[14420] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
| 3 | * Copyright (C) 2002-2016 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 |
|
---|
| 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
| 24 | using System.ComponentModel;
|
---|
| 25 | using System.Linq;
|
---|
| 26 | using System.Runtime.CompilerServices;
|
---|
| 27 | using System.Threading;
|
---|
[14450] | 28 | using HeuristicLab.Algorithms.MemPR.Interfaces;
|
---|
[14420] | 29 | using HeuristicLab.Algorithms.MemPR.Util;
|
---|
| 30 | using HeuristicLab.Analysis;
|
---|
| 31 | using HeuristicLab.Common;
|
---|
| 32 | using HeuristicLab.Core;
|
---|
| 33 | using HeuristicLab.Data;
|
---|
| 34 | using HeuristicLab.Optimization;
|
---|
| 35 | using HeuristicLab.Parameters;
|
---|
| 36 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 37 |
|
---|
| 38 | namespace HeuristicLab.Algorithms.MemPR {
|
---|
| 39 | [Item("MemPR Algorithm", "Base class for MemPR algorithms")]
|
---|
| 40 | [StorableClass]
|
---|
[14450] | 41 | public abstract class MemPRAlgorithm<TProblem, TSolution, TPopulationContext, TSolutionContext> : BasicAlgorithm, INotifyPropertyChanged
|
---|
| 42 | where TProblem : class, IItem, ISingleObjectiveHeuristicOptimizationProblem, ISingleObjectiveProblemDefinition
|
---|
[14420] | 43 | where TSolution : class, IItem
|
---|
[14450] | 44 | where TPopulationContext : MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext>, new()
|
---|
| 45 | where TSolutionContext : MemPRSolutionContext<TProblem, TSolution, TPopulationContext, TSolutionContext> {
|
---|
[14420] | 46 | private const double MutationProbabilityMagicConst = 0.1;
|
---|
| 47 |
|
---|
| 48 | public override Type ProblemType {
|
---|
[14450] | 49 | get { return typeof(TProblem); }
|
---|
[14420] | 50 | }
|
---|
| 51 |
|
---|
[14450] | 52 | public new TProblem Problem {
|
---|
| 53 | get { return (TProblem)base.Problem; }
|
---|
[14420] | 54 | set { base.Problem = value; }
|
---|
| 55 | }
|
---|
| 56 |
|
---|
| 57 | protected string QualityName {
|
---|
| 58 | get { return Problem != null && Problem.Evaluator != null ? Problem.Evaluator.QualityParameter.ActualName : null; }
|
---|
| 59 | }
|
---|
| 60 |
|
---|
| 61 | public int? MaximumEvaluations {
|
---|
| 62 | get {
|
---|
| 63 | var val = ((OptionalValueParameter<IntValue>)Parameters["MaximumEvaluations"]).Value;
|
---|
| 64 | return val != null ? val.Value : (int?)null;
|
---|
| 65 | }
|
---|
| 66 | set {
|
---|
| 67 | var param = (OptionalValueParameter<IntValue>)Parameters["MaximumEvaluations"];
|
---|
| 68 | param.Value = value.HasValue ? new IntValue(value.Value) : null;
|
---|
| 69 | }
|
---|
| 70 | }
|
---|
| 71 |
|
---|
| 72 | public TimeSpan? MaximumExecutionTime {
|
---|
| 73 | get {
|
---|
| 74 | var val = ((OptionalValueParameter<TimeSpanValue>)Parameters["MaximumExecutionTime"]).Value;
|
---|
| 75 | return val != null ? val.Value : (TimeSpan?)null;
|
---|
| 76 | }
|
---|
| 77 | set {
|
---|
| 78 | var param = (OptionalValueParameter<TimeSpanValue>)Parameters["MaximumExecutionTime"];
|
---|
| 79 | param.Value = value.HasValue ? new TimeSpanValue(value.Value) : null;
|
---|
| 80 | }
|
---|
| 81 | }
|
---|
| 82 |
|
---|
| 83 | public double? TargetQuality {
|
---|
| 84 | get {
|
---|
| 85 | var val = ((OptionalValueParameter<DoubleValue>)Parameters["TargetQuality"]).Value;
|
---|
| 86 | return val != null ? val.Value : (double?)null;
|
---|
| 87 | }
|
---|
| 88 | set {
|
---|
| 89 | var param = (OptionalValueParameter<DoubleValue>)Parameters["TargetQuality"];
|
---|
| 90 | param.Value = value.HasValue ? new DoubleValue(value.Value) : null;
|
---|
| 91 | }
|
---|
| 92 | }
|
---|
| 93 |
|
---|
| 94 | protected FixedValueParameter<IntValue> MaximumPopulationSizeParameter {
|
---|
| 95 | get { return ((FixedValueParameter<IntValue>)Parameters["MaximumPopulationSize"]); }
|
---|
| 96 | }
|
---|
| 97 | public int MaximumPopulationSize {
|
---|
| 98 | get { return MaximumPopulationSizeParameter.Value.Value; }
|
---|
| 99 | set { MaximumPopulationSizeParameter.Value.Value = value; }
|
---|
| 100 | }
|
---|
| 101 |
|
---|
| 102 | public bool SetSeedRandomly {
|
---|
| 103 | get { return ((FixedValueParameter<BoolValue>)Parameters["SetSeedRandomly"]).Value.Value; }
|
---|
| 104 | set { ((FixedValueParameter<BoolValue>)Parameters["SetSeedRandomly"]).Value.Value = value; }
|
---|
| 105 | }
|
---|
| 106 |
|
---|
| 107 | public int Seed {
|
---|
| 108 | get { return ((FixedValueParameter<IntValue>)Parameters["Seed"]).Value.Value; }
|
---|
| 109 | set { ((FixedValueParameter<IntValue>)Parameters["Seed"]).Value.Value = value; }
