[1806] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[3842] | 3 | * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[1806] | 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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[3842] | 25 | using HeuristicLab.Operators;
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| 26 | using HeuristicLab.Parameters;
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[4068] | 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[2415] | 28 | using SVM;
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[1806] | 29 |
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[3842] | 30 | namespace HeuristicLab.Problems.DataAnalysis.SupportVectorMachine {
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| 31 | /// <summary>
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| 32 | /// Represents an operator that creates a support vector machine model.
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| 33 | /// </summary>
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| 34 | [StorableClass]
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| 35 | [Item("SupportVectorMachineModelCreator", "Represents an operator that creates a support vector machine model.")]
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| 36 | public class SupportVectorMachineModelCreator : SingleSuccessorOperator {
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| 37 | private const string DataAnalysisProblemDataParameterName = "DataAnalysisProblemData";
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| 38 | private const string SvmTypeParameterName = "SvmType";
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| 39 | private const string KernelTypeParameterName = "KernelType";
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| 40 | private const string CostParameterName = "Cost";
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| 41 | private const string NuParameterName = "Nu";
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| 42 | private const string GammaParameterName = "Gamma";
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[3877] | 43 | private const string EpsilonParameterName = "Epsilon";
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| 44 | private const string SamplesStartParameterName = "SamplesStart";
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| 45 | private const string SamplesEndParameterName = "SamplesEnd";
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[3842] | 46 | private const string ModelParameterName = "SupportVectorMachineModel";
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[1806] | 47 |
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[3842] | 48 | #region parameter properties
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| 49 | public IValueLookupParameter<DataAnalysisProblemData> DataAnalysisProblemDataParameter {
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| 50 | get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[DataAnalysisProblemDataParameterName]; }
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[1848] | 51 | }
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[3877] | 52 | public IValueLookupParameter<StringValue> SvmTypeParameter {
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| 53 | get { return (IValueLookupParameter<StringValue>)Parameters[SvmTypeParameterName]; }
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[3842] | 54 | }
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[3877] | 55 | public IValueLookupParameter<StringValue> KernelTypeParameter {
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| 56 | get { return (IValueLookupParameter<StringValue>)Parameters[KernelTypeParameterName]; }
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[3842] | 57 | }
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| 58 | public IValueLookupParameter<DoubleValue> NuParameter {
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| 59 | get { return (IValueLookupParameter<DoubleValue>)Parameters[NuParameterName]; }
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| 60 | }
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| 61 | public IValueLookupParameter<DoubleValue> CostParameter {
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| 62 | get { return (IValueLookupParameter<DoubleValue>)Parameters[CostParameterName]; }
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| 63 | }
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| 64 | public IValueLookupParameter<DoubleValue> GammaParameter {
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| 65 | get { return (IValueLookupParameter<DoubleValue>)Parameters[GammaParameterName]; }
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| 66 | }
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[3877] | 67 | public IValueLookupParameter<DoubleValue> EpsilonParameter {
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| 68 | get { return (IValueLookupParameter<DoubleValue>)Parameters[EpsilonParameterName]; }
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| 69 | }
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| 70 | public IValueLookupParameter<IntValue> SamplesStartParameter {
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| 71 | get { return (IValueLookupParameter<IntValue>)Parameters[SamplesStartParameterName]; }
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| 72 | }
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| 73 | public IValueLookupParameter<IntValue> SamplesEndParameter {
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| 74 | get { return (IValueLookupParameter<IntValue>)Parameters[SamplesEndParameterName]; }
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| 75 | }
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[3842] | 76 | public ILookupParameter<SupportVectorMachineModel> SupportVectorMachineModelParameter {
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| 77 | get { return (ILookupParameter<SupportVectorMachineModel>)Parameters[ModelParameterName]; }
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| 78 | }
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| 79 | #endregion
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| 80 | #region properties
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| 81 | public DataAnalysisProblemData DataAnalysisProblemData {
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| 82 | get { return DataAnalysisProblemDataParameter.ActualValue; }
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| 83 | }
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| 84 | public StringValue SvmType {
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| 85 | get { return SvmTypeParameter.Value; }
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| 86 | }
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| 87 | public StringValue KernelType {
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| 88 | get { return KernelTypeParameter.Value; }
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| 89 | }
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| 90 | public DoubleValue Nu {
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| 91 | get { return NuParameter.ActualValue; }
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| 92 | }
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| 93 | public DoubleValue Cost {
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| 94 | get { return CostParameter.ActualValue; }
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| 95 | }
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| 96 | public DoubleValue Gamma {
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| 97 | get { return GammaParameter.ActualValue; }
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| 98 | }
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[3877] | 99 | public DoubleValue Epsilon {
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| 100 | get { return EpsilonParameter.ActualValue; }
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| 101 | }
