[16268] | 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.Linq;
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| 24 | using System.Threading;
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| 25 | using HeuristicLab.Algorithms.DataAnalysis;
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| 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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| 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Problems.DataAnalysis;
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 34 | using HeuristicLab.Random;
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[16329] | 35 | using System.Collections.Generic;
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[16663] | 36 | using HEAL.Attic;
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[16268] | 37 |
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| 38 | namespace HeuristicLab.Problems.DynamicalSystemsModelling {
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| 39 | [Item("OdeParameterIdentification", "TODO")]
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| 40 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 120)]
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[16663] | 41 | [StorableType("93C151A2-9579-4013-9E67-7611F9378962")]
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[16268] | 42 | public sealed class OdeParameterIdentification : FixedDataAnalysisAlgorithm<Problem> {
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| 43 | private const string RegressionSolutionResultName = "Regression solution";
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| 44 | private const string ModelStructureParameterName = "Model structure";
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| 45 | private const string IterationsParameterName = "Iterations";
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| 46 | private const string RestartsParameterName = "Restarts";
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| 47 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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| 48 | private const string SeedParameterName = "Seed";
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| 49 | private const string InitParamsRandomlyParameterName = "InitializeParametersRandomly";
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| 50 |
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| 51 | public IValueParameter<StringArray> ModelStructureParameter {
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| 52 | get { return (IValueParameter<StringArray>)Parameters[ModelStructureParameterName]; }
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| 53 | }
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| 54 | public IFixedValueParameter<IntValue> IterationsParameter {
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| 55 | get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
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| 56 | }
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| 57 |
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| 58 | public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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| 59 | get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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| 60 | }
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| 61 |
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| 62 | public IFixedValueParameter<IntValue> SeedParameter {
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| 63 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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| 64 | }
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| 65 |
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| 66 | public IFixedValueParameter<IntValue> RestartsParameter {
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| 67 | get { return (IFixedValueParameter<IntValue>)Parameters[RestartsParameterName]; }
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| 68 | }
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| 69 |
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| 70 | public IFixedValueParameter<BoolValue> InitParametersRandomlyParameter {
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| 71 | get { return (IFixedValueParameter<BoolValue>)Parameters[InitParamsRandomlyParameterName]; }
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| 72 | }
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| 73 |
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| 74 | public StringArray ModelStructure {
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| 75 | get { return ModelStructureParameter.Value; }
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| 76 | set { ModelStructureParameter.Value = value; }
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| 77 | }
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| 78 |
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| 79 | public int Iterations {
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| 80 | get { return IterationsParameter.Value.Value; }
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| 81 | set { IterationsParameter.Value.Value = value; }
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| 82 | }
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| 83 |
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| 84 | public int Restarts {
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| 85 | get { return RestartsParameter.Value.Value; }
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| 86 | set { RestartsParameter.Value.Value = value; }
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| 87 | }
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| 88 |
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| 89 | public int Seed {
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| 90 | get { return SeedParameter.Value.Value; }
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| 91 | set { SeedParameter.Value.Value = value; }
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| 92 | }
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| 93 |
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| 94 | public bool SetSeedRandomly {
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| 95 | get { return SetSeedRandomlyParameter.Value.Value; }
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| 96 | set { SetSeedRandomlyParameter.Value.Value = value; }
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| 97 | }
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| 98 |
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| 99 | public bool InitializeParametersRandomly {
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| 100 | get { return InitParametersRandomlyParameter.Value.Value; }
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| 101 | set { InitParametersRandomlyParameter.Value.Value = value; }
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| 102 | }
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| 103 |
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| 104 | [StorableConstructor]
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[16663] | 105 | private OdeParameterIdentification(StorableConstructorFlag _) : base(_) { }
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[16268] | 106 | private OdeParameterIdentification(OdeParameterIdentification original, Cloner cloner)
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| 107 | : base(original, cloner) {
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| 108 | }
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| 109 | public OdeParameterIdentification()
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| 110 | : base() {
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| 111 | Problem = new Problem();
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| 112 | Parameters.Add(new ValueParameter<StringArray>(ModelStructureParameterName, "The function for which the parameters must be fit (only numeric constants are tuned).", new StringArray(new string[] { "1.0 * x*x + 0.0" })));
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| 113 | Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "The maximum number of iterations for constants optimization.", new IntValue(200)));
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| 114 | Parameters.Add(new FixedValueParameter<IntValue>(RestartsParameterName, "The number of independent random restarts (>0)", new IntValue(10)));
