[5624] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5624] | 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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[5777] | 23 | using System.Collections.Generic;
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[5624] | 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.Optimization;
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| 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 | using HeuristicLab.Problems.DataAnalysis;
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[8609] | 32 | using LibSVM;
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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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| 36 | /// Support vector machine regression data analysis algorithm.
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| 37 | /// </summary>
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[13238] | 38 | [Item("Support Vector Regression (SVM)", "Support vector machine regression data analysis algorithm (wrapper for libSVM).")]
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[12504] | 39 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 110)]
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[5624] | 40 | [StorableClass]
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| 41 | public sealed class SupportVectorRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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| 42 | private const string SvmTypeParameterName = "SvmType";
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| 43 | private const string KernelTypeParameterName = "KernelType";
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| 44 | private const string CostParameterName = "Cost";
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| 45 | private const string NuParameterName = "Nu";
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| 46 | private const string GammaParameterName = "Gamma";
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| 47 | private const string EpsilonParameterName = "Epsilon";
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[8613] | 48 | private const string DegreeParameterName = "Degree";
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[12934] | 49 | private const string CreateSolutionParameterName = "CreateSolution";
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[5624] | 50 |
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| 51 | #region parameter properties
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[8121] | 52 | public IConstrainedValueParameter<StringValue> SvmTypeParameter {
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| 53 | get { return (IConstrainedValueParameter<StringValue>)Parameters[SvmTypeParameterName]; }
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[5624] | 54 | }
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[8121] | 55 | public IConstrainedValueParameter<StringValue> KernelTypeParameter {
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| 56 | get { return (IConstrainedValueParameter<StringValue>)Parameters[KernelTypeParameterName]; }
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[5624] | 57 | }
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| 58 | public IValueParameter<DoubleValue> NuParameter {
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| 59 | get { return (IValueParameter<DoubleValue>)Parameters[NuParameterName]; }
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| 60 | }
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| 61 | public IValueParameter<DoubleValue> CostParameter {
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| 62 | get { return (IValueParameter<DoubleValue>)Parameters[CostParameterName]; }
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| 63 | }
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| 64 | public IValueParameter<DoubleValue> GammaParameter {
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| 65 | get { return (IValueParameter<DoubleValue>)Parameters[GammaParameterName]; }
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| 66 | }
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| 67 | public IValueParameter<DoubleValue> EpsilonParameter {
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| 68 | get { return (IValueParameter<DoubleValue>)Parameters[EpsilonParameterName]; }
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| 69 | }
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[8613] | 70 | public IValueParameter<IntValue> DegreeParameter {
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| 71 | get { return (IValueParameter<IntValue>)Parameters[DegreeParameterName]; }
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| 72 | }
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[12934] | 73 | public IFixedValueParameter<BoolValue> CreateSolutionParameter {
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| 74 | get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
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| 75 | }
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[5624] | 76 | #endregion
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| 77 | #region properties
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| 78 | public StringValue SvmType {
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| 79 | get { return SvmTypeParameter.Value; }
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[8121] | 80 | set { SvmTypeParameter.Value = value; }
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[5624] | 81 | }
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| 82 | public StringValue KernelType {
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| 83 | get { return KernelTypeParameter.Value; }
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[8121] | 84 | set { KernelTypeParameter.Value = value; }
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[5624] | 85 | }
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| 86 | public DoubleValue Nu {
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| 87 | get { return NuParameter.Value; }
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| 88 | }
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| 89 | public DoubleValue Cost {
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| 90 | get { return CostParameter.Value; }
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| 91 | }
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| 92 | public DoubleValue Gamma {
