1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using System.Runtime.CompilerServices;
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26 | using System.Threading;
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27 | using HeuristicLab.Algorithms.DataAnalysis;
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28 | using HeuristicLab.Algorithms.MemPR.Interfaces;
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29 | using HeuristicLab.Common;
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30 | using HeuristicLab.Core;
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31 | using HeuristicLab.Data;
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32 | using HeuristicLab.Optimization;
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33 | using HeuristicLab.Parameters;
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34 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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35 | using HeuristicLab.Problems.DataAnalysis;
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36 | using HeuristicLab.Random;
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37 | using ExecutionContext = HeuristicLab.Core.ExecutionContext;
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38 |
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39 | namespace HeuristicLab.Algorithms.MemPR {
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40 | [Item("MemPRContext", "Abstract base class for MemPR contexts.")]
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41 | [StorableClass]
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42 | public abstract class MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext> : ParameterizedNamedItem,
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43 | IPopulationBasedHeuristicAlgorithmContext<TProblem, TSolution>, ISolutionModelContext<TSolution>, IEvaluationServiceContext<TSolution>
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44 | where TProblem : class, IItem, ISingleObjectiveHeuristicOptimizationProblem
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45 | where TSolution : class, IItem
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46 | where TPopulationContext : MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext>
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47 | where TSolutionContext : MemPRSolutionContext<TProblem, TSolution, TPopulationContext, TSolutionContext> {
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48 |
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49 | private IExecutionContext parent;
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50 | public IExecutionContext Parent {
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51 | get { return parent; }
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52 | set { parent = value; }
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53 | }
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54 |
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55 | [Storable]
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56 | private IScope scope;
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57 | public IScope Scope {
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58 | get { return scope; }
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59 | private set { scope = value; }
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60 | }
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61 |
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62 | IKeyedItemCollection<string, IParameter> IExecutionContext.Parameters {
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63 | get { return Parameters; }
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64 | }
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65 |
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66 | [Storable]
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67 | private IValueParameter<TProblem> problem;
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68 | public TProblem Problem {
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69 | get { return problem.Value; }
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70 | set { problem.Value = value; }
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71 | }
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72 | public bool Maximization {
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73 | get { return ((IValueParameter<BoolValue>)Problem.MaximizationParameter).Value.Value; }
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74 | }
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75 |
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76 | [Storable]
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77 | private IValueParameter<BoolValue> initialized;
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78 | public bool Initialized {
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79 | get { return initialized.Value.Value; }
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80 | set { initialized.Value.Value = value; }
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81 | }
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82 |
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83 | [Storable]
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84 | private IValueParameter<IntValue> iterations;
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85 | public int Iterations {
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86 | get { return iterations.Value.Value; }
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87 | set { iterations.Value.Value = value; }
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88 | }
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89 |
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90 | [Storable]
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91 | private IValueParameter<IntValue> evaluatedSolutions;
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92 | public int EvaluatedSolutions {
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93 | get { return evaluatedSolutions.Value.Value; }
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94 | set { evaluatedSolutions.Value.Value = value; }
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95 | }
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96 |
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97 | [Storable]
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98 | private IValueParameter<DoubleValue> bestQuality;
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99 | public double BestQuality {
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100 | get { return bestQuality.Value.Value; }
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101 | set { bestQuality.Value.Value = value; }
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102 | }
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103 |
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104 | [Storable]
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105 | private IValueParameter<TSolution> bestSolution;
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106 | public TSolution BestSolution {
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107 | get { return bestSolution.Value; }
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108 | set { bestSolution.Value = value; }
