1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 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.Collections.Generic;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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26 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Symbols;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 | using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Symbols;
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29 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
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30 | using System.Linq;
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31 | using System.Drawing;
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32 | using System;
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33 |
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34 | namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic {
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35 | [StorableClass]
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36 | [Item("SymbolicTimeSeriesPrognosisSolution", "Represents a solution for time series prognosis.")]
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37 | public class SymbolicTimeSeriesPrognosisSolution : NamedItem, IMultiVariateDataAnalysisSolution, IStorableContent {
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38 | [Storable]
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39 | private MultiVariateDataAnalysisProblemData problemData;
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40 | [Storable]
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41 | private SymbolicTimeSeriesPrognosisModel model;
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42 | [Storable]
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43 | private int horizon;
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44 | [Storable]
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45 | private string conditionalEvaluationVariable;
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46 |
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47 | public string FileName { get; set; }
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48 |
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49 | [StorableConstructor]
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50 | protected SymbolicTimeSeriesPrognosisSolution(bool deserializing) : base(deserializing) { }
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51 |
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52 | public SymbolicTimeSeriesPrognosisSolution() {
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53 | horizon = 1;
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54 | }
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55 |
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56 | public SymbolicTimeSeriesPrognosisSolution(MultiVariateDataAnalysisProblemData problemData, SymbolicTimeSeriesPrognosisModel model, int horizon, string conditionalEvaluationVariable)
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57 | : this() {
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58 | this.problemData = problemData;
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59 | this.model = model;
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60 | this.horizon = horizon;
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61 | this.conditionalEvaluationVariable = conditionalEvaluationVariable;
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62 | }
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63 |
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64 | [StorableHook(HookType.AfterDeserialization)]
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65 | private void Initialize() {
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66 | if (problemData != null)
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67 | RegisterProblemDataEvents();
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68 | }
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69 |
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70 | public override Image ItemImage {
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71 | get { return HeuristicLab.Common.Resources.VS2008ImageLibrary.Function; }
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72 | }
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73 |
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74 | public int Horizon {
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75 | get { return horizon; }
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76 | set {
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77 | if (value <= 0) throw new ArgumentException();
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78 | horizon = value;
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79 | }
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80 | }
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81 |
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82 | public SymbolicTimeSeriesPrognosisModel Model {
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83 | get { return model; }
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84 | set {
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85 | if (model != value) {
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86 | if (value == null) throw new ArgumentNullException();
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87 | model = value;
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88 | RaiseModelChanged();
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89 | }
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90 | }
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91 | }
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92 |
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93 | public string ConditionalEvaluationVariable {
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94 | get { return conditionalEvaluationVariable; }
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95 | set {
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96 | if (conditionalEvaluationVariable != value) {
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97 | conditionalEvaluationVariable = value;
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98 | RaiseEstimatedValuesChanged();
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99 | }
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100 | }
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101 | }
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102 |
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103 |
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104 | public IEnumerable<double[]> GetPrognosis(int t) {
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105 | return model.GetEstimatedValues(problemData, t, t + 1);
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106 | }
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107 |
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108 | #region IMultiVariateDataAnalysisSolution Members
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109 |
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110 | public MultiVariateDataAnalysisProblemData ProblemData {
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111 | get { return problemData; }
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112 | set {
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113 | if (problemData != value) {
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114 | if (value == null) throw new ArgumentNullException();
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115 | if (model != null && problemData != null &&
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116 | !(problemData.InputVariables
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117 | .Select(c => c.Value)
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118 | .SequenceEqual(value.InputVariables
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119 | .Select(c => c.Value)) &&
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120 | problemData.TargetVariables
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121 | .Select(c => c.Value)
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122 | .SequenceEqual(value.TargetVariables
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123 | .Select(c => c.Value)))) {
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124 | throw new ArgumentException("Could not set new problem data with different structure");
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125 | }
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126 |
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127 | if (problemData != null) DeregisterProblemDataEvents();
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128 | problemData = value;
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129 | RaiseProblemDataChanged();
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130 | RegisterProblemDataEvents();
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131 | }
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132 | }
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133 | }
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134 |
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135 | IMultiVariateDataAnalysisModel IMultiVariateDataAnalysisSolution.Model {
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136 | get { return model; }
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137 | }
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138 |
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139 | public IEnumerable<double[]> EstimatedValues {
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140 | get {
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141 | return model.GetEstimatedValues(problemData, 0, problemData.Dataset.Rows);
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142 | }
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143 | }
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144 |
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145 | public IEnumerable<double[]> EstimatedTrainingValues {
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146 | get {
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147 | return model.GetEstimatedValues(problemData, problemData.TrainingSamplesStart.Value, problemData.TrainingSamplesEnd.Value);
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148 | }
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149 | }
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150 |
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151 | public IEnumerable<double[]> EstimatedTestValues {
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152 | get {
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153 | return model.GetEstimatedValues(problemData, problemData.TestSamplesStart.Value, problemData.TestSamplesEnd.Value);
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154 | }
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155 | }
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156 |
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157 | #endregion
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158 |
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159 |
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160 | #region Events
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161 | protected virtual void RegisterProblemDataEvents() {
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162 | ProblemData.ProblemDataChanged += new EventHandler(ProblemData_Changed);
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163 | }
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164 | protected virtual void DeregisterProblemDataEvents() {
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165 | ProblemData.ProblemDataChanged += new EventHandler(ProblemData_Changed);
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166 | }
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167 | private void ProblemData_Changed(object sender, EventArgs e) {
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168 | RaiseProblemDataChanged();
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169 | }
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170 |
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171 | public event EventHandler ProblemDataChanged;
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172 | protected virtual void RaiseProblemDataChanged() {
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173 | var listeners = ProblemDataChanged;
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174 | if (listeners != null)
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175 | listeners(this, EventArgs.Empty);
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176 | }
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177 |
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178 | public event EventHandler ModelChanged;
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179 | protected virtual void RaiseModelChanged() {
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180 | EventHandler handler = ModelChanged;
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181 | if (handler != null)
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182 | handler(this, EventArgs.Empty);
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183 | }
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184 |
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185 | public event EventHandler EstimatedValuesChanged;
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186 | protected virtual void RaiseEstimatedValuesChanged() {
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187 | var listeners = EstimatedValuesChanged;
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188 | if (listeners != null)
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189 | listeners(this, EventArgs.Empty);
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190 | }
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191 | #endregion
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192 |
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193 |
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194 | public override IDeepCloneable Clone(Cloner cloner) {
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195 | SymbolicTimeSeriesPrognosisSolution clone = (SymbolicTimeSeriesPrognosisSolution)base.Clone(cloner);
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196 | clone.problemData = (MultiVariateDataAnalysisProblemData)cloner.Clone(problemData);
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197 | clone.model = (SymbolicTimeSeriesPrognosisModel)cloner.Clone(model);
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198 | clone.horizon = horizon;
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199 | clone.conditionalEvaluationVariable = conditionalEvaluationVariable;
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200 | return clone;
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201 | }
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202 | }
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203 | } |
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