[5624] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 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.IO;
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| 25 | using System.Linq;
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| 26 | using System.Text;
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| 27 | using HeuristicLab.Common;
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| 28 | using HeuristicLab.Core;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 | using SVM;
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| 31 | using HeuristicLab.Problems.DataAnalysis;
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[5649] | 32 | using System.Drawing;
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[5624] | 33 |
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| 34 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 35 | /// <summary>
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[5626] | 36 | /// Represents a support vector machine model.
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[5624] | 37 | /// </summary>
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| 38 | [StorableClass]
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[5626] | 39 | [Item("SupportVectorMachineModel", "Represents a support vector machine model.")]
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| 40 | public sealed class SupportVectorMachineModel : NamedItem, IRegressionModel, IClassificationModel {
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[5624] | 41 |
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| 42 | private SVM.Model model;
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| 43 | /// <summary>
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| 44 | /// Gets or sets the SVM model.
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| 45 | /// </summary>
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| 46 | public SVM.Model Model {
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| 47 | get { return model; }
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| 48 | set {
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| 49 | if (value != model) {
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| 50 | if (value == null) throw new ArgumentNullException();
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| 51 | model = value;
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| 52 | OnChanged(EventArgs.Empty);
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| 53 | }
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| 54 | }
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| 55 | }
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| 56 |
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| 57 | /// <summary>
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| 58 | /// Gets or sets the range transformation for the model.
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| 59 | /// </summary>
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| 60 | private SVM.RangeTransform rangeTransform;
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| 61 | public SVM.RangeTransform RangeTransform {
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| 62 | get { return rangeTransform; }
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| 63 | set {
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| 64 | if (value != rangeTransform) {
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| 65 | if (value == null) throw new ArgumentNullException();
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| 66 | rangeTransform = value;
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| 67 | OnChanged(EventArgs.Empty);
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| 68 | }
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| 69 | }
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| 70 | }
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[5649] | 71 |
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[5690] | 72 | public Dataset SupportVectors {
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| 73 | get {
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| 74 | var data = new double[Model.SupportVectorCount, allowedInputVariables.Count()];
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| 75 | for (int i = 0; i < Model.SupportVectorCount; i++) {
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| 76 | var sv = Model.SupportVectors[i];
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| 77 | for (int j = 0; j < sv.Length; j++) {
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| 78 | data[i, sv[j].Index] = sv[j].Value;
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| 79 | }
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| 80 | }
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| 81 | return new Dataset(allowedInputVariables, data);
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| 82 | }
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| 83 | }
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| 84 |
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[5649] | 85 | [Storable]
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| 86 | private string targetVariable;
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| 87 | [Storable]
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| 88 | private string[] allowedInputVariables;
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[5690] | 89 | [Storable]
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| 90 | private double[] classValues; // only for SVM classification models
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[5649] | 91 |
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| 92 | [StorableConstructor]
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| 93 | private SupportVectorMachineModel(bool deserializing) : base(deserializing) { }
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| 94 | private SupportVectorMachineModel(SupportVectorMachineModel original, Cloner cloner)
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| 95 | : base(original, cloner) {
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| 96 | // only using a shallow copy here! (gkronber)
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| 97 | this.model = original.model;
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| 98 | this.rangeTransform = original.rangeTransform;
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[5690] | 99 | this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
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| 100 | if (original.classValues != null)
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| 101 | this.classValues = (double[])original.classValues.Clone();
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[5649] | 102 | }
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[5690] | 103 | public SupportVectorMachineModel(SVM.Model model, SVM.RangeTransform rangeTransform, string targetVariable, IEnumerable<string> allowedInputVariables, IEnumerable<double> classValues)
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| 104 | : this(model, rangeTransform, targetVariable, allowedInputVariables) {
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| 105 | this.classValues = classValues.ToArray();
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| 106 | }
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[5649] | 107 | public SupportVectorMachineModel(SVM.Model model, SVM.RangeTransform rangeTransform, string targetVariable, IEnumerable<string> allowedInputVariables)
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| 108 | : base() {
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| 109 | this.name = ItemName;
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| 110 | this.description = ItemDescription;
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| 111 | this.model = model;
