[3915] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17180] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[3915] | 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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[17472] | 23 | using System.Threading;
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[6256] | 24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[15371] | 25 | using HeuristicLab.MainForm;
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[5699] | 26 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
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[3915] | 27 |
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[5699] | 28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
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| 29 | public partial class InteractiveSymbolicRegressionSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
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[5717] | 30 | public new SymbolicRegressionSolution Content {
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| 31 | get { return (SymbolicRegressionSolution)base.Content; }
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| 32 | set { base.Content = value; }
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| 33 | }
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| 34 |
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[5699] | 35 | public InteractiveSymbolicRegressionSolutionSimplifierView()
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[15371] | 36 | : base(new SymbolicRegressionSolutionImpactValuesCalculator()) {
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[5699] | 37 | InitializeComponent();
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| 38 | this.Caption = "Interactive Regression Solution Simplifier";
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[3915] | 39 | }
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| 40 |
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[17456] | 41 | protected override void SetEnabledStateOfControls() {
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| 42 | base.SetEnabledStateOfControls();
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| 43 |
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| 44 | var tree = Content?.Model?.SymbolicExpressionTree;
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[17633] | 45 | btnOptimizeConstants.Enabled = tree != null && NonlinearLeastSquaresConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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| 46 | btnVectorOptimizeConstants.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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| 47 | nudLearningRate.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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[17786] | 48 | btnUnrollingVectorOptimizeConstants.Enabled = tree != null && VectorUnrollingNonlinearLeastSquaresConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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[18239] | 49 | #if INCLUDE_DIFFSHARP
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[17786] | 50 | btnDiffSharpOptimizeConstants.Enabled = tree != null && NonlinearLeastSquaresVectorConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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[18239] | 51 | #endif
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[17456] | 52 | }
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| 53 |
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[5717] | 54 | protected override void UpdateModel(ISymbolicExpressionTree tree) {
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[13941] | 55 | var model = new SymbolicRegressionModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit);
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[8972] | 56 | model.Scale(Content.ProblemData);
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[5818] | 57 | Content.Model = model;
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[3915] | 58 | }
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| 59 |
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[17472] | 60 | protected override ISymbolicExpressionTree OptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
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[15371] | 61 | const int constOptIterations = 50;
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[17456] | 62 | const int maxRepetitions = 100;
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| 63 | const double minimumImprovement = 1e-10;
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[15400] | 64 | var regressionProblemData = Content.ProblemData;
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| 65 | var model = Content.Model;
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[16798] | 66 | progress.CanBeStopped = true;
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[17456] | 67 | double prevResult = 0.0, improvement = 0.0;
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[16798] | 68 | var result = 0.0;
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| 69 | int reps = 0;
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| 70 |
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[17633] | 71 | do {
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| 72 | prevResult = result;
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| 73 | tree = NonlinearLeastSquaresConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData, regressionProblemData.TrainingIndices,
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| 74 | applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
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| 75 | cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
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| 76 | double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
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| 77 | progress.ProgressValue = Math.Min(newProgressValue, 1.0);
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[18238] | 78 | progress.Message = $"MSE: { func / regressionProblemData.TrainingPartition.Size }";
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[17633] | 79 | });
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| 80 | result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
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| 81 | model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
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| 82 | reps++;
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| 83 | improvement = result - prevResult;
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| 84 | } while (improvement > minimumImprovement && reps < maxRepetitions &&
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| 85 | progress.ProgressState != ProgressState.StopRequested &&
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| 86 | progress.ProgressState != ProgressState.CancelRequested);
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| 87 | return tree;
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| 88 | }
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| 89 |
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| 90 | protected override ISymbolicExpressionTree VectorOptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
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[17502] | 91 | const int maxIterations = 1000;
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[17633] | 92 | var regressionProblemData = Content.ProblemData;
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| 93 | progress.CanBeStopped = true;
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| 94 |
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| 95 | var learningRate = Math.Pow(10, (double)nudLearningRate.Value);
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| 96 |
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[17502] | 97 | return TensorFlowConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData,
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| 98 | regressionProblemData.TrainingIndices,
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[18240] | 99 | applyLinearScaling: true, updateVariableWeights: true, maxIterations: maxIterations, learningRate: learningRate,
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[17502] | 100 | cancellationToken: cancellationToken,
