1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Diagnostics;
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25 | using System.Linq;
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26 | using HeuristicLab.Analysis;
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27 | using HeuristicLab.Collections;
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28 | using HeuristicLab.Common;
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29 | using HeuristicLab.Core;
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30 | using HeuristicLab.Data;
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31 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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32 | using HeuristicLab.Optimization;
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33 | using HeuristicLab.Parameters;
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34 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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35 | using HeuristicLab.Problems.DataAnalysis;
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36 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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37 | using HeuristicLab.Problems.Instances;
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38 | using Variable = HeuristicLab.Problems.DataAnalysis.Symbolic.Variable;
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39 |
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40 | namespace HeuristicLab.Problems.DynamicalSystemsModelling {
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41 |
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42 |
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43 |
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44 |
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45 | // Eine weitere Möglichkeit ist spline-smoothing der Daten (über Zeit) um damit für jede Zielvariable
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46 | // einen bereinigten (Rauschen) Wert und die Ableitung dy/dt für alle Beobachtungen zu bekommen
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47 | // danach kann man die Regression direkt für dy/dt anwenden (mit bereinigten y als inputs)
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48 | [Item("Dynamical Systems Modelling Problem", "TODO")]
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49 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 900)]
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50 | [StorableClass]
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51 | public sealed class Problem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
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52 | #region parameter names
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53 | private const string ProblemDataParameterName = "Data";
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54 | private const string TargetVariablesParameterName = "Target variables";
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55 | private const string FunctionSetParameterName = "Function set";
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56 | private const string MaximumLengthParameterName = "Size limit";
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57 | private const string MaximumParameterOptimizationIterationsParameterName = "Max. parameter optimization iterations";
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58 | private const string NumberOfLatentVariablesParameterName = "Number of latent variables";
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59 | private const string NumericIntegrationStepsParameterName = "Steps for numeric integration";
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60 | private const string TrainingEpisodesParameterName = "Training episodes";
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61 | private const string OptimizeParametersForEpisodesParameterName = "Optimize parameters for episodes";
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62 | private const string OdeSolverParameterName = "ODE Solver";
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63 | #endregion
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64 |
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65 | #region Parameter Properties
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66 | IParameter IDataAnalysisProblem.ProblemDataParameter { get { return ProblemDataParameter; } }
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67 |
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68 | public IValueParameter<IRegressionProblemData> ProblemDataParameter {
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69 | get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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70 | }
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71 | public IValueParameter<ReadOnlyCheckedItemList<StringValue>> TargetVariablesParameter {
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72 | get { return (IValueParameter<ReadOnlyCheckedItemList<StringValue>>)Parameters[TargetVariablesParameterName]; }
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73 | }
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74 | public IValueParameter<ReadOnlyCheckedItemList<StringValue>> FunctionSetParameter {
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75 | get { return (IValueParameter<ReadOnlyCheckedItemList<StringValue>>)Parameters[FunctionSetParameterName]; }
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76 | }
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77 | public IFixedValueParameter<IntValue> MaximumLengthParameter {
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78 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumLengthParameterName]; }
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79 | }
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80 | public IFixedValueParameter<IntValue> MaximumParameterOptimizationIterationsParameter {
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81 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumParameterOptimizationIterationsParameterName]; }
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82 | }
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83 | public IFixedValueParameter<IntValue> NumberOfLatentVariablesParameter {
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84 | get { return (IFixedValueParameter<IntValue>)Parameters[NumberOfLatentVariablesParameterName]; }
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85 | }
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86 | public IFixedValueParameter<IntValue> NumericIntegrationStepsParameter {
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87 | get { return (IFixedValueParameter<IntValue>)Parameters[NumericIntegrationStepsParameterName]; }
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88 | }
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89 | public IValueParameter<ItemList<IntRange>> TrainingEpisodesParameter {
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90 | get { return (IValueParameter<ItemList<IntRange>>)Parameters[TrainingEpisodesParameterName]; }
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91 | }
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92 | public IFixedValueParameter<BoolValue> OptimizeParametersForEpisodesParameter {
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93 | get { return (IFixedValueParameter<BoolValue>)Parameters[OptimizeParametersForEpisodesParameterName]; }
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94 | }
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95 | public IConstrainedValueParameter<StringValue> OdeSolverParameter {
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96 | get { return (IConstrainedValueParameter<StringValue>)Parameters[OdeSolverParameterName]; }
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97 | }
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98 | #endregion
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99 |
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100 | #region Properties
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101 | public IRegressionProblemData ProblemData {
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102 | get { return ProblemDataParameter.Value; }
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103 | set { ProblemDataParameter.Value = value; }
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104 | }
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105 | IDataAnalysisProblemData IDataAnalysisProblem.ProblemData { get { return ProblemData; } }
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106 |
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107 | public ReadOnlyCheckedItemList<StringValue> TargetVariables {
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108 | get { return TargetVariablesParameter.Value; }
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109 | }
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110 |
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111 | public ReadOnlyCheckedItemList<StringValue> FunctionSet {
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112 | get { return FunctionSetParameter.Value; }
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113 | }
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114 |
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115 | public int MaximumLength {
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116 | get { return MaximumLengthParameter.Value.Value; }
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117 | }
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118 | public int MaximumParameterOptimizationIterations {
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119 | get { return MaximumParameterOptimizationIterationsParameter.Value.Value; }
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120 | }
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121 | public int NumberOfLatentVariables {
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122 | get { return NumberOfLatentVariablesParameter.Value.Value; }
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123 | }
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124 | public int NumericIntegrationSteps {
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125 | get { return NumericIntegrationStepsParameter.Value.Value; }
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126 | }
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127 | public IEnumerable<IntRange> TrainingEpisodes {
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128 | get { return TrainingEpisodesParameter.Value; }
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129 | }
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130 | public bool OptimizeParametersForEpisodes {
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131 | get { return OptimizeParametersForEpisodesParameter.Value.Value; }
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132 | }
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133 |
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134 | public string OdeSolver {
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135 | get { return OdeSolverParameter.Value.Value; }
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136 | set {
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137 | var matchingValue = OdeSolverParameter.ValidValues.FirstOrDefault(v => v.Value == value);
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138 | if (matchingValue == null) throw new ArgumentOutOfRangeException();
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139 | else OdeSolverParameter.Value = matchingValue;
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140 | }
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141 | }
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142 |
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143 | #endregion
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144 |
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145 | public event EventHandler ProblemDataChanged;
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146 |
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147 | public override bool Maximization {
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148 | get { return false; } // we minimize NMSE
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149 | }
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150 |
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151 | #region item cloning and persistence
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152 | // persistence
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153 | [StorableConstructor]
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154 | private Problem(bool deserializing) : base(deserializing) { }
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155 | [StorableHook(HookType.AfterDeserialization)]
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156 | private void AfterDeserialization() {
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157 | if (!Parameters.ContainsKey(OptimizeParametersForEpisodesParameterName)) {
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158 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersForEpisodesParameterName, "Flag to select if parameters should be optimized globally or for each episode individually.", new BoolValue(false)));
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159 | }
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160 | RegisterEventHandlers();
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161 | }
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162 |
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163 | // cloning
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164 | private Problem(Problem original, Cloner cloner)
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165 | : base(original, cloner) {
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166 | RegisterEventHandlers();
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167 | }
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168 | public override IDeepCloneable Clone(Cloner cloner) { return new Problem(this, cloner); }
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169 | #endregion
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170 |
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171 | public Problem()
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172 | : base() {
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173 | var targetVariables = new CheckedItemList<StringValue>().AsReadOnly(); // HACK: it would be better to provide a new class derived from IDataAnalysisProblem
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174 | var functions = CreateFunctionSet();
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175 | Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, "The data captured from the dynamical system. Use CSV import functionality to import data.", new RegressionProblemData()));
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176 | Parameters.Add(new ValueParameter<ReadOnlyCheckedItemList<StringValue>>(TargetVariablesParameterName, "Target variables (overrides setting in ProblemData)", targetVariables));
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177 | Parameters.Add(new ValueParameter<ReadOnlyCheckedItemList<StringValue>>(FunctionSetParameterName, "The list of allowed functions", functions));
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178 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumLengthParameterName, "The maximally allowed length of each expression. Set to a small value (5 - 25). Default = 10", new IntValue(10)));
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179 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumParameterOptimizationIterationsParameterName, "The maximum number of iterations for optimization of parameters (using L-BFGS). More iterations makes the algorithm slower, fewer iterations might prevent convergence in the optimization scheme. Default = 100", new IntValue(100)));
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180 | Parameters.Add(new FixedValueParameter<IntValue>(NumberOfLatentVariablesParameterName, "Latent variables (unobserved variables) allow us to produce expressions which are integrated up and can be used in other expressions. They are handled similarly to target variables in forward simulation / integration. The difference to target variables is that there are no data to which the calculated values of latent variables are compared. Set to a small value (0 .. 5) as necessary (default = 0)", new IntValue(0)));
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181 | Parameters.Add(new FixedValueParameter<IntValue>(NumericIntegrationStepsParameterName, "Number of steps in the numeric integration that are taken from one row to the next (set to 1 to 100). More steps makes the algorithm slower, less steps worsens the accuracy of the numeric integration scheme.", new IntValue(10)));
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182 | Parameters.Add(new ValueParameter<ItemList<IntRange>>(TrainingEpisodesParameterName, "A list of ranges that should be used for training, each range represents an independent episode. This overrides the TrainingSet parameter in ProblemData.", new ItemList<IntRange>()));
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183 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersForEpisodesParameterName, "Flag to select if parameters should be optimized globally or for each episode individually.", new BoolValue(false)));
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184 |
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185 | var solversStr = new string[] { "HeuristicLab", "CVODES" };
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186 | var solvers = new ItemSet<StringValue>(
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187 | solversStr.Select(s => new StringValue(s).AsReadOnly())
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188 | );
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189 | Parameters.Add(new ConstrainedValueParameter<StringValue>(OdeSolverParameterName, "The solver to use for solving the initial value ODE problems", solvers, solvers.First()));
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190 |
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191 | RegisterEventHandlers();
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192 | InitAllParameters();
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193 |
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194 | // TODO: do not clear selection of target variables when the input variables are changed (keep selected target variables)
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195 | // TODO: UI hangs when selecting / deselecting input variables because the encoding is updated on each item
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196 | // TODO: use training range as default training episode
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197 | // TODO: write back optimized parameters to solution?
