| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 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 HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HEAL.Attic;
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| 30 |
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| 31 | namespace HeuristicLab.Problems.DataAnalysis {
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| 32 | [StorableType("1C8DCCCF-4E2A-421D-9C61-7C017D584054")]
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| 33 | [Item("ClassificationProblemData", "Represents an item containing all data defining a classification problem.")]
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| 34 | public class ClassificationProblemData : DataAnalysisProblemData, IClassificationProblemData, IStorableContent {
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| 35 | protected const string TargetVariableParameterName = "TargetVariable";
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| 36 | protected const string ClassNamesParameterName = "ClassNames";
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| 37 | protected const string ClassificationPenaltiesParameterName = "ClassificationPenalties";
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| 38 | protected const string PositiveClassParameterName = "PositiveClass";
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| 39 | protected const int MaximumNumberOfClasses = 100;
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| 40 | protected const int InspectedRowsToDetermineTargets = 2000;
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| 41 |
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| 42 | public string Filename { get; set; }
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| 43 |
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| 44 | #region default data
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| 45 | private static string[] defaultVariableNames = new string[] { "sample", "clump thickness", "cell size", "cell shape", "marginal adhesion", "epithelial cell size", "bare nuclei", "chromatin", "nucleoli", "mitoses", "class" };
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| 46 | private static double[,] defaultData = new double[,]{
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| 47 | {1000025,5,1,1,1,2,1,3,1,1,2 },
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| 48 | {1002945,5,4,4,5,7,10,3,2,1,2 },
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| 49 | {1015425,3,1,1,1,2,2,3,1,1,2 },
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| 50 | {1016277,6,8,8,1,3,4,3,7,1,2 },
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| 51 | {1017023,4,1,1,3,2,1,3,1,1,2 },
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| 52 | {1017122,8,10,10,8,7,10,9,7,1,4 },
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| 53 | {1018099,1,1,1,1,2,10,3,1,1,2 },
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| 54 | {1018561,2,1,2,1,2,1,3,1,1,2 },
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| 55 | {1033078,2,1,1,1,2,1,1,1,5,2 },
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| 56 | {1033078,4,2,1,1,2,1,2,1,1,2 },
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| 57 | {1035283,1,1,1,1,1,1,3,1,1,2 },
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| 58 | {1036172,2,1,1,1,2,1,2,1,1,2 },
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| 59 | {1041801,5,3,3,3,2,3,4,4,1,4 },
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| 60 | {1043999,1,1,1,1,2,3,3,1,1,2 },
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| 61 | {1044572,8,7,5,10,7,9,5,5,4,4 },
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| 62 | {1047630,7,4,6,4,6,1,4,3,1,4 },
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| 63 | {1048672,4,1,1,1,2,1,2,1,1,2 },
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| 64 | {1049815,4,1,1,1,2,1,3,1,1,2 },
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| 65 | {1050670,10,7,7,6,4,10,4,1,2,4 },
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| 66 | {1050718,6,1,1,1,2,1,3,1,1,2 },
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| 67 | {1054590,7,3,2,10,5,10,5,4,4,4 },
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| 68 | {1054593,10,5,5,3,6,7,7,10,1,4 },
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| 69 | {1056784,3,1,1,1,2,1,2,1,1,2 },
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| 70 | {1057013,8,4,5,1,2,2,7,3,1,4 },
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| 71 | {1059552,1,1,1,1,2,1,3,1,1,2 },
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| 72 | {1065726,5,2,3,4,2,7,3,6,1,4 },
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| 73 | {1066373,3,2,1,1,1,1,2,1,1,2 },
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| 74 | {1066979,5,1,1,1,2,1,2,1,1,2 },
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| 75 | {1067444,2,1,1,1,2,1,2,1,1,2 },
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| 76 | {1070935,1,1,3,1,2,1,1,1,1,2 },
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| 77 | {1070935,3,1,1,1,1,1,2,1,1,2 },
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| 78 | {1071760,2,1,1,1,2,1,3,1,1,2 },
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| 79 | {1072179,10,7,7,3,8,5,7,4,3,4 },
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| 80 | {1074610,2,1,1,2,2,1,3,1,1,2 },
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| 81 | {1075123,3,1,2,1,2,1,2,1,1,2 },
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| 82 | {1079304,2,1,1,1,2,1,2,1,1,2 },
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| 83 | {1080185,10,10,10,8,6,1,8,9,1,4 },
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| 84 | {1081791,6,2,1,1,1,1,7,1,1,2 },
