1 | #region License Information
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2 |
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3 | /* HeuristicLab
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4 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 |
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22 | #endregion
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23 |
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24 | using System.Drawing;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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28 | using HEAL.Attic;
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29 |
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30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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31 | [StorableType("87C4FF17-FC59-4D0F-80F5-2C84499E1222")]
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32 | [Item("NormalDistributedThresholdsModelCreator", "")]
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33 | public sealed class NormalDistributedThresholdsModelCreator : Item, ISymbolicDiscriminantFunctionClassificationModelCreator {
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34 | public static new Image StaticItemImage {
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35 | get { return HeuristicLab.Common.Resources.VSImageLibrary.Method; }
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36 | }
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37 | public override Image ItemImage {
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38 | get { return HeuristicLab.Common.Resources.VSImageLibrary.Method; }
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39 | }
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40 | [StorableConstructor]
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41 | private NormalDistributedThresholdsModelCreator(StorableConstructorFlag _) : base(_) { }
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42 | private NormalDistributedThresholdsModelCreator(NormalDistributedThresholdsModelCreator original, Cloner cloner) : base(original, cloner) { }
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43 | public NormalDistributedThresholdsModelCreator() : base() { }
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44 |
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45 | public override IDeepCloneable Clone(Cloner cloner) { return new NormalDistributedThresholdsModelCreator(this, cloner); }
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46 |
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47 |
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48 | public ISymbolicClassificationModel CreateSymbolicClassificationModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) {
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49 | return CreateSymbolicDiscriminantFunctionClassificationModel(targetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit);
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50 | }
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51 | public ISymbolicDiscriminantFunctionClassificationModel CreateSymbolicDiscriminantFunctionClassificationModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) {
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52 | return new SymbolicDiscriminantFunctionClassificationModel(targetVariable, tree, interpreter, new NormalDistributionCutPointsThresholdCalculator(), lowerEstimationLimit, upperEstimationLimit);
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53 | }
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54 |
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55 | }
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56 | }
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