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109 | }
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110 |
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111 | [Storable]
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112 | private IValueParameter<IntValue> localSearchEvaluations;
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113 | public int LocalSearchEvaluations {
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114 | get { return localSearchEvaluations.Value.Value; }
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115 | set { localSearchEvaluations.Value.Value = value; }
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116 | }
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117 |
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118 | [Storable]
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119 | private IValueParameter<DoubleValue> localOptimaLevel;
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120 | public double LocalOptimaLevel {
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121 | get { return localOptimaLevel.Value.Value; }
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122 | set { localOptimaLevel.Value.Value = value; }
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123 | }
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124 |
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125 | [Storable]
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126 | private IValueParameter<IntValue> byBreeding;
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127 | public int ByBreeding {
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128 | get { return byBreeding.Value.Value; }
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129 | set { byBreeding.Value.Value = value; }
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130 | }
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131 |
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132 | [Storable]
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133 | private IValueParameter<IntValue> byRelinking;
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134 | public int ByRelinking {
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135 | get { return byRelinking.Value.Value; }
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136 | set { byRelinking.Value.Value = value; }
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137 | }
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138 |
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139 | [Storable]
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140 | private IValueParameter<IntValue> byDelinking;
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141 | public int ByDelinking {
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142 | get { return byDelinking.Value.Value; }
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143 | set { byDelinking.Value.Value = value; }
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144 | }
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145 |
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146 | [Storable]
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147 | private IValueParameter<IntValue> bySampling;
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148 | public int BySampling {
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149 | get { return bySampling.Value.Value; }
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150 | set { bySampling.Value.Value = value; }
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151 | }
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152 |
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153 | [Storable]
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154 | private IValueParameter<IntValue> byHillclimbing;
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155 | public int ByHillclimbing {
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156 | get { return byHillclimbing.Value.Value; }
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157 | set { byHillclimbing.Value.Value = value; }
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158 | }
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159 |
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160 | [Storable]
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161 | private IValueParameter<IntValue> byAdaptivewalking;
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162 | public int ByAdaptivewalking {
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163 | get { return byAdaptivewalking.Value.Value; }
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164 | set { byAdaptivewalking.Value.Value = value; }
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165 | }
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166 |
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167 | [Storable]
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168 | private IValueParameter<IRandom> random;
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169 | public IRandom Random {
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170 | get { return random.Value; }
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171 | set { random.Value = value; }
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172 | }
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173 |
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174 | public IEnumerable<ISingleObjectiveSolutionScope<TSolution>> Population {
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175 | get { return scope.SubScopes.OfType<ISingleObjectiveSolutionScope<TSolution>>(); }
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176 | }
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177 | public void AddToPopulation(ISingleObjectiveSolutionScope<TSolution> solScope) {
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178 | scope.SubScopes.Add(solScope);
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179 | }
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180 | public void ReplaceAtPopulation(int index, ISingleObjectiveSolutionScope<TSolution> solScope) {
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181 | scope.SubScopes[index] = solScope;
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182 | }
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183 | public ISingleObjectiveSolutionScope<TSolution> AtPopulation(int index) {
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184 | return scope.SubScopes[index] as ISingleObjectiveSolutionScope<TSolution>;
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185 | }
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186 | public void SortPopulation() {
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187 | scope.SubScopes.Replace(scope.SubScopes.OfType<ISingleObjectiveSolutionScope<TSolution>>().OrderBy(x => Maximization ? -x.Fitness : x.Fitness).ToList());
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188 | }
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189 | public int PopulationCount {
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190 | get { return scope.SubScopes.Count; }
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191 | }
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192 |
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193 | [Storable]
