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source: branches/3.2/sources/HeuristicLab.GP.StructureIdentification.Classification/3.3/GPClassificationEvaluatorBase.cs @ 16386

Last change on this file since 16386 was 2578, checked in by gkronber, 15 years ago

Implemented #824 (Refactor: ITreeEvaluator interface to provide a method that evaluates a tree on a range of samples.)

File size: 2.3 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 System;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.DataAnalysis;
26using HeuristicLab.GP.Interfaces;
27
28namespace HeuristicLab.GP.StructureIdentification.Classification {
29  public abstract class GPClassificationEvaluatorBase : GPEvaluatorBase {
30
31    public GPClassificationEvaluatorBase()
32      : base() {
33      AddVariableInfo(new VariableInfo("TargetClassValues", "The original class values of target variable (for instance negative=0 and positive=1).", typeof(ItemList<DoubleData>), VariableKind.In));
34    }
35
36    public override void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end) {
37
38      ItemList<DoubleData> classes = GetVariableValue<ItemList<DoubleData>>("TargetClassValues", scope, true);
39      double[] classesArr = new double[classes.Count];
40      for (int i = 0; i < classesArr.Length; i++) classesArr[i] = classes[i].Data;
41      Array.Sort(classesArr);
42      double[] thresholds = new double[classes.Count - 1];
43      for (int i = 0; i < classesArr.Length - 1; i++) {
44        thresholds[i] = (classesArr[i] + classesArr[i + 1]) / 2.0;
45      }
46
47      Evaluate(scope, tree, evaluator, dataset, targetVariable, classesArr, thresholds, start, end);
48    }
49
50    public abstract void Evaluate(IScope scope, IFunctionTree tree, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end);
51  }
52}
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