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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.Regression/3.3/SupportVectorRegression/SupportVectorRegressionSolution.cs @ 6452

Last change on this file since 6452 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 4.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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
22using System;
23using System.Collections.Generic;
24using System.Drawing;
25using System.Linq;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis.SupportVectorMachine;
30using SVM;
31
32namespace 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 {
40      get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
41    }
42
43    public new SupportVectorMachineModel Model {
44      get { return (SupportVectorMachineModel)base.Model; }
45      set { base.Model = value; }
46    }
47
48    public Dataset SupportVectors {
49      get { return CalculateSupportVectors(); }
50    }
51
52    private List<double> estimatedValues;
53    public override IEnumerable<double> EstimatedValues {
54      get {
55        if (estimatedValues == null) RecalculateEstimatedValues();
56        return estimatedValues;
57      }
58    }
59
60    public override IEnumerable<double> EstimatedTrainingValues {
61      get {
62        return GetEstimatedValues(ProblemData.TrainingIndizes);
63      }
64    }
65
66    public override IEnumerable<double> EstimatedTestValues {
67      get {
68        return GetEstimatedValues(ProblemData.TestIndizes);
69      }
70    }
71
72    [StorableConstructor]
73    private SupportVectorRegressionSolution(bool deserializing) : base(deserializing) { }
74    private SupportVectorRegressionSolution(SupportVectorRegressionSolution original, Cloner cloner) : base(original, cloner) { }
75    public SupportVectorRegressionSolution() : base() { }
76    public SupportVectorRegressionSolution(DataAnalysisProblemData problemData, SupportVectorMachineModel model, IEnumerable<string> inputVariables, double lowerEstimationLimit, double upperEstimationLimit)
77      : base(problemData, lowerEstimationLimit, upperEstimationLimit) {
78      this.Model = model;
79    }
80
81    public override IDeepCloneable Clone(Cloner cloner) {
82      return new SupportVectorRegressionSolution(this, cloner);
83    }
84
85    protected override void OnProblemDataChanged() {
86      Model.Model.SupportVectorIndizes = new int[0];
87      base.OnProblemDataChanged();
88    }
89
90    private Dataset CalculateSupportVectors() {
91      if (Model.Model.SupportVectorIndizes.Length == 0)
92        return new Dataset(new List<string>(), new double[0, 0]);
93
94      double[,] data = new double[Model.Model.SupportVectorIndizes.Length, ProblemData.Dataset.Columns];
95      for (int i = 0; i < Model.Model.SupportVectorIndizes.Length; i++) {
96        for (int column = 0; column < ProblemData.Dataset.Columns; column++)
97          data[i, column] = ProblemData.Dataset[Model.Model.SupportVectorIndizes[i], column];
98      }
99      return new Dataset(ProblemData.Dataset.VariableNames, data);
100    }
101
102    protected override void RecalculateEstimatedValues() {
103      SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(ProblemData, Enumerable.Range(0, ProblemData.Dataset.Rows));
104      SVM.Problem scaledProblem = Scaling.Scale(Model.RangeTransform, problem);
105
106      estimatedValues = (from row in Enumerable.Range(0, scaledProblem.Count)
107                         let prediction = SVM.Prediction.Predict(Model.Model, scaledProblem.X[row])
108                         let boundedX = Math.Min(UpperEstimationLimit, Math.Max(LowerEstimationLimit, prediction))
109                         select double.IsNaN(boundedX) ? UpperEstimationLimit : boundedX).ToList();
110      OnEstimatedValuesChanged();
111    }
112
113
114    private IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
115      if (estimatedValues == null) RecalculateEstimatedValues();
116      foreach (int row in rows)
117        yield return estimatedValues[row];
118    }
119  }
120}
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