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| 85 | {1084584,5,4,4,9,2,10,5,6,1,4 },
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| 86 | {1091262,2,5,3,3,6,7,7,5,1,4 },
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| 87 | {1096800,6,6,6,9,6,4,7,8,1,2 },
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| 88 | {1099510,10,4,3,1,3,3,6,5,2,4 },
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| 89 | {1100524,6,10,10,2,8,10,7,3,3,4 },
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| 90 | {1102573,5,6,5,6,10,1,3,1,1,4 },
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| 91 | {1103608,10,10,10,4,8,1,8,10,1,4 },
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| 92 | {1103722,1,1,1,1,2,1,2,1,2,2 },
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| 93 | {1105257,3,7,7,4,4,9,4,8,1,4 },
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| 94 | {1105524,1,1,1,1,2,1,2,1,1,2 },
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| 95 | {1106095,4,1,1,3,2,1,3,1,1,2 },
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| 96 | {1106829,7,8,7,2,4,8,3,8,2,4 },
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| 97 | {1108370,9,5,8,1,2,3,2,1,5,4 },
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| 98 | {1108449,5,3,3,4,2,4,3,4,1,4 },
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| 99 | {1110102,10,3,6,2,3,5,4,10,2,4 },
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| 100 | {1110503,5,5,5,8,10,8,7,3,7,4 },
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| 101 | {1110524,10,5,5,6,8,8,7,1,1,4 },
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| 102 | {1111249,10,6,6,3,4,5,3,6,1,4 },
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| 103 | {1112209,8,10,10,1,3,6,3,9,1,4 },
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| 104 | {1113038,8,2,4,1,5,1,5,4,4,4 },
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| 105 | {1113483,5,2,3,1,6,10,5,1,1,4 },
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| 106 | {1113906,9,5,5,2,2,2,5,1,1,4 },
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| 107 | {1115282,5,3,5,5,3,3,4,10,1,4 },
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| 108 | {1115293,1,1,1,1,2,2,2,1,1,2 },
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| 109 | {1116116,9,10,10,1,10,8,3,3,1,4 },
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| 110 | {1116132,6,3,4,1,5,2,3,9,1,4 },
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| 111 | {1116192,1,1,1,1,2,1,2,1,1,2 },
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| 112 | {1116998,10,4,2,1,3,2,4,3,10,4 },
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| 113 | {1117152,4,1,1,1,2,1,3,1,1,2 },
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| 114 | {1118039,5,3,4,1,8,10,4,9,1,4 },
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| 115 | {1120559,8,3,8,3,4,9,8,9,8,4 },
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| 116 | {1121732,1,1,1,1,2,1,3,2,1,2 },
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| 117 | {1121919,5,1,3,1,2,1,2,1,1,2 },
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| 118 | {1123061,6,10,2,8,10,2,7,8,10,4 },
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| 119 | {1124651,1,3,3,2,2,1,7,2,1,2 },
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| 120 | {1125035,9,4,5,10,6,10,4,8,1,4 },
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| 121 | {1126417,10,6,4,1,3,4,3,2,3,4 },
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| 122 | {1131294,1,1,2,1,2,2,4,2,1,2 },
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| 123 | {1132347,1,1,4,1,2,1,2,1,1,2 },
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| 124 | {1133041,5,3,1,2,2,1,2,1,1,2 },
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| 125 | {1133136,3,1,1,1,2,3,3,1,1,2 },
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| 126 | {1136142,2,1,1,1,3,1,2,1,1,2 },
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| 127 | {1137156,2,2,2,1,1,1,7,1,1,2 },
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| 128 | {1143978,4,1,1,2,2,1,2,1,1,2 },
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| 129 | {1143978,5,2,1,1,2,1,3,1,1,2 },
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| 130 | {1147044,3,1,1,1,2,2,7,1,1,2 },
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| 131 | {1147699,3,5,7,8,8,9,7,10,7,4 },
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| 132 | {1147748,5,10,6,1,10,4,4,10,10,4 },
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| 133 | {1148278,3,3,6,4,5,8,4,4,1,4 },
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| 134 | {1148873,3,6,6,6,5,10,6,8,3,4 },
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| 135 | {1152331,4,1,1,1,2,1,3,1,1,2 },
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| 136 | {1155546,2,1,1,2,3,1,2,1,1,2 },
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| 137 | {1156272,1,1,1,1,2,1,3,1,1,2 },
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| 138 | {1156948,3,1,1,2,2,1,1,1,1,2 },
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| 139 | {1157734,4,1,1,1,2,1,3,1,1,2 },
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| 140 | {1158247,1,1,1,1,2,1,2,1,1,2 },
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| 141 | {1160476,2,1,1,1,2,1,3,1,1,2 },
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| 142 | {1164066,1,1,1,1,2,1,3,1,1,2 },
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| 143 | {1165297,2,1,1,2,2,1,1,1,1,2 },
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| 144 | {1165790,5,1,1,1,2,1,3,1,1,2 },
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| 145 | {1165926,9,6,9,2,10,6,2,9,10,4 },
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| 146 | {1166630,7,5,6,10,5,10,7,9,4,4 },