|
---|
| 110 | }
|
---|
| 111 |
|
---|
| 112 | public IAnalyzer Analyzer {
|
---|
| 113 | get { return ((ValueParameter<IAnalyzer>)Parameters["Analyzer"]).Value; }
|
---|
| 114 | set { ((ValueParameter<IAnalyzer>)Parameters["Analyzer"]).Value = value; }
|
---|
| 115 | }
|
---|
| 116 |
|
---|
[14450] | 117 | public IConstrainedValueParameter<ISolutionModelTrainer<TPopulationContext>> SolutionModelTrainerParameter {
|
---|
| 118 | get { return (IConstrainedValueParameter<ISolutionModelTrainer<TPopulationContext>>)Parameters["SolutionModelTrainer"]; }
|
---|
[14420] | 119 | }
|
---|
| 120 |
|
---|
[14450] | 121 | public IConstrainedValueParameter<ILocalSearch<TSolutionContext>> LocalSearchParameter {
|
---|
| 122 | get { return (IConstrainedValueParameter<ILocalSearch<TSolutionContext>>)Parameters["LocalSearch"]; }
|
---|
[14420] | 123 | }
|
---|
| 124 |
|
---|
| 125 | [Storable]
|
---|
[14450] | 126 | private TPopulationContext context;
|
---|
| 127 | public TPopulationContext Context {
|
---|
[14420] | 128 | get { return context; }
|
---|
| 129 | protected set {
|
---|
| 130 | if (context == value) return;
|
---|
| 131 | context = value;
|
---|
| 132 | OnPropertyChanged("State");
|
---|
| 133 | }
|
---|
| 134 | }
|
---|
| 135 |
|
---|
| 136 | [Storable]
|
---|
| 137 | private BestAverageWorstQualityAnalyzer qualityAnalyzer;
|
---|
| 138 |
|
---|
| 139 | [StorableConstructor]
|
---|
| 140 | protected MemPRAlgorithm(bool deserializing) : base(deserializing) { }
|
---|
[14450] | 141 | protected MemPRAlgorithm(MemPRAlgorithm<TProblem, TSolution, TPopulationContext, TSolutionContext> original, Cloner cloner) : base(original, cloner) {
|
---|
[14420] | 142 | context = cloner.Clone(original.context);
|
---|
| 143 | qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
|
---|
| 144 | RegisterEventHandlers();
|
---|
| 145 | }
|
---|
| 146 | protected MemPRAlgorithm() {
|
---|
| 147 | Parameters.Add(new ValueParameter<IAnalyzer>("Analyzer", "The analyzer to apply to the population.", new MultiAnalyzer()));
|
---|
| 148 | Parameters.Add(new FixedValueParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population that is evolved.", new IntValue(20)));
|
---|
| 149 | Parameters.Add(new OptionalValueParameter<IntValue>("MaximumEvaluations", "The maximum number of solution evaluations."));
|
---|
| 150 | Parameters.Add(new OptionalValueParameter<TimeSpanValue>("MaximumExecutionTime", "The maximum runtime.", new TimeSpanValue(TimeSpan.FromMinutes(1))));
|
---|
| 151 | Parameters.Add(new OptionalValueParameter<DoubleValue>("TargetQuality", "The target quality at which the algorithm terminates."));
|
---|
| 152 | Parameters.Add(new FixedValueParameter<BoolValue>("SetSeedRandomly", "Whether each run of the algorithm should be conducted with a new random seed.", new BoolValue(true)));
|
---|
| 153 | Parameters.Add(new FixedValueParameter<IntValue>("Seed", "The random number seed that is used in case SetSeedRandomly is false.", new IntValue(0)));
|
---|
[14450] | 154 | Parameters.Add(new ConstrainedValueParameter<ISolutionModelTrainer<TPopulationContext>>("SolutionModelTrainer", "The object that creates a solution model that can be sampled."));
|
---|
| 155 | Parameters.Add(new ConstrainedValueParameter<ILocalSearch<TSolutionContext>>("LocalSearch", "The local search operator to use."));
|
---|
[14420] | 156 |
|
---|
| 157 | qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
|
---|
| 158 | RegisterEventHandlers();
|
---|
| 159 | }
|
---|
| 160 |
|
---|
| 161 | [StorableHook(HookType.AfterDeserialization)]
|
---|
| 162 | private void AfterDeserialization() {
|
---|
| 163 | RegisterEventHandlers();
|
---|
| 164 | }
|
---|
| 165 |
|
---|
| 166 | private void RegisterEventHandlers() {
|
---|
| 167 | MaximumPopulationSizeParameter.Value.ValueChanged += MaximumPopulationSizeOnChanged;
|
---|
| 168 | }
|
---|
| 169 |
|
---|
| 170 | private void MaximumPopulationSizeOnChanged(object sender, EventArgs eventArgs) {
|
---|
| 171 | if (ExecutionState == ExecutionState.Started || ExecutionState == ExecutionState.Paused)
|
---|
| 172 | throw new InvalidOperationException("Cannot change maximum population size before algorithm finishes.");
|
---|
| 173 | Prepare();
|
---|
| 174 | }
|
---|
| 175 |
|
---|
| 176 | protected override void OnProblemChanged() {
|
---|
| 177 | base.OnProblemChanged();
|
---|
| 178 | qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
|
---|
| 179 | qualityAnalyzer.MaximizationParameter.Hidden = true;
|
---|
| 180 | qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
|
---|