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198 |
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199 | }
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200 |
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201 | public override double Evaluate(Individual individual, IRandom random) {
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202 | var trees = individual.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
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203 | // write back optimized parameters to tree nodes instead of the separate OptTheta variable
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204 | // retreive optimized parameters from nodes?
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205 |
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206 | if (OptimizeParametersForEpisodes) {
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207 | int eIdx = 0;
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208 | double totalNMSE = 0.0;
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209 | int totalSize = 0;
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210 | foreach (var episode in TrainingEpisodes) {
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211 | double[] optTheta;
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212 | double nmse;
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213 | OptimizeForEpisodes(trees, random, new[] { episode }, out optTheta, out nmse);
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214 | individual["OptTheta_" + eIdx] = new DoubleArray(optTheta); // write back optimized parameters so that we can use them in the Analysis method
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215 | eIdx++;
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216 | totalNMSE += nmse * episode.Size;
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217 | totalSize += episode.Size;
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218 | }
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219 | return totalNMSE / totalSize;
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220 | } else {
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221 | double[] optTheta;
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222 | double nmse;
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223 | OptimizeForEpisodes(trees, random, TrainingEpisodes, out optTheta, out nmse);
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224 | individual["OptTheta"] = new DoubleArray(optTheta); // write back optimized parameters so that we can use them in the Analysis method
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225 | return nmse;
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226 | }
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227 | }
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228 |
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229 | private void OptimizeForEpisodes(
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230 | ISymbolicExpressionTree[] trees,
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231 | IRandom random,
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232 | IEnumerable<IntRange> episodes,
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233 | out double[] optTheta,
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234 | out double nmse) {
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235 | var rows = episodes.SelectMany(e => Enumerable.Range(e.Start, e.End - e.Start)).ToArray();
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236 | var problemData = ProblemData;
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237 | var targetVars = TargetVariables.CheckedItems.OrderBy(i => i.Index).Select(i => i.Value.Value).ToArray();
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238 | var latentVariables = Enumerable.Range(1, NumberOfLatentVariables).Select(i => "λ" + i).ToArray(); // TODO: must coincide with the variables which are actually defined in the grammar and also for which we actually have trees
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239 | var targetValues = new double[rows.Length, targetVars.Length];
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240 |
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241 | // collect values of all target variables
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242 | var colIdx = 0;
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243 | foreach (var targetVar in targetVars) {
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244 | int rowIdx = 0;
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245 | foreach (var value in problemData.Dataset.GetDoubleValues(targetVar, rows)) {
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246 | targetValues[rowIdx, colIdx] = value;
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247 | rowIdx++;
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248 | }
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249 | colIdx++;
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250 | }
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251 |
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252 | // NOTE: the order of values in parameter matches prefix order of constant nodes in trees
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253 | var paramNodes = new List<ISymbolicExpressionTreeNode>();
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254 | foreach (var t in trees) {
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255 | foreach (var n in t.IterateNodesPrefix()) {
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256 | if (IsConstantNode(n))
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257 | paramNodes.Add(n);
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258 | }
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259 | }
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260 | // init params randomly from Unif(-1e-5, 1e-5)
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261 | // theta contains parameter values for trees and then the initial values for latent variables (a separate vector for each episode)
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262 | // inital values for latent variables are also optimized
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263 | var theta = new double[paramNodes.Count + latentVariables.Length * episodes.Count()];
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264 | for (int i = 0; i < theta.Length; i++)
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265 | theta[i] = random.NextDouble() * 2.0e-2 - 1.0e-2;
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266 |
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267 | optTheta = new double[0];
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268 | if (theta.Length > 0) {
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269 | alglib.minlbfgsstate state;
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270 | alglib.minlbfgsreport report;
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271 | alglib.minlbfgscreate(Math.Min(theta.Length, 5), theta, out state);
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272 | alglib.minlbfgssetcond(state, 0.0, 0.0, 0.0, MaximumParameterOptimizationIterations);
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273 | //alglib.minlbfgssetgradientcheck(state, 1e-6);
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274 | alglib.minlbfgsoptimize(state, EvaluateObjectiveAndGradient, null,
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275 | new object[] { trees, targetVars, problemData, targetValues, episodes.ToArray(), NumericIntegrationSteps, latentVariables, OdeSolver }); //TODO: create a type
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276 |
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277 | alglib.minlbfgsresults(state, out optTheta, out report);
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278 |
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279 | /*
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280 | *
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281 | * L-BFGS algorithm results
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282 |
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283 | INPUT PARAMETERS:
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284 | State - algorithm state
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285 |
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286 | OUTPUT PARAMETERS:
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287 | X - array[0..N-1], solution
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288 | Rep - optimization report:
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289 | * Rep.TerminationType completetion code:
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290 | * -7 gradient verification failed.
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291 | See MinLBFGSSetGradientCheck() for more information.
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292 | * -2 rounding errors prevent further improvement.
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293 | X contains best point found.
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294 | * -1 incorrect parameters were specified
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295 | * 1 relative function improvement is no more than
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296 | EpsF.
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297 | * 2 relative step is no more than EpsX.
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298 | * 4 gradient norm is no more than EpsG
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299 | * 5 MaxIts steps was taken
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300 | * 7 stopping conditions are too stringent,
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301 | further improvement is impossible
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302 | * Rep.IterationsCount contains iterations count
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303 | * NFEV countains number of function calculations
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304 | */
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305 | if (report.terminationtype < 0) { nmse = 10E6; return; }
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306 | }
