1 | using System;
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2 | using System.Collections.Generic;
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3 | using HeuristicLab.Problems.DataAnalysis;
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4 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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5 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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6 | using System.Linq;
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7 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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8 |
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9 | namespace Tests {
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10 | [TestClass]
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11 | public class AutoDiffTest {
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12 | [TestMethod]
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13 | public void TestMethod1() {
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14 | {
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15 | var parser = new InfixExpressionParser();
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16 | var t = parser.Parse("2.0*x+y");
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17 |
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18 | var evaluator = new IntervalEvaluator();
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19 | var intervals = new Dictionary<string, AlgebraicInterval>();
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20 | intervals.Add("x", new AlgebraicInterval(-1.0, 1.0));
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21 | intervals.Add("y", new AlgebraicInterval(2.0, 10.0));
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22 | var resultInterval = evaluator.Evaluate(t, intervals);
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23 | Assert.AreEqual(0, resultInterval.LowerBound);
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24 | Assert.AreEqual(12, resultInterval.UpperBound);
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25 | }
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26 |
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27 | {
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28 | var parser = new InfixExpressionParser();
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29 | var t = parser.Parse("2.0*x+y");
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30 |
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31 | var evaluator = new VectorEvaluator();
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32 | var vars = new string[] { "x", "y", "f(x)" };
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33 | var values = new double[,] {
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34 | { 1, 1, 0 },
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35 | { 2, 1, 0 },
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36 | { 3, -1, 0 },
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37 | { 4, -1, 0 },
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38 | { 5, -1, 0 },
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39 | };
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40 |
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41 | var ds = new Dataset(vars, values);
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42 | var problemData = new RegressionProblemData(ds, vars, "f(x)");
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43 | var train = evaluator.Evaluate(t, ds, problemData.TrainingIndices.ToArray());
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44 | Assert.AreEqual(2, train.Length);
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45 | Assert.AreEqual(3, train[0]);
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46 | Assert.AreEqual(5, train[1]);
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47 |
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48 | var test = evaluator.Evaluate(t, ds, problemData.TestIndices.ToArray());
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49 | Assert.AreEqual(3, test.Length);
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50 | Assert.AreEqual(5, test[0]);
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51 | Assert.AreEqual(7, test[1]);
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52 | Assert.AreEqual(9, test[2]);
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53 | }
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54 |
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55 | {
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56 | var parser = new InfixExpressionParser();
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57 | var t = parser.Parse("2.0*x+y");
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58 | var p0 = t.IterateNodesPostfix().First(n => n is ConstantTreeNode);
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59 | var p1 = t.IterateNodesPostfix().First(n => (n is VariableTreeNode var) && var.VariableName == "y");
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60 | var paramNodes = new ISymbolicExpressionTreeNode[] { p0, p1 };
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61 |
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62 | var evaluator = new VectorAutoDiffEvaluator();
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63 | var vars = new string[] { "x", "y", "f(x)" };
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64 | var values = new double[,] {
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65 | { 1, 1, 0 },
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66 | { 2, 1, 0 },
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67 | { 3, -1, 0 },
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68 | { 4, -1, 0 },
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69 | { 5, -1, 0 },
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70 | };
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71 |
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72 | var ds = new Dataset(vars, values);
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73 | var problemData = new RegressionProblemData(ds, vars, "f(x)");
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74 | evaluator.Evaluate(t, ds, problemData.TrainingIndices.ToArray(), paramNodes, out double[] train, out double[,] trainJac);
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75 | Assert.AreEqual(2, train.Length);
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76 | Assert.AreEqual(3, train[0]);
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77 | Assert.AreEqual(5, train[1]);
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78 | // check jac
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79 | Assert.AreEqual(1, trainJac[0, 0]);
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80 | Assert.AreEqual(1, trainJac[0, 1]);
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81 | Assert.AreEqual(2, trainJac[1, 0]);
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82 | Assert.AreEqual(1, trainJac[1, 1]);
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83 |
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84 | evaluator.Evaluate(t, ds, problemData.TestIndices.ToArray(), paramNodes, out double[] test, out double[,] testJac);
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85 | Assert.AreEqual(3, test.Length);
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86 | Assert.AreEqual(5, test[0]);
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87 | Assert.AreEqual(7, test[1]);
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88 | Assert.AreEqual(9, test[2]);
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89 |
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90 | // check jac
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91 | Assert.AreEqual(3, testJac[0, 0]);
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92 | Assert.AreEqual(-1, testJac[0, 1]);
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93 | Assert.AreEqual(4, testJac[1, 0]);
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94 | Assert.AreEqual(-1, testJac[1, 1]);
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95 | Assert.AreEqual(5, testJac[2, 0]);
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96 | Assert.AreEqual(-1, testJac[2, 1]);
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97 |
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98 | }
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99 |
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100 | }
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101 | }
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102 | }
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