| 181 | qualityAnalyzer.QualityParameter.Depth = 1;
|
---|
| 182 | qualityAnalyzer.QualityParameter.Hidden = true;
|
---|
| 183 | qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
|
---|
| 184 | qualityAnalyzer.BestKnownQualityParameter.Hidden = true;
|
---|
| 185 |
|
---|
| 186 | var multiAnalyzer = Analyzer as MultiAnalyzer;
|
---|
| 187 | if (multiAnalyzer != null) {
|
---|
| 188 | multiAnalyzer.Operators.Clear();
|
---|
| 189 | if (Problem != null) {
|
---|
| 190 | foreach (var analyzer in Problem.Operators.OfType<IAnalyzer>()) {
|
---|
| 191 | foreach (var param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
|
---|
| 192 | param.Depth = 1;
|
---|
| 193 | multiAnalyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
|
---|
| 194 | }
|
---|
| 195 | }
|
---|
| 196 | multiAnalyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
|
---|
| 197 | }
|
---|
| 198 | }
|
---|
| 199 |
|
---|
| 200 | public override void Prepare() {
|
---|
| 201 | base.Prepare();
|
---|
| 202 | Results.Clear();
|
---|
| 203 | Context = null;
|
---|
| 204 | }
|
---|
| 205 |
|
---|
[14450] | 206 | protected virtual TPopulationContext CreateContext() {
|
---|
| 207 | return new TPopulationContext();
|
---|
[14420] | 208 | }
|
---|
| 209 |
|
---|
| 210 | protected sealed override void Run(CancellationToken token) {
|
---|
| 211 | if (Context == null) {
|
---|
| 212 | Context = CreateContext();
|
---|
| 213 | if (SetSeedRandomly) Seed = new System.Random().Next();
|
---|
| 214 | Context.Random.Reset(Seed);
|
---|
| 215 | Context.Scope.Variables.Add(new Variable("Results", Results));
|
---|
[14450] | 216 | Context.Problem = Problem;
|
---|
[14420] | 217 | }
|
---|
| 218 |
|
---|
[14477] | 219 | if (MaximumExecutionTime.HasValue)
|
---|
| 220 | CancellationTokenSource.CancelAfter(MaximumExecutionTime.Value);
|
---|
| 221 |
|
---|
[14420] | 222 | IExecutionContext context = null;
|
---|
| 223 | foreach (var item in Problem.ExecutionContextItems)
|
---|
| 224 | context = new Core.ExecutionContext(context, item, Context.Scope);
|
---|
| 225 | context = new Core.ExecutionContext(context, this, Context.Scope);
|
---|
| 226 | Context.Parent = context;
|
---|
| 227 |
|
---|
| 228 | if (!Context.Initialized) {
|
---|
| 229 | // We initialize the population with two local optima
|
---|
| 230 | while (Context.PopulationCount < 2) {
|
---|
| 231 | var child = Create(token);
|
---|
| 232 | Context.HcSteps += HillClimb(child, token);
|
---|
| 233 | Context.AddToPopulation(child);
|
---|
| 234 | Analyze(token);
|
---|
| 235 | token.ThrowIfCancellationRequested();
|
---|
[14456] | 236 | if (Terminate()) return;
|
---|
[14420] | 237 | }
|
---|
| 238 | Context.HcSteps /= 2;
|
---|
| 239 | Context.Initialized = true;
|
---|
| 240 | }
|
---|
| 241 |
|
---|
| 242 | while (!Terminate()) {
|
---|
| 243 | Iterate(token);
|
---|
| 244 | Analyze(token);
|
---|
| 245 | token.ThrowIfCancellationRequested();
|
---|
| 246 | }
|
---|
| 247 | }
|
---|
| 248 |
|
---|
| 249 | private void Iterate(CancellationToken token) {
|
---|
| 250 | var replaced = false;
|
---|
| 251 |
|
---|
| 252 | var i1 = Context.Random.Next(Context.PopulationCount);
|
---|
| 253 | var i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 254 | while (i1 == i2) i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 255 |
|
---|
| 256 | var p1 = Context.AtPopulation(i1);
|
---|
| 257 | var p2 = Context.AtPopulation(i2);
|
---|
| 258 |
|
---|
| 259 | var parentDist = Dist(p1, p2);
|
---|
| 260 |
|
---|
| 261 | ISingleObjectiveSolutionScope<TSolution> offspring = null;
|
---|
| 262 | int replPos = -1;
|
---|
| 263 |
|
---|
| 264 | if (Context.Random.NextDouble() > parentDist) {
|
---|
| 265 | offspring = BreedAndImprove(p1, p2, token);
|
---|
| 266 | replPos = Replace(offspring, token);
|
---|
| 267 | if (replPos >= 0) {
|
---|
| 268 | replaced = true;
|
---|
| 269 | Context.ByBreeding++;
|
---|
| 270 | }
|
---|
| 271 | }
|
---|
| 272 |
|
---|
| 273 | if (Context.Random.NextDouble() < parentDist) {
|
---|
| 274 | offspring = RelinkAndImprove(p1, p2, token);
|
---|
| 275 | replPos = Replace(offspring, token);
|
---|
| 276 | if (replPos >= 0) {
|
---|
| 277 | replaced = true;
|
---|
| 278 | Context.ByRelinking++;
|
---|
| 279 | }
|
---|
| 280 | }
|
---|
| 281 |
|
---|
| 282 | offspring = PerformSampling(token);
|
---|
| 283 | replPos = Replace(offspring, token);
|
---|
| 284 | if (replPos >= 0) {
|
---|
| 285 | replaced = true;
|
---|
| 286 | Context.BySampling++;
|
---|
| 287 | }
|
---|
| 288 |
|
---|
| 289 | if (!replaced) {
|
---|
| 290 | offspring = Create(token);
|
---|
| 291 | if (HillclimbingSuited(offspring)) {
