#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.Views {
public abstract partial class InteractiveSymbolicClassificationSolutionSimplifierViewBase : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
private readonly SymbolicClassificationSolutionImpactValuesCalculator calculator;
public new ISymbolicClassificationSolution Content {
get { return (ISymbolicClassificationSolution)base.Content; }
set { base.Content = value; }
}
public InteractiveSymbolicClassificationSolutionSimplifierViewBase()
: base() {
InitializeComponent();
this.Caption = "Interactive Classification Solution Simplifier";
calculator = new SymbolicClassificationSolutionImpactValuesCalculator();
}
///
/// It is necessary to create new models of an unknown type with new trees in the simplifier.
/// For this purpose the cloner is used by registering the new tree as already cloned object and invoking the clone mechanism.
/// This results in a new model of the same type as the old one with an exchanged tree.
///
/// The new tree that should be included in the new object
///
protected ISymbolicClassificationModel CreateModel(ISymbolicExpressionTree tree) {
var cloner = new Cloner();
cloner.RegisterClonedObject(Content.Model.SymbolicExpressionTree, tree);
var model = (ISymbolicClassificationModel)Content.Model.Clone(cloner);
model.RecalculateModelParameters(Content.ProblemData, Content.ProblemData.TrainingIndices);
return model;
}
protected override Dictionary CalculateReplacementValues(ISymbolicExpressionTree tree) {
return tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToDictionary(
n => n,
n => calculator.CalculateReplacementValue(Content.Model, n, Content.ProblemData, Content.ProblemData.TrainingIndices)
);
}
protected override Dictionary CalculateImpactValues(ISymbolicExpressionTree tree) {
var values = CalculateImpactAndReplacementValues(tree);
return values.ToDictionary(x => x.Key, x => x.Value.Item1);
}
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);
impactAndReplacementValues.Add(node, new Tuple(impactValue, replacementValue));
}
return impactAndReplacementValues;
}
protected override void btnOptimizeConstants_Click(object sender, EventArgs e) {
}
}
}