[2041] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Text;
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| 25 | using System.Xml;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.DataAnalysis;
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| 29 | using System.Linq;
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| 30 |
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[2043] | 31 | namespace HeuristicLab.SupportVectorMachines {
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[2041] | 32 | public class VariableEvaluationImpactCalculator : HeuristicLab.Modeling.VariableEvaluationImpactCalculator {
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| 33 |
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| 34 | public VariableEvaluationImpactCalculator()
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| 35 | : base() {
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[2043] | 36 | AddVariableInfo(new VariableInfo("SVMModel", "The model that should be evaluated", typeof(SVMModel), VariableKind.In));
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[2041] | 37 | }
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| 38 |
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| 39 |
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[2043] | 40 | protected override double[] GetOutputs(IScope scope, Dataset dataset, int targetVariable, ItemList<IntData> allowedFeatures, int start, int end) {
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| 41 | SVMModel model = GetVariableValue<SVMModel>("SVMModel", scope, true);
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| 42 | SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
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| 43 | SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, model.RangeTransform);
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[2041] | 44 |
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[2043] | 45 | double[] values = new double[end - start];
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| 46 | for (int i = 0; i < end - start; i++) {
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| 47 | values[i] = SVM.Prediction.Predict(model.Model, scaledProblem.X[i]);
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[2041] | 48 | }
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[2043] | 49 | return values;
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[2041] | 50 | }
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| 51 | }
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| 52 | }
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