|
---|
| 292 | HillClimb(offspring, token);
|
---|
| 293 | replPos = Replace(offspring, token);
|
---|
| 294 | if (replPos >= 0) {
|
---|
| 295 | Context.ByHillclimbing++;
|
---|
| 296 | replaced = true;
|
---|
| 297 | }
|
---|
| 298 | } else {
|
---|
[14450] | 299 | offspring = (ISingleObjectiveSolutionScope<TSolution>)Context.AtPopulation(Context.Random.Next(Context.PopulationCount)).Clone();
|
---|
[14420] | 300 | Mutate(offspring, token);
|
---|
| 301 | PerformTabuWalk(offspring, Context.HcSteps, token);
|
---|
| 302 | replPos = Replace(offspring, token);
|
---|
| 303 | if (replPos >= 0) {
|
---|
| 304 | Context.ByTabuwalking++;
|
---|
| 305 | replaced = true;
|
---|
| 306 | }
|
---|
| 307 | }
|
---|
| 308 | }
|
---|
| 309 | Context.Iterations++;
|
---|
| 310 | }
|
---|
| 311 |
|
---|
| 312 | protected void Analyze(CancellationToken token) {
|
---|
| 313 | IResult res;
|
---|
| 314 | if (!Results.TryGetValue("EvaluatedSolutions", out res))
|
---|
| 315 | Results.Add(new Result("EvaluatedSolutions", new IntValue(Context.EvaluatedSolutions)));
|
---|
| 316 | else ((IntValue)res.Value).Value = Context.EvaluatedSolutions;
|
---|
| 317 | if (!Results.TryGetValue("Iterations", out res))
|
---|
| 318 | Results.Add(new Result("Iterations", new IntValue(Context.Iterations)));
|
---|
| 319 | else ((IntValue)res.Value).Value = Context.Iterations;
|
---|
[14456] | 320 | if (!Results.TryGetValue("HcSteps", out res))
|
---|
| 321 | Results.Add(new Result("HcSteps", new IntValue(Context.HcSteps)));
|
---|
| 322 | else ((IntValue)res.Value).Value = Context.HcSteps;
|
---|
[14420] | 323 | if (!Results.TryGetValue("ByBreeding", out res))
|
---|
| 324 | Results.Add(new Result("ByBreeding", new IntValue(Context.ByBreeding)));
|
---|
| 325 | else ((IntValue)res.Value).Value = Context.ByBreeding;
|
---|
| 326 | if (!Results.TryGetValue("ByRelinking", out res))
|
---|
| 327 | Results.Add(new Result("ByRelinking", new IntValue(Context.ByRelinking)));
|
---|
| 328 | else ((IntValue)res.Value).Value = Context.ByRelinking;
|
---|
| 329 | if (!Results.TryGetValue("BySampling", out res))
|
---|
| 330 | Results.Add(new Result("BySampling", new IntValue(Context.BySampling)));
|
---|
| 331 | else ((IntValue)res.Value).Value = Context.BySampling;
|
---|
| 332 | if (!Results.TryGetValue("ByHillclimbing", out res))
|
---|
| 333 | Results.Add(new Result("ByHillclimbing", new IntValue(Context.ByHillclimbing)));
|
---|
| 334 | else ((IntValue)res.Value).Value = Context.ByHillclimbing;
|
---|
| 335 | if (!Results.TryGetValue("ByTabuwalking", out res))
|
---|
| 336 | Results.Add(new Result("ByTabuwalking", new IntValue(Context.ByTabuwalking)));
|
---|
| 337 | else ((IntValue)res.Value).Value = Context.ByTabuwalking;
|
---|
| 338 |
|
---|
| 339 | var sp = new ScatterPlot("Parent1 vs Offspring", "");
|
---|
| 340 | sp.Rows.Add(new ScatterPlotDataRow("corr", "", Context.BreedingStat.Select(x => new Point2D<double>(x.Item1, x.Item3))) { VisualProperties = { PointSize = 6 }});
|
---|
| 341 | if (!Results.TryGetValue("BreedingStat1", out res)) {
|
---|
| 342 | Results.Add(new Result("BreedingStat1", sp));
|
---|
| 343 | } else res.Value = sp;
|
---|
| 344 |
|
---|
| 345 | sp = new ScatterPlot("Parent2 vs Offspring", "");
|
---|
| 346 | sp.Rows.Add(new ScatterPlotDataRow("corr", "", Context.BreedingStat.Select(x => new Point2D<double>(x.Item2, x.Item3))) { VisualProperties = { PointSize = 6 } });
|
---|
| 347 | if (!Results.TryGetValue("BreedingStat2", out res)) {
|
---|
| 348 | Results.Add(new Result("BreedingStat2", sp));
|
---|
| 349 | } else res.Value = sp;
|
---|
| 350 |
|
---|
| 351 | sp = new ScatterPlot("Solution vs Local Optimum", "");
|
---|
| 352 | sp.Rows.Add(new ScatterPlotDataRow("corr", "", Context.HillclimbingStat.Select(x => new Point2D<double>(x.Item1, x.Item2))) { VisualProperties = { PointSize = 6 } });
|
---|
| 353 | if (!Results.TryGetValue("HillclimbingStat", out res)) {
|
---|
| 354 | Results.Add(new Result("HillclimbingStat", sp));
|
---|
| 355 | } else res.Value = sp;
|
---|
| 356 |
|
---|
| 357 | sp = new ScatterPlot("Solution vs Tabu Walk", "");
|
---|
| 358 | sp.Rows.Add(new ScatterPlotDataRow("corr", "", Context.TabuwalkingStat.Select(x => new Point2D<double>(x.Item1, x.Item2))) { VisualProperties = { PointSize = 6 } });
|
---|
| 359 | if (!Results.TryGetValue("TabuwalkingStat", out res)) {
|
---|
| 360 | Results.Add(new Result("TabuwalkingStat", sp));
|
---|
| 361 | } else res.Value = sp;
|
---|
| 362 |
|
---|
| 363 | RunOperator(Analyzer, Context.Scope, token);
|
---|
| 364 | }
|
---|
| 365 |
|
---|
