[3842] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[5445] | 3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[3842] | 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using System;
|
---|
[4068] | 23 | using System.Collections.Generic;
|
---|
| 24 | using System.Drawing;
|
---|
| 25 | using System.Linq;
|
---|
[4722] | 26 | using HeuristicLab.Common;
|
---|
[3842] | 27 | using HeuristicLab.Core;
|
---|
| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 29 | using HeuristicLab.Problems.DataAnalysis.SupportVectorMachine;
|
---|
| 30 | using SVM;
|
---|
| 31 |
|
---|
| 32 | namespace HeuristicLab.Problems.DataAnalysis.Regression.SupportVectorRegression {
|
---|
| 33 | /// <summary>
|
---|
| 34 | /// Represents a support vector solution for a regression problem which can be visualized in the GUI.
|
---|
| 35 | /// </summary>
|
---|
| 36 | [Item("SupportVectorRegressionSolution", "Represents a support vector solution for a regression problem which can be visualized in the GUI.")]
|
---|
| 37 | [StorableClass]
|
---|
| 38 | public sealed class SupportVectorRegressionSolution : DataAnalysisSolution {
|
---|
| 39 | public override Image ItemImage {
|
---|
[5287] | 40 | get { return HeuristicLab.Common.Resources.VSImageLibrary.Function; }
|
---|
[3842] | 41 | }
|
---|
| 42 |
|
---|
[3884] | 43 | public new SupportVectorMachineModel Model {
|
---|
| 44 | get { return (SupportVectorMachineModel)base.Model; }
|
---|
| 45 | set { base.Model = value; }
|
---|
[3842] | 46 | }
|
---|
| 47 |
|
---|
[4722] | 48 | private List<double> estimatedValues;
|
---|
| 49 | public override IEnumerable<double> EstimatedValues {
|
---|
| 50 | get {
|
---|
| 51 | if (estimatedValues == null) RecalculateEstimatedValues();
|
---|
| 52 | return estimatedValues;
|
---|
| 53 | }
|
---|
| 54 | }
|
---|
| 55 |
|
---|
| 56 | public override IEnumerable<double> EstimatedTrainingValues {
|
---|
| 57 | get {
|
---|
| 58 | return GetEstimatedValues(ProblemData.TrainingIndizes);
|
---|
| 59 | }
|
---|
| 60 | }
|
---|
| 61 |
|
---|
| 62 | public override IEnumerable<double> EstimatedTestValues {
|
---|
| 63 | get {
|
---|
| 64 | return GetEstimatedValues(ProblemData.TestIndizes);
|
---|
| 65 | }
|
---|
| 66 | }
|
---|
| 67 |
|
---|
[5692] | 68 | public Dataset SupportVectors {
|
---|
| 69 | get {
|
---|
| 70 | int nCol = inputVariables.Count;
|
---|
| 71 | double[,] data = new double[Model.Model.SupportVectorCount, nCol];
|
---|
| 72 | int row = 0;
|
---|
| 73 | foreach (var sv in Model.SupportVectors) {
|
---|
| 74 | for (int col = 0; col < nCol; col++) {
|
---|
| 75 | data[row, col] = sv[col];
|
---|
| 76 | }
|
---|
| 77 | row++;
|
---|
| 78 | }
|
---|
| 79 | return new Dataset(inputVariables, data);
|
---|
| 80 | }
|
---|
| 81 | }
|
---|
| 82 |
|
---|
| 83 | [Storable]
|
---|
| 84 | private List<string> inputVariables;
|
---|
| 85 |
|
---|
[4722] | 86 | [StorableConstructor]
|
---|
| 87 | private SupportVectorRegressionSolution(bool deserializing) : base(deserializing) { }
|
---|
[5692] | 88 | private SupportVectorRegressionSolution(SupportVectorRegressionSolution original, Cloner cloner)
|
---|
| 89 | : base(original, cloner) {
|
---|
| 90 | this.inputVariables = new List<string>(original.inputVariables);
|
---|
| 91 | }
|
---|
[4722] | 92 | public SupportVectorRegressionSolution() : base() { }
|
---|
| 93 | public SupportVectorRegressionSolution(DataAnalysisProblemData problemData, SupportVectorMachineModel model, IEnumerable<string> inputVariables, double lowerEstimationLimit, double upperEstimationLimit)
|
---|
| 94 | : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
|
---|
| 95 | this.Model = model;
|
---|
[5692] | 96 | this.inputVariables = new List<string>(inputVariables);
|
---|
[4722] | 97 | }
|
---|
| 98 |
|
---|
[5692] | 99 | [StorableHook(HookType.AfterDeserialization)]
|
---|
| 100 | private void AfterDeserialization() {
|
---|
| 101 | #region backwards compatibility
|
---|
| 102 | if (inputVariables == null) inputVariables = ProblemData.InputVariables.CheckedItems.Select(x => x.Value.Value).ToList();
|
---|
| 103 | #endregion
|
---|
| 104 | }
|
---|
| 105 |
|
---|
[4722] | 106 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 107 | return new SupportVectorRegressionSolution(this, cloner);
|
---|
| 108 | }
|
---|
| 109 |
|
---|
[3884] | 110 | protected override void RecalculateEstimatedValues() {
|
---|
[4543] | 111 | SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(ProblemData, Enumerable.Range(0, ProblemData.Dataset.Rows));
|
---|
[3884] | 112 | SVM.Problem scaledProblem = Scaling.Scale(Model.RangeTransform, problem);
|
---|
[3842] | 113 |
|
---|
| 114 | estimatedValues = (from row in Enumerable.Range(0, scaledProblem.Count)
|
---|
[3884] | 115 | let prediction = SVM.Prediction.Predict(Model.Model, scaledProblem.X[row])
|
---|
[3842] | 116 | let boundedX = Math.Min(UpperEstimationLimit, Math.Max(LowerEstimationLimit, prediction))
|
---|
| 117 | select double.IsNaN(boundedX) ? UpperEstimationLimit : boundedX).ToList();
|
---|
[3884] | 118 | OnEstimatedValuesChanged();
|
---|
[3842] | 119 | }
|
---|
| 120 |
|
---|
| 121 |
|
---|
[4543] | 122 | private IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
|
---|
| 123 | if (estimatedValues == null) RecalculateEstimatedValues();
|
---|
| 124 | foreach (int row in rows)
|
---|
| 125 | yield return estimatedValues[row];
|
---|
| 126 | }
|
---|
[3842] | 127 | }
|
---|
| 128 | }
|
---|