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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableConditionTreeNode.cs @ 14419

Last change on this file since 14419 was 14345, checked in by gkronber, 8 years ago

#2690: implemented methods to generate symbolic expression tree solutions for decision tree models (random forest and gradient boosted) as well as views which make it possible to inspect each of the individual trees in a GBT and RF solution

File size: 3.9 KB
RevLine 
[5060]1#region License Information
2/* HeuristicLab
[14185]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[5060]4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27using HeuristicLab.Random;
28
[5532]29namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
[5060]30  [StorableClass]
31  public sealed class VariableConditionTreeNode : SymbolicExpressionTreeNode {
32    #region properties
33    public new VariableCondition Symbol {
34      get { return (VariableCondition)base.Symbol; }
35    }
36    [Storable]
37    private double threshold;
38    public double Threshold {
39      get { return threshold; }
40      set { threshold = value; }
41    }
42    [Storable]
43    private string variableName;
44    public string VariableName {
45      get { return variableName; }
46      set { variableName = value; }
47    }
48    [Storable]
49    private double slope;
50    public double Slope {
51      get { return slope; }
52      set { slope = value; }
53    }
54    #endregion
55
56    [StorableConstructor]
57    private VariableConditionTreeNode(bool deserializing) : base(deserializing) { }
58    private VariableConditionTreeNode(VariableConditionTreeNode original, Cloner cloner)
59      : base(original, cloner) {
60      threshold = original.threshold;
61      variableName = original.variableName;
62      slope = original.slope;
63    }
64    public override IDeepCloneable Clone(Cloner cloner) {
65      return new VariableConditionTreeNode(this, cloner);
66    }
67
68    public VariableConditionTreeNode(VariableCondition variableConditionSymbol) : base(variableConditionSymbol) { }
69    public override bool HasLocalParameters {
70      get { return true; }
71    }
72
73    public override void ResetLocalParameters(IRandom random) {
74      base.ResetLocalParameters(random);
75      threshold = NormalDistributedRandom.NextDouble(random, Symbol.ThresholdInitializerMu, Symbol.ThresholdInitializerSigma);
[12422]76
77#pragma warning disable 612, 618
[5060]78      variableName = Symbol.VariableNames.SelectRandom(random);
[12422]79#pragma warning restore 612, 618
80
[5060]81      slope = NormalDistributedRandom.NextDouble(random, Symbol.SlopeInitializerMu, Symbol.SlopeInitializerSigma);
82    }
83
84    public override void ShakeLocalParameters(IRandom random, double shakingFactor) {
85      base.ShakeLocalParameters(random, shakingFactor);
86      double x = NormalDistributedRandom.NextDouble(random, Symbol.ThresholdManipulatorMu, Symbol.ThresholdManipulatorSigma);
87      threshold = threshold + x * shakingFactor;
[12422]88
89#pragma warning disable 612, 618
[5060]90      variableName = Symbol.VariableNames.SelectRandom(random);
[12422]91#pragma warning restore 612, 618
92
[5060]93      x = NormalDistributedRandom.NextDouble(random, Symbol.SlopeManipulatorMu, Symbol.SlopeManipulatorSigma);
94      slope = slope + x * shakingFactor;
95    }
96
97    public override string ToString() {
[14345]98      if (slope.IsAlmost(0.0) || Symbol.IgnoreSlope) {
99        return variableName + " < " + threshold.ToString("E4");
100      } else {
[7671]101        return variableName + " > " + threshold.ToString("E4") + Environment.NewLine +
[14345]102               "slope: " + slope.ToString("E4");
103      }
[5060]104    }
105  }
106}
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