| 366 | protected int Replace(ISingleObjectiveSolutionScope<TSolution> child, CancellationToken token) {
|
---|
[14453] | 367 | if (double.IsNaN(child.Fitness)) {
|
---|
| 368 | Evaluate(child, token);
|
---|
| 369 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 370 | }
|
---|
| 371 | if (IsBetter(child.Fitness, Context.BestQuality)) {
|
---|
| 372 | Context.BestQuality = child.Fitness;
|
---|
| 373 | Context.BestSolution = (TSolution)child.Solution.Clone();
|
---|
| 374 | }
|
---|
[14420] | 375 |
|
---|
| 376 | var popSize = MaximumPopulationSize;
|
---|
| 377 | if (Context.Population.All(p => !Eq(p, child))) {
|
---|
| 378 |
|
---|
| 379 | if (Context.PopulationCount < popSize) {
|
---|
| 380 | Context.AddToPopulation(child);
|
---|
| 381 | return Context.PopulationCount - 1;
|
---|
| 382 | }
|
---|
| 383 |
|
---|
| 384 | // The set of replacement candidates consists of all solutions at least as good as the new one
|
---|
| 385 | var candidates = Context.Population.Select((p, i) => new { Index = i, Individual = p })
|
---|
| 386 | .Where(x => x.Individual.Fitness == child.Fitness
|
---|
| 387 | || IsBetter(child, x.Individual)).ToList();
|
---|
| 388 | if (candidates.Count == 0) return -1;
|
---|
| 389 |
|
---|
| 390 | var repCand = -1;
|
---|
| 391 | var avgChildDist = 0.0;
|
---|
| 392 | var minChildDist = double.MaxValue;
|
---|
| 393 | var plateau = new List<int>();
|
---|
| 394 | var worstPlateau = -1;
|
---|
| 395 | var minAvgPlateauDist = double.MaxValue;
|
---|
| 396 | var minPlateauDist = double.MaxValue;
|
---|
| 397 | // If there are equally good solutions it is first tried to replace one of those
|
---|
| 398 | // The criteria for replacement is that the new solution has better average distance
|
---|
| 399 | // to all other solutions at this "plateau"
|
---|
| 400 | foreach (var c in candidates.Where(x => x.Individual.Fitness == child.Fitness)) {
|
---|
| 401 | var dist = Dist(c.Individual, child);
|
---|
| 402 | avgChildDist += dist;
|
---|
| 403 | if (dist < minChildDist) minChildDist = dist;
|
---|
| 404 | plateau.Add(c.Index);
|
---|
| 405 | }
|
---|
| 406 | if (plateau.Count > 2) {
|
---|
| 407 | avgChildDist /= plateau.Count;
|
---|
| 408 | foreach (var p in plateau) {
|
---|
| 409 | var avgDist = 0.0;
|
---|
| 410 | var minDist = double.MaxValue;
|
---|
| 411 | foreach (var q in plateau) {
|
---|
| 412 | if (p == q) continue;
|
---|
| 413 | var dist = Dist(Context.AtPopulation(p), Context.AtPopulation(q));
|
---|
| 414 | avgDist += dist;
|
---|
| 415 | if (dist < minDist) minDist = dist;
|
---|
| 416 | }
|
---|
| 417 |
|
---|
| 418 | var d = Dist(Context.AtPopulation(p), child);
|
---|
| 419 | avgDist += d;
|
---|
| 420 | avgDist /= plateau.Count;
|
---|
| 421 | if (d < minDist) minDist = d;
|
---|
| 422 |
|
---|
| 423 | if (minDist < minPlateauDist || (minDist == minPlateauDist && avgDist < avgChildDist)) {
|
---|
| 424 | minAvgPlateauDist = avgDist;
|
---|
| 425 | minPlateauDist = minDist;
|
---|
| 426 | worstPlateau = p;
|
---|
| 427 | }
|
---|
| 428 | }
|
---|
| 429 | if (minPlateauDist < minChildDist || (minPlateauDist == minChildDist && minAvgPlateauDist < avgChildDist))
|
---|
| 430 | repCand = worstPlateau;
|
---|
| 431 | }
|
---|
| 432 |
|
---|
| 433 | if (repCand < 0) {
|
---|
| 434 | // If no solution at the same plateau were identified for replacement
|
---|
| 435 | // a worse solution with smallest distance is chosen
|
---|
| 436 | var minDist = double.MaxValue;
|
---|
| 437 | foreach (var c in candidates.Where(x => IsBetter(child, x.Individual))) {
|
---|
| 438 | var d = Dist(c.Individual, child);
|
---|
| 439 | if (d < minDist) {
|
---|
| 440 | minDist = d;
|
---|
| 441 | repCand = c.Index;
|
---|
| 442 | }
|
---|
| 443 | }
|
---|
| 444 | }
|
---|
| 445 |
|
---|
| 446 | // If no replacement was identified, this can only mean that there are
|
---|
| 447 | // no worse solutions and those on the same plateau are all better
|
---|
| 448 | // stretched out than the new one
|
---|
| 449 | if (repCand < 0) return -1;
|
---|
| 450 |
|
---|
| 451 | Context.ReplaceAtPopulation(repCand, child);
|
---|
| 452 | return repCand;
|
---|
| 453 | }
|
---|
| 454 | return -1;
|
---|
| 455 | }
|
---|
| 456 |
|
---|
| 457 | [MethodImpl(MethodImplOptions.AggressiveInlining)]
|
---|
| 458 | protected bool IsBetter(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b) {
|
---|
| 459 | return IsBetter(a.Fitness, b.Fitness);
|
---|
| 460 | }
|
---|
| 461 | [MethodImpl(MethodImplOptions.AggressiveInlining)]
|
---|
| 462 | protected bool IsBetter(double a, double b) {
|
---|