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194 | private IConfidenceRegressionModel breedingPerformanceModel;
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195 | public IConfidenceRegressionModel BreedingPerformanceModel {
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196 | get { return breedingPerformanceModel; }
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197 | }
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198 | [Storable]
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199 | private List<Tuple<double, double, double, double>> breedingStat;
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200 | public IEnumerable<Tuple<double, double, double, double>> BreedingStat {
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201 | get { return breedingStat; }
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202 | }
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203 | [Storable]
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204 | private IConfidenceRegressionModel relinkingPerformanceModel;
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205 | public IConfidenceRegressionModel RelinkingPerformanceModel {
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206 | get { return relinkingPerformanceModel; }
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207 | }
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208 | [Storable]
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209 | private List<Tuple<double, double, double, double>> relinkingStat;
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210 | public IEnumerable<Tuple<double, double, double, double>> RelinkingStat {
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211 | get { return relinkingStat; }
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212 | }
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213 | [Storable]
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214 | private IConfidenceRegressionModel delinkingPerformanceModel;
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215 | public IConfidenceRegressionModel DelinkingPerformanceModel {
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216 | get { return delinkingPerformanceModel; }
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217 | }
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218 | [Storable]
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219 | private List<Tuple<double, double, double, double>> delinkingStat;
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220 | public IEnumerable<Tuple<double, double, double, double>> DelinkingStat {
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221 | get { return delinkingStat; }
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222 | }
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223 | [Storable]
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224 | private IConfidenceRegressionModel samplingPerformanceModel;
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225 | public IConfidenceRegressionModel SamplingPerformanceModel {
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226 | get { return samplingPerformanceModel; }
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227 | }
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228 | [Storable]
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229 | private List<Tuple<double, double>> samplingStat;
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230 | public IEnumerable<Tuple<double, double>> SamplingStat {
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231 | get { return samplingStat; }
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232 | }
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233 | [Storable]
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234 | private IConfidenceRegressionModel hillclimbingPerformanceModel;
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235 | public IConfidenceRegressionModel HillclimbingPerformanceModel {
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236 | get { return hillclimbingPerformanceModel; }
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237 | }
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238 | [Storable]
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239 | private List<Tuple<double, double>> hillclimbingStat;
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240 | public IEnumerable<Tuple<double, double>> HillclimbingStat {
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241 | get { return hillclimbingStat; }
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242 | }
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243 | [Storable]
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244 | private IConfidenceRegressionModel adaptiveWalkPerformanceModel;
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245 | public IConfidenceRegressionModel AdaptiveWalkPerformanceModel {
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246 | get { return adaptiveWalkPerformanceModel; }
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247 | }
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248 | [Storable]
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249 | private List<Tuple<double, double>> adaptivewalkingStat;
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250 | public IEnumerable<Tuple<double, double>> AdaptivewalkingStat {
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251 | get { return adaptivewalkingStat; }
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252 | }
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253 |
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254 | [Storable]
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255 | public ISolutionModel<TSolution> Model { get; set; }
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256 |
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257 | [StorableConstructor]
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258 | protected MemPRPopulationContext(bool deserializing) : base(deserializing) { }
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259 | protected MemPRPopulationContext(MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext> original, Cloner cloner)
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260 | : base(original, cloner) {
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261 | scope = cloner.Clone(original.scope);
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262 | problem = cloner.Clone(original.problem);
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263 | initialized = cloner.Clone(original.initialized);
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264 | iterations = cloner.Clone(original.iterations);
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265 | evaluatedSolutions = cloner.Clone(original.evaluatedSolutions);
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266 | bestQuality = cloner.Clone(original.bestQuality);
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267 | bestSolution = cloner.Clone(original.bestSolution);
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268 | localSearchEvaluations = cloner.Clone(original.localSearchEvaluations);
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269 | localOptimaLevel = cloner.Clone(original.localOptimaLevel);
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270 | byBreeding = cloner.Clone(original.byBreeding);
