds.ReplaceVariable("Y", ys.ToList());
|
---|
856 |
|
---|
857 | var modifiedProblemData = new RegressionProblemData(ds, regProblem.AllowedInputVariables, regProblem.TargetVariable);
|
---|
858 |
|
---|
859 |
|
---|
860 | TestMctsWithoutConstants(modifiedProblemData, nVarRefs: 15, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
861 | }
|
---|
862 |
|
---|
863 | [TestMethod]
|
---|
864 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
865 | [TestProperty("Time", "short")]
|
---|
866 | public void MctsSymbReg_NoConstants_Poly10_Part2() {
|
---|
867 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
868 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
869 |
|
---|
870 | // Y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10
|
---|
871 | // Y' = X1*X7*X9 + X3*X6*X10
|
---|
872 | // simplify problem by changing target
|
---|
873 | var ds = ((Dataset)regProblem.Dataset).ToModifiable();
|
---|
874 | var ys = ds.GetDoubleValues("Y").ToArray();
|
---|
875 | var x1 = ds.GetDoubleValues("X1").ToArray();
|
---|
876 | var x2 = ds.GetDoubleValues("X2").ToArray();
|
---|
877 | var x3 = ds.GetDoubleValues("X3").ToArray();
|
---|
878 | var x4 = ds.GetDoubleValues("X4").ToArray();
|
---|
879 | var x5 = ds.GetDoubleValues("X5").ToArray();
|
---|
880 | var x6 = ds.GetDoubleValues("X6").ToArray();
|
---|
881 | var x7 = ds.GetDoubleValues("X7").ToArray();
|
---|
882 | var x8 = ds.GetDoubleValues("X8").ToArray();
|
---|
883 | var x9 = ds.GetDoubleValues("X9").ToArray();
|
---|
884 | var x10 = ds.GetDoubleValues("X10").ToArray();
|
---|
885 | for (int i = 0; i < ys.Length; i++) {
|
---|
886 | ys[i] -= x1[i] * x2[i];
|
---|
887 | ys[i] -= x3[i] * x4[i];
|
---|
888 | ys[i] -= x5[i] * x6[i];
|
---|
889 | }
|
---|
890 | ds.ReplaceVariable("Y", ys.ToList());
|
---|
891 |
|
---|
892 | var modifiedProblemData = new RegressionProblemData(ds, regProblem.AllowedInputVariables, regProblem.TargetVariable);
|
---|
893 |
|
---|
894 |
|
---|
895 | TestMctsWithoutConstants(modifiedProblemData, nVarRefs: 15, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
896 | }
|
---|
897 |
|
---|
898 | [TestMethod]
|
---|
899 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
900 | [TestProperty("Time", "short")]
|
---|
901 | public void MctsSymbReg_NoConstants_Poly10_Part3() {
|
---|
902 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
903 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
904 |
|
---|
905 | // Y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10
|
---|
906 | // Y' = X1*X2 + X1*X7*X9
|
---|
907 | // simplify problem by changing target
|
---|
908 | var ds = ((Dataset)regProblem.Dataset).ToModifiable();
|
---|
909 | var ys = ds.GetDoubleValues("Y").ToArray();
|
---|
910 | var x1 = ds.GetDoubleValues("X1").ToArray();
|
---|
911 | var x2 = ds.GetDoubleValues("X2").ToArray();
|
---|
912 | var x3 = ds.GetDoubleValues("X3").ToArray();
|
---|
913 | var x4 = ds.GetDoubleValues("X4").ToArray();
|
---|
914 | var x5 = ds.GetDoubleValues("X5").ToArray();
|
---|
915 | var x6 = ds.GetDoubleValues("X6").ToArray();
|
---|
916 | var x7 = ds.GetDoubleValues("X7").ToArray();
|
---|
917 | var x8 = ds.GetDoubleValues("X8").ToArray();
|
---|
918 | var x9 = ds.GetDoubleValues("X9").ToArray();
|
---|
919 | var x10 = ds.GetDoubleValues("X10").ToArray();
|
---|
920 | for (int i = 0; i < ys.Length; i++) {
|
---|
921 | ys[i] -= x3[i] * x4[i];
|
---|
922 | ys[i] -= x5[i] * x6[i];
|
---|
923 | ys[i] -= x3[i] * x6[i] * x10[i];
|
---|
924 | }
|
---|
925 | ds.ReplaceVariable("Y", ys.ToList());
|
---|
926 |
|
---|
927 | var modifiedProblemData = new RegressionProblemData(ds, regProblem.AllowedInputVariables, regProblem.TargetVariable);
|
---|
928 |
|
---|
929 |
|
---|
930 | TestMctsWithoutConstants(modifiedProblemData, nVarRefs: 15, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
931 | }
|
---|
932 |
|
---|
933 | [TestMethod]
|
---|