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| 147 | {1166654,10,3,5,1,10,5,3,10,2,4 },
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| 148 | {1167439,2,3,4,4,2,5,2,5,1,4 },
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| 149 | {1167471,4,1,2,1,2,1,3,1,1,2 },
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| 150 | {1168359,8,2,3,1,6,3,7,1,1,4 },
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| 151 | {1168736,10,10,10,10,10,1,8,8,8,4 },
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| 152 | {1169049,7,3,4,4,3,3,3,2,7,4 },
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| 153 | {1170419,10,10,10,8,2,10,4,1,1,4 },
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| 154 | {1170420,1,6,8,10,8,10,5,7,1,4 },
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| 155 | {1171710,1,1,1,1,2,1,2,3,1,2 },
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| 156 | {1171710,6,5,4,4,3,9,7,8,3,4 },
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| 157 | {1171795,1,3,1,2,2,2,5,3,2,2 },
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| 158 | {1171845,8,6,4,3,5,9,3,1,1,4 },
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| 159 | {1172152,10,3,3,10,2,10,7,3,3,4 },
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| 160 | {1173216,10,10,10,3,10,8,8,1,1,4 },
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| 161 | {1173235,3,3,2,1,2,3,3,1,1,2 },
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| 162 | {1173347,1,1,1,1,2,5,1,1,1,2 },
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| 163 | {1173347,8,3,3,1,2,2,3,2,1,2 },
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| 164 | {1173509,4,5,5,10,4,10,7,5,8,4 },
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| 165 | {1173514,1,1,1,1,4,3,1,1,1,2 },
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| 166 | {1173681,3,2,1,1,2,2,3,1,1,2 },
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| 167 | {1174057,1,1,2,2,2,1,3,1,1,2 },
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| 168 | {1174057,4,2,1,1,2,2,3,1,1,2 },
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| 169 | {1174131,10,10,10,2,10,10,5,3,3,4 },
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| 170 | {1174428,5,3,5,1,8,10,5,3,1,4 },
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| 171 | {1175937,5,4,6,7,9,7,8,10,1,4 },
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| 172 | {1176406,1,1,1,1,2,1,2,1,1,2 },
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| 173 | {1176881,7,5,3,7,4,10,7,5,5,4 }
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| 174 | };
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| 175 | private static readonly Dataset defaultDataset;
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| 176 | private static readonly IEnumerable<string> defaultAllowedInputVariables;
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| 177 | private static readonly string defaultTargetVariable;
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| 178 |
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| 179 | private static readonly ClassificationProblemData emptyProblemData;
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| 180 | public static ClassificationProblemData EmptyProblemData {
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| 181 | get { return EmptyProblemData; }
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| 182 | }
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| 183 |
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| 184 | static ClassificationProblemData() {
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| 185 | defaultDataset = new Dataset(defaultVariableNames, defaultData);
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| 186 | defaultDataset.Name = "Wisconsin classification problem";
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| 187 | defaultDataset.Description = "subset from to ..";
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| 188 |
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| 189 | defaultAllowedInputVariables = defaultVariableNames.Except(new List<string>() { "sample", "class" });
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| 190 | defaultTargetVariable = "class";
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| 191 |
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| 192 | var problemData = new ClassificationProblemData();
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| 193 | problemData.Parameters.Clear();
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| 194 | problemData.Name = "Empty Classification ProblemData";
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| 195 | problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
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| 196 | problemData.isEmpty = true;
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| 197 |
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| 198 | problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
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| 199 | problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
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| 200 | problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
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| 201 | problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
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| 202 | problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
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| 203 | problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
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| 204 | problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