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271 | byRelinking = cloner.Clone(original.byRelinking);
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272 | byDelinking = cloner.Clone(original.byDelinking);
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273 | bySampling = cloner.Clone(original.bySampling);
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274 | byHillclimbing = cloner.Clone(original.byHillclimbing);
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275 | byAdaptivewalking = cloner.Clone(original.byAdaptivewalking);
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276 | random = cloner.Clone(original.random);
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277 | breedingPerformanceModel = cloner.Clone(original.breedingPerformanceModel);
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278 | breedingStat = original.breedingStat.Select(x => Tuple.Create(x.Item1, x.Item2, x.Item3, x.Item4)).ToList();
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279 | relinkingPerformanceModel = cloner.Clone(original.relinkingPerformanceModel);
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280 | relinkingStat = original.relinkingStat.Select(x => Tuple.Create(x.Item1, x.Item2, x.Item3, x.Item4)).ToList();
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281 | delinkingPerformanceModel = cloner.Clone(original.delinkingPerformanceModel);
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282 | delinkingStat = original.delinkingStat.Select(x => Tuple.Create(x.Item1, x.Item2, x.Item3, x.Item4)).ToList();
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283 | samplingPerformanceModel = cloner.Clone(original.samplingPerformanceModel);
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284 | samplingStat = original.samplingStat.Select(x => Tuple.Create(x.Item1, x.Item2)).ToList();
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285 | hillclimbingPerformanceModel = cloner.Clone(original.hillclimbingPerformanceModel);
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286 | hillclimbingStat = original.hillclimbingStat.Select(x => Tuple.Create(x.Item1, x.Item2)).ToList();
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287 | adaptiveWalkPerformanceModel = cloner.Clone(original.adaptiveWalkPerformanceModel);
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288 | adaptivewalkingStat = original.adaptivewalkingStat.Select(x => Tuple.Create(x.Item1, x.Item2)).ToList();
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289 |
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290 | Model = cloner.Clone(original.Model);
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291 | }
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292 | public MemPRPopulationContext() : this("MemPRContext") { }
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293 | public MemPRPopulationContext(string name) : base(name) {
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294 | scope = new Scope("Global");
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295 |
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296 | Parameters.Add(problem = new ValueParameter<TProblem>("Problem"));
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297 | Parameters.Add(initialized = new ValueParameter<BoolValue>("Initialized", new BoolValue(false)));
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298 | Parameters.Add(iterations = new ValueParameter<IntValue>("Iterations", new IntValue(0)));
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299 | Parameters.Add(evaluatedSolutions = new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
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300 | Parameters.Add(bestQuality = new ValueParameter<DoubleValue>("BestQuality", new DoubleValue(double.NaN)));
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301 | Parameters.Add(bestSolution = new ValueParameter<TSolution>("BestFoundSolution"));
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302 | Parameters.Add(localSearchEvaluations = new ValueParameter<IntValue>("LocalSearchEvaluations", new IntValue(0)));
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303 | Parameters.Add(localOptimaLevel = new ValueParameter<DoubleValue>("LocalOptimaLevel", new DoubleValue(0)));
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304 | Parameters.Add(byBreeding = new ValueParameter<IntValue>("ByBreeding", new IntValue(0)));
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305 | Parameters.Add(byRelinking = new ValueParameter<IntValue>("ByRelinking", new IntValue(0)));
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306 | Parameters.Add(byDelinking = new ValueParameter<IntValue>("ByDelinking", new IntValue(0)));
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307 | Parameters.Add(bySampling = new ValueParameter<IntValue>("BySampling", new IntValue(0)));
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308 | Parameters.Add(byHillclimbing = new ValueParameter<IntValue>("ByHillclimbing", new IntValue(0)));
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309 | Parameters.Add(byAdaptivewalking = new ValueParameter<IntValue>("ByAdaptivewalking", new IntValue(0)));
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310 | Parameters.Add(random = new ValueParameter<IRandom>("Random", new MersenneTwister()));
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311 |
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312 | breedingStat = new List<Tuple<double, double, double, double>>();
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313 | relinkingStat = new List<Tuple<double, double, double, double>>();
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314 | delinkingStat = new List<Tuple<double, double, double, double>>();
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315 | samplingStat = new List<Tuple<double, double>>();
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316 | hillclimbingStat = new List<Tuple<double, double>>();
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317 | adaptivewalkingStat = new List<Tuple<double, double>>();
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318 | }
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319 |
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320 | public abstract ISingleObjectiveSolutionScope<TSolution> ToScope(TSolution code, double fitness = double.NaN);
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321 |
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322 | public virtual double Evaluate(TSolution solution, CancellationToken token) {
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323 | var solScope = ToScope(solution);
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324 | Evaluate(solScope, token);
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325 | return solScope.Fitness;
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326 | }
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327 |
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328 | public virtual void Evaluate(ISingleObjectiveSolutionScope<TSolution> solScope, CancellationToken token) {
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329 | var pdef = Problem as ISingleObjectiveProblemDefinition;
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330 | if (pdef != null) {
