[702] | 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 HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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| 25 | using HeuristicLab.DataAnalysis;
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| 26 |
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| 27 | namespace HeuristicLab.GP.StructureIdentification.Classification {
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| 28 | public abstract class GPClassificationEvaluatorBase : GPEvaluatorBase {
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| 29 |
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| 30 | public GPClassificationEvaluatorBase()
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| 31 | : base() {
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| 32 | AddVariableInfo(new VariableInfo("TargetClassValues", "The original class values of target variable (for instance negative=0 and positive=1).", typeof(ItemList<DoubleData>), VariableKind.In));
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| 33 | }
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| 34 |
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[1891] | 35 | public override void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
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[702] | 36 |
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| 37 | ItemList<DoubleData> classes = GetVariableValue<ItemList<DoubleData>>("TargetClassValues", scope, true);
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| 38 | double[] classesArr = new double[classes.Count];
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| 39 | for(int i = 0; i < classesArr.Length; i++) classesArr[i] = classes[i].Data;
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| 40 | Array.Sort(classesArr);
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| 41 | double[] thresholds = new double[classes.Count - 1];
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| 42 | for(int i = 0; i < classesArr.Length - 1; i++) {
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| 43 | thresholds[i] = (classesArr[i] + classesArr[i + 1]) / 2.0;
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| 44 | }
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| 45 |
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[1891] | 46 | Evaluate(scope, evaluator, dataset, targetVariable, classesArr, thresholds, start, end);
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[702] | 47 | }
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| 48 |
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[1891] | 49 | public abstract void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, double[] classes, double[] thresholds, int start, int end);
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[702] | 50 | }
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| 51 | }
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