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| 102 | public IntValue SamplesStart {
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| 103 | get { return SamplesStartParameter.ActualValue; }
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| 104 | }
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| 105 | public IntValue SamplesEnd {
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| 106 | get { return SamplesEndParameter.ActualValue; }
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| 107 | }
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[3842] | 108 | #endregion
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[1806] | 109 |
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[3842] | 110 | public SupportVectorMachineModelCreator()
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| 111 | : base() {
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| 112 | StringValue nuSvrType = new StringValue("NU_SVR").AsReadOnly();
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| 113 | StringValue rbfKernelType = new StringValue("RBF").AsReadOnly();
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| 114 | Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(DataAnalysisProblemDataParameterName, "The data analysis problem data to use for training."));
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[3877] | 115 | Parameters.Add(new ValueLookupParameter<StringValue>(SvmTypeParameterName, "The type of SVM to use.", nuSvrType));
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| 116 | Parameters.Add(new ValueLookupParameter<StringValue>(KernelTypeParameterName, "The kernel type to use for the SVM.", rbfKernelType));
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[3842] | 117 | Parameters.Add(new ValueLookupParameter<DoubleValue>(NuParameterName, "The value of the nu parameter nu-SVC, one-class SVM and nu-SVR."));
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| 118 | Parameters.Add(new ValueLookupParameter<DoubleValue>(CostParameterName, "The value of the C (cost) parameter of C-SVC, epsilon-SVR and nu-SVR."));
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| 119 | Parameters.Add(new ValueLookupParameter<DoubleValue>(GammaParameterName, "The value of the gamma parameter in the kernel function."));
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[3877] | 120 | Parameters.Add(new ValueLookupParameter<DoubleValue>(EpsilonParameterName, "The value of the epsilon parameter for epsilon-SVR."));
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| 121 | Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first index of the data set partition the support vector machine should use for training."));
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| 122 | Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The last index of the data set partition the support vector machine should use for training."));
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[3842] | 123 | Parameters.Add(new LookupParameter<SupportVectorMachineModel>(ModelParameterName, "The result model generated by the SVM."));
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[1806] | 124 | }
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| 125 |
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[3842] | 126 | public override IOperation Apply() {
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| 127 | SupportVectorMachineModel model = TrainModel(DataAnalysisProblemData,
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[3877] | 128 | SamplesStart.Value, SamplesEnd.Value,
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[3842] | 129 | SvmType.Value, KernelType.Value,
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[3877] | 130 | Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value);
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[3842] | 131 | SupportVectorMachineModelParameter.ActualValue = model;
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[1806] | 132 |
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[3842] | 133 | return base.Apply();
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[2550] | 134 | }
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| 135 |
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[3877] | 136 | private static SupportVectorMachineModel TrainModel(
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| 137 | DataAnalysisProblemData problemData,
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| 138 | string svmType, string kernelType,
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| 139 | double cost, double nu, double gamma, double epsilon) {
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| 140 | return TrainModel(problemData, problemData.TrainingSamplesStart.Value, problemData.TrainingSamplesEnd.Value, svmType, kernelType, cost, nu, gamma, epsilon);
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| 141 | }
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| 142 |
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[3842] | 143 | public static SupportVectorMachineModel TrainModel(
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| 144 | DataAnalysisProblemData problemData,
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[3877] | 145 | int start, int end,
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[2550] | 146 | string svmType, string kernelType,
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[3877] | 147 | double cost, double nu, double gamma, double epsilon) {
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[3842] | 148 | int targetVariableIndex = problemData.Dataset.GetVariableIndex(problemData.TargetVariable.Value);
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[2550] | 149 |
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| 150 | //extract SVM parameters from scope and set them
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| 151 | SVM.Parameter parameter = new SVM.Parameter();
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| 152 | parameter.SvmType = (SVM.SvmType)Enum.Parse(typeof(SVM.SvmType), svmType, true);
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| 153 | parameter.KernelType = (SVM.KernelType)Enum.Parse(typeof(SVM.KernelType), kernelType, true);
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| 154 | parameter.C = cost;
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| 155 | parameter.Nu = nu;
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| 156 | parameter.Gamma = gamma;
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[3877] | 157 | parameter.P = epsilon;
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[2550] | 158 | parameter.CacheSize = 500;
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| 159 | parameter.Probability = false;
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| 160 |
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| 161 |
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[3877] | 162 | SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(problemData, start, end);
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[2550] | 163 | SVM.RangeTransform rangeTransform = SVM.RangeTransform.Compute(problem);
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[3842] | 164 | SVM.Problem scaledProblem = Scaling.Scale(rangeTransform, problem);
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| 165 | var model = new SupportVectorMachineModel();
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[2550] | 166 | model.Model = SVM.Training.Train(scaledProblem, parameter);
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| 167 | model.RangeTransform = rangeTransform;
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| 168 |
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[1848] | 169 | return model;
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| 170 | }
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[1806] | 171 | }
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| 172 | }
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