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307 |
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308 | // perform evaluation for optimal theta to get quality value
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309 | double[] grad = new double[optTheta.Length];
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310 | nmse = double.NaN;
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311 | EvaluateObjectiveAndGradient(optTheta, ref nmse, grad,
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312 | new object[] { trees, targetVars, problemData, targetValues, episodes.ToArray(), NumericIntegrationSteps, latentVariables, OdeSolver });
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313 | if (double.IsNaN(nmse) || double.IsInfinity(nmse)) { nmse = 10E6; return; } // return a large value (TODO: be consistent by using NMSE)
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314 | }
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315 |
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316 | private static void EvaluateObjectiveAndGradient(double[] x, ref double f, double[] grad, object obj) {
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317 | var trees = (ISymbolicExpressionTree[])((object[])obj)[0];
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318 | var targetVariables = (string[])((object[])obj)[1];
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319 | var problemData = (IRegressionProblemData)((object[])obj)[2];
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320 | var targetValues = (double[,])((object[])obj)[3];
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321 | var episodes = (IntRange[])((object[])obj)[4];
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322 | var numericIntegrationSteps = (int)((object[])obj)[5];
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323 | var latentVariables = (string[])((object[])obj)[6];
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324 | var odeSolver = (string)((object[])obj)[7];
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325 |
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326 |
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327 | Tuple<double, Vector>[][] predicted = null; // one array of predictions for each episode
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328 | // TODO: stop integration early for diverging solutions
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329 | predicted = Integrate(
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330 | trees, // we assume trees contain expressions for the change of each target variable over time y'(t)
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331 | problemData.Dataset,
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332 | problemData.AllowedInputVariables.ToArray(),
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333 | targetVariables,
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334 | latentVariables,
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335 | episodes,
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336 | x,
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337 | odeSolver,
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338 | numericIntegrationSteps).ToArray();
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339 |
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340 | if (predicted.Length != targetValues.GetLength(0)) {
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341 | f = double.MaxValue;
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342 | Array.Clear(grad, 0, grad.Length);
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343 | return;
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344 | }
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345 |
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346 | // for normalized MSE = 1/variance(t) * MSE(t, pred)
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347 | // TODO: Perf. (by standardization of target variables before evaluation of all trees)
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348 | var invVar = Enumerable.Range(0, targetVariables.Length)
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349 | .Select(c => Enumerable.Range(0, targetValues.GetLength(0)).Select(row => targetValues[row, c])) // column vectors
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350 | .Select(vec => vec.Variance())
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351 | .Select(v => 1.0 / v)
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352 | .ToArray();
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353 |
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354 | // objective function is NMSE
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355 | f = 0.0;
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356 | int n = predicted.Length;
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357 | double invN = 1.0 / n;
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358 | var g = Vector.Zero;
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359 | int r = 0;
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360 | foreach (var y_pred in predicted) {
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361 | // y_pred contains the predicted values for target variables first and then predicted values for latent variables
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362 | for (int c = 0; c < targetVariables.Length; c++) {
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363 |
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364 | var y_pred_f = y_pred[c].Item1;
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365 | var y = targetValues[r, c];
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366 |
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367 | var res = (y - y_pred_f);
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368 | var ressq = res * res;
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369 | f += ressq * invN * invVar[c];
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370 | g += -2.0 * res * y_pred[c].Item2 * invN * invVar[c];
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371 | }
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372 | r++;
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373 | }
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374 |
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375 | g.CopyTo(grad);
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376 | }
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377 |
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378 | public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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379 | base.Analyze(individuals, qualities, results, random);
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380 |
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381 | if (!results.ContainsKey("Prediction (training)")) {
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382 | results.Add(new Result("Prediction (training)", typeof(ReadOnlyItemList<DataTable>)));
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383 | }
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384 | if (!results.ContainsKey("Prediction (test)")) {
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385 | results.Add(new Result("Prediction (test)", typeof(ReadOnlyItemList<DataTable>)));
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386 | }
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387 | if (!results.ContainsKey("Models")) {
|
---|
388 | results.Add(new Result("Models", typeof(VariableCollection)));
|
---|
389 | }
|
---|
390 |
|
---|
391 | var bestIndividualAndQuality = this.GetBestIndividual(individuals, qualities);
|
---|
392 | var trees = bestIndividualAndQuality.Item1.Values.Select(v => v.Value).OfType<ISymbolicExpressionTree>().ToArray(); // extract all trees from individual
|
---|
393 |
|
---|
394 | var problemData = ProblemData;
|
---|
395 | var targetVars = TargetVariables.CheckedItems.OrderBy(i => i.Index).Select(i => i.Value.Value).ToArray();
|
---|
396 | var latentVariables = Enumerable.Range(1, NumberOfLatentVariables).Select(i => "λ" + i).ToArray(); // TODO: must coincide with the variables which are actually defined in the grammar and also for which we actually have trees
|
---|
397 |
|
---|
398 | var trainingList = new ItemList<DataTable>();
|
---|
399 |
|
---|
400 | if (OptimizeParametersForEpisodes) {
|
---|
401 | var eIdx = 0;
|
---|
402 | var trainingPredictions = new List<Tuple<double, Vector>[][]>();
|
---|
403 | foreach (var episode in TrainingEpisodes) {
|
---|
404 | var episodes = new[] { episode };
|
---|
405 | var optTheta = ((DoubleArray)bestIndividualAndQuality.Item1["OptTheta_" + eIdx]).ToArray(); // see evaluate
|
---|
406 | var trainingPrediction = Integrate(
|
---|
407 | trees, // we assume trees contain expressions for the change of each target variable over time y'(t)
|
---|
408 | problemData.Dataset,
|
---|
409 | problemData.AllowedInputVariables.ToArray(),
|
---|
410 | targetVars,
|
---|
411 | latentVariables,
|
---|
412 | episodes,
|
---|
413 | optTheta,
|
---|
414 | OdeSolver,
|
---|
415 | NumericIntegrationSteps).ToArray();
|
---|
416 | trainingPredictions.Add(trainingPrediction);
|
---|
417 | eIdx++;
|
---|
418 | }
|
---|
419 |
|
---|
420 | // only for target values
|
---|
421 | var trainingRows = TrainingEpisodes.SelectMany(e => Enumerable.Range(e.Start, e.End - e.Start));
|
---|
422 | for (int colIdx = 0; colIdx < targetVars.Length; colIdx++) {
|
---|
423 | var targetVar = targetVars[colIdx];
|
---|
424 | var trainingDataTable = new DataTable(targetVar + " prediction (training)");
|
---|
425 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, trainingRows));
|
---|
426 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, trainingPredictions.SelectMany(arr => arr.Select(row => row[colIdx].Item1)).ToArray());
|
---|
427 | trainingDataTable.Rows.Add(actualValuesRow);
|
---|
428 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
429 | trainingList.Add(trainingDataTable);
|
---|
430 | }
|
---|
431 | results["Prediction (training)"].Value = trainingList.AsReadOnly();
|
---|
432 |
|
---|
433 |
|
---|
434 | var models = new VariableCollection();
|
---|
435 |
|
---|
436 | foreach (var tup in targetVars.Zip(trees, Tuple.Create)) {
|
---|
437 | var targetVarName = tup.Item1;