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331 | var ind = new SingleEncodingIndividual(pdef.Encoding, solScope);
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332 | solScope.Fitness = pdef.Evaluate(ind, Random);
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333 | } else {
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334 | RunOperator(Problem.Evaluator, solScope, token);
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335 | }
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336 | }
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337 |
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338 | public abstract TSolutionContext CreateSingleSolutionContext(ISingleObjectiveSolutionScope<TSolution> solution);
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339 |
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340 | public void IncrementEvaluatedSolutions(int byEvaluations) {
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341 | if (byEvaluations < 0) throw new ArgumentException("Can only increment and not decrement evaluated solutions.");
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342 | EvaluatedSolutions += byEvaluations;
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343 | }
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344 |
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345 | public void RelearnBreedingPerformanceModel() {
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346 | breedingPerformanceModel = RunRegression(PrepareRegression(ToListRow(breedingStat)), breedingPerformanceModel).Model;
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347 | }
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348 | public bool BreedingSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, double dist) {
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349 | if (breedingPerformanceModel == null) return true;
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350 | double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
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351 | foreach (var d in BreedingStat) {
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352 | if (d.Item1 < minI1) minI1 = d.Item1;
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353 | if (d.Item1 > maxI1) maxI1 = d.Item1;
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354 | if (d.Item2 < minI2) minI2 = d.Item2;
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355 | if (d.Item2 > maxI2) maxI2 = d.Item2;
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356 | }
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357 | if (p1.Fitness < minI1 || p1.Fitness > maxI1 || p2.Fitness < minI2 || p2.Fitness > maxI2)
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358 | return true;
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359 |
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360 | return Random.NextDouble() < ProbabilityAcceptAbsolutePerformanceModel(new List<double> { p1.Fitness, p2.Fitness, dist }, breedingPerformanceModel);
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361 | }
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362 |
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363 | public void RelearnRelinkingPerformanceModel() {
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364 | relinkingPerformanceModel = RunRegression(PrepareRegression(ToListRow(relinkingStat)), relinkingPerformanceModel).Model;
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365 | }
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366 | public bool RelinkSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, double dist) {
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367 | if (relinkingPerformanceModel == null) return true;
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368 | double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
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369 | foreach (var d in RelinkingStat) {
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370 | if (d.Item1 < minI1) minI1 = d.Item1;
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371 | if (d.Item1 > maxI1) maxI1 = d.Item1;
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372 | if (d.Item2 < minI2) minI2 = d.Item2;
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373 | if (d.Item2 > maxI2) maxI2 = d.Item2;
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374 | }
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375 | if (p1.Fitness < minI1 || p1.Fitness > maxI1 || p2.Fitness < minI2 || p2.Fitness > maxI2)
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376 | return true;
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377 |
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378 | if (IsBetter(p1, p2)) {
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379 | return Random.NextDouble() < ProbabilityAcceptRelativePerformanceModel(p1.Fitness, new List<double> { p1.Fitness, p2.Fitness, dist }, relinkingPerformanceModel);
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380 | }
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381 | return Random.NextDouble() < ProbabilityAcceptRelativePerformanceModel(p2.Fitness, new List<double> { p1.Fitness, p2.Fitness, dist }, relinkingPerformanceModel);
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382 | }
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383 |
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384 | public void RelearnDelinkingPerformanceModel() {
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385 | delinkingPerformanceModel = RunRegression(PrepareRegression(ToListRow(delinkingStat)), delinkingPerformanceModel).Model;
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386 | }
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387 | public bool DelinkSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, double dist) {
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388 | if (delinkingPerformanceModel == null) return true;
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389 | double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
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390 | foreach (var d in DelinkingStat) {
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391 | if (d.Item1 < minI1) minI1 = d.Item1;
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392 | if (d.Item1 > maxI1) maxI1 = d.Item1;
|
---|
393 | if (d.Item2 < minI2) minI2 = d.Item2;
|
---|
394 | if (d.Item2 > maxI2) maxI2 = d.Item2;
|
---|
395 | }
|
---|
396 | if (p1.Fitness < minI1 || p1.Fitness > maxI1 || p2.Fitness < minI2 || p2.Fitness > maxI2)
|
---|
397 | return true;
|
---|
398 | if (IsBetter(p1, p2)) {
|
---|
399 | return Random.NextDouble() < ProbabilityAcceptRelativePerformanceModel(p1.Fitness, new List<double> { p1.Fitness, p2.Fitness, dist }, delinkingPerformanceModel);
|
---|
400 | }
|
---|
401 | return Random.NextDouble() < ProbabilityAcceptRelativePerformanceModel(p2.Fitness, new List<double> { p1.Fitness, p2.Fitness, dist }, delinkingPerformanceModel);
|
---|
402 | }
|
---|
403 |
|
---|
404 | public void RelearnSamplingPerformanceModel() {
|
---|
405 | samplingPerformanceModel = RunRegression(PrepareRegression(ToListRow(samplingStat)), samplingPerformanceModel).Model;
|
---|
406 | }
|
---|
407 | public bool SamplingSuited(double avgDist) {
|
---|
408 | if (samplingPerformanceModel == null) return true;