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| 205 | emptyProblemData = problemData;
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| 206 | }
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| 207 | #endregion
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| 208 |
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| 209 | #region parameter properties
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| 210 | public IConstrainedValueParameter<StringValue> TargetVariableParameter {
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| 211 | get { return (IConstrainedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
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| 212 | }
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| 213 | public IFixedValueParameter<StringMatrix> ClassNamesParameter {
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| 214 | get { return (IFixedValueParameter<StringMatrix>)Parameters[ClassNamesParameterName]; }
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| 215 | }
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| 216 | public IConstrainedValueParameter<StringValue> PositiveClassParameter {
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| 217 | get { return (IConstrainedValueParameter<StringValue>)Parameters[PositiveClassParameterName]; }
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| 218 | }
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| 219 | public IFixedValueParameter<DoubleMatrix> ClassificationPenaltiesParameter {
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| 220 | get { return (IFixedValueParameter<DoubleMatrix>)Parameters[ClassificationPenaltiesParameterName]; }
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| 221 | }
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| 222 | #endregion
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| 223 |
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| 224 | #region properties
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| 225 | public string TargetVariable {
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| 226 | get { return TargetVariableParameter.Value.Value; }
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| 227 | set {
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| 228 | if (value == null) throw new ArgumentNullException("targetVariable", "The provided value for the targetVariable is null.");
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| 229 | if (value == TargetVariable) return;
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| 230 |
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| 231 |
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| 232 | var matchingParameterValue = TargetVariableParameter.ValidValues.FirstOrDefault(v => v.Value == value);
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| 233 | if (matchingParameterValue == null) throw new ArgumentException("The provided value is not valid as the targetVariable.", "targetVariable");
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| 234 | TargetVariableParameter.Value = matchingParameterValue;
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| 235 | }
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| 236 | }
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| 237 |
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| 238 | private List<double> classValuesCache;
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| 239 | private List<double> ClassValuesCache {
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| 240 | get {
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| 241 | if (classValuesCache == null) {
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| 242 | classValuesCache = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().OrderBy(x => x).ToList();
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| 243 | }
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| 244 | return classValuesCache;
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| 245 | }
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| 246 | }
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| 247 | public IEnumerable<double> ClassValues {
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| 248 | get { return ClassValuesCache; }
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| 249 | }
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| 250 | public int Classes {
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| 251 | get { return ClassValuesCache.Count; }
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| 252 | }
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| 253 |
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| 254 | private List<string> classNamesCache;
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| 255 | private List<string> ClassNamesCache {
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| 256 | get {
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| 257 | if (classNamesCache == null) {
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| 258 | classNamesCache = new List<string>();
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| 259 | for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
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| 260 | classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
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| 261 | }
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| 262 | return classNamesCache;
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| 263 | }
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| 264 | }
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| 265 | public IEnumerable<string> ClassNames {