934 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
935 | [TestProperty("Time", "short")]
|
---|
936 | public void MctsSymbReg_NoConstants_Poly10_Part4() {
|
---|
937 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
938 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
939 |
|
---|
940 | // Y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10
|
---|
941 | // Y' = X3*X4 + X5*X6 + X3*X6*X10
|
---|
942 | // simplify problem by changing target
|
---|
943 | var ds = ((Dataset)regProblem.Dataset).ToModifiable();
|
---|
944 | var ys = ds.GetDoubleValues("Y").ToArray();
|
---|
945 | var x1 = ds.GetDoubleValues("X1").ToArray();
|
---|
946 | var x2 = ds.GetDoubleValues("X2").ToArray();
|
---|
947 | var x3 = ds.GetDoubleValues("X3").ToArray();
|
---|
948 | var x4 = ds.GetDoubleValues("X4").ToArray();
|
---|
949 | var x5 = ds.GetDoubleValues("X5").ToArray();
|
---|
950 | var x6 = ds.GetDoubleValues("X6").ToArray();
|
---|
951 | var x7 = ds.GetDoubleValues("X7").ToArray();
|
---|
952 | var x8 = ds.GetDoubleValues("X8").ToArray();
|
---|
953 | var x9 = ds.GetDoubleValues("X9").ToArray();
|
---|
954 | var x10 = ds.GetDoubleValues("X10").ToArray();
|
---|
955 | for (int i = 0; i < ys.Length; i++) {
|
---|
956 | ys[i] -= x1[i] * x2[i];
|
---|
957 | ys[i] -= x1[i] * x7[i] * x9[i];
|
---|
958 | }
|
---|
959 | ds.ReplaceVariable("Y", ys.ToList());
|
---|
960 | var modifiedProblemData = new RegressionProblemData(ds, regProblem.AllowedInputVariables, regProblem.TargetVariable);
|
---|
961 |
|
---|
962 |
|
---|
963 | TestMctsWithoutConstants(modifiedProblemData, nVarRefs: 15, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
964 | }
|
---|
965 |
|
---|
966 | [TestMethod]
|
---|
967 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
968 | [TestProperty("Time", "short")]
|
---|
969 | public void MctsSymbReg_NoConstants_Poly10_Part5() {
|
---|
970 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
971 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
972 |
|
---|
973 | // Y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10
|
---|
974 | // Y' = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9
|
---|
975 | // simplify problem by changing target
|
---|
976 | var ds = ((Dataset)regProblem.Dataset).ToModifiable();
|
---|
977 | var ys = ds.GetDoubleValues("Y").ToArray();
|
---|
978 | var x1 = ds.GetDoubleValues("X1").ToArray();
|
---|
979 | var x2 = ds.GetDoubleValues("X2").ToArray();
|
---|
980 | var x3 = ds.GetDoubleValues("X3").ToArray();
|
---|
981 | var x4 = ds.GetDoubleValues("X4").ToArray();
|
---|
982 | var x5 = ds.GetDoubleValues("X5").ToArray();
|
---|
983 | var x6 = ds.GetDoubleValues("X6").ToArray();
|
---|
984 | var x7 = ds.GetDoubleValues("X7").ToArray();
|
---|
985 | var x8 = ds.GetDoubleValues("X8").ToArray();
|
---|
986 | var x9 = ds.GetDoubleValues("X9").ToArray();
|
---|
987 | var x10 = ds.GetDoubleValues("X10").ToArray();
|
---|
988 | for (int i = 0; i < ys.Length; i++) {
|
---|
989 | ys[i] -= x3[i] * x6[i] * x10[i];
|
---|
990 | }
|
---|
991 | ds.ReplaceVariable("Y", ys.ToList());
|
---|
992 | var modifiedProblemData = new RegressionProblemData(ds, regProblem.AllowedInputVariables, regProblem.TargetVariable);
|
---|
993 |
|
---|
994 |
|
---|
995 | TestMctsWithoutConstants(modifiedProblemData, nVarRefs: 15, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
996 | }
|
---|
997 |
|
---|
998 | [TestMethod]
|
---|
999 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1000 | [TestProperty("Time", "short")]
|
---|
1001 | public void MctsSymbReg_NoConstants_Poly10_Part6() {
|
---|
1002 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
1003 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
1004 |
|
---|
1005 | // Y = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10
|
---|
1006 | // Y' = X1*X2 + X3*X4 + X5*X6 + X3*X6*X10
|
---|
1007 | // simplify problem by changing target
|
---|