|
---|
409 | if (avgDist < samplingStat.Min(x => x.Item1) || avgDist > samplingStat.Max(x => x.Item1)) return true;
|
---|
410 | return Random.NextDouble() < ProbabilityAcceptAbsolutePerformanceModel(new List<double> { avgDist }, samplingPerformanceModel);
|
---|
411 | }
|
---|
412 |
|
---|
413 | public void RelearnHillclimbingPerformanceModel() {
|
---|
414 | hillclimbingPerformanceModel = RunRegression(PrepareRegression(ToListRow(hillclimbingStat)), hillclimbingPerformanceModel).Model;
|
---|
415 | }
|
---|
416 | public bool HillclimbingSuited(ISingleObjectiveSolutionScope<TSolution> scope) {
|
---|
417 | return HillclimbingSuited(scope.Fitness);
|
---|
418 | }
|
---|
419 | public bool HillclimbingSuited(double startingFitness) {
|
---|
420 | if (hillclimbingPerformanceModel == null) return true;
|
---|
421 | if (startingFitness < HillclimbingStat.Min(x => x.Item1) || startingFitness > HillclimbingStat.Max(x => x.Item1))
|
---|
422 | return true;
|
---|
423 | return Random.NextDouble() < ProbabilityAcceptRelativePerformanceModel(startingFitness, new List<double> { startingFitness }, hillclimbingPerformanceModel);
|
---|
424 | }
|
---|
425 |
|
---|
426 | public void RelearnAdaptiveWalkPerformanceModel() {
|
---|
427 | adaptiveWalkPerformanceModel = RunRegression(PrepareRegression(ToListRow(adaptivewalkingStat)), adaptiveWalkPerformanceModel).Model;
|
---|
428 | }
|
---|
429 | public bool AdaptivewalkingSuited(ISingleObjectiveSolutionScope<TSolution> scope) {
|
---|
430 | return AdaptivewalkingSuited(scope.Fitness);
|
---|
431 | }
|
---|
432 | public bool AdaptivewalkingSuited(double startingFitness) {
|
---|
433 | if (adaptiveWalkPerformanceModel == null) return true;
|
---|
434 | if (startingFitness < AdaptivewalkingStat.Min(x => x.Item1) || startingFitness > AdaptivewalkingStat.Max(x => x.Item1))
|
---|
435 | return true;
|
---|
436 | return Random.NextDouble() < ProbabilityAcceptAbsolutePerformanceModel(new List<double> { startingFitness }, adaptiveWalkPerformanceModel);
|
---|
437 | }
|
---|
438 |
|
---|
439 | public IConfidenceRegressionSolution GetSolution(IConfidenceRegressionModel model, IEnumerable<Tuple<double, double>> data) {
|
---|
440 | return new ConfidenceRegressionSolution(model, PrepareRegression(ToListRow(data.ToList())));
|
---|
441 | }
|
---|
442 | public IConfidenceRegressionSolution GetSolution(IConfidenceRegressionModel model, IEnumerable<Tuple<double, double, double>> data) {
|
---|
443 | return new ConfidenceRegressionSolution(model, PrepareRegression(ToListRow(data.ToList())));
|
---|
444 | }
|
---|
445 | public IConfidenceRegressionSolution GetSolution(IConfidenceRegressionModel model, IEnumerable<Tuple<double, double, double, double>> data) {
|
---|
446 | return new ConfidenceRegressionSolution(model, PrepareRegression(ToListRow(data.ToList())));
|
---|
447 | }
|
---|
448 |
|
---|
449 | protected RegressionProblemData PrepareRegression(List<List<double>> sample) {
|
---|
450 | var columns = sample.First().Select(y => new List<double>()).ToList();
|
---|
451 | foreach (var next in sample.Shuffle(Random)) {
|
---|
452 | for (var i = 0; i < next.Count; i++) {
|
---|
453 | columns[i].Add(next[i]);
|
---|
454 | }
|
---|
455 | }
|
---|
456 | var ds = new Dataset(columns.Select((v, i) => i < columns.Count - 1 ? "in" + i : "out").ToList(), columns);
|
---|
457 | var regPrb = new RegressionProblemData(ds, Enumerable.Range(0, columns.Count - 1).Select(x => "in" + x), "out") {
|
---|
458 | TrainingPartition = { Start = 0, End = Math.Min(50, sample.Count) },
|
---|
459 | TestPartition = { Start = Math.Min(50, sample.Count), End = sample.Count }
|
---|
460 | };
|
---|
461 | return regPrb;
|
---|
462 | }
|
---|
463 |
|
---|
464 | protected static IConfidenceRegressionSolution RunRegression(RegressionProblemData trainingData, IConfidenceRegressionModel baseLineModel = null) {
|
---|
465 | var baseline = baseLineModel != null ? new ConfidenceRegressionSolution(baseLineModel, trainingData) : null;
|
---|
466 | var gpr = new GaussianProcessRegression { Problem = { ProblemData = trainingData } };
|
---|
467 | if (trainingData.InputVariables.CheckedItems.Any(x => alglib.pearsoncorr2(trainingData.Dataset.GetDoubleValues(x.Value.Value).ToArray(), trainingData.TargetVariableValues.ToArray()) > 0.8)) {
|
---|
468 | gpr.MeanFunction = new MeanZero();
|
---|
469 | var cov1 = new CovarianceSum();
|
---|
470 | cov1.Terms.Add(new CovarianceLinearArd());
|
---|
471 | cov1.Terms.Add(new CovarianceConst());
|
---|
472 | gpr.CovarianceFunction = cov1;
|
---|
473 | }
|
---|
474 | IConfidenceRegressionSolution solution = null;
|
---|
475 | var cnt = 0;
|
---|
476 | do {
|
---|
477 | ExecuteAlgorithm(gpr);
|
---|
478 | solution = (IConfidenceRegressionSolution)gpr.Results["Solution"].Value;
|
---|
479 | cnt++;
|
---|
480 | } while (cnt < 10 && (solution == null || solution.TrainingRSquared.IsAlmost(0)));
|
---|
481 | if (baseline == null) return solution;
|
---|
482 | if (trainingData.Dataset.Rows < 60)
|
---|
483 | return solution.TrainingMeanAbsoluteError < baseline.TrainingMeanAbsoluteError ? solution : baseline;
|
---|
484 | return solution.TestMeanAbsoluteError < baseline.TestMeanAbsoluteError ? solution : baseline;
|
---|
485 | }
|
---|
486 |
|
---|
487 | protected static void ExecuteAlgorithm(IAlgorithm algorithm) {
|
---|
488 | using (var evt = new AutoResetEvent(false)) {
|
---|
489 | EventHandler exeStateChanged = (o, args) => {
|
---|
490 | if (algorithm.ExecutionState == ExecutionState.Paused || algorithm.ExecutionState == ExecutionState.Stopped)
|
---|
491 | evt.Set();
|
---|
492 | };
|
---|
493 | algorithm.ExecutionStateChanged += exeStateChanged;
|
---|
494 | algorithm.Prepare(true);
|
---|
495 | algorithm.Start();
|
---|
496 | evt.WaitOne();
|
---|
497 | algorithm.ExecutionStateChanged -= exeStateChanged;
|
---|
498 | }
|
---|
499 | }
|
---|
500 |
|
---|
501 | private double ProbabilityAcceptAbsolutePerformanceModel(List<double> inputs, IConfidenceRegressionModel model) {
|
---|
502 | var inputVariables = inputs.Select((v, i) => "in" + i);
|
---|
503 | var ds = new Dataset(inputVariables.Concat( new [] { "out" }), inputs.Select(x => new List<double> { x }).Concat(new [] { new List<double> { double.NaN } }));
|
---|
504 | var mean = model.GetEstimatedValues(ds, new[] { 0 }).Single();
|
---|
505 | var sdev = Math.Sqrt(model.GetEstimatedVariances(ds, new[] { 0 }).Single());
|
---|
506 |
|
---|
507 | // calculate the fitness goal
|
---|
508 | var goal = Maximization ? Population.Min(x => x.Fitness) : Population.Max(x => x.Fitness);
|
---|
509 | var z = (goal - mean) / sdev;
|
---|
510 | // return the probability of achieving or surpassing that goal
|
---|
511 | var y = alglib.invnormaldistribution(z);
|
---|
512 | return Maximization ? 1.0 - y /* P(X >= z) */ : y; // P(X <= z)
|
---|
513 | }
|
---|
514 |
|
---|
515 | private double ProbabilityAcceptRelativePerformanceModel(double basePerformance, List<double> inputs, IConfidenceRegressionModel model) {
|
---|
516 | var inputVariables = inputs.Select((v, i) => "in" + i);
|
---|
517 | var ds = new Dataset(inputVariables.Concat(new[] { "out" }), inputs.Select(x => new List<double> { x }).Concat(new[] { new List<double> { double.NaN } }));
|
---|
518 | var mean = model.GetEstimatedValues(ds, new[] { 0 }).Single();
|
---|
519 | var sdev = Math.Sqrt(model.GetEstimatedVariances(ds, new[] { 0 }).Single());
|
---|
520 |
|
---|
521 | // calculate the improvement goal