|
---|
438 | var tree = tup.Item2;
|
---|
439 |
|
---|
440 | var origTreeVar = new HeuristicLab.Core.Variable(targetVarName + "(original)");
|
---|
441 | origTreeVar.Value = (ISymbolicExpressionTree)tree.Clone();
|
---|
442 | models.Add(origTreeVar);
|
---|
443 | }
|
---|
444 | results["Models"].Value = models;
|
---|
445 | } else {
|
---|
446 | var optTheta = ((DoubleArray)bestIndividualAndQuality.Item1["OptTheta"]).ToArray(); // see evaluate
|
---|
447 | var trainingPrediction = Integrate(
|
---|
448 | trees, // we assume trees contain expressions for the change of each target variable over time dy/dt
|
---|
449 | problemData.Dataset,
|
---|
450 | problemData.AllowedInputVariables.ToArray(),
|
---|
451 | targetVars,
|
---|
452 | latentVariables,
|
---|
453 | TrainingEpisodes,
|
---|
454 | optTheta,
|
---|
455 | OdeSolver,
|
---|
456 | NumericIntegrationSteps).ToArray();
|
---|
457 | // for target values and latent variables
|
---|
458 | var trainingRows = TrainingEpisodes.SelectMany(e => Enumerable.Range(e.Start, e.End - e.Start));
|
---|
459 | for (int colIdx = 0; colIdx < trees.Length; colIdx++) {
|
---|
460 | // is target variable
|
---|
461 | if (colIdx < targetVars.Length) {
|
---|
462 | var targetVar = targetVars[colIdx];
|
---|
463 | var trainingDataTable = new DataTable(targetVar + " prediction (training)");
|
---|
464 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, trainingRows));
|
---|
465 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, trainingPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
466 | trainingDataTable.Rows.Add(actualValuesRow);
|
---|
467 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
468 | trainingList.Add(trainingDataTable);
|
---|
469 | } else {
|
---|
470 | var latentVar = latentVariables[colIdx - targetVars.Length];
|
---|
471 | var trainingDataTable = new DataTable(latentVar + " prediction (training)");
|
---|
472 | var predictedValuesRow = new DataRow(latentVar + " pred.", "Predicted values for " + latentVar, trainingPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
473 | var emptyRow = new DataRow(latentVar);
|
---|
474 | trainingDataTable.Rows.Add(emptyRow);
|
---|
475 | trainingDataTable.Rows.Add(predictedValuesRow);
|
---|
476 | trainingList.Add(trainingDataTable);
|
---|
477 | }
|
---|
478 | }
|
---|
479 | // TODO: DRY for training and test
|
---|
480 | var testList = new ItemList<DataTable>();
|
---|
481 | var testRows = ProblemData.TestIndices.ToArray();
|
---|
482 | var testPrediction = Integrate(
|
---|
483 | trees, // we assume trees contain expressions for the change of each target variable over time y'(t)
|
---|
484 | problemData.Dataset,
|
---|
485 | problemData.AllowedInputVariables.ToArray(),
|
---|
486 | targetVars,
|
---|
487 | latentVariables,
|
---|
488 | new IntRange[] { ProblemData.TestPartition },
|
---|
489 | optTheta,
|
---|
490 | OdeSolver,
|
---|
491 | NumericIntegrationSteps).ToArray();
|
---|
492 |
|
---|
493 | for (int colIdx = 0; colIdx < trees.Length; colIdx++) {
|
---|
494 | // is target variable
|
---|
495 | if (colIdx < targetVars.Length) {
|
---|
496 | var targetVar = targetVars[colIdx];
|
---|
497 | var testDataTable = new DataTable(targetVar + " prediction (test)");
|
---|
498 | var actualValuesRow = new DataRow(targetVar, "The values of " + targetVar, problemData.Dataset.GetDoubleValues(targetVar, testRows));
|
---|
499 | var predictedValuesRow = new DataRow(targetVar + " pred.", "Predicted values for " + targetVar, testPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
500 | testDataTable.Rows.Add(actualValuesRow);
|
---|
501 | testDataTable.Rows.Add(predictedValuesRow);
|
---|
502 | testList.Add(testDataTable);
|
---|
503 |
|
---|
504 | } else {
|
---|
505 | var latentVar = latentVariables[colIdx - targetVars.Length];
|
---|
506 | var testDataTable = new DataTable(latentVar + " prediction (test)");
|
---|
507 | var predictedValuesRow = new DataRow(latentVar + " pred.", "Predicted values for " + latentVar, testPrediction.Select(arr => arr[colIdx].Item1).ToArray());
|
---|
508 | var emptyRow = new DataRow(latentVar);
|
---|
509 | testDataTable.Rows.Add(emptyRow);
|
---|
510 | testDataTable.Rows.Add(predictedValuesRow);
|
---|
511 | testList.Add(testDataTable);
|
---|
512 | }
|
---|
513 | }
|
---|
514 |
|
---|
515 | results["Prediction (training)"].Value = trainingList.AsReadOnly();
|
---|
516 | results["Prediction (test)"].Value = testList.AsReadOnly();
|
---|
517 | #region simplification of models
|
---|
518 | // TODO the dependency of HeuristicLab.Problems.DataAnalysis.Symbolic is not ideal
|
---|
519 | var models = new VariableCollection(); // to store target var names and original version of tree
|
---|
520 |
|
---|
521 | int nextParIdx = 0;
|
---|
522 | for (int idx = 0; idx < trees.Length; idx++) {
|
---|
523 | var varName = string.Empty;
|
---|
524 | if (idx < targetVars.Length) {
|
---|
525 | varName = targetVars[idx];
|
---|
526 | } else {
|
---|
527 | varName = latentVariables[idx - targetVars.Length];
|
---|
528 | }
|
---|
529 | var tree = trees[idx];
|
---|
530 |
|
---|
531 | // when we reference HeuristicLab.Problems.DataAnalysis.Symbolic we can translate symbols
|
---|
532 | var shownTree = new SymbolicExpressionTree(TranslateTreeNode(tree.Root, optTheta.ToArray(),
|
---|
533 | ref nextParIdx));
|
---|
534 |
|
---|
535 | // var shownTree = (SymbolicExpressionTree)tree.Clone();
|
---|
536 | // var constantsNodeOrig = tree.IterateNodesPrefix().Where(IsConstantNode);
|
---|
537 | // var constantsNodeShown = shownTree.IterateNodesPrefix().Where(IsConstantNode);
|
---|
538 | //
|
---|
539 | // foreach (var n in constantsNodeOrig.Zip(constantsNodeShown, (original, shown) => new { original, shown })) {
|
---|
540 | // double constantsVal = optTheta[nodeIdx[n.original]];
|
---|
541 | //
|
---|
542 | // ConstantTreeNode replacementNode = new ConstantTreeNode(new Constant()) { Value = constantsVal };
|
---|
543 | //
|
---|
544 | // var parentNode = n.shown.Parent;
|
---|
545 | // int replacementIndex = parentNode.IndexOfSubtree(n.shown);
|
---|
546 | // parentNode.RemoveSubtree(replacementIndex);
|
---|
547 | // parentNode.InsertSubtree(replacementIndex, replacementNode);
|
---|
548 | // }
|
---|
549 |
|
---|
550 | var origTreeVar = new HeuristicLab.Core.Variable(varName + "(original)");
|
---|
551 | origTreeVar.Value = (ISymbolicExpressionTree)tree.Clone();
|
---|
552 | models.Add(origTreeVar);
|
---|
553 | var simplifiedTreeVar = new HeuristicLab.Core.Variable(varName + "(simplified)");
|
---|
554 | simplifiedTreeVar.Value = TreeSimplifier.Simplify(shownTree);
|
---|
555 | models.Add(simplifiedTreeVar);
|
---|
556 |
|
---|
557 | }
|
---|
558 | results["Models"].Value = models;
|
---|
559 | #endregion
|
---|
560 | }
|
---|
561 | }
|
---|
562 |
|
---|
563 |
|
---|
564 | #region interpretation
|
---|
565 |
|
---|
566 | // the following uses auto-diff to calculate the gradient w.r.t. the parameters forward in time.
|
---|
567 | // this is basically the method described in Gronwall T. Note on the derivatives with respect to a parameter of the solutions of a system of differential equations. Ann. Math. 1919;20:292–296.
|
---|
568 |
|
---|
569 | // a comparison of three potential calculation methods for the gradient is given in:
|
---|
570 | // Sengupta, B., Friston, K. J., & Penny, W. D. (2014). Efficient gradient computation for dynamical models. Neuroimage, 98(100), 521–527. http://doi.org/10.1016/j.neuroimage.2014.04.040
|
---|
571 | // "Our comparison establishes that the adjoint method is computationally more efficient for numerical estimation of parametric gradients
|
---|
572 | // for state-space models — both linear and non-linear, as in the case of a dynamical causal model (DCM)"
|
---|
573 |
|
---|
574 | // for a solver with the necessary features see: https://computation.llnl.gov/projects/sundials/cvodes
|
---|
575 |
|
---|
576 | private static IEnumerable<Tuple<double, Vector>[]> Integrate(
|
---|
577 | ISymbolicExpressionTree[] trees, IDataset dataset,
|
---|
578 | string[] inputVariables, string[] targetVariables, string[] latentVariables, IEnumerable<IntRange> episodes,
|
---|
579 | double[] parameterValues,
|
---|
580 | string odeSolver, int numericIntegrationSteps = 100) {
|
---|
581 |
|
---|
582 | // TODO: numericIntegrationSteps is only relevant for the HeuristicLab solver
|
---|
583 | var episodeIdx = 0;
|
---|
584 | foreach (var episode in episodes) {
|
---|
585 | var rows = Enumerable.Range(episode.Start, episode.End - episode.Start);
|
---|
586 |
|
---|
587 | // integrate forward starting with known values for the target in t0
|
---|
588 |
|
---|
589 | var variableValues = new Dictionary<string, Tuple<double, Vector>>();
|
---|
590 | var t0 = rows.First();
|
---|
591 | foreach (var varName in inputVariables) {
|
---|
592 | variableValues.Add(varName, Tuple.Create(dataset.GetDoubleValue(varName, t0), Vector.Zero));
|
---|
593 | }
|
---|
594 | foreach (var varName in targetVariables) {
|
---|
595 | variableValues.Add(varName, Tuple.Create(dataset.GetDoubleValue(varName, t0), Vector.Zero));
|
---|
596 | }
|
---|
597 | // add value entries for latent variables which are also integrated
|
---|
598 | // initial values are at the end of the parameter vector
|
---|
599 | // separete initial values for each episode
|
---|
600 | var initialValueIdx = parameterValues.Length - episodes.Count() * latentVariables.Length + episodeIdx * latentVariables.Length;
|
---|
601 | foreach (var latentVar in latentVariables) {
|
---|
602 | var arr = new double[parameterValues.Length]; // backing array
|
---|
603 | arr[initialValueIdx] = 1.0;
|
---|
604 | var g = new Vector(arr);
|
---|
605 | variableValues.Add(latentVar,
|
---|
606 | Tuple.Create(parameterValues[initialValueIdx], g)); // we don't have observations for latent variables therefore we optimize the initial value for each episode
|
---|
607 | initialValueIdx++;
|
---|
608 | }
|
---|
609 |
|
---|
610 | var calculatedVariables = targetVariables.Concat(latentVariables).ToArray(); // TODO: must conincide with the order of trees in the encoding
|
---|
611 |
|
---|
612 | // return first value as stored in the dataset
|
---|
613 | yield return calculatedVariables
|
---|
614 | .Select(calcVarName => variableValues[calcVarName])
|
---|
615 | .ToArray();
|
---|
616 |
|
---|
617 | var prevT = rows.First(); // TODO: here we should use a variable for t if it is available. Right now we assume equidistant measurements.