1008 | var ds = ((Dataset)regProblem.Dataset).ToModifiable();
|
---|
1009 | var ys = ds.GetDoubleValues("Y").ToArray();
|
---|
1010 | var x1 = ds.GetDoubleValues("X1").ToArray();
|
---|
1011 | var x2 = ds.GetDoubleValues("X2").ToArray();
|
---|
1012 | var x3 = ds.GetDoubleValues("X3").ToArray();
|
---|
1013 | var x4 = ds.GetDoubleValues("X4").ToArray();
|
---|
1014 | var x5 = ds.GetDoubleValues("X5").ToArray();
|
---|
1015 | var x6 = ds.GetDoubleValues("X6").ToArray();
|
---|
1016 | var x7 = ds.GetDoubleValues("X7").ToArray();
|
---|
1017 | var x8 = ds.GetDoubleValues("X8").ToArray();
|
---|
1018 | var x9 = ds.GetDoubleValues("X9").ToArray();
|
---|
1019 | var x10 = ds.GetDoubleValues("X10").ToArray();
|
---|
1020 | for (int i = 0; i < ys.Length; i++) {
|
---|
1021 | ys[i] -= x1[i] * x7[i] * x9[i];
|
---|
1022 | }
|
---|
1023 | ds.ReplaceVariable("Y", ys.ToList());
|
---|
1024 | var modifiedProblemData = new RegressionProblemData(ds, regProblem.AllowedInputVariables, regProblem.TargetVariable);
|
---|
1025 |
|
---|
1026 |
|
---|
1027 | TestMctsWithoutConstants(modifiedProblemData, nVarRefs: 9, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
1028 | }
|
---|
1029 |
|
---|
1030 |
|
---|
1031 | [TestMethod]
|
---|
1032 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1033 | [TestProperty("Time", "long")]
|
---|
1034 | public void MctsSymbReg_NoConstants_Poly10_250rows() {
|
---|
1035 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider(seed: 1234);
|
---|
1036 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Poly-10")));
|
---|
1037 | regProblem.TrainingPartition.Start = 0;
|
---|
1038 | regProblem.TrainingPartition.End = regProblem.Dataset.Rows;
|
---|
1039 | regProblem.TestPartition.Start = 0;
|
---|
1040 | regProblem.TestPartition.End = 2;
|
---|
1041 | TestMctsWithoutConstants(regProblem, nVarRefs: 15, iterations: 200000, allowExp: false, allowLog: false, allowInv: false);
|
---|
1042 | }
|
---|
1043 | [TestMethod]
|
---|
1044 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1045 | [TestProperty("Time", "long")]
|
---|
1046 | public void MctsSymbReg_NoConstants_Poly10_10000rows() {
|
---|
1047 | // as poly-10 but more rows
|
---|
1048 | var rand = new FastRandom(1234);
|
---|
1049 | var x1 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1050 | var x2 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1051 | var x3 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1052 | var x4 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1053 | var x5 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1054 | var x6 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1055 | var x7 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1056 | var x8 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1057 | var x9 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1058 | var x10 = Enumerable.Range(0, 10000).Select(_ => rand.NextDouble()).ToList();
|
---|
1059 | var ys = new List<double>();
|
---|
1060 | for (int i = 0; i < x1.Count; i++) {
|
---|
1061 | ys.Add(x1[i] * x2[i] + x3[i] * x4[i] + x5[i] * x6[i] + x1[i] * x7[i] * x9[i] + x3[i] * x6[i] * x10[i]);
|
---|
1062 | }
|
---|
1063 |
|
---|
1064 | var ds = new Dataset(new string[] { "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "y" },
|
---|
1065 | new[] { x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, ys });
|
---|
1066 |
|
---|
1067 |
|
---|
1068 | var problemData = new RegressionProblemData(ds, new string[] { "a", "b", "c", "d", "e", "f", "g", "h", "i", "j" }, "y");
|
---|
1069 |
|
---|
1070 | problemData.TrainingPartition.Start = 0;
|
---|
1071 | problemData.TrainingPartition.End = problemData.Dataset.Rows;
|
---|
1072 | problemData.TestPartition.Start = 0;
|
---|
1073 | problemData.TestPartition.End = 2; // must not be empty
|
---|
1074 |
|
---|
1075 |
|
---|
1076 | TestMctsWithoutConstants(problemData, nVarRefs: 15, iterations: 100000, allowExp: false, allowLog: false, allowInv: false);