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| 115 | Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The PRNG seed value.", new IntValue()));
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| 116 | Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
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| 117 | Parameters.Add(new FixedValueParameter<BoolValue>(InitParamsRandomlyParameterName, "Switch to determine if the real-valued model parameters should be initialized randomly in each restart.", new BoolValue(false)));
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| 118 |
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| 119 | SetParameterHiddenState();
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| 120 |
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| 121 | InitParametersRandomlyParameter.Value.ValueChanged += (sender, args) => {
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| 122 | SetParameterHiddenState();
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| 123 | };
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| 124 | }
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| 125 |
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| 126 | private void SetParameterHiddenState() {
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| 127 | var hide = !InitializeParametersRandomly;
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| 128 | RestartsParameter.Hidden = hide;
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| 129 | SeedParameter.Hidden = hide;
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| 130 | SetSeedRandomlyParameter.Hidden = hide;
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| 131 | }
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| 132 |
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| 133 | [StorableHook(HookType.AfterDeserialization)]
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| 134 | private void AfterDeserialization() {
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| 135 | }
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| 136 |
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| 137 | public override IDeepCloneable Clone(Cloner cloner) {
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| 138 | return new OdeParameterIdentification(this, cloner);
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| 139 | }
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| 140 |
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| 141 | #region nonlinear regression
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| 142 | protected override void Run(CancellationToken cancellationToken) {
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| 143 | if (SetSeedRandomly) Seed = (new System.Random()).Next();
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| 144 | var rand = new MersenneTwister((uint)Seed);
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| 145 | if (InitializeParametersRandomly) {
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| 146 | throw new NotImplementedException();
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| 147 | // var qualityTable = new DataTable("RMSE table");
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| 148 | // qualityTable.VisualProperties.YAxisLogScale = true;
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| 149 | // var trainRMSERow = new DataRow("RMSE (train)");
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| 150 | // trainRMSERow.VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
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| 151 | // var testRMSERow = new DataRow("RMSE test");
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| 152 | // testRMSERow.VisualProperties.ChartType = DataRowVisualProperties.DataRowChartType.Points;
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| 153 | //
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| 154 | // qualityTable.Rows.Add(trainRMSERow);
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| 155 | // qualityTable.Rows.Add(testRMSERow);
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| 156 | // Results.Add(new Result(qualityTable.Name, qualityTable.Name + " for all restarts", qualityTable));
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| 157 |
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| 158 | // CreateSolution(Problem, ModelStructure.ToArray(), Iterations, rand);
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| 159 | //
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| 160 | // for (int r = 0; r < Restarts; r++) {
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| 161 | // CreateSolution(Problem, ModelStructure.ToArray(), Iterations, rand);
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| 162 | // trainRMSERow.Values.Add(solution.TrainingRootMeanSquaredError);
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| 163 | // testRMSERow.Values.Add(solution.TestRootMeanSquaredError);
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| 164 | // if (solution.TrainingRootMeanSquaredError < bestSolution.TrainingRootMeanSquaredError) {
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| 165 | // bestSolution = solution;
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| 166 | // }
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| 167 | // }
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| 168 | } else {
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| 169 | CreateSolution(Problem, ModelStructure.ToArray(), Iterations, rand);
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| 170 | }
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| 171 |
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| 172 | // Results.Add(new Result(RegressionSolutionResultName, "The nonlinear regression solution.", bestSolution));
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| 173 | // Results.Add(new Result("Root mean square error (train)", "The root of the mean of squared errors of the regression solution on the training set.", new DoubleValue(bestSolution.TrainingRootMeanSquaredError)));
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| 174 | // Results.Add(new Result("Root mean square error (test)", "The root of the mean of squared errors of the regression solution on the test set.", new DoubleValue(bestSolution.TestRootMeanSquaredError)));
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| 175 |
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| 176 | }
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| 177 |
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| 178 | public void CreateSolution(Problem problem, string[] modelStructure, int maxIterations, IRandom rand) {
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| 179 | var parser = new InfixExpressionParser();
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[16602] | 180 | var trees = modelStructure.Select(expr => parser.Parse(expr)).ToArray();
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[16268] | 181 | var names = problem.Encoding.Encodings.Select(enc => enc.Name).ToArray();
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| 182 | if (trees.Length != names.Length) throw new ArgumentException("The number of expressions must match the number of target variables exactly");
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| 183 |
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| 184 | var scope = new Scope();
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| 185 | for (int i = 0; i < names.Length; i++) {
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| 186 | scope.Variables.Add(new Core.Variable(names[i], trees[i]));
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| 187 | }
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| 188 | var ind = problem.Encoding.GetIndividual(scope);
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| 189 | var quality = problem.Evaluate(ind, rand);
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| 190 | problem.Analyze(new[] { ind }, new[] { quality }, Results, rand);
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| 191 | }
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| 192 | #endregion
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| 193 | }
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| 194 | }
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