|
---|
618 | foreach (var t in rows.Skip(1)) {
|
---|
619 | if (odeSolver == "HeuristicLab")
|
---|
620 | IntegrateHL(trees, calculatedVariables, variableValues, parameterValues, numericIntegrationSteps);
|
---|
621 | else if (odeSolver == "CVODES")
|
---|
622 | IntegrateCVODES(trees, calculatedVariables, variableValues, parameterValues, t - prevT);
|
---|
623 | else throw new InvalidOperationException("Unknown ODE solver " + odeSolver);
|
---|
624 | prevT = t;
|
---|
625 |
|
---|
626 | // This check doesn't work with the HeuristicLab integrator if there are input variables
|
---|
627 | //if (variableValues.Count == targetVariables.Length) {
|
---|
628 | // only return the target variables for calculation of errors
|
---|
629 | var res = calculatedVariables
|
---|
630 | .Select(targetVar => variableValues[targetVar])
|
---|
631 | .ToArray();
|
---|
632 | if (res.Any(ri => double.IsNaN(ri.Item1) || double.IsInfinity(ri.Item1))) yield break;
|
---|
633 | yield return res;
|
---|
634 | //} else {
|
---|
635 | // yield break; // stop early on problems
|
---|
636 | //}
|
---|
637 |
|
---|
638 |
|
---|
639 | // update for next time step
|
---|
640 | foreach (var varName in inputVariables) {
|
---|
641 | variableValues[varName] = Tuple.Create(dataset.GetDoubleValue(varName, t), Vector.Zero);
|
---|
642 | }
|
---|
643 | }
|
---|
644 | episodeIdx++;
|
---|
645 | }
|
---|
646 | }
|
---|
647 |
|
---|
648 |
|
---|
649 | /// <summary>
|
---|
650 | /// Here we use CVODES to solve the ODE. Forward sensitivities are used to calculate the gradient for parameter optimization
|
---|
651 | /// </summary>
|
---|
652 | /// <param name="trees">Each equation in the ODE represented as a tree</param>
|
---|
653 | /// <param name="calculatedVariables">The names of the calculated variables</param>
|
---|
654 | /// <param name="variableValues">The start values of the calculated variables as well as their sensitivites over parameters</param>
|
---|
655 | /// <param name="parameterValues">The current parameter values</param>
|
---|
656 | /// <param name="t">The time t up to which we need to integrate.</param>
|
---|
657 | private static void IntegrateCVODES(
|
---|
658 | ISymbolicExpressionTree[] trees, // f(y,p) in tree representation
|
---|
659 | string[] calculatedVariables, // names of elements of y
|
---|
660 | Dictionary<string, Tuple<double, Vector>> variableValues, // y (intput and output) input: y(t0), output: y(t0+t)
|
---|
661 | double[] parameterValues, // p
|
---|
662 | double t // duration t for which we want to integrate
|
---|
663 | ) {
|
---|
664 |
|
---|
665 | // the RHS of the ODE
|
---|
666 | // dy/dt = f(y_t,x_t,p)
|
---|
667 | CVODES.CVRhsFunc f = CreateOdeRhs(trees, calculatedVariables, parameterValues);
|
---|
668 | // the Jacobian ∂f/∂y
|
---|
669 | CVODES.CVDlsJacFunc jac = CreateJac(trees, calculatedVariables, parameterValues);
|
---|
670 |
|
---|
671 | // the RHS for the forward sensitivities (∂f/∂y)s_i(t) + ∂f/∂p_i
|
---|
672 | CVODES.CVSensRhsFn sensF = CreateSensitivityRhs(trees, calculatedVariables, parameterValues);
|
---|
673 |
|
---|
674 | // setup solver
|
---|
675 | int numberOfEquations = trees.Length;
|
---|
676 | IntPtr y = IntPtr.Zero;
|
---|
677 | IntPtr cvode_mem = IntPtr.Zero;
|
---|
678 | IntPtr A = IntPtr.Zero;
|
---|
679 | IntPtr yS0 = IntPtr.Zero;
|
---|
680 | IntPtr linearSolver = IntPtr.Zero;
|
---|
681 | var ns = parameterValues.Length; // number of parameters
|
---|
682 |
|
---|
683 | try {
|
---|
684 | y = CVODES.N_VNew_Serial(numberOfEquations);
|
---|
685 | // init y to current values of variables
|
---|
686 | // y must be initialized before calling CVodeInit
|
---|
687 | for (int i = 0; i < calculatedVariables.Length; i++) {
|
---|
688 | CVODES.NV_Set_Ith_S(y, i, variableValues[calculatedVariables[i]].Item1);
|
---|
689 | }
|
---|
690 |
|
---|
691 | cvode_mem = CVODES.CVodeCreate(CVODES.MultistepMethod.CV_ADAMS, CVODES.NonlinearSolverIteration.CV_FUNCTIONAL);
|
---|
692 |
|
---|
693 | var flag = CVODES.CVodeInit(cvode_mem, f, 0.0, y);
|
---|
694 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
695 |
|
---|
696 | double relTol = 1.0e-2;
|
---|
697 | double absTol = 1.0;
|
---|
698 | flag = CVODES.CVodeSStolerances(cvode_mem, relTol, absTol); // TODO: probably need to adjust absTol per variable
|
---|
699 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
700 |
|
---|
701 | A = CVODES.SUNDenseMatrix(numberOfEquations, numberOfEquations);
|
---|
702 | Debug.Assert(A != IntPtr.Zero);
|
---|
703 |
|
---|
704 | linearSolver = CVODES.SUNDenseLinearSolver(y, A);
|
---|
705 | Debug.Assert(linearSolver != IntPtr.Zero);
|
---|
706 |
|
---|
707 | flag = CVODES.CVDlsSetLinearSolver(cvode_mem, linearSolver, A);
|
---|
708 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
709 |
|
---|
710 | flag = CVODES.CVDlsSetJacFn(cvode_mem, jac);
|
---|
711 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
712 |
|
---|
713 | yS0 = CVODES.N_VCloneVectorArray_Serial(ns, y); // clone the output vector for each parameter
|
---|
714 | unsafe {
|
---|
715 | // set to initial sensitivities supplied by caller
|
---|
716 | for (int pIdx = 0; pIdx < ns; pIdx++) {
|
---|
717 | var yS0_i = *((IntPtr*)yS0.ToPointer() + pIdx);
|
---|
718 | for (var varIdx = 0; varIdx < calculatedVariables.Length; varIdx++) {
|
---|
719 | CVODES.NV_Set_Ith_S(yS0_i, varIdx, variableValues[calculatedVariables[varIdx]].Item2[pIdx]);
|
---|
720 | }
|
---|
721 | }
|
---|
722 | }
|
---|
723 |
|
---|
724 | flag = CVODES.CVodeSensInit(cvode_mem, ns, CVODES.CV_SIMULTANEOUS, sensF, yS0);
|
---|
725 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
726 |
|
---|
727 | flag = CVODES.CVodeSensEEtolerances(cvode_mem);
|
---|
728 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
729 |
|
---|
730 | // make one forward integration step
|
---|
731 | double tout = 0.0; // first output time
|
---|
732 | flag = CVODES.CVode(cvode_mem, t, y, ref tout, CVODES.CV_NORMAL);
|
---|
733 | if (flag == CVODES.CV_SUCCESS) {
|
---|
734 | Debug.Assert(t == tout);
|
---|
735 |
|
---|
736 | // get sensitivities
|
---|
737 | flag = CVODES.CVodeGetSens(cvode_mem, ref tout, yS0);
|
---|
738 | Debug.Assert(CVODES.CV_SUCCESS == flag);
|
---|
739 |
|
---|
740 | // update variableValues based on integration results
|
---|
741 | for (int varIdx = 0; varIdx < calculatedVariables.Length; varIdx++) {
|
---|
742 | var yi = CVODES.NV_Get_Ith_S(y, varIdx);
|
---|
743 | var gArr = new double[parameterValues.Length];
|
---|
744 | for (var pIdx = 0; pIdx < parameterValues.Length; pIdx++) {
|
---|
745 | unsafe {
|
---|
746 | var yS0_pi = *((IntPtr*)yS0.ToPointer() + pIdx);
|
---|
747 | gArr[pIdx] = CVODES.NV_Get_Ith_S(yS0_pi, varIdx);
|
---|
748 | }
|
---|
749 | }
|
---|
750 | variableValues[calculatedVariables[varIdx]] = Tuple.Create(yi, new Vector(gArr));
|
---|
751 | }
|
---|
752 | } else {
|
---|
753 | variableValues.Clear(); // indicate problems by not returning new values
|
---|
754 | }
|
---|
755 |
|
---|
756 | // cleanup all allocated objects
|
---|
757 | } finally {
|
---|
758 | if (y != IntPtr.Zero) CVODES.N_VDestroy_Serial(y);
|
---|
759 | if (cvode_mem != IntPtr.Zero) CVODES.CVodeFree(ref cvode_mem);
|
---|
760 | if (linearSolver != IntPtr.Zero) CVODES.SUNLinSolFree(linearSolver);
|
---|
761 | if (A != IntPtr.Zero) CVODES.SUNMatDestroy(A);
|
---|
762 | if (yS0 != IntPtr.Zero) CVODES.N_VDestroyVectorArray_Serial(yS0, ns);
|
---|
763 | }
|
---|
764 | }
|
---|
765 |
|
---|
766 |
|
---|
767 | private static CVODES.CVRhsFunc CreateOdeRhs(
|
---|
768 | ISymbolicExpressionTree[] trees,
|
---|
769 | string[] calculatedVariables,
|
---|
770 | double[] parameterValues) {
|
---|
771 | // we don't need to calculate a gradient here -> no nodes are selected for
|
---|
772 | // --> no nodes are selected to be included in the gradient calculation
|
---|
773 | var nodeIdx = new Dictionary<ISymbolicExpressionTreeNode, int>();
|
---|
774 | return (double t,
|
---|
775 | IntPtr y, // N_Vector, current value of y (input)
|
---|
776 | IntPtr ydot, // N_Vector (calculated value of y' (output)
|
---|
777 | IntPtr user_data // optional user data, (unused here)
|
---|
778 | ) => {
|
---|
779 | // TODO: perf
|
---|
780 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
781 |
|
---|
782 | int pIdx = 0;
|
---|
783 | foreach (var tree in trees) {
|
---|
784 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
785 | if (IsConstantNode(n)) {
|
---|
786 | nodeValues.Add(n, Tuple.Create(parameterValues[pIdx], Vector.Zero)); // here we do not need a gradient
|
---|
787 | pIdx++;
|
---|
788 | } else if (n.SubtreeCount == 0) {
|
---|
789 | // for variables and latent variables get the value from variableValues
|
---|
790 | var varName = n.Symbol.Name;
|
---|
791 | var varIdx = Array.IndexOf(calculatedVariables, varName); // TODO: perf!