|
---|
522 | var goal = Maximization ? Population.Min(x => x.Fitness) - basePerformance : basePerformance - Population.Max(x => x.Fitness);
|
---|
523 | var z = (goal - mean) / sdev;
|
---|
524 | // return the probability of achieving or surpassing that goal
|
---|
525 | return 1.0 - alglib.invnormaldistribution(z); /* P(X >= z) */
|
---|
526 | }
|
---|
527 |
|
---|
528 | private static List<List<double>> ToListRow(List<Tuple<double, double>> rows) {
|
---|
529 | return rows.Select(x => new List<double> { x.Item1, x.Item2 }).ToList();
|
---|
530 | }
|
---|
531 | private static List<List<double>> ToListRow(List<Tuple<double, double, double>> rows) {
|
---|
532 | return rows.Select(x => new List<double> { x.Item1, x.Item2, x.Item3 }).ToList();
|
---|
533 | }
|
---|
534 | private static List<List<double>> ToListRow(List<Tuple<double, double, double, double>> rows) {
|
---|
535 | return rows.Select(x => new List<double> { x.Item1, x.Item2, x.Item3, x.Item4 }).ToList();
|
---|
536 | }
|
---|
537 |
|
---|
538 | [MethodImpl(MethodImplOptions.AggressiveInlining)]
|
---|
539 | public bool IsBetter(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b) {
|
---|
540 | return IsBetter(a.Fitness, b.Fitness);
|
---|
541 | }
|
---|
542 | [MethodImpl(MethodImplOptions.AggressiveInlining)]
|
---|
543 | public bool IsBetter(double a, double b) {
|
---|
544 | return double.IsNaN(b) && !double.IsNaN(a)
|
---|
545 | || Maximization && a > b
|
---|
546 | || !Maximization && a < b;
|
---|
547 | }
|
---|
548 |
|
---|
549 | public void AddBreedingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, double parentDist, ISingleObjectiveSolutionScope<TSolution> child) {
|
---|
550 | if (IsBetter(a, b))
|
---|
551 | breedingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, child.Fitness));
|
---|
552 | else breedingStat.Add(Tuple.Create(b.Fitness, a.Fitness, parentDist, child.Fitness));
|
---|
553 | if (breedingStat.Count % 10 == 0) RelearnBreedingPerformanceModel();
|
---|
554 | }
|
---|
555 |
|
---|
556 | public void AddRelinkingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, double parentDist, ISingleObjectiveSolutionScope<TSolution> child) {
|
---|
557 | if (IsBetter(a, b))
|
---|
558 | relinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, Maximization ? child.Fitness - a.Fitness : a.Fitness - child.Fitness));
|
---|
559 | else relinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, Maximization ? child.Fitness - b.Fitness : b.Fitness - child.Fitness));
|
---|
560 | if (relinkingStat.Count % 10 == 0) RelearnRelinkingPerformanceModel();
|
---|
561 | }
|
---|
562 |
|
---|
563 | public void AddDelinkingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, double parentDist, ISingleObjectiveSolutionScope<TSolution> child) {
|
---|
564 | if (IsBetter(a, b))
|
---|
565 | delinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, Maximization ? child.Fitness - a.Fitness : a.Fitness - child.Fitness));
|
---|
566 | else delinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, Maximization ? child.Fitness - b.Fitness : b.Fitness - child.Fitness));
|
---|
567 | if (delinkingStat.Count % 10 == 0) RelearnDelinkingPerformanceModel();
|
---|
568 | }
|
---|
569 |
|
---|
570 | public void AddSamplingResult(ISingleObjectiveSolutionScope<TSolution> sample, double avgDist) {
|
---|
571 | samplingStat.Add(Tuple.Create(avgDist, sample.Fitness));
|
---|
572 | if (samplingStat.Count % 10 == 0) RelearnSamplingPerformanceModel();
|
---|
573 | }
|
---|
574 |
|
---|
575 | public void AddHillclimbingResult(ISingleObjectiveSolutionScope<TSolution> input, ISingleObjectiveSolutionScope<TSolution> outcome) {
|
---|
576 | hillclimbingStat.Add(Tuple.Create(input.Fitness, Maximization ? outcome.Fitness - input.Fitness : input.Fitness - outcome.Fitness));
|
---|
577 | if (hillclimbingStat.Count % 10 == 0) RelearnHillclimbingPerformanceModel();
|
---|
578 | }
|
---|
579 |
|
---|
580 | public void AddAdaptivewalkingResult(ISingleObjectiveSolutionScope<TSolution> input, ISingleObjectiveSolutionScope<TSolution> outcome) {
|
---|
581 | adaptivewalkingStat.Add(Tuple.Create(input.Fitness, outcome.Fitness));
|
---|
582 | if (adaptivewalkingStat.Count % 10 == 0) RelearnAdaptiveWalkPerformanceModel();
|
---|
583 | }
|
---|
584 |
|
---|
585 | #region IExecutionContext members
|
---|
586 | public IAtomicOperation CreateOperation(IOperator op) {
|
---|
587 | return new ExecutionContext(this, op, Scope);
|
---|
588 | }
|
---|
589 |
|
---|
590 | public IAtomicOperation CreateOperation(IOperator op, IScope s) {
|
---|
591 | return new ExecutionContext(this, op, s);
|
---|
592 | }
|
---|
593 |
|
---|
594 | public IAtomicOperation CreateChildOperation(IOperator op) {
|
---|
595 | return new ExecutionContext(this, op, Scope);
|
---|
596 | }
|
---|
597 |
|
---|
598 | public IAtomicOperation CreateChildOperation(IOperator op, IScope s) {
|
---|
599 | return new ExecutionContext(this, op, s);
|
---|
600 | }
|
---|
601 | #endregion
|
---|
602 |
|
---|
603 | #region Math Helper
|
---|
604 | // normal distribution CDF (left of x) for N(0;1) standard normal distribution
|
---|
605 | // from http://www.johndcook.com/blog/csharp_phi/
|
---|
606 | // license: "This code is in the public domain. Do whatever you want with it, no strings attached."
|
---|
607 | // added: 2016-11-19 21:46 CET
|
---|
608 | /*protected static double Phi(double x) {
|
---|
609 | // constants
|
---|
610 | double a1 = 0.254829592;
|
---|
611 | double a2 = -0.284496736;
|
---|
612 | double a3 = 1.421413741;
|
---|
613 | double a4 = -1.453152027;
|
---|
614 | double a5 = 1.061405429;
|
---|
615 | double p = 0.3275911;
|
---|
616 |
|
---|
617 | // Save the sign of x
|
---|
618 | int sign = 1;
|
---|
619 | if (x < 0)
|
---|
620 | sign = -1;
|
---|
621 | x = Math.Abs(x) / Math.Sqrt(2.0);
|
---|
622 |
|
---|
623 | // A&S formula 7.1.26
|
---|
624 | double t = 1.0 / (1.0 + p * x);
|
---|
625 | double y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.Exp(-x * x);
|
---|
626 |
|
---|
627 | return 0.5 * (1.0 + sign * y);
|
---|
628 | }*/
|
---|
629 | #endregion
|
---|
630 |
|
---|
631 | #region Engine Helper
|
---|
632 | public void RunOperator(IOperator op, IScope scope, CancellationToken cancellationToken) {
|
---|
633 | var stack = new Stack<IOperation>();
|
---|
634 | stack.Push(CreateChildOperation(op, scope));
|
---|
635 |
|
---|
636 | while (stack.Count > 0) {
|
---|
637 | cancellationToken.ThrowIfCancellationRequested();
|
---|
638 |
|
---|
639 | var next = stack.Pop();
|
---|
640 | if (next is OperationCollection) {
|
---|
641 | var coll = (OperationCollection)next;
|
---|
642 | for (int i = coll.Count - 1; i >= 0; i--)
|
---|
643 | if (coll[i] != null) stack.Push(coll[i]);
|
---|
644 | } else if (next is IAtomicOperation) {
|
---|
645 | var operation = (IAtomicOperation)next;
|
---|
646 | try {
|
---|
647 | next = operation.Operator.Execute((IExecutionContext)operation, cancellationToken);
|
---|
648 | } catch (Exception ex) {
|
---|
649 | stack.Push(operation);
|
---|
650 | if (ex is OperationCanceledException) throw ex;
|
---|
651 | else throw new OperatorExecutionException(operation.Operator, ex);
|
---|
652 | }
|
---|
653 | if (next != null) stack.Push(next);
|
---|
654 | }
|
---|
655 | }
|
---|
656 | }
|
---|
657 | #endregion
|
---|
658 | }
|
---|
659 |
|
---|
660 | [Item("SingleSolutionMemPRContext", "Abstract base class for single solution MemPR contexts.")]
