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source: trunk/sources/HeuristicLab.SupportVectorMachines/3.2/VariableEvaluationImpactCalculator.cs @ 2301

Last change on this file since 2301 was 2165, checked in by gkronber, 15 years ago

Removed variable AllowedFeatures in all modeling algorithms. #709

File size: 2.0 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.Text;
25using System.Xml;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.DataAnalysis;
29using System.Linq;
30
31namespace HeuristicLab.SupportVectorMachines {
32  public class VariableEvaluationImpactCalculator : HeuristicLab.Modeling.VariableEvaluationImpactCalculator {
33
34    public VariableEvaluationImpactCalculator()
35      : base() {
36      AddVariableInfo(new VariableInfo("SVMModel", "The model that should be evaluated", typeof(SVMModel), VariableKind.In));
37    }
38
39
40    protected override double[] GetOutputs(IScope scope, Dataset dataset, int targetVariable, int start, int end) {
41      SVMModel model = GetVariableValue<SVMModel>("SVMModel", scope, true);
42      SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, targetVariable, start, end);
43      SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, model.RangeTransform);
44
45      double[] values = new double[end - start];
46      for (int i = 0; i < end - start; i++) {
47        values[i] = SVM.Prediction.Predict(model.Model, scaledProblem.X[i]);
48      }
49      return values;
50    }
51  }
52}
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