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| 101 | progress: new SynchronousProgress<double>(cost => {
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| 102 | var newProgress = progress.ProgressValue + (1.0 / (maxIterations + 1));
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| 103 | progress.ProgressValue = Math.Min(newProgress, 1.0);
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| 104 | progress.Message = $"MSE: {cost}";
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| 105 | })
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| 106 | );
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[10492] | 107 | }
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[17502] | 108 |
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[17786] | 109 | protected override ISymbolicExpressionTree UnrollingVectorOptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
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| 110 | const int constOptIterations = 50;
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| 111 | const int maxRepetitions = 100;
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| 112 | const double minimumImprovement = 1e-10;
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| 113 | var regressionProblemData = Content.ProblemData;
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| 114 | var model = Content.Model;
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| 115 | progress.CanBeStopped = true;
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| 116 | double prevResult = 0.0, improvement = 0.0;
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| 117 | var result = 0.0;
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| 118 | int reps = 0;
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| 119 | var interpreter = new SymbolicDataAnalysisExpressionTreeVectorInterpreter();
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| 120 |
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| 121 | do {
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| 122 | prevResult = result;
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| 123 | tree = VectorUnrollingNonlinearLeastSquaresConstantOptimizationEvaluator.OptimizeTree(
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| 124 | tree, interpreter,
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[18239] | 125 | regressionProblemData,
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| 126 | regressionProblemData.TrainingIndices,
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[18240] | 127 | applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
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[17786] | 128 | cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
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| 129 | double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
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| 130 | progress.ProgressValue = Math.Min(newProgressValue, 1.0);
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[18238] | 131 | progress.Message = $"MSE: { func / regressionProblemData.TrainingPartition.Size }";
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[17786] | 132 | });
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| 133 | result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
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| 134 | model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
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| 135 | reps++;
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| 136 | improvement = result - prevResult;
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| 137 | } while (improvement > minimumImprovement && reps < maxRepetitions &&
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| 138 | progress.ProgressState != ProgressState.StopRequested &&
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| 139 | progress.ProgressState != ProgressState.CancelRequested);
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| 140 | return tree;
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| 141 | }
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| 142 |
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| 143 |
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| 144 | protected override ISymbolicExpressionTree DiffSharpVectorOptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
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| 145 | const int constOptIterations = 50;
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| 146 | const int maxRepetitions = 100;
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| 147 | const double minimumImprovement = 1e-10;
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| 148 | var regressionProblemData = Content.ProblemData;
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| 149 | var model = Content.Model;
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| 150 | progress.CanBeStopped = true;
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| 151 | double prevResult = 0.0, improvement = 0.0;
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| 152 | var result = 0.0;
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| 153 | int reps = 0;
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| 154 |
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[18239] | 155 | #if INCLUDE_DIFFSHARP
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[17786] | 156 | do {
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| 157 | prevResult = result;
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| 158 | tree = NonlinearLeastSquaresVectorConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData, regressionProblemData.TrainingIndices,
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| 159 | applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
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| 160 | cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
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| 161 | double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
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| 162 | progress.ProgressValue = Math.Min(newProgressValue, 1.0);
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[18238] | 163 | progress.Message = $"MSE: { func / regressionProblemData.TrainingPartition.Size }";
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[17786] | 164 | });
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| 165 | result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
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| 166 | model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
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| 167 | reps++;
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| 168 | improvement = result - prevResult;
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| 169 | } while (improvement > minimumImprovement && reps < maxRepetitions &&
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| 170 | progress.ProgressState != ProgressState.StopRequested &&
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| 171 | progress.ProgressState != ProgressState.CancelRequested);
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[18239] | 172 | #endif
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[17786] | 173 | return tree;
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| 174 | }
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| 175 |
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| 176 |
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[17502] | 177 | internal class SynchronousProgress<T> : IProgress<T> {
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| 178 | private readonly Action<T> callback;
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| 179 | public SynchronousProgress(Action<T> callback) {
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| 180 | this.callback = callback;
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| 181 | }
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| 182 | public void Report(T value) {
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| 183 | callback(value);
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| 184 | }
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| 185 | }
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[3915] | 186 | }
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| 187 | }
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