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source: trunk/sources/HeuristicLab.GP.StructureIdentification.ConditionalEvaluation/3.3/ConditionalSimpleEvaluator.cs @ 1902

Last change on this file since 1902 was 1902, checked in by mkommend, 15 years ago

added initial version of conditional evaluators (ticket #515)

File size: 3.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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 System.Collections.Generic;
24using System.Linq;
25using System.Text;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.DataAnalysis;
29
30namespace HeuristicLab.GP.StructureIdentification.ConditionalEvaluation {
31  public class ConditionalSimpleEvaluator : GPEvaluatorBase {
32    public ConditionalSimpleEvaluator()
33      : base() {
34      AddVariableInfo(new VariableInfo("MaxTimeOffset", "Maximal time offset for all feature", typeof(IntData), VariableKind.In));
35      AddVariableInfo(new VariableInfo("MinTimeOffset", "Minimal time offset for all feature", typeof(IntData), VariableKind.In));
36      AddVariableInfo(new VariableInfo("ConditionVariable", "Variable index which indicates if the row should be evaluated (0 means do not evaluate, != 0 evaluate)", typeof(IntData), VariableKind.In));
37      AddVariableInfo(new VariableInfo("Values", "The values of the target variable as predicted by the model and the original value of the target variable", typeof(ItemList), VariableKind.New | VariableKind.Out));
38    }
39
40    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
41      ItemList values = GetVariableValue<ItemList>("Values", scope, false, false);
42      if (values == null) {
43        values = new ItemList();
44        IVariableInfo info = GetVariableInfo("Values");
45        if (info.Local)
46          AddVariable(new HeuristicLab.Core.Variable(info.ActualName, values));
47        else
48          scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName(info.FormalName), values));
49      }
50      values.Clear();
51
52      int maxTimeOffset = GetVariableValue<IntData>("MaxTimeOffset", scope, true).Data;
53      int minTimeOffset = GetVariableValue<IntData>("MinTimeOffset", scope, true).Data;
54      int conditionVariable = GetVariableValue<IntData>("ConditionVariable", scope, true).Data;
55
56      for (int sample = start; sample < end; sample++) {
57        // check if condition variable is true between sample - minTimeOffset and sample - maxTimeOffset
58        bool skip = false;
59        for (int checkIndex = sample - minTimeOffset; checkIndex <= sample - maxTimeOffset && !skip ; checkIndex++) {
60          if (dataset.GetValue(checkIndex, conditionVariable) == 0)
61            skip = true;
62        }
63        if (!skip) {
64          ItemList row = new ItemList();
65          double estimated = evaluator.Evaluate(sample);
66          double original = dataset.GetValue(sample, targetVariable);
67          if (updateTargetValues) {
68            dataset.SetValue(sample, targetVariable, estimated);
69          }
70          row.Add(new DoubleData(estimated));
71          row.Add(new DoubleData(original));
72          values.Add(row);
73        }
74      }
75    }
76  }
77}
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