| 463 | return double.IsNaN(b) && !double.IsNaN(a)
|
---|
[14450] | 464 | || Problem.Maximization && a > b
|
---|
| 465 | || !Problem.Maximization && a < b;
|
---|
[14420] | 466 | }
|
---|
| 467 |
|
---|
| 468 | protected abstract bool Eq(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b);
|
---|
| 469 | protected abstract double Dist(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b);
|
---|
| 470 | protected abstract ISingleObjectiveSolutionScope<TSolution> ToScope(TSolution code, double fitness = double.NaN);
|
---|
[14450] | 471 | protected abstract ISolutionSubspace<TSolution> CalculateSubspace(IEnumerable<TSolution> solutions, bool inverse = false);
|
---|
[14420] | 472 | protected virtual void Evaluate(ISingleObjectiveSolutionScope<TSolution> scope, CancellationToken token) {
|
---|
| 473 | var prob = Problem as ISingleObjectiveProblemDefinition;
|
---|
| 474 | if (prob != null) {
|
---|
| 475 | var ind = new SingleEncodingIndividual(prob.Encoding, scope);
|
---|
| 476 | scope.Fitness = prob.Evaluate(ind, Context.Random);
|
---|
| 477 | } else RunOperator(Problem.Evaluator, scope, token);
|
---|
| 478 | }
|
---|
| 479 |
|
---|
| 480 | #region Create
|
---|
[14450] | 481 | protected virtual ISingleObjectiveSolutionScope<TSolution> Create(CancellationToken token) {
|
---|
| 482 | var child = ToScope(null);
|
---|
| 483 | RunOperator(Problem.SolutionCreator, child, token);
|
---|
| 484 | return child;
|
---|
| 485 | }
|
---|
[14420] | 486 | #endregion
|
---|
| 487 |
|
---|
| 488 | #region Improve
|
---|
[14450] | 489 | protected virtual int HillClimb(ISingleObjectiveSolutionScope<TSolution> scope, CancellationToken token, ISolutionSubspace<TSolution> subspace = null) {
|
---|
[14453] | 490 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 491 | Evaluate(scope, token);
|
---|
| 492 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 493 | }
|
---|
[14420] | 494 | var before = scope.Fitness;
|
---|
| 495 | var lscontext = Context.CreateSingleSolutionContext(scope);
|
---|
| 496 | LocalSearchParameter.Value.Optimize(lscontext);
|
---|
| 497 | var after = scope.Fitness;
|
---|
| 498 | Context.HillclimbingStat.Add(Tuple.Create(before, after));
|
---|
[14453] | 499 | Context.IncrementEvaluatedSolutions(lscontext.EvaluatedSolutions);
|
---|
[14456] | 500 | return lscontext.EvaluatedSolutions;
|
---|
[14420] | 501 | }
|
---|
| 502 |
|
---|
[14450] | 503 | protected virtual void PerformTabuWalk(ISingleObjectiveSolutionScope<TSolution> scope, int steps, CancellationToken token, ISolutionSubspace<TSolution> subspace = null) {
|
---|
[14453] | 504 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 505 | Evaluate(scope, token);
|
---|
| 506 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 507 | }
|
---|
[14420] | 508 | var before = scope.Fitness;
|
---|
| 509 | var newScope = (ISingleObjectiveSolutionScope<TSolution>)scope.Clone();
|
---|
[14456] | 510 | var newSteps = TabuWalk(newScope, steps, token, subspace);
|
---|
[14420] | 511 | Context.TabuwalkingStat.Add(Tuple.Create(before, newScope.Fitness));
|
---|
[14456] | 512 | //Context.HcSteps = (int)Math.Ceiling(Context.HcSteps * (1.0 + Context.TabuwalkingStat.Count) / (2.0 + Context.TabuwalkingStat.Count) + newSteps / (2.0 + Context.TabuwalkingStat.Count));
|
---|
[14420] | 513 | if (IsBetter(newScope, scope) || (newScope.Fitness == scope.Fitness && Dist(newScope, scope) > 0))
|
---|
| 514 | scope.Adopt(newScope);
|
---|
| 515 | }
|
---|
[14456] | 516 | protected abstract int TabuWalk(ISingleObjectiveSolutionScope<TSolution> scope, int maxEvals, CancellationToken token, ISolutionSubspace<TSolution> subspace = null);
|
---|
[14450] | 517 | protected virtual void TabuClimb(ISingleObjectiveSolutionScope<TSolution> scope, int steps, CancellationToken token, ISolutionSubspace<TSolution> subspace = null) {
|
---|
[14453] | 518 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 519 | Evaluate(scope, token);
|
---|
| 520 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 521 | }
|
---|
[14420] | 522 | var before = scope.Fitness;
|
---|
| 523 | var newScope = (ISingleObjectiveSolutionScope<TSolution>)scope.Clone();
|
---|
[14456] | 524 | var newSteps = TabuWalk(newScope, steps, token, subspace);
|
---|
[14420] | 525 | Context.TabuwalkingStat.Add(Tuple.Create(before, newScope.Fitness));
|
---|
[14456] | 526 | //Context.HcSteps = (int)Math.Ceiling(Context.HcSteps * (1.0 + Context.TabuwalkingStat.Count) / (2.0 + Context.TabuwalkingStat.Count) + newSteps / (2.0 + Context.TabuwalkingStat.Count));
|
---|
[14420] | 527 | if (IsBetter(newScope, scope) || (newScope.Fitness == scope.Fitness && Dist(newScope, scope) > 0))