|
---|
792 | if (varIdx < 0) throw new InvalidProgramException();
|
---|
793 | var y_i = CVODES.NV_Get_Ith_S(y, (long)varIdx);
|
---|
794 | nodeValues.Add(n, Tuple.Create(y_i, Vector.Zero)); // no gradient needed
|
---|
795 | }
|
---|
796 | }
|
---|
797 | }
|
---|
798 | for (int i = 0; i < trees.Length; i++) {
|
---|
799 | var tree = trees[i];
|
---|
800 | var res_i = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
801 | CVODES.NV_Set_Ith_S(ydot, i, res_i.Item1);
|
---|
802 | }
|
---|
803 | return 0;
|
---|
804 | };
|
---|
805 | }
|
---|
806 |
|
---|
807 | private static CVODES.CVDlsJacFunc CreateJac(
|
---|
808 | ISymbolicExpressionTree[] trees,
|
---|
809 | string[] calculatedVariables,
|
---|
810 | double[] parameterValues) {
|
---|
811 |
|
---|
812 | return (
|
---|
813 | double t, // current time (input)
|
---|
814 | IntPtr y, // N_Vector, current value of y (input)
|
---|
815 | IntPtr fy, // N_Vector, current value of f (input)
|
---|
816 | IntPtr Jac, // SUNMatrix ∂f/∂y (output, rows i contains are ∂f_i/∂y vector)
|
---|
817 | IntPtr user_data, // optional (unused here)
|
---|
818 | IntPtr tmp1, // N_Vector, optional (unused here)
|
---|
819 | IntPtr tmp2, // N_Vector, optional (unused here)
|
---|
820 | IntPtr tmp3 // N_Vector, optional (unused here)
|
---|
821 | ) => {
|
---|
822 | // here we need to calculate partial derivatives for the calculated variables y
|
---|
823 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
824 | int pIdx = 0;
|
---|
825 | foreach (var tree in trees) {
|
---|
826 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
827 | if (IsConstantNode(n)) {
|
---|
828 | nodeValues.Add(n, Tuple.Create(parameterValues[pIdx], Vector.Zero)); // here we need a gradient over y which is zero for parameters
|
---|
829 | pIdx++;
|
---|
830 | } else if (n.SubtreeCount == 0) {
|
---|
831 | // for variables and latent variables we use values supplied in y and init gradient vectors accordingly
|
---|
832 | var varName = n.Symbol.Name;
|
---|
833 | var varIdx = Array.IndexOf(calculatedVariables, varName); // TODO: perf!
|
---|
834 | if (varIdx < 0) throw new InvalidProgramException();
|
---|
835 |
|
---|
836 | var y_i = CVODES.NV_Get_Ith_S(y, (long)varIdx);
|
---|
837 | var gArr = new double[CVODES.NV_LENGTH_S(y)]; // backing array
|
---|
838 | gArr[varIdx] = 1.0;
|
---|
839 | var g = new Vector(gArr);
|
---|
840 | nodeValues.Add(n, Tuple.Create(y_i, g));
|
---|
841 | }
|
---|
842 | }
|
---|
843 | }
|
---|
844 |
|
---|
845 | for (int i = 0; i < trees.Length; i++) {
|
---|
846 | var tree = trees[i];
|
---|
847 | var res = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
848 | var g = res.Item2;
|
---|
849 | for (int j = 0; j < calculatedVariables.Length; j++) {
|
---|
850 | CVODES.SUNDenseMatrix_Set(Jac, i, j, g[j]);
|
---|
851 | }
|
---|
852 | }
|
---|
853 | return 0; // on success
|
---|
854 | };
|
---|
855 | }
|
---|
856 |
|
---|
857 |
|
---|
858 | // to calculate sensitivities RHS for all equations at once
|
---|
859 | // must compute (∂f/∂y)s_i(t) + ∂f/∂p_i and store in ySdot.
|
---|
860 | // Index i refers to parameters, dimensionality of matrix and vectors is number of equations
|
---|
861 | private static CVODES.CVSensRhsFn CreateSensitivityRhs(ISymbolicExpressionTree[] trees, string[] calculatedVariables, double[] parameterValues) {
|
---|
862 | return (
|
---|
863 | int Ns, // number of parameters
|
---|
864 | double t, // current time
|
---|
865 | IntPtr y, // N_Vector y(t) (input)
|
---|
866 | IntPtr ydot, // N_Vector dy/dt(t) (input)
|
---|
867 | IntPtr yS, // N_Vector*, one vector for each parameter (input)
|
---|
868 | IntPtr ySdot, // N_Vector*, one vector for each parameter (output)
|
---|
869 | IntPtr user_data, // optional (unused here)
|
---|
870 | IntPtr tmp1, // N_Vector, optional (unused here)
|
---|
871 | IntPtr tmp2 // N_Vector, optional (unused here)
|
---|
872 | ) => {
|
---|
873 | // here we need to calculate partial derivatives for the calculated variables y as well as for the parameters
|
---|
874 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
875 | var d = calculatedVariables.Length + parameterValues.Length; // dimensionality of gradient
|
---|
876 | // first collect variable values
|
---|
877 | foreach (var tree in trees) {
|
---|
878 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
879 | if (IsVariableNode(n)) {
|
---|
880 | // for variables and latent variables we use values supplied in y and init gradient vectors accordingly
|
---|
881 | var varName = n.Symbol.Name;
|
---|
882 | var varIdx = Array.IndexOf(calculatedVariables, varName); // TODO: perf!
|
---|
883 | if (varIdx < 0) throw new InvalidProgramException();
|
---|
884 |
|
---|
885 | var y_i = CVODES.NV_Get_Ith_S(y, (long)varIdx);
|
---|
886 | var gArr = new double[d]; // backing array
|
---|
887 | gArr[varIdx] = 1.0;
|
---|
888 | var g = new Vector(gArr);
|
---|
889 | nodeValues.Add(n, Tuple.Create(y_i, g));
|
---|
890 | }
|
---|
891 | }
|
---|
892 | }
|
---|
893 | // then collect constants
|
---|
894 | int pIdx = 0;
|
---|
895 | foreach (var tree in trees) {
|
---|
896 | foreach (var n in tree.IterateNodesPrefix()) {
|
---|
897 | if (IsConstantNode(n)) {
|
---|
898 | var gArr = new double[d];
|
---|
899 | gArr[calculatedVariables.Length + pIdx] = 1.0;
|
---|
900 | var g = new Vector(gArr);
|
---|
901 | nodeValues.Add(n, Tuple.Create(parameterValues[pIdx], g));
|
---|
902 | pIdx++;
|
---|
903 | }
|
---|
904 | }
|
---|
905 | }
|
---|
906 | // gradient vector is [∂f/∂y_1, ∂f/∂y_2, ... ∂f/∂yN, ∂f/∂p_1 ... ∂f/∂p_K]
|
---|
907 |
|
---|
908 |
|
---|
909 | for (pIdx = 0; pIdx < Ns; pIdx++) {
|
---|
910 | unsafe {
|
---|
911 | var sDot_pi = *((IntPtr*)ySdot.ToPointer() + pIdx);
|
---|
912 | CVODES.N_VConst_Serial(0.0, sDot_pi);
|
---|
913 | }
|
---|
914 | }
|
---|
915 |
|
---|
916 | for (int i = 0; i < trees.Length; i++) {
|
---|
917 | var tree = trees[i];
|
---|
918 | var res = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
919 | var g = res.Item2;
|
---|
920 |
|
---|
921 |
|
---|
922 | // update ySdot = (∂f/∂y)s_i(t) + ∂f/∂p_i
|
---|
923 |
|
---|
924 | for (pIdx = 0; pIdx < Ns; pIdx++) {
|
---|
925 | unsafe {
|
---|
926 | var sDot_pi = *((IntPtr*)ySdot.ToPointer() + pIdx);
|
---|
927 | var s_pi = *((IntPtr*)yS.ToPointer() + pIdx);
|
---|
928 |
|
---|
929 | var v = CVODES.NV_Get_Ith_S(sDot_pi, i);
|
---|
930 | // (∂f/∂y)s_i(t)
|
---|
931 | var p = 0.0;
|
---|
932 | for (int yIdx = 0; yIdx < calculatedVariables.Length; yIdx++) {
|
---|
933 | p += g[yIdx] * CVODES.NV_Get_Ith_S(s_pi, yIdx);
|
---|
934 | }
|
---|
935 | // + ∂f/∂p_i
|
---|
936 | CVODES.NV_Set_Ith_S(sDot_pi, i, v + p + g[calculatedVariables.Length + pIdx]);
|
---|
937 | }
|
---|
938 | }
|
---|
939 |
|
---|
940 | }
|
---|
941 | return 0; // on success
|
---|
942 | };
|
---|
943 | }
|
---|
944 |
|
---|
945 | private static void IntegrateHL(
|
---|
946 | ISymbolicExpressionTree[] trees,
|
---|
947 | string[] calculatedVariables, // names of integrated variables
|
---|
948 | Dictionary<string, Tuple<double, Vector>> variableValues, //y (intput and output) input: y(t0), output: y(t0+1)
|
---|
949 | double[] parameterValues,
|
---|
950 | int numericIntegrationSteps) {
|
---|
951 |
|
---|
952 | var nodeValues = new Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>>();
|
---|
953 |
|
---|
954 | // the nodeValues for parameters are constant over time
|
---|
955 | // TODO: this needs to be done only once for each iteration in gradient descent (whenever parameter values change)
|
---|
956 | // NOTE: the order must be the same as above (prefix order for nodes)
|
---|
957 | int pIdx = 0;
|
---|