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| 112 | this.rangeTransform = rangeTransform;
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| 113 | this.targetVariable = targetVariable;
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| 114 | this.allowedInputVariables = allowedInputVariables.ToArray();
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| 115 | }
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| 116 |
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| 117 | public override IDeepCloneable Clone(Cloner cloner) {
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| 118 | return new SupportVectorMachineModel(this, cloner);
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| 119 | }
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| 120 |
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| 121 |
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[5626] | 122 | #region IRegressionModel Members
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[5649] | 123 | public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
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| 124 | return GetEstimatedValuesHelper(dataset, rows);
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[5626] | 125 | }
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| 126 | #endregion
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| 127 | #region IClassificationModel Members
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[5649] | 128 | public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
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[5690] | 129 | if (classValues == null) throw new NotSupportedException();
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| 130 | // return the original class value instead of the predicted value of the model
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| 131 | // svm classification only works for integer classes
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| 132 | foreach (var estimated in GetEstimatedValuesHelper(dataset, rows)) {
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| 133 | // find closest class
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| 134 | double bestDist = double.MaxValue;
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| 135 | double bestClass = -1;
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| 136 | for (int i = 0; i < classValues.Length; i++) {
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| 137 | double d = Math.Abs(estimated - classValues[i]);
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| 138 | if (d < bestDist) {
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| 139 | bestDist = d;
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| 140 | bestClass = classValues[i];
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| 141 | if (d.IsAlmost(0.0)) break; // exact match no need to look further
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| 142 | }
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| 143 | }
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| 144 | yield return bestClass;
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| 145 | }
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[5626] | 146 | }
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| 147 | #endregion
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[5649] | 148 | private IEnumerable<double> GetEstimatedValuesHelper(Dataset dataset, IEnumerable<int> rows) {
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| 149 | SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
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[5624] | 150 | SVM.Problem scaledProblem = Scaling.Scale(RangeTransform, problem);
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| 151 |
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[5690] | 152 | foreach (var row in Enumerable.Range(0, scaledProblem.Count)) {
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| 153 | yield return SVM.Prediction.Predict(Model, scaledProblem.X[row]);
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| 154 | }
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[5624] | 155 | }
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| 156 | #region events
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| 157 | public event EventHandler Changed;
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| 158 | private void OnChanged(EventArgs e) {
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| 159 | var handlers = Changed;
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| 160 | if (handlers != null)
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| 161 | handlers(this, e);
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| 162 | }
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| 163 | #endregion
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| 164 |
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| 165 | #region persistence
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| 166 | [Storable]
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| 167 | private string ModelAsString {
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| 168 | get {
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| 169 | using (MemoryStream stream = new MemoryStream()) {
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| 170 | SVM.Model.Write(stream, Model);
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| 171 | stream.Seek(0, System.IO.SeekOrigin.Begin);
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| 172 | StreamReader reader = new StreamReader(stream);
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| 173 | return reader.ReadToEnd();
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| 174 | }
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| 175 | }
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| 176 | set {
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| 177 | using (MemoryStream stream = new MemoryStream(Encoding.ASCII.GetBytes(value))) {
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| 178 | model = SVM.Model.Read(stream);
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| 179 | }
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| 180 | }
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| 181 | }
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| 182 | [Storable]
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| 183 | private string RangeTransformAsString {
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| 184 | get {
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| 185 | using (MemoryStream stream = new MemoryStream()) {
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| 186 | SVM.RangeTransform.Write(stream, RangeTransform);
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| 187 | stream.Seek(0, System.IO.SeekOrigin.Begin);
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| 188 | StreamReader reader = new StreamReader(stream);
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| 189 | return reader.ReadToEnd();
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| 190 | }
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| 191 | }
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| 192 | set {
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| 193 | using (MemoryStream stream = new MemoryStream(Encoding.ASCII.GetBytes(value))) {
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| 194 | RangeTransform = SVM.RangeTransform.Read(stream);
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| 195 | }
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| 196 | }
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| 197 | }
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| 198 | #endregion
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| 199 | }
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| 200 | }
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