|
---|
1077 | }
|
---|
1078 |
|
---|
1079 | [TestMethod]
|
---|
1080 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1081 | [TestProperty("Time", "short")]
|
---|
1082 | public void MctsSymbReg_NoConstants_TwoVars() {
|
---|
1083 |
|
---|
1084 | // y = x1 + x2 + x1*x2 + x1*x2*x2 + x1*x1*x2
|
---|
1085 | var rand = new FastRandom(1234);
|
---|
1086 | var x1 = Enumerable.Range(0, 100).Select(_ => rand.NextDouble()).ToList();
|
---|
1087 | var x2 = Enumerable.Range(0, 100).Select(_ => rand.NextDouble()).ToList();
|
---|
1088 | var ys = x1.Zip(x2, (x1i, x2i) => x1i + x2i + x1i * x2i + x1i * x2i * x2i + x1i * x1i * x2i).ToList();
|
---|
1089 |
|
---|
1090 | var ds = new Dataset(new string[] { "a", "b", "y" }, new[] { x1, x2, ys });
|
---|
1091 |
|
---|
1092 | var problemData = new RegressionProblemData(ds, new string[] { "a", "b" }, "y");
|
---|
1093 |
|
---|
1094 |
|
---|
1095 | TestMctsWithoutConstants(problemData, nVarRefs: 10, iterations: 10000, allowExp: false, allowLog: false, allowInv: false);
|
---|
1096 | }
|
---|
1097 |
|
---|
1098 | [TestMethod]
|
---|
1099 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1100 | [TestProperty("Time", "short")]
|
---|
1101 | public void MctsSymbReg_NoConstants_Misleading() {
|
---|
1102 |
|
---|
1103 | // y = a + baaaaa (the effect of the second term should be very small)
|
---|
1104 | // the alg will quickly find that a has big effect and will search below a
|
---|
1105 | // since we prevent a + a... the algorithm must find the correct expression via a + b...
|
---|
1106 | // however b has a small effect so the branch might not be identified as relevant
|
---|
1107 |
|
---|
1108 | var rand = new FastRandom(1234);
|
---|
1109 | var @as = Enumerable.Range(0, 100).Select(_ => rand.NextDouble()).ToList();
|
---|
1110 | var bs = Enumerable.Range(0, 100).Select(_ => rand.NextDouble()).ToList();
|
---|
1111 | var cs = Enumerable.Range(0, 100).Select(_ => rand.NextDouble() * 1.0e-3).ToList();
|
---|
1112 | var ds = Enumerable.Range(0, 100).Select(_ => rand.NextDouble()).ToList();
|
---|
1113 | var es = Enumerable.Range(0, 100).Select(_ => rand.NextDouble()).ToList();
|
---|
1114 | var ys = new double[@as.Count];
|
---|
1115 | for (int i = 0; i < ys.Length; i++)
|
---|
1116 | ys[i] = @as[i] + bs[i] + @as[i] * bs[i] * cs[i];
|
---|
1117 |
|
---|
1118 | var dataset = new Dataset(new string[] { "a", "b", "c", "d", "e", "y" }, new[] { @as, bs, cs, ds, es, ys.ToList() });
|
---|
1119 |
|
---|
1120 | var problemData = new RegressionProblemData(dataset, new string[] { "a", "b", "c", "d", "e" }, "y");
|
---|
1121 |
|
---|
1122 |
|
---|
1123 | TestMctsWithoutConstants(problemData, nVarRefs: 10, iterations: 10000, allowExp: false, allowLog: false, allowInv: false);
|
---|
1124 | }
|
---|
1125 | #endregion
|
---|
1126 |
|
---|
1127 | #region restricted structure but including numeric constants
|
---|
1128 |
|
---|
1129 | [TestMethod]
|
---|
1130 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1131 | [TestProperty("Time", "short")]
|
---|
1132 | public void MctsSymbRegKeijzer7() {
|
---|
1133 | // ln(x)
|
---|
1134 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1135 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 7 f(")));
|
---|
1136 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1137 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1138 | TestMcts(regProblem, allowExp: false, allowLog: true, allowInv: false);
|
---|
1139 | }
|
---|
1140 |
|
---|
1141 | /*
|
---|
1142 | // [TestMethod]
|
---|
1143 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1144 | [TestProperty("Time", "short")]
|
---|
1145 | public void MctsSymbRegBenchmarkNguyen5() {
|
---|
1146 | // sin(x²)cos(x) - 1
|
---|
1147 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider();
|
---|
1148 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F5 ")));
