#region License Information /* HeuristicLab * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Threading.Tasks; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Problems.DataAnalysis.Symbolic.Views; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views { public partial class InteractiveSymbolicRegressionSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView { private readonly SymbolicRegressionSolutionImpactValuesCalculator calculator; public new SymbolicRegressionSolution Content { get { return (SymbolicRegressionSolution)base.Content; } set { base.Content = value; } } public InteractiveSymbolicRegressionSolutionSimplifierView() : base() { InitializeComponent(); this.Caption = "Interactive Regression Solution Simplifier"; calculator = new SymbolicRegressionSolutionImpactValuesCalculator(); } protected override void UpdateModel(ISymbolicExpressionTree tree) { var model = new SymbolicRegressionModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit); model.Scale(Content.ProblemData); Content.Model = model; } protected override Dictionary> CalculateImpactAndReplacementValues(ISymbolicExpressionTree tree) { var impactAndReplacementValues = new Dictionary>(); foreach (var node in tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix()) { double impactValue, replacementValue, newQualityForImpactsCalculation; calculator.CalculateImpactAndReplacementValues(Content.Model, node, Content.ProblemData, Content.ProblemData.TrainingIndices, out impactValue, out replacementValue, out newQualityForImpactsCalculation); Progress.ProgressValue += 1.0 / (tree.Length - 2); impactAndReplacementValues.Add(node, new Tuple(impactValue, replacementValue)); } return impactAndReplacementValues; } protected override async void btnOptimizeConstants_Click(object sender, EventArgs e) { const int constOptIterations = 50; var model = Content.Model; Progress.Start("Optimizing Constants ...", 0); await Task.Run(() => { SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(model.Interpreter, model.SymbolicExpressionTree, Content.ProblemData, Content.ProblemData.TrainingIndices, applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true, lowerEstimationLimit: model.LowerEstimationLimit, upperEstimationLimit: model.UpperEstimationLimit, iterationReport: (args, func, iter) => { Progress.ProgressValue = (double)iter / (constOptIterations + 1); // (maxIterations + 1) iterations are reported }); }); UpdateModel(Content.Model.SymbolicExpressionTree); // UpdateModel calls Progress.Finish } } }