|
---|
| 528 | scope.Adopt(newScope);
|
---|
| 529 | }
|
---|
| 530 | #endregion
|
---|
| 531 |
|
---|
| 532 | #region Breed
|
---|
| 533 | protected virtual ISingleObjectiveSolutionScope<TSolution> PerformBreeding(CancellationToken token) {
|
---|
| 534 | if (Context.PopulationCount < 2) throw new InvalidOperationException("Cannot breed from population with less than 2 individuals.");
|
---|
| 535 | var i1 = Context.Random.Next(Context.PopulationCount);
|
---|
| 536 | var i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 537 | while (i1 == i2) i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 538 |
|
---|
| 539 | var p1 = Context.AtPopulation(i1);
|
---|
| 540 | var p2 = Context.AtPopulation(i2);
|
---|
| 541 |
|
---|
[14453] | 542 | if (double.IsNaN(p1.Fitness)) {
|
---|
| 543 | Evaluate(p1, token);
|
---|
| 544 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 545 | }
|
---|
| 546 | if (double.IsNaN(p2.Fitness)) {
|
---|
| 547 | Evaluate(p2, token);
|
---|
| 548 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 549 | }
|
---|
[14420] | 550 |
|
---|
| 551 | return BreedAndImprove(p1, p2, token);
|
---|
| 552 | }
|
---|
| 553 |
|
---|
| 554 | protected virtual ISingleObjectiveSolutionScope<TSolution> BreedAndImprove(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, CancellationToken token) {
|
---|
| 555 | var offspring = Cross(p1, p2, token);
|
---|
| 556 | var subspace = CalculateSubspace(new[] { p1.Solution, p2.Solution });
|
---|
| 557 | if (Context.Random.NextDouble() < MutationProbabilityMagicConst) {
|
---|
| 558 | Mutate(offspring, token, subspace); // mutate the solutions, especially to widen the sub-space
|
---|
| 559 | }
|
---|
[14453] | 560 | if (double.IsNaN(offspring.Fitness)) {
|
---|
| 561 | Evaluate(offspring, token);
|
---|
| 562 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 563 | }
|
---|
[14420] | 564 | Context.BreedingStat.Add(Tuple.Create(p1.Fitness, p2.Fitness, offspring.Fitness));
|
---|
| 565 | if ((IsBetter(offspring, p1) && IsBetter(offspring, p2))
|
---|
| 566 | || Context.Population.Any(p => IsBetter(offspring, p))) return offspring;
|
---|
| 567 |
|
---|
[14456] | 568 | if (HillclimbingSuited(offspring))
|
---|
[14420] | 569 | HillClimb(offspring, token, subspace); // perform hillclimb in the solution sub-space
|
---|
| 570 | return offspring;
|
---|
| 571 | }
|
---|
| 572 |
|
---|
| 573 | protected abstract ISingleObjectiveSolutionScope<TSolution> Cross(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, CancellationToken token);
|
---|
[14450] | 574 | protected abstract void Mutate(ISingleObjectiveSolutionScope<TSolution> offspring, CancellationToken token, ISolutionSubspace<TSolution> subspace = null);
|
---|
[14420] | 575 | #endregion
|
---|
| 576 |
|
---|
| 577 | #region Relink
|
---|
| 578 | protected virtual ISingleObjectiveSolutionScope<TSolution> PerformRelinking(CancellationToken token) {
|
---|
| 579 | if (Context.PopulationCount < 2) throw new InvalidOperationException("Cannot breed from population with less than 2 individuals.");
|
---|
| 580 | var i1 = Context.Random.Next(Context.PopulationCount);
|
---|
| 581 | var i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 582 | while (i1 == i2) i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 583 |
|
---|
| 584 | var p1 = Context.AtPopulation(i1);
|
---|
| 585 | var p2 = Context.AtPopulation(i2);
|
---|
| 586 |
|
---|
| 587 | return RelinkAndImprove(p1, p2, token);
|
---|
| 588 | }
|
---|
| 589 |
|
---|
| 590 | protected virtual ISingleObjectiveSolutionScope<TSolution> RelinkAndImprove(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, CancellationToken token) {
|
---|
| 591 | var child = Relink(a, b, token);
|
---|
| 592 | if (IsBetter(child, a) && IsBetter(child, b)) return child;
|
---|
| 593 |
|
---|
| 594 | var dist1 = Dist(child, a);
|
---|
| 595 | var dist2 = Dist(child, b);
|
---|
| 596 | if (dist1 > 0 && dist2 > 0) {
|
---|
| 597 | var subspace = CalculateSubspace(new[] { a.Solution, b.Solution }, inverse: true);
|
---|
[14456] | 598 | if (HillclimbingSuited(child)) {
|
---|
[14454] | 599 | HillClimb(child, token, subspace); // perform hillclimb in solution sub-space
|
---|
[14420] | 600 | }
|
---|
| 601 | }
|
---|
| 602 | return child;
|
---|
| 603 | }
|
---|
| 604 |
|
---|
| 605 | protected abstract ISingleObjectiveSolutionScope<TSolution> Relink(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, CancellationToken token);
|
---|
| 606 | #endregion
|
---|
| 607 |
|
---|
| 608 | #region Sample
|
---|