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| 93 | get { return GammaParameter.Value; }
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| 94 | }
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| 95 | public DoubleValue Epsilon {
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| 96 | get { return EpsilonParameter.Value; }
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| 97 | }
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[8613] | 98 | public IntValue Degree {
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| 99 | get { return DegreeParameter.Value; }
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| 100 | }
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[12934] | 101 | public bool CreateSolution {
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| 102 | get { return CreateSolutionParameter.Value.Value; }
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| 103 | set { CreateSolutionParameter.Value.Value = value; }
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| 104 | }
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[5624] | 105 | #endregion
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| 106 | [StorableConstructor]
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| 107 | private SupportVectorRegression(bool deserializing) : base(deserializing) { }
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| 108 | private SupportVectorRegression(SupportVectorRegression original, Cloner cloner)
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| 109 | : base(original, cloner) {
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| 110 | }
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| 111 | public SupportVectorRegression()
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| 112 | : base() {
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[5649] | 113 | Problem = new RegressionProblem();
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| 114 |
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[5626] | 115 | List<StringValue> svrTypes = (from type in new List<string> { "NU_SVR", "EPSILON_SVR" }
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| 116 | select new StringValue(type).AsReadOnly())
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| 117 | .ToList();
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| 118 | ItemSet<StringValue> svrTypeSet = new ItemSet<StringValue>(svrTypes);
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| 119 | List<StringValue> kernelTypes = (from type in new List<string> { "LINEAR", "POLY", "SIGMOID", "RBF" }
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| 120 | select new StringValue(type).AsReadOnly())
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| 121 | .ToList();
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[5649] | 122 | ItemSet<StringValue> kernelTypeSet = new ItemSet<StringValue>(kernelTypes);
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[5626] | 123 | Parameters.Add(new ConstrainedValueParameter<StringValue>(SvmTypeParameterName, "The type of SVM to use.", svrTypeSet, svrTypes[0]));
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| 124 | Parameters.Add(new ConstrainedValueParameter<StringValue>(KernelTypeParameterName, "The kernel type to use for the SVM.", kernelTypeSet, kernelTypes[3]));
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| 125 | Parameters.Add(new ValueParameter<DoubleValue>(NuParameterName, "The value of the nu parameter of the nu-SVR.", new DoubleValue(0.5)));
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| 126 | Parameters.Add(new ValueParameter<DoubleValue>(CostParameterName, "The value of the C (cost) parameter of epsilon-SVR and nu-SVR.", new DoubleValue(1.0)));
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[5624] | 127 | Parameters.Add(new ValueParameter<DoubleValue>(GammaParameterName, "The value of the gamma parameter in the kernel function.", new DoubleValue(1.0)));
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[5626] | 128 | Parameters.Add(new ValueParameter<DoubleValue>(EpsilonParameterName, "The value of the epsilon parameter for epsilon-SVR.", new DoubleValue(0.1)));
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[8613] | 129 | Parameters.Add(new ValueParameter<IntValue>(DegreeParameterName, "The degree parameter for the polynomial kernel function.", new IntValue(3)));
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[12934] | 130 | Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
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| 131 | Parameters[CreateSolutionParameterName].Hidden = true;
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[5624] | 132 | }
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| 133 | [StorableHook(HookType.AfterDeserialization)]
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[8613] | 134 | private void AfterDeserialization() {
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| 135 | #region backwards compatibility (change with 3.4)
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[12934] | 136 |
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| 137 | if (!Parameters.ContainsKey(DegreeParameterName)) {
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| 138 | Parameters.Add(new ValueParameter<IntValue>(DegreeParameterName,
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| 139 | "The degree parameter for the polynomial kernel function.", new IntValue(3)));
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| 140 | }
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| 141 | if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
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| 142 | Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
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| 143 | Parameters[CreateSolutionParameterName].Hidden = true;
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| 144 | }
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[8613] | 145 | #endregion
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| 146 | }
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[5624] | 147 |
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| 148 | public override IDeepCloneable Clone(Cloner cloner) {
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| 149 | return new SupportVectorRegression(this, cloner);
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| 150 | }
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| 151 |