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| 266 | get { return ClassNamesCache; }
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| 267 | }
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| 268 |
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| 269 | public string PositiveClass {
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| 270 | get { return PositiveClassParameter.Value.Value; }
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| 271 | set {
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| 272 | var matchingValue = PositiveClassParameter.ValidValues.SingleOrDefault(x => x.Value == value);
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| 273 | if (matchingValue == null) throw new ArgumentException(string.Format("{0} cannot be set as positive class.", value));
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| 274 | PositiveClassParameter.Value = matchingValue;
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| 275 | }
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| 276 | }
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| 277 | #endregion
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| 278 |
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| 279 |
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| 280 | [StorableConstructor]
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| 281 | protected ClassificationProblemData(StorableConstructorFlag _) : base(_) { }
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| 282 | [StorableHook(HookType.AfterDeserialization)]
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| 283 | private void AfterDeserialization() {
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| 284 | RegisterParameterEvents();
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| 285 |
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| 286 | classNamesCache = new List<string>();
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| 287 | for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
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| 288 | classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
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| 289 |
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| 290 | // BackwardsCompatibility3.4
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| 291 | #region Backwards compatible code, remove with 3.5
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| 292 | if (!Parameters.ContainsKey(PositiveClassParameterName)) {
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| 293 | var validValues = new ItemSet<StringValue>(ClassNames.Select(s => new StringValue(s).AsReadOnly()));
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| 294 | Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName,
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| 295 | "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)", validValues, validValues.First()));
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| 296 | }
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| 297 | #endregion
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| 298 |
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| 299 | }
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| 300 |
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| 301 | protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
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| 302 | : base(original, cloner) {
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| 303 | RegisterParameterEvents();
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| 304 | classNamesCache = new List<string>();
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| 305 | for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
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| 306 | classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
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| 307 | }
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| 308 |
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| 309 | public override IDeepCloneable Clone(Cloner cloner) {
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| 310 | if (this == emptyProblemData) return emptyProblemData;
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| 311 | return new ClassificationProblemData(this, cloner);
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| 312 | }
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| 313 |
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| 314 | public ClassificationProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable, Enumerable.Empty<string>()) { }
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| 315 |
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| 316 | public ClassificationProblemData(IClassificationProblemData classificationProblemData)
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| 317 | : this(classificationProblemData, classificationProblemData.Dataset) {
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| 318 | }
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| 319 |
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| 320 | /// <summary>
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| 321 | /// This method satisfies a common use case: making a copy of the problem but providing a different dataset.
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| 322 | /// One must be careful here that the dataset passed is not modified, as that would invalidate the problem data internals.
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| 323 | /// Passing a ModifiableDataset to this constructor is therefore discouraged.