|
---|
661 | [StorableClass]
|
---|
662 | public abstract class MemPRSolutionContext<TProblem, TSolution, TContext, TSolutionContext> : ParameterizedNamedItem,
|
---|
663 | ISingleSolutionHeuristicAlgorithmContext<TProblem, TSolution>, IEvaluationServiceContext<TSolution>
|
---|
664 | where TProblem : class, IItem, ISingleObjectiveHeuristicOptimizationProblem
|
---|
665 | where TSolution : class, IItem
|
---|
666 | where TContext : MemPRPopulationContext<TProblem, TSolution, TContext, TSolutionContext>
|
---|
667 | where TSolutionContext : MemPRSolutionContext<TProblem, TSolution, TContext, TSolutionContext> {
|
---|
668 |
|
---|
669 | private TContext parent;
|
---|
670 | public IExecutionContext Parent {
|
---|
671 | get { return parent; }
|
---|
672 | set { throw new InvalidOperationException("Cannot set the parent of a single-solution context."); }
|
---|
673 | }
|
---|
674 |
|
---|
675 | [Storable]
|
---|
676 | private ISingleObjectiveSolutionScope<TSolution> scope;
|
---|
677 | public IScope Scope {
|
---|
678 | get { return scope; }
|
---|
679 | }
|
---|
680 |
|
---|
681 | IKeyedItemCollection<string, IParameter> IExecutionContext.Parameters {
|
---|
682 | get { return Parameters; }
|
---|
683 | }
|
---|
684 |
|
---|
685 | public TProblem Problem {
|
---|
686 | get { return parent.Problem; }
|
---|
687 | }
|
---|
688 | public bool Maximization {
|
---|
689 | get { return parent.Maximization; }
|
---|
690 | }
|
---|
691 |
|
---|
692 | public double BestQuality {
|
---|
693 | get { return parent.BestQuality; }
|
---|
694 | set { parent.BestQuality = value; }
|
---|
695 | }
|
---|
696 |
|
---|
697 | public TSolution BestSolution {
|
---|
698 | get { return parent.BestSolution; }
|
---|
699 | set { parent.BestSolution = value; }
|
---|
700 | }
|
---|
701 |
|
---|
702 | public IRandom Random {
|
---|
703 | get { return parent.Random; }
|
---|
704 | }
|
---|
705 |
|
---|
706 | [Storable]
|
---|
707 | private IValueParameter<IntValue> evaluatedSolutions;
|
---|
708 | public int EvaluatedSolutions {
|
---|
709 | get { return evaluatedSolutions.Value.Value; }
|
---|
710 | set { evaluatedSolutions.Value.Value = value; }
|
---|
711 | }
|
---|
712 |
|
---|
713 | [Storable]
|
---|
714 | private IValueParameter<IntValue> iterations;
|
---|
715 | public int Iterations {
|
---|
716 | get { return iterations.Value.Value; }
|
---|
717 | set { iterations.Value.Value = value; }
|
---|
718 | }
|
---|
719 |
|
---|
720 | ISingleObjectiveSolutionScope<TSolution> ISingleSolutionHeuristicAlgorithmContext<TProblem, TSolution>.Solution {
|
---|
721 | get { return scope; }
|
---|
722 | }
|
---|
723 |
|
---|
724 | [StorableConstructor]
|
---|
725 | protected MemPRSolutionContext(bool deserializing) : base(deserializing) { }
|
---|
726 | protected MemPRSolutionContext(MemPRSolutionContext<TProblem, TSolution, TContext, TSolutionContext> original, Cloner cloner)
|
---|
727 | : base(original, cloner) {
|
---|
728 | scope = cloner.Clone(original.scope);
|
---|
729 | evaluatedSolutions = cloner.Clone(original.evaluatedSolutions);
|
---|
730 | iterations = cloner.Clone(original.iterations);
|
---|
731 | }
|
---|
732 | public MemPRSolutionContext(TContext baseContext, ISingleObjectiveSolutionScope<TSolution> solution) {
|
---|
733 | parent = baseContext;
|
---|
734 | scope = solution;
|
---|
735 |
|
---|
736 | Parameters.Add(evaluatedSolutions = new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
|
---|
737 | Parameters.Add(iterations = new ValueParameter<IntValue>("Iterations", new IntValue(0)));
|
---|
738 | }
|
---|
739 |
|
---|
740 | public void IncrementEvaluatedSolutions(int byEvaluations) {
|
---|
741 | if (byEvaluations < 0) throw new ArgumentException("Can only increment and not decrement evaluated solutions.");
|
---|
742 | EvaluatedSolutions += byEvaluations;
|
---|
743 | }
|
---|
744 | public virtual double Evaluate(TSolution solution, CancellationToken token) {
|
---|
745 | return parent.Evaluate(solution, token);
|
---|
746 | }
|
---|
747 |
|
---|
748 | public virtual void Evaluate(ISingleObjectiveSolutionScope<TSolution> solScope, CancellationToken token) {
|
---|
749 | parent.Evaluate(solScope, token);
|
---|
750 | }
|
---|
751 |
|
---|
752 | #region IExecutionContext members
|
---|
753 | public IAtomicOperation CreateOperation(IOperator op) {
|
---|
754 | return new ExecutionContext(this, op, Scope);
|
---|
755 | }
|
---|
756 |
|
---|
757 | public IAtomicOperation CreateOperation(IOperator op, IScope s) {
|
---|
758 | return new ExecutionContext(this, op, s);
|
---|
759 | }
|
---|
760 |
|
---|
761 | public IAtomicOperation CreateChildOperation(IOperator op) {
|
---|
762 | return new ExecutionContext(this, op, Scope);
|
---|
763 | }
|
---|
764 |
|
---|
765 | public IAtomicOperation CreateChildOperation(IOperator op, IScope s) {
|
---|
766 | return new ExecutionContext(this, op, s);
|
---|
767 | }
|
---|
768 | #endregion
|
---|
769 | }
|
---|
770 | }
|
---|