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| 152 | #region support vector regression
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| 153 | protected override void Run() {
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| 154 | IRegressionProblemData problemData = Problem.ProblemData;
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[5649] | 155 | IEnumerable<string> selectedInputVariables = problemData.AllowedInputVariables;
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[7306] | 156 | int nSv;
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[12934] | 157 | ISupportVectorMachineModel model;
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| 158 | Run(problemData, selectedInputVariables, SvmType.Value, KernelType.Value, Cost.Value, Nu.Value, Gamma.Value, Epsilon.Value, Degree.Value, out model, out nSv);
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[5624] | 159 |
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[12934] | 160 | if (CreateSolution) {
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| 161 | var solution = new SupportVectorRegressionSolution((SupportVectorMachineModel)model, (IRegressionProblemData)problemData.Clone());
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| 162 | Results.Add(new Result("Support vector regression solution", "The support vector regression solution.", solution));
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| 163 | }
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| 164 |
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[7306] | 165 | Results.Add(new Result("Number of support vectors", "The number of support vectors of the SVR solution.", new IntValue(nSv)));
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[12934] | 166 |
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| 167 |
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| 168 | {
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| 169 | // calculate regression model metrics
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| 170 | var ds = problemData.Dataset;
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| 171 | var trainRows = problemData.TrainingIndices;
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| 172 | var testRows = problemData.TestIndices;
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| 173 | var yTrain = ds.GetDoubleValues(problemData.TargetVariable, trainRows);
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| 174 | var yTest = ds.GetDoubleValues(problemData.TargetVariable, testRows);
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| 175 | var yPredTrain = model.GetEstimatedValues(ds, trainRows).ToArray();
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| 176 | var yPredTest = model.GetEstimatedValues(ds, testRows).ToArray();
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| 177 |
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| 178 | OnlineCalculatorError error;
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| 179 | var trainMse = OnlineMeanSquaredErrorCalculator.Calculate(yPredTrain, yTrain, out error);
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| 180 | if (error != OnlineCalculatorError.None) trainMse = double.MaxValue;
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| 181 | var testMse = OnlineMeanSquaredErrorCalculator.Calculate(yPredTest, yTest, out error);
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| 182 | if (error != OnlineCalculatorError.None) testMse = double.MaxValue;
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| 183 |
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| 184 | Results.Add(new Result("Mean squared error (training)", "The mean of squared errors of the SVR solution on the training partition.", new DoubleValue(trainMse)));
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| 185 | Results.Add(new Result("Mean squared error (test)", "The mean of squared errors of the SVR solution on the test partition.", new DoubleValue(testMse)));
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| 186 |
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| 187 |
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| 188 | var trainMae = OnlineMeanAbsoluteErrorCalculator.Calculate(yPredTrain, yTrain, out error);
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| 189 | if (error != OnlineCalculatorError.None) trainMae = double.MaxValue;
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| 190 | var testMae = OnlineMeanAbsoluteErrorCalculator.Calculate(yPredTest, yTest, out error);
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| 191 | if (error != OnlineCalculatorError.None) testMae = double.MaxValue;
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| 192 |
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| 193 | Results.Add(new Result("Mean absolute error (training)", "The mean of absolute errors of the SVR solution on the training partition.", new DoubleValue(trainMae)));
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| 194 | Results.Add(new Result("Mean absolute error (test)", "The mean of absolute errors of the SVR solution on the test partition.", new DoubleValue(testMae)));
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| 195 |
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| 196 |
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| 197 | var trainRelErr = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(yPredTrain, yTrain, out error);
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| 198 | if (error != OnlineCalculatorError.None) trainRelErr = double.MaxValue;
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| 199 | var testRelErr = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(yPredTest, yTest, out error);
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| 200 | if (error != OnlineCalculatorError.None) testRelErr = double.MaxValue;
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| 201 |
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| 202 | Results.Add(new Result("Average relative error (training)", "The mean of relative errors of the SVR solution on the training partition.", new DoubleValue(trainRelErr)));
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| 203 | Results.Add(new Result("Average relative error (test)", "The mean of relative errors of the SVR solution on the test partition.", new DoubleValue(testRelErr)));
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| 204 | }
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[5624] | 205 | }