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| 324 | /// </summary>
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| 325 | /// <param name="classificationProblemData">The original instance of classification problem data.</param>
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| 326 | /// <param name="dataset">The new dataset.</param>
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| 327 | public ClassificationProblemData(IClassificationProblemData classificationProblemData, IDataset dataset)
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| 328 | : this(classificationProblemData.Dataset, classificationProblemData.AllowedInputVariables, classificationProblemData.TargetVariable, classificationProblemData.ClassNames, classificationProblemData.PositiveClass) {
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| 329 |
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| 330 | TrainingPartition.Start = classificationProblemData.TrainingPartition.Start;
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| 331 | TrainingPartition.End = classificationProblemData.TrainingPartition.End;
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| 332 | TestPartition.Start = classificationProblemData.TestPartition.Start;
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| 333 | TestPartition.End = classificationProblemData.TestPartition.End;
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| 334 |
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| 335 | for (int i = 0; i < Classes; i++) {
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| 336 | for (int j = 0; j < Classes; j++) {
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| 337 | ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
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| 338 | }
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| 339 | }
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| 340 | }
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| 341 |
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| 342 | public ClassificationProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable,
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| 343 | IEnumerable<string> classNames = null,
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| 344 | string positiveClass = null, // can be null in which case it's set as the first class name
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| 345 | IEnumerable<ITransformation> transformations = null)
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| 346 | : base(dataset, allowedInputVariables, transformations ?? Enumerable.Empty<ITransformation>()) {
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| 347 | var validTargetVariableValues = CheckVariablesForPossibleTargetVariables(dataset).Select(x => new StringValue(x).AsReadOnly()).ToList();
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| 348 | var target = validTargetVariableValues.Where(x => x.Value == targetVariable).DefaultIfEmpty(validTargetVariableValues.First()).First();
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| 349 |
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| 350 | Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>(validTargetVariableValues), target));
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| 351 | Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix()));
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| 352 | Parameters.Add(new ConstrainedValueParameter<StringValue>(PositiveClassParameterName, "The positive class which is used for quality measure calculation (e.g., specifity, sensitivity,...)"));
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| 353 | Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
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| 354 |
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| 355 | RegisterParameterEvents();
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| 356 | ResetTargetVariableDependentMembers(); // correctly set the values of the parameters added above
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| 357 |
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| 358 | // set the class names
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| 359 | if (classNames != null && classNames.Any()) {
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| 360 | // better to allocate lists because we use these multiple times below
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| 361 | var names = classNames.ToList();
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| 362 | var values = ClassValuesCache;
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| 363 |
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| 364 | if (names.Count != values.Count) {
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| 365 | throw new ArgumentException();
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| 366 | }
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| 367 |
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| 368 | ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
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| 369 | ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = names.Count;
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| 370 |
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| 371 | for (int i = 0; i < names.Count; ++i) {
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| 372 | SetClassName(values[i], names[i]);
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| 373 | }
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| 374 | }
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| 375 |
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| 376 | // set the positive class value
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| 377 | if (positiveClass != null) {
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| 378 | PositiveClass = positiveClass;
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| 379 | }
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| 380 | }
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| 381 |
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| 382 | public static IEnumerable<string> CheckVariablesForPossibleTargetVariables(IDataset dataset) {
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| 383 | int maxSamples = Math.Min(InspectedRowsToDetermineTargets, dataset.Rows);
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| 384 | var validTargetVariables = (from v in dataset.DoubleVariables
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| 385 | let distinctValues = dataset.GetDoubleValues(v)
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| 386 | .Take(maxSamples)
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| 387 | .Distinct()
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| 388 | .Count()
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| 389 | where distinctValues <= MaximumNumberOfClasses
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| 390 | select v).ToArray();
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| 391 |
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| 392 | if (!validTargetVariables.Any())
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| 393 | throw new ArgumentException("Import of classification problem data was not successful, because no target variable was found." +
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| 394 | " A target variable must have at most " + MaximumNumberOfClasses + " distinct values to be applicable to classification.");
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| 395 | return validTargetVariables;
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| 396 | }
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| 397 |
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| 398 |
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| 399 | private void ResetTargetVariableDependentMembers() {
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| 400 | DeregisterParameterEvents();
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| 401 |
