[1808] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2009 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.Linq;
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| 25 | using System.Text;
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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 |
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| 30 | namespace HeuristicLab.SupportVectorMachines {
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| 31 | public class SupportVectorEvaluator : OperatorBase {
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| 32 |
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| 33 | public SupportVectorEvaluator()
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| 34 | : base() {
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| 35 | //Dataset infos
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| 36 | AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
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| 37 | AddVariableInfo(new VariableInfo("AllowedFeatures", "List of indexes of allowed features", typeof(ItemList<IntData>), VariableKind.In));
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| 38 | AddVariableInfo(new VariableInfo("TargetVariable", "Index of the column of the dataset that holds the target variable", typeof(IntData), VariableKind.In));
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| 39 | AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
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| 40 | AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
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| 41 |
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[1837] | 42 | AddVariableInfo(new VariableInfo("SVMModel", "Represent the model learned by the SVM", typeof(SVMModel), VariableKind.In));
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| 43 | AddVariableInfo(new VariableInfo("SVMRangeTransform", "The applied transformation during the learning the model", typeof(SVMRangeTransform), VariableKind.In));
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[1808] | 44 |
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| 45 | AddVariableInfo(new VariableInfo("Values", "Target vs predicted values", typeof(ItemList), VariableKind.New | VariableKind.Out));
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| 46 | }
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| 47 |
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| 48 |
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| 49 | public override IOperation Apply(IScope scope) {
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| 50 | Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
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| 51 | ItemList<IntData> allowedFeatures = GetVariableValue<ItemList<IntData>>("AllowedFeatures", scope, true);
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| 52 | int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
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| 53 | int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
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| 54 | int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
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| 55 |
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[1837] | 56 | SVM.Model model = GetVariableValue<SVMModel>("SVMModel", scope, true).Data;
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| 57 | SVM.RangeTransform rangeTransform = GetVariableValue<SVMRangeTransform>("SVMRangeTransform", scope, true).Data;
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[1808] | 58 |
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| 59 | SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
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| 60 | SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, rangeTransform);
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| 61 |
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| 62 | ItemList predictedValues = new ItemList();
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| 63 | ItemList row;
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| 64 | for (int i = 0; i < end - start; i++) {
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| 65 | row = new ItemList();
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| 66 | row.Add(new DoubleData(SVM.Prediction.Predict(model, scaledProblem.X[i])));
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| 67 | row.Add(new DoubleData(dataset.Samples[(start + i) * dataset.Columns + targetVariable]));
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| 68 | predictedValues.Add(row);
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| 69 | }
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| 70 |
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| 71 | scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Values"), predictedValues));
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| 72 | return null;
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| 73 | }
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| 74 | }
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| 75 | }
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