|
---|
1149 | TestMcts(regProblem);
|
---|
1150 | }
|
---|
1151 | // [TestMethod]
|
---|
1152 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1153 | [TestProperty("Time", "short")]
|
---|
1154 | public void MctsSymbRegBenchmarkNguyen6() {
|
---|
1155 | // sin(x) + sin(x + x²)
|
---|
1156 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider();
|
---|
1157 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F6 ")));
|
---|
1158 | TestMcts(regProblem);
|
---|
1159 | }
|
---|
1160 | */
|
---|
1161 | [TestMethod]
|
---|
1162 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1163 | [TestProperty("Time", "short")]
|
---|
1164 | public void MctsSymbRegBenchmarkNguyen7() {
|
---|
1165 | // log(x + 1) + log(x² + 1)
|
---|
1166 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
1167 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F7 ")));
|
---|
1168 | TestMcts(regProblem, maxVariableReferences: 5, allowExp: false, allowLog: true, allowInv: false);
|
---|
1169 | }
|
---|
1170 | [TestMethod]
|
---|
1171 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1172 | [TestProperty("Time", "short")]
|
---|
1173 | public void MctsSymbRegBenchmarkNguyen8() {
|
---|
1174 | // Sqrt(x)
|
---|
1175 | // = x ^ 0.5
|
---|
1176 | // = exp(0.5 * log(x))
|
---|
1177 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
1178 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F8 ")));
|
---|
1179 | TestMcts(regProblem, maxVariableReferences: 5, allowExp: true, allowLog: true, allowInv: false);
|
---|
1180 | }
|
---|
1181 | /*
|
---|
1182 | // [TestMethod]
|
---|
1183 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1184 | [TestProperty("Time", "short")]
|
---|
1185 | public void MctsSymbRegBenchmarkNguyen9() {
|
---|
1186 | // sin(x) + sin(y²)
|
---|
1187 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider();
|
---|
1188 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F9 ")));
|
---|
1189 | TestMcts(regProblem);
|
---|
1190 | }
|
---|
1191 | // [TestMethod]
|
---|
1192 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1193 | [TestProperty("Time", "short")]
|
---|
1194 | public void MctsSymbRegBenchmarkNguyen10() {
|
---|
1195 | // 2sin(x)cos(y)
|
---|
1196 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider();
|
---|
1197 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F10 ")));
|
---|
1198 | TestMcts(regProblem);
|
---|
1199 | }
|
---|
1200 | */
|
---|
1201 | [TestMethod]
|
---|
1202 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1203 | [TestProperty("Time", "short")]
|
---|
1204 | public void MctsSymbRegBenchmarkNguyen11() {
|
---|
1205 | // x ^ y , x > 0, y > 0
|
---|
1206 | // = exp(y * log(x))
|
---|
1207 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
1208 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F11 ")));
|
---|
1209 | TestMcts(regProblem, maxVariableReferences: 5, allowExp: true, allowLog: true, allowInv: false);
|
---|
1210 | }
|
---|
1211 | [TestMethod]
|
---|
1212 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1213 | [TestProperty("Time", "short")]
|
---|
1214 | public void MctsSymbRegBenchmarkNguyen12() {
|
---|
1215 | // x^4 - x³ + y²/2 - y
|
---|
1216 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.NguyenInstanceProvider(seed: 1234);
|
---|
1217 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("F12 ")));
|
---|
1218 | TestMcts(regProblem, maxVariableReferences: 20, allowExp: false, allowLog: false, allowInv: false);
|
---|
1219 | }
|
---|
1220 |
|
---|
1221 | #endregion
|
---|
1222 |
|
---|
1223 | #region keijzer
|
---|
1224 | [TestMethod]
|
---|
1225 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1226 | [TestProperty("Time", "long")]
|
---|
1227 | public void MctsSymbRegBenchmarkKeijzer5() {
|
---|
1228 | // (30 * x * z) / ((x - 10) * y²)
|
---|
1229 | // = 30 x z / (xy² - y²)
|
---|
