Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/GPEvaluatorBase.cs @ 2222

Last change on this file since 2222 was 2222, checked in by gkronber, 15 years ago

Merged changes from GP-refactoring branch back into the trunk #713.

File size: 5.3 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 HeuristicLab.Core;
23using HeuristicLab.Data;
24using HeuristicLab.DataAnalysis;
25using HeuristicLab.GP.Interfaces;
26
27namespace HeuristicLab.GP.StructureIdentification {
28  public abstract class GPEvaluatorBase : OperatorBase {
29    public GPEvaluatorBase()
30      : base() {
31      AddVariableInfo(new VariableInfo("TreeEvaluator", "The evaluator that should be used to evaluate the expression tree", typeof(ITreeEvaluator), VariableKind.In));
32      AddVariableInfo(new VariableInfo("FunctionTree", "The function tree that should be evaluated", typeof(IGeneticProgrammingModel), VariableKind.In));
33      AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
34      AddVariableInfo(new VariableInfo("TargetVariable", "Index of the column of the dataset that holds the target variable", typeof(IntData), VariableKind.In));
35      AddVariableInfo(new VariableInfo("PunishmentFactor", "Punishment factor for invalid estimations", typeof(DoubleData), VariableKind.In));
36      AddVariableInfo(new VariableInfo("TotalEvaluatedNodes", "Number of evaluated nodes", typeof(DoubleData), VariableKind.In | VariableKind.Out));
37      AddVariableInfo(new VariableInfo("TrainingSamplesStart", "Start index of training samples in dataset", typeof(IntData), VariableKind.In));
38      AddVariableInfo(new VariableInfo("TrainingSamplesEnd", "End index of training samples in dataset", typeof(IntData), VariableKind.In));
39      AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
40      AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
41      AddVariableInfo(new VariableInfo("UseEstimatedTargetValue", "Wether to use the original (measured) or the estimated (calculated) value for the target variable for autoregressive modelling", typeof(BoolData), VariableKind.In));
42    }
43
44    public override IOperation Apply(IScope scope) {
45      // get all variable values
46      int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
47      Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
48      IGeneticProgrammingModel gpModel = GetVariableValue<IGeneticProgrammingModel>("FunctionTree", scope, true);
49      double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;
50      double totalEvaluatedNodes = scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data;
51      int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
52      int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
53      int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
54      int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
55      bool useEstimatedValues = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, true).Data;
56      ITreeEvaluator evaluator = GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true);
57      evaluator.PrepareForEvaluation(dataset, targetVariable, trainingStart, trainingEnd, punishmentFactor, gpModel.FunctionTree);
58
59      double[] backupValues = null;
60      // prepare for autoregressive modelling by saving the original values of the target-variable to a backup array
61      if (useEstimatedValues &&
62        (backupValues == null || backupValues.Length != end - start)) {
63        backupValues = new double[end - start];
64        for (int i = start; i < end; i++) {
65          backupValues[i - start] = dataset.GetValue(i, targetVariable);
66        }
67      }
68      dataset.FireChangeEvents = false;
69
70      Evaluate(scope, evaluator, dataset, targetVariable, start, end, useEstimatedValues);
71
72      // restore the values of the target variable from the backup array if necessary
73      if (useEstimatedValues) {
74        for (int i = start; i < end; i++) {
75          dataset.SetValue(i, targetVariable, backupValues[i - start]);
76        }
77      }
78      dataset.FireChangeEvents = true;
79      dataset.FireChanged();
80
81      // update the value of total evaluated nodes
82      scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data = totalEvaluatedNodes + gpModel.Size * (end - start);
83      return null;
84    }
85
86    public abstract void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues);
87  }
88}
Note: See TracBrowser for help on using the repository browser.