958 | foreach (var tree in trees) {
|
---|
959 | foreach (var node in tree.Root.IterateNodesPrefix()) {
|
---|
960 | if (IsConstantNode(node)) {
|
---|
961 | var gArr = new double[parameterValues.Length]; // backing array
|
---|
962 | gArr[pIdx] = 1.0;
|
---|
963 | var g = new Vector(gArr);
|
---|
964 | nodeValues.Add(node, new Tuple<double, Vector>(parameterValues[pIdx], g));
|
---|
965 | pIdx++;
|
---|
966 | } else if (node.SubtreeCount == 0) {
|
---|
967 | // for (latent) variables get the values from variableValues
|
---|
968 | var varName = node.Symbol.Name;
|
---|
969 | nodeValues.Add(node, variableValues[varName]);
|
---|
970 | }
|
---|
971 | }
|
---|
972 | }
|
---|
973 |
|
---|
974 |
|
---|
975 | double h = 1.0 / numericIntegrationSteps;
|
---|
976 | for (int step = 0; step < numericIntegrationSteps; step++) {
|
---|
977 | var deltaValues = new Dictionary<string, Tuple<double, Vector>>();
|
---|
978 | for (int i = 0; i < trees.Length; i++) {
|
---|
979 | var tree = trees[i];
|
---|
980 | var targetVarName = calculatedVariables[i];
|
---|
981 |
|
---|
982 | // Root.GetSubtree(0).GetSubtree(0) skips programRoot and startSymbol
|
---|
983 | var res = InterpretRec(tree.Root.GetSubtree(0).GetSubtree(0), nodeValues);
|
---|
984 | deltaValues.Add(targetVarName, res);
|
---|
985 | }
|
---|
986 |
|
---|
987 | // update variableValues for next step, trapezoid integration
|
---|
988 | foreach (var kvp in deltaValues) {
|
---|
989 | var oldVal = variableValues[kvp.Key];
|
---|
990 | var newVal = Tuple.Create(
|
---|
991 | oldVal.Item1 + h * kvp.Value.Item1,
|
---|
992 | oldVal.Item2 + h * kvp.Value.Item2
|
---|
993 | );
|
---|
994 | variableValues[kvp.Key] = newVal;
|
---|
995 | }
|
---|
996 | // update nodeValues from variableValues
|
---|
997 | // TODO: perf using dictionary with list of nodes for each variable
|
---|
998 | foreach (var tree in trees) {
|
---|
999 | foreach (var node in tree.Root.IterateNodesPrefix().Where(n => IsVariableNode(n))) {
|
---|
1000 | var varName = node.Symbol.Name;
|
---|
1001 | nodeValues[node] = variableValues[varName];
|
---|
1002 | }
|
---|
1003 | }
|
---|
1004 | }
|
---|
1005 | }
|
---|
1006 |
|
---|
1007 | private static Tuple<double, Vector> InterpretRec(
|
---|
1008 | ISymbolicExpressionTreeNode node,
|
---|
1009 | Dictionary<ISymbolicExpressionTreeNode, Tuple<double, Vector>> nodeValues // contains value and gradient vector for a node (variables and constants only)
|
---|
1010 | ) {
|
---|
1011 |
|
---|
1012 | switch (node.Symbol.Name) {
|
---|
1013 | case "+": {
|
---|
1014 | var l = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1015 | var r = InterpretRec(node.GetSubtree(1), nodeValues);
|
---|
1016 |
|
---|
1017 | return Tuple.Create(l.Item1 + r.Item1, l.Item2 + r.Item2);
|
---|
1018 | }
|
---|
1019 | case "*": {
|
---|
1020 | var l = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1021 | var r = InterpretRec(node.GetSubtree(1), nodeValues);
|
---|
1022 |
|
---|
1023 | return Tuple.Create(l.Item1 * r.Item1, l.Item2 * r.Item1 + l.Item1 * r.Item2);
|
---|
1024 | }
|
---|
1025 |
|
---|
1026 | case "-": {
|
---|
1027 | var l = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1028 | var r = InterpretRec(node.GetSubtree(1), nodeValues);
|
---|
1029 |
|
---|
1030 | return Tuple.Create(l.Item1 - r.Item1, l.Item2 - r.Item2);
|
---|
1031 | }
|
---|
1032 | case "%": {
|
---|
1033 | var l = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1034 | var r = InterpretRec(node.GetSubtree(1), nodeValues);
|
---|
1035 |
|
---|
1036 | // protected division
|
---|
1037 | if (r.Item1.IsAlmost(0.0)) {
|
---|
1038 | return Tuple.Create(0.0, Vector.Zero);
|
---|
1039 | } else {
|
---|
1040 | return Tuple.Create(
|
---|
1041 | l.Item1 / r.Item1,
|
---|
1042 | l.Item1 * -1.0 / (r.Item1 * r.Item1) * r.Item2 + 1.0 / r.Item1 * l.Item2 // (f/g)' = f * (1/g)' + 1/g * f' = f * -1/g² * g' + 1/g * f'
|
---|
1043 | );
|
---|
1044 | }
|
---|
1045 | }
|
---|
1046 | case "sin": {
|
---|
1047 | var x = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1048 | return Tuple.Create(
|
---|
1049 | Math.Sin(x.Item1),
|
---|
1050 | Vector.Cos(x.Item2) * x.Item2
|
---|
1051 | );
|
---|
1052 | }
|
---|
1053 | case "cos": {
|
---|
1054 | var x = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1055 | return Tuple.Create(
|
---|
1056 | Math.Cos(x.Item1),
|
---|
1057 | -Vector.Sin(x.Item2) * x.Item2
|
---|
1058 | );
|
---|
1059 | }
|
---|
1060 | case "sqr": {
|
---|
1061 | var x = InterpretRec(node.GetSubtree(0), nodeValues);
|
---|
1062 | return Tuple.Create(
|
---|
1063 | x.Item1 * x.Item1,
|
---|
1064 | 2.0 * x.Item1 * x.Item2
|
---|
1065 | );
|
---|
1066 | }
|
---|
1067 | default: {
|
---|
1068 | return nodeValues[node]; // value and gradient for constants and variables must be set by the caller
|
---|
1069 | }
|
---|
1070 | }
|
---|
1071 | }
|
---|
1072 | #endregion
|
---|
1073 |
|
---|
1074 | #region events
|
---|
1075 | /*
|
---|
1076 | * Dependencies between parameters:
|
---|
1077 | *
|
---|
1078 | * ProblemData
|
---|
1079 | * |
|
---|
1080 | * V
|
---|
1081 | * TargetVariables FunctionSet MaximumLength NumberOfLatentVariables
|
---|
1082 | * | | | |
|
---|
1083 | * V V | |
|
---|
1084 | * Grammar <---------------+-------------------
|
---|
1085 | * |
|
---|
1086 | * V
|
---|
1087 | * Encoding
|
---|
1088 | */
|
---|
1089 | private void RegisterEventHandlers() {
|
---|
1090 | ProblemDataParameter.ValueChanged += ProblemDataParameter_ValueChanged;
|
---|
1091 | if (ProblemDataParameter.Value != null) ProblemDataParameter.Value.Changed += ProblemData_Changed;
|
---|
1092 |
|
---|
1093 | TargetVariablesParameter.ValueChanged += TargetVariablesParameter_ValueChanged;
|
---|
1094 | if (TargetVariablesParameter.Value != null) TargetVariablesParameter.Value.CheckedItemsChanged += CheckedTargetVariablesChanged;
|
---|
1095 |
|
---|
1096 | FunctionSetParameter.ValueChanged += FunctionSetParameter_ValueChanged;
|
---|
1097 | if (FunctionSetParameter.Value != null) FunctionSetParameter.Value.CheckedItemsChanged += CheckedFunctionsChanged;
|
---|
1098 |
|
---|
1099 | MaximumLengthParameter.Value.ValueChanged += MaximumLengthChanged;
|
---|
1100 |
|
---|
1101 | NumberOfLatentVariablesParameter.Value.ValueChanged += NumLatentVariablesChanged;
|
---|
1102 | }
|
---|
1103 |
|
---|
1104 | private void NumLatentVariablesChanged(object sender, EventArgs e) {
|
---|
1105 | UpdateGrammarAndEncoding();
|
---|
1106 | }
|
---|
1107 |
|
---|
1108 | private void MaximumLengthChanged(object sender, EventArgs e) {
|
---|
1109 | UpdateGrammarAndEncoding();
|
---|
1110 | }
|
---|
1111 |
|
---|
1112 | private void FunctionSetParameter_ValueChanged(object sender, EventArgs e) {
|
---|
1113 | FunctionSetParameter.Value.CheckedItemsChanged += CheckedFunctionsChanged;
|
---|
1114 | }
|
---|
1115 |
|
---|
1116 | private void CheckedFunctionsChanged(object sender, CollectionItemsChangedEventArgs<IndexedItem<StringValue>> e) {
|
---|
1117 | UpdateGrammarAndEncoding();
|
---|
1118 | }
|
---|
1119 |
|
---|
1120 | private void TargetVariablesParameter_ValueChanged(object sender, EventArgs e) {
|
---|
1121 | TargetVariablesParameter.Value.CheckedItemsChanged += CheckedTargetVariablesChanged;
|
---|
1122 | }
|
---|
1123 |
|
---|
1124 | private void CheckedTargetVariablesChanged(object sender, CollectionItemsChangedEventArgs<IndexedItem<StringValue>> e) {
|
---|
1125 | UpdateGrammarAndEncoding();
|
---|
1126 | }
|
---|
1127 |
|
---|
1128 | private void ProblemDataParameter_ValueChanged(object sender, EventArgs e) {
|
---|
1129 | ProblemDataParameter.Value.Changed += ProblemData_Changed;
|
---|
1130 | OnProblemDataChanged();