1230 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1231 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 5 f(")));
|
---|
1232 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1233 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1234 | TestMcts(regProblem, maxVariableReferences: 20, allowExp: false, allowLog: false, allowInv: true);
|
---|
1235 | }
|
---|
1236 |
|
---|
1237 | [TestMethod]
|
---|
1238 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1239 | [TestProperty("Time", "short")]
|
---|
1240 | public void MctsSymbRegBenchmarkKeijzer6() {
|
---|
1241 | // Keijzer 6 f(x) = Sum(1 / i) From 1 to X , x \in [0..120]
|
---|
1242 | // we can only approximate this
|
---|
1243 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1244 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 6 f(")));
|
---|
1245 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1246 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1247 | TestMcts(regProblem, maxVariableReferences: 20, allowExp: false, allowLog: false, allowInv: true);
|
---|
1248 | }
|
---|
1249 |
|
---|
1250 |
|
---|
1251 | [TestMethod]
|
---|
1252 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1253 | [TestProperty("Time", "short")]
|
---|
1254 | public void MctsSymbRegBenchmarkKeijzer8() {
|
---|
1255 | // sqrt(x)
|
---|
1256 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1257 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 8 f(")));
|
---|
1258 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1259 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1260 | TestMcts(regProblem, maxVariableReferences: 5, allowExp: true, allowLog: true, allowInv: false);
|
---|
1261 | }
|
---|
1262 |
|
---|
1263 | [TestMethod]
|
---|
1264 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1265 | [TestProperty("Time", "short")]
|
---|
1266 | public void MctsSymbRegBenchmarkKeijzer9() {
|
---|
1267 | // arcsinh(x) i.e. ln(x + sqrt(x² + 1))
|
---|
1268 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1269 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 9 f(")));
|
---|
1270 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1271 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1272 | TestMcts(regProblem, maxVariableReferences: 5, allowExp: true, allowLog: true, allowInv: false);
|
---|
1273 | }
|
---|
1274 |
|
---|
1275 | /*
|
---|
1276 | [TestMethod]
|
---|
1277 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1278 | [TestProperty("Time", "short")]
|
---|
1279 | public void MctsSymbRegBenchmarkKeijzer11() {
|
---|
1280 | // xy + sin( (x-1) (y-1) )
|
---|
1281 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider();
|
---|
1282 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 11 f(")));
|
---|
1283 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1284 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1285 | TestMcts(regProblem, successThreshold: 0.99); // cannot solve this yet
|
---|
1286 | }
|
---|
1287 | */
|
---|
1288 | [TestMethod]
|
---|
1289 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1290 | [TestProperty("Time", "short")]
|
---|
1291 | public void MctsSymbRegBenchmarkKeijzer12() {
|
---|
1292 | // x^4 - x³ + y² / 2 - y, same as Nguyen 12
|
---|
1293 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1294 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 12 f(")));
|
---|
1295 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1296 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1297 | TestMcts(regProblem, maxVariableReferences: 15, allowExp: false, allowLog: false, allowInv: false);
|
---|
1298 | }
|
---|
1299 | [TestMethod]
|
---|
1300 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1301 | [TestProperty("Time", "short")]
|
---|
1302 | public void MctsSymbRegBenchmarkKeijzer14() {
|
---|
1303 | // 8 / (2 + x² + y²)
|
---|
1304 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1305 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 14 f(")));