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| 206 |
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[12934] | 207 | // BackwardsCompatibility3.4
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| 208 | #region Backwards compatible code, remove with 3.5
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| 209 | // for compatibility with old API
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| 210 | public static SupportVectorRegressionSolution CreateSupportVectorRegressionSolution(
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| 211 | IRegressionProblemData problemData, IEnumerable<string> allowedInputVariables,
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[8613] | 212 | string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
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[7306] | 213 | out double trainingR2, out double testR2, out int nSv) {
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[12934] | 214 | ISupportVectorMachineModel model;
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| 215 | Run(problemData, allowedInputVariables, svmType, kernelType, cost, nu, gamma, epsilon, degree, out model, out nSv);
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| 216 |
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| 217 | var solution = new SupportVectorRegressionSolution((SupportVectorMachineModel)model, (IRegressionProblemData)problemData.Clone());
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| 218 | trainingR2 = solution.TrainingRSquared;
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| 219 | testR2 = solution.TestRSquared;
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| 220 | return solution;
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| 221 | }
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| 222 | #endregion
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| 223 |
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| 224 | public static void Run(IRegressionProblemData problemData, IEnumerable<string> allowedInputVariables,
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| 225 | string svmType, string kernelType, double cost, double nu, double gamma, double epsilon, int degree,
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| 226 | out ISupportVectorMachineModel model, out int nSv) {
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[12509] | 227 | var dataset = problemData.Dataset;
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[5624] | 228 | string targetVariable = problemData.TargetVariable;
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[8139] | 229 | IEnumerable<int> rows = problemData.TrainingIndices;
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[5624] | 230 |
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[12934] | 231 | svm_parameter parameter = new svm_parameter {
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| 232 | svm_type = GetSvmType(svmType),
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| 233 | kernel_type = GetKernelType(kernelType),
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| 234 | C = cost,
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| 235 | nu = nu,
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| 236 | gamma = gamma,
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| 237 | p = epsilon,
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| 238 | cache_size = 500,
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| 239 | probability = 0,
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| 240 | eps = 0.001,
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| 241 | degree = degree,
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| 242 | shrinking = 1,
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| 243 | coef0 = 0
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| 244 | };
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[5624] | 245 |
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[8609] | 246 | svm_problem problem = SupportVectorMachineUtil.CreateSvmProblem(dataset, targetVariable, allowedInputVariables, rows);
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| 247 | RangeTransform rangeTransform = RangeTransform.Compute(problem);
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| 248 | svm_problem scaledProblem = rangeTransform.Scale(problem);
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| 249 | var svmModel = svm.svm_train(scaledProblem, parameter);
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| 250 | nSv = svmModel.SV.Length;
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[12934] | 251 |
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| 252 | model = new SupportVectorMachineModel(svmModel, rangeTransform, targetVariable, allowedInputVariables);
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[5624] | 253 | }
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[8609] | 254 |
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| 255 | private static int GetSvmType(string svmType) {
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| 256 | if (svmType == "NU_SVR") return svm_parameter.NU_SVR;
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| 257 | if (svmType == "EPSILON_SVR") return svm_parameter.EPSILON_SVR;
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| 258 | throw new ArgumentException("Unknown SVM type");
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| 259 | }
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| 260 |
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| 261 | private static int GetKernelType(string kernelType) {
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| 262 | if (kernelType == "LINEAR") return svm_parameter.LINEAR;
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| 263 | if (kernelType == "POLY") return svm_parameter.POLY;
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| 264 | if (kernelType == "SIGMOID") return svm_parameter.SIGMOID;
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| 265 | if (kernelType == "RBF") return svm_parameter.RBF;
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| 266 | throw new ArgumentException("Unknown kernel type");
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| 267 | }
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[5624] | 268 | #endregion
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| 269 | }
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| 270 | }
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