|
---|
1131 | OnReset();
|
---|
1132 | }
|
---|
1133 |
|
---|
1134 | private void ProblemData_Changed(object sender, EventArgs e) {
|
---|
1135 | OnProblemDataChanged();
|
---|
1136 | OnReset();
|
---|
1137 | }
|
---|
1138 |
|
---|
1139 | private void OnProblemDataChanged() {
|
---|
1140 | UpdateTargetVariables(); // implicitly updates other dependent parameters
|
---|
1141 | var handler = ProblemDataChanged;
|
---|
1142 | if (handler != null) handler(this, EventArgs.Empty);
|
---|
1143 | }
|
---|
1144 |
|
---|
1145 | #endregion
|
---|
1146 |
|
---|
1147 | #region helper
|
---|
1148 |
|
---|
1149 | private void InitAllParameters() {
|
---|
1150 | UpdateTargetVariables(); // implicitly updates the grammar and the encoding
|
---|
1151 | }
|
---|
1152 |
|
---|
1153 | private ReadOnlyCheckedItemList<StringValue> CreateFunctionSet() {
|
---|
1154 | var l = new CheckedItemList<StringValue>();
|
---|
1155 | l.Add(new StringValue("+").AsReadOnly());
|
---|
1156 | l.Add(new StringValue("*").AsReadOnly());
|
---|
1157 | l.Add(new StringValue("%").AsReadOnly());
|
---|
1158 | l.Add(new StringValue("-").AsReadOnly());
|
---|
1159 | l.Add(new StringValue("sin").AsReadOnly());
|
---|
1160 | l.Add(new StringValue("cos").AsReadOnly());
|
---|
1161 | l.Add(new StringValue("sqr").AsReadOnly());
|
---|
1162 | return l.AsReadOnly();
|
---|
1163 | }
|
---|
1164 |
|
---|
1165 | private static bool IsConstantNode(ISymbolicExpressionTreeNode n) {
|
---|
1166 | return n.Symbol.Name.StartsWith("θ");
|
---|
1167 | }
|
---|
1168 | private static bool IsLatentVariableNode(ISymbolicExpressionTreeNode n) {
|
---|
1169 | return n.Symbol.Name.StartsWith("λ");
|
---|
1170 | }
|
---|
1171 | private static bool IsVariableNode(ISymbolicExpressionTreeNode n) {
|
---|
1172 | return (n.SubtreeCount == 0) && !IsConstantNode(n) && !IsLatentVariableNode(n);
|
---|
1173 | }
|
---|
1174 |
|
---|
1175 |
|
---|
1176 | private void UpdateTargetVariables() {
|
---|
1177 | var currentlySelectedVariables = TargetVariables.CheckedItems
|
---|
1178 | .OrderBy(i => i.Index)
|
---|
1179 | .Select(i => i.Value.Value)
|
---|
1180 | .ToArray();
|
---|
1181 |
|
---|
1182 | var newVariablesList = new CheckedItemList<StringValue>(ProblemData.Dataset.VariableNames.Select(str => new StringValue(str).AsReadOnly()).ToArray()).AsReadOnly();
|
---|
1183 | var matchingItems = newVariablesList.Where(item => currentlySelectedVariables.Contains(item.Value)).ToArray();
|
---|
1184 | foreach (var matchingItem in matchingItems) {
|
---|
1185 | newVariablesList.SetItemCheckedState(matchingItem, true);
|
---|
1186 | }
|
---|
1187 | TargetVariablesParameter.Value = newVariablesList;
|
---|
1188 | }
|
---|
1189 |
|
---|
1190 | private void UpdateGrammarAndEncoding() {
|
---|
1191 | var encoding = new MultiEncoding();
|
---|
1192 | var g = CreateGrammar();
|
---|
1193 | foreach (var targetVar in TargetVariables.CheckedItems) {
|
---|
1194 | encoding = encoding.Add(new SymbolicExpressionTreeEncoding(targetVar + "_tree", g, MaximumLength, MaximumLength)); // only limit by length
|
---|
1195 | }
|
---|
1196 | for (int i = 1; i <= NumberOfLatentVariables; i++) {
|
---|
1197 | encoding = encoding.Add(new SymbolicExpressionTreeEncoding("λ" + i + "_tree", g, MaximumLength, MaximumLength));
|
---|
1198 | }
|
---|
1199 | Encoding = encoding;
|
---|
1200 | }
|
---|
1201 |
|
---|
1202 | private ISymbolicExpressionGrammar CreateGrammar() {
|
---|
1203 | // whenever ProblemData is changed we create a new grammar with the necessary symbols
|
---|
1204 | var g = new SimpleSymbolicExpressionGrammar();
|
---|
1205 | g.AddSymbols(FunctionSet.CheckedItems.OrderBy(i => i.Index).Select(i => i.Value.Value).ToArray(), 2, 2);
|
---|
1206 |
|
---|
1207 | // TODO
|
---|
1208 | //g.AddSymbols(new[] {
|
---|
1209 | // "exp",
|
---|
1210 | // "log", // log( <expr> ) // TODO: init a theta to ensure the value is always positive
|
---|
1211 | // "exp_minus" // exp((-1) * <expr>
|
---|
1212 | //}, 1, 1);
|
---|
1213 |
|
---|
1214 | foreach (var variableName in ProblemData.AllowedInputVariables.Union(TargetVariables.CheckedItems.Select(i => i.Value.Value)))
|
---|
1215 | g.AddTerminalSymbol(variableName);
|
---|
1216 |
|
---|
1217 | // generate symbols for numeric parameters for which the value is optimized using AutoDiff
|
---|
1218 | // we generate multiple symbols to balance the probability for selecting a numeric parameter in the generation of random trees
|
---|
1219 | var numericConstantsFactor = 2.0;
|
---|
1220 | for (int i = 0; i < numericConstantsFactor * (ProblemData.AllowedInputVariables.Count() + TargetVariables.CheckedItems.Count()); i++) {
|
---|
1221 | g.AddTerminalSymbol("θ" + i); // numeric parameter for which the value is optimized using AutoDiff
|
---|
1222 | }
|
---|
1223 |
|
---|
1224 | // generate symbols for latent variables
|
---|
1225 | for (int i = 1; i <= NumberOfLatentVariables; i++) {
|
---|
1226 | g.AddTerminalSymbol("λ" + i); // numeric parameter for which the value is optimized using AutoDiff
|
---|
1227 | }
|
---|
1228 |
|
---|
1229 | return g;
|
---|
1230 | }
|
---|
1231 |
|
---|
1232 |
|
---|
1233 | private ISymbolicExpressionTreeNode TranslateTreeNode(ISymbolicExpressionTreeNode n, double[] parameterValues, ref int nextParIdx) {
|
---|
1234 | ISymbolicExpressionTreeNode translatedNode = null;
|
---|
1235 | if (n.Symbol is StartSymbol) {
|
---|
1236 | translatedNode = new StartSymbol().CreateTreeNode();
|
---|
1237 | } else if (n.Symbol is ProgramRootSymbol) {
|
---|
1238 | translatedNode = new ProgramRootSymbol().CreateTreeNode();
|
---|
1239 | } else if (n.Symbol.Name == "+") {
|
---|
1240 | translatedNode = new Addition().CreateTreeNode();
|
---|
1241 | } else if (n.Symbol.Name == "-") {
|
---|
1242 | translatedNode = new Subtraction().CreateTreeNode();
|
---|
1243 | } else if (n.Symbol.Name == "*") {
|
---|
1244 | translatedNode = new Multiplication().CreateTreeNode();
|
---|
1245 | } else if (n.Symbol.Name == "%") {
|
---|
1246 | translatedNode = new Division().CreateTreeNode();
|
---|
1247 | } else if (n.Symbol.Name == "sin") {
|
---|
1248 | translatedNode = new Sine().CreateTreeNode();
|
---|
1249 | } else if (n.Symbol.Name == "cos") {
|
---|
1250 | translatedNode = new Cosine().CreateTreeNode();
|
---|
1251 | } else if (n.Symbol.Name == "sqr") {
|
---|
1252 | translatedNode = new Square().CreateTreeNode();
|
---|
1253 | } else if (IsConstantNode(n)) {
|
---|
1254 | var constNode = (ConstantTreeNode)new Constant().CreateTreeNode();
|
---|
1255 | constNode.Value = parameterValues[nextParIdx];
|
---|
1256 | nextParIdx++;
|
---|
1257 | translatedNode = constNode;
|
---|
1258 | } else {
|
---|
1259 | // assume a variable name
|
---|
1260 | var varName = n.Symbol.Name;
|
---|
1261 | var varNode = (VariableTreeNode)new Variable().CreateTreeNode();
|
---|
1262 | varNode.Weight = 1.0;
|
---|
1263 | varNode.VariableName = varName;
|
---|
1264 | translatedNode = varNode;
|
---|
1265 | }
|
---|
1266 | foreach (var child in n.Subtrees) {
|
---|
1267 | translatedNode.AddSubtree(TranslateTreeNode(child, parameterValues, ref nextParIdx));
|
---|
1268 | }
|
---|
1269 | return translatedNode;
|
---|
1270 | }
|
---|
1271 | #endregion
|
---|
1272 |
|
---|
1273 | #region Import & Export
|
---|
1274 | public void Load(IRegressionProblemData data) {
|
---|
1275 | Name = data.Name;
|
---|
1276 | Description = data.Description;
|
---|
1277 | ProblemData = data;
|
---|
1278 | }
|
---|
1279 |
|
---|
1280 | public IRegressionProblemData Export() {
|
---|
1281 | return ProblemData;
|
---|
1282 | }
|
---|
1283 | #endregion
|
---|
1284 |
|
---|
1285 | }
|
---|
1286 | }
|
---|