|
---|
1306 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1307 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1308 | TestMcts(regProblem, maxVariableReferences: 10, allowExp: false, allowLog: false, allowInv: true);
|
---|
1309 | }
|
---|
1310 | [TestMethod]
|
---|
1311 | [TestCategory("Algorithms.DataAnalysis")]
|
---|
1312 | [TestProperty("Time", "short")]
|
---|
1313 | public void MctsSymbRegBenchmarkKeijzer15() {
|
---|
1314 | // x³ / 5 + y³ / 2 - y - x
|
---|
1315 | var provider = new HeuristicLab.Problems.Instances.DataAnalysis.KeijzerInstanceProvider(seed: 1234);
|
---|
1316 | var regProblem = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name.Contains("Keijzer 15 f(")));
|
---|
1317 | // some Keijzer problem instances have very large test partitions (here we are not concerened about test performance)
|
---|
1318 | if (regProblem.TestPartition.End - regProblem.TestPartition.Start > 1000) regProblem.TestPartition.End = regProblem.TestPartition.Start + 1000;
|
---|
1319 | TestMcts(regProblem, maxVariableReferences: 10, allowExp: false, allowLog: false, allowInv: false);
|
---|
1320 | }
|
---|
1321 | #endregion
|
---|
1322 |
|
---|
1323 | private void TestMcts(IRegressionProblemData problemData,
|
---|
1324 | int iterations = 20000,
|
---|
1325 | double successThreshold = 0.99999,
|
---|
1326 | int maxVariableReferences = 5,
|
---|
1327 | bool allowExp = true,
|
---|
1328 | bool allowLog = true,
|
---|
1329 | bool allowInv = true,
|
---|
1330 | bool allowSum = true
|
---|
1331 | ) {
|
---|
1332 | var mctsSymbReg = new MctsSymbolicRegressionAlgorithm();
|
---|
1333 | var regProblem = new RegressionProblem();
|
---|
1334 | regProblem.ProblemDataParameter.Value = problemData;
|
---|
1335 | #region Algorithm Configuration
|
---|
1336 | mctsSymbReg.Problem = regProblem;
|
---|
1337 | mctsSymbReg.Iterations = iterations;
|
---|
1338 | mctsSymbReg.MaxVariableReferences = maxVariableReferences;
|
---|
1339 |
|
---|
1340 | mctsSymbReg.SetSeedRandomly = false;
|
---|
1341 | mctsSymbReg.Seed = 1234;
|
---|
1342 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("exp")), allowExp);
|
---|
1343 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("log")), allowLog);
|
---|
1344 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("1 /")), allowInv);
|
---|
1345 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("t1(x) + t2(x) + ... ")), allowSum);
|
---|
1346 |
|
---|
1347 | mctsSymbReg.ScaleVariables = true;
|
---|
1348 | mctsSymbReg.ConstantOptimizationIterations = 0;
|
---|
1349 |
|
---|
1350 | #endregion
|
---|
1351 | RunAlgorithm(mctsSymbReg);
|
---|
1352 |
|
---|
1353 | Console.WriteLine(mctsSymbReg.ExecutionTime);
|
---|
1354 | var eps = 1.0 - successThreshold;
|
---|
1355 | Assert.AreEqual(1.0, ((DoubleValue)mctsSymbReg.Results["Best solution quality (train)"].Value).Value, eps);
|
---|
1356 | Assert.AreEqual(1.0, ((DoubleValue)mctsSymbReg.Results["Best solution quality (test)"].Value).Value, eps);
|
---|
1357 | }
|
---|
1358 |
|
---|
1359 |
|
---|
1360 | private void TestMctsWithoutConstants(IRegressionProblemData problemData,
|
---|
1361 | int nVarRefs = 10,
|
---|
1362 | int iterations = 200000, double successThreshold = 0.99999,
|
---|
1363 | bool allowExp = true,
|
---|
1364 | bool allowLog = true,
|
---|
1365 | bool allowInv = true,
|
---|
1366 | bool allowSum = true
|
---|
1367 | ) {
|
---|
1368 | var mctsSymbReg = new MctsSymbolicRegressionAlgorithm();
|
---|
1369 | var regProblem = new RegressionProblem();
|
---|
1370 | regProblem.ProblemDataParameter.Value = problemData;
|
---|
1371 | #region Algorithm Configuration
|
---|
1372 | mctsSymbReg.Problem = regProblem;
|
---|
1373 | mctsSymbReg.Iterations = iterations;
|
---|
1374 | mctsSymbReg.MaxVariableReferences = nVarRefs;
|
---|
1375 | mctsSymbReg.SetSeedRandomly = false;
|
---|