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| 402 | ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
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| 403 | ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
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| 404 | for (int i = 0; i < Classes; i++)
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| 405 | ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
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| 406 | ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
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| 407 | ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
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| 408 |
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| 409 | PositiveClassParameter.ValidValues.Clear();
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| 410 | foreach (var className in ClassNames) {
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| 411 | PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
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| 412 | }
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| 413 |
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| 414 | ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
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| 415 | ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
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| 416 | ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
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| 417 | ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
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| 418 | for (int i = 0; i < Classes; i++) {
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| 419 | for (int j = 0; j < Classes; j++) {
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| 420 | if (i != j) ClassificationPenaltiesParameter.Value[i, j] = 1;
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| 421 | else ClassificationPenaltiesParameter.Value[i, j] = 0;
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| 422 | }
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| 423 | }
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| 424 | RegisterParameterEvents();
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| 425 | }
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| 426 |
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| 427 | public string GetClassName(double classValue) {
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| 428 | if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
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| 429 | int index = ClassValuesCache.IndexOf(classValue);
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| 430 | return ClassNamesCache[index];
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| 431 | }
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| 432 | public double GetClassValue(string className) {
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| 433 | if (!ClassNamesCache.Contains(className)) throw new ArgumentException();
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| 434 | int index = ClassNamesCache.IndexOf(className);
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| 435 | return ClassValuesCache[index];
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| 436 | }
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| 437 | public void SetClassName(double classValue, string className) {
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| 438 | if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
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| 439 | int index = ClassValuesCache.IndexOf(classValue);
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| 440 | ClassNamesParameter.Value[index, 0] = className;
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| 441 | // updating of class names cache is not necessary here as the parameter value fires a changed event which updates the cache
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| 442 | }
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| 443 |
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| 444 | public double GetClassificationPenalty(string correctClassName, string estimatedClassName) {
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| 445 | return GetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName));
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| 446 | }
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| 447 | public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
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| 448 | int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
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| 449 | int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
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| 450 | return ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
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| 451 | }
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| 452 | public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
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|---|
| 453 | SetClassificationPenalty(GetClassValue(correctClassName), GetClassValue(estimatedClassName), penalty);
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| 454 | }
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|---|
| 455 | public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
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|---|
| 456 | int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
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| 457 | int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
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| 458 |
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| 459 | ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
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| 460 | }
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| 461 |
|
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| 462 | #region events
|
|---|
| 463 | private void RegisterParameterEvents() {
|
|---|
| 464 | TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
|
|---|
| 465 | ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
|
|---|
| 466 | ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
|---|
| 467 | ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
|---|
| 468 | ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
|
|---|
| 469 | }
|
|---|
| 470 | private void DeregisterParameterEvents() {
|
|---|
| 471 | TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
|
|---|
| 472 | ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
|
|---|
| 473 | ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
|---|
| 474 | ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
|
|---|
| 475 | ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
|
|---|
| 476 | }
|
|---|
| 477 |
|
|---|
| 478 | private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
|
|---|
| 479 | classValuesCache = null;
|
|---|
| 480 | classNamesCache = null;
|
|---|
| 481 | ResetTargetVariableDependentMembers();
|
|---|
| 482 | OnChanged();
|
|---|
| 483 | }
|
|---|
| 484 | private void Parameter_ValueChanged(object sender, EventArgs e) {
|
|---|
| 485 | var oldPositiveClass = PositiveClass;
|
|---|
| 486 | var oldClassNames = classNamesCache;
|
|---|
| 487 | var index = oldClassNames.IndexOf(oldPositiveClass);
|
|---|
| 488 |
|
|---|
| 489 | classNamesCache = null;
|
|---|
| 490 | ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
|
|---|
| 491 | ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
|
|---|
| 492 |
|
|---|
| 493 | PositiveClassParameter.ValidValues.Clear();
|
|---|
| 494 | foreach (var className in ClassNames) {
|
|---|
| 495 | PositiveClassParameter.ValidValues.Add(new StringValue(className).AsReadOnly());
|
|---|
| 496 | }
|
|---|
| 497 | PositiveClassParameter.Value = PositiveClassParameter.ValidValues.ElementAt(index);
|
|---|
| 498 |
|
|---|
| 499 | OnChanged();
|
|---|
| 500 | }
|
|---|
| 501 | #endregion
|
|---|
| 502 | }
|
|---|
| 503 | }
|
|---|