1376 | mctsSymbReg.Seed = 1234;
|
---|
1377 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("exp")), allowExp);
|
---|
1378 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("log")), allowLog);
|
---|
1379 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("1 /")), allowInv);
|
---|
1380 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("t1(x) + t2(x) + ... ")), allowSum);
|
---|
1381 |
|
---|
1382 | // no constants
|
---|
1383 | mctsSymbReg.ScaleVariables = false;
|
---|
1384 | mctsSymbReg.ConstantOptimizationIterations = -1;
|
---|
1385 |
|
---|
1386 | // random policy
|
---|
1387 | // var epsPolicy = new EpsilonGreedy();
|
---|
1388 | // epsPolicy.Eps = 1.0;
|
---|
1389 | // mctsSymbReg.Policy = epsPolicy;
|
---|
1390 |
|
---|
1391 | // UCB tuned
|
---|
1392 | var ucbTuned = new UcbTuned();
|
---|
1393 | ucbTuned.C = 1;
|
---|
1394 | mctsSymbReg.Policy = ucbTuned;
|
---|
1395 |
|
---|
1396 |
|
---|
1397 | #endregion
|
---|
1398 | RunAlgorithm(mctsSymbReg);
|
---|
1399 |
|
---|
1400 | Console.WriteLine(mctsSymbReg.ExecutionTime);
|
---|
1401 | var eps = 1.0 - successThreshold;
|
---|
1402 | Assert.AreEqual(1.0, ((DoubleValue)mctsSymbReg.Results["Best solution quality (train)"].Value).Value, eps);
|
---|
1403 | Assert.AreEqual(1.0, ((DoubleValue)mctsSymbReg.Results["Best solution quality (test)"].Value).Value, eps);
|
---|
1404 | }
|
---|
1405 |
|
---|
1406 | private void TestMctsNumberOfSolutions(IRegressionProblemData problemData, int maxNumberOfVariables, int expectedNumberOfSolutions,
|
---|
1407 | bool allowProd = true,
|
---|
1408 | bool allowExp = true,
|
---|
1409 | bool allowLog = true,
|
---|
1410 | bool allowInv = true,
|
---|
1411 | bool allowSum = true
|
---|
1412 | ) {
|
---|
1413 | var mctsSymbReg = new MctsSymbolicRegressionAlgorithm();
|
---|
1414 | var regProblem = new RegressionProblem();
|
---|
1415 | regProblem.ProblemDataParameter.Value = problemData;
|
---|
1416 | #region Algorithm Configuration
|
---|
1417 |
|
---|
1418 | mctsSymbReg.SetSeedRandomly = false;
|
---|
1419 | mctsSymbReg.Seed = 1234;
|
---|
1420 | mctsSymbReg.Problem = regProblem;
|
---|
1421 | mctsSymbReg.Iterations = int.MaxValue; // stopping when all solutions have been enumerated
|
---|
1422 | mctsSymbReg.MaxVariableReferences = maxNumberOfVariables;
|
---|
1423 | var ucbPolicy = new Ucb();
|
---|
1424 | ucbPolicy.C = 1000; // essentially breadth first search
|
---|
1425 | mctsSymbReg.Policy = ucbPolicy;
|
---|
1426 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.StartsWith("x * y * ...")), allowProd);
|
---|
1427 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("exp(c * x * y ...)")), allowExp);
|
---|
1428 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("log(c + c1 x + c2 x + ...)")), allowLog);
|
---|
1429 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("1 / (1 + c1 x + c2 x + ...)")), allowInv);
|
---|
1430 | mctsSymbReg.AllowedFactors.SetItemCheckedState(mctsSymbReg.AllowedFactors.Single(s => s.Value.Contains("t1(x) + t2(x) + ... ")), allowSum);
|
---|
1431 | #endregion
|
---|
1432 | RunAlgorithm(mctsSymbReg);
|
---|
1433 |
|
---|
1434 | Console.WriteLine(mctsSymbReg.ExecutionTime);
|
---|
1435 | Assert.AreEqual(expectedNumberOfSolutions, ((IntValue)mctsSymbReg.Results["Iterations"].Value).Value);
|
---|
1436 | }
|
---|
1437 |
|
---|
1438 |
|
---|
1439 | // same as in SamplesUtil
|
---|
1440 | private void RunAlgorithm(IAlgorithm a) {
|
---|
1441 | var trigger = new EventWaitHandle(false, EventResetMode.ManualReset);
|
---|
1442 | Exception ex = null;
|
---|
1443 | a.Stopped += (src, e) => { trigger.Set(); };
|
---|
1444 | a.ExceptionOccurred += (src, e) => { ex = e.Value; trigger.Set(); };
|
---|
1445 | a.Prepare();
|
---|
1446 | a.Start();
|
---|
1447 | trigger.WaitOne();
|
---|
1448 |
|
---|
1449 | Assert.AreEqual(ex, null);
|
---|
1450 | }
|
---|
1451 |
|
---|
1452 | }
|
---|
1453 | }
|
---|