| 609 | protected virtual ISingleObjectiveSolutionScope<TSolution> PerformSampling(CancellationToken token) {
|
---|
| 610 | SolutionModelTrainerParameter.Value.TrainModel(Context);
|
---|
| 611 | var sample = ToScope(Context.Model.Sample());
|
---|
[14453] | 612 | Evaluate(sample, token);
|
---|
| 613 | Context.IncrementEvaluatedSolutions(1);
|
---|
[14420] | 614 | if (Context.Population.Any(p => IsBetter(sample, p) || sample.Fitness == p.Fitness)) return sample;
|
---|
| 615 |
|
---|
| 616 | if (HillclimbingSuited(sample)) {
|
---|
| 617 | var subspace = CalculateSubspace(Context.Population.Select(x => x.Solution));
|
---|
| 618 | HillClimb(sample, token, subspace);
|
---|
| 619 | }
|
---|
| 620 | return sample;
|
---|
| 621 | }
|
---|
| 622 | #endregion
|
---|
| 623 |
|
---|
| 624 | protected bool HillclimbingSuited(ISingleObjectiveSolutionScope<TSolution> scope) {
|
---|
| 625 | return Context.Random.NextDouble() < ProbabilityAccept(scope, Context.HillclimbingStat);
|
---|
| 626 | }
|
---|
| 627 | protected bool HillclimbingSuited(double startingFitness) {
|
---|
| 628 | return Context.Random.NextDouble() < ProbabilityAccept(startingFitness, Context.HillclimbingStat);
|
---|
| 629 | }
|
---|
| 630 | protected bool TabuwalkingSuited(ISingleObjectiveSolutionScope<TSolution> scope) {
|
---|
| 631 | return Context.Random.NextDouble() < ProbabilityAccept(scope, Context.TabuwalkingStat);
|
---|
| 632 | }
|
---|
| 633 | protected bool TabuwalkingSuited(double startingFitness) {
|
---|
| 634 | return Context.Random.NextDouble() < ProbabilityAccept(startingFitness, Context.TabuwalkingStat);
|
---|
| 635 | }
|
---|
| 636 |
|
---|
| 637 | protected double ProbabilityAccept(ISingleObjectiveSolutionScope<TSolution> scope, IList<Tuple<double, double>> data) {
|
---|
[14453] | 638 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 639 | Evaluate(scope, CancellationToken.None);
|
---|
| 640 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 641 | }
|
---|
[14420] | 642 | return ProbabilityAccept(scope.Fitness, data);
|
---|
| 643 | }
|
---|
| 644 | protected double ProbabilityAccept(double startingFitness, IList<Tuple<double, double>> data) {
|
---|
| 645 | if (data.Count < 10) return 1.0;
|
---|
| 646 | int[] clusterValues;
|
---|
| 647 | var centroids = CkMeans1D.Cluster(data.Select(x => x.Item1).ToArray(), 2, out clusterValues);
|
---|
| 648 | var cluster = Math.Abs(startingFitness - centroids.First().Key) < Math.Abs(startingFitness - centroids.Last().Key) ? centroids.First().Value : centroids.Last().Value;
|
---|
| 649 |
|
---|
| 650 | var samples = 0;
|
---|
| 651 | double meanStart = 0, meanStartOld = 0, meanEnd = 0, meanEndOld = 0;
|
---|
| 652 | double varStart = 0, varStartOld = 0, varEnd = 0, varEndOld = 0;
|
---|
| 653 | for (var i = 0; i < data.Count; i++) {
|
---|
| 654 | if (clusterValues[i] != cluster) continue;
|
---|
| 655 |
|
---|
| 656 | samples++;
|
---|
| 657 | var x = data[i].Item1;
|
---|
| 658 | var y = data[i].Item2;
|
---|
| 659 |
|
---|
| 660 | if (samples == 1) {
|
---|
| 661 | meanStartOld = x;
|
---|
| 662 | meanEndOld = y;
|
---|
| 663 | } else {
|
---|
| 664 | meanStart = meanStartOld + (x - meanStartOld) / samples;
|
---|
| 665 | meanEnd = meanEndOld + (x - meanEndOld) / samples;
|
---|
| 666 | varStart = varStartOld + (x - meanStartOld) * (x - meanStart) / (samples - 1);
|
---|
| 667 | varEnd = varEndOld + (x - meanEndOld) * (x - meanEnd) / (samples - 1);
|
---|
| 668 |
|
---|
| 669 | meanStartOld = meanStart;
|
---|
| 670 | meanEndOld = meanEnd;
|
---|
| 671 | varStartOld = varStart;
|
---|
| 672 | varEndOld = varEnd;
|
---|
| 673 | }
|
---|
| 674 | }
|
---|
| 675 | if (samples < 5) return 1.0;
|
---|
| 676 | var cov = data.Select((v, i) => new { Index = i, Value = v }).Where(x => clusterValues[x.Index] == cluster).Select(x => x.Value).Sum(x => (x.Item1 - meanStart) * (x.Item2 - meanEnd)) / data.Count;
|
---|
| 677 |
|
---|
| 678 | var biasedMean = meanEnd + cov / varStart * (startingFitness - meanStart);
|
---|
| 679 | var biasedStdev = Math.Sqrt(varEnd - (cov * cov) / varStart);
|
---|
| 680 |
|
---|
[14450] | 681 | if (Problem.Maximization) {
|
---|
[14420] | 682 | var goal = Context.Population.Min(x => x.Fitness);
|
---|
| 683 | var z = (goal - biasedMean) / biasedStdev;
|
---|
| 684 | return 1.0 - Phi(z); // P(X >= z)
|
---|
| 685 | } else {
|
---|
| 686 | var goal = Context.Population.Max(x => x.Fitness);
|
---|
| 687 | var z = (goal - biasedMean) / biasedStdev;
|
---|
| 688 | return Phi(z); // P(X <= z)
|
---|
| 689 | }
|
---|
| 690 | }
|
---|
| 691 |
|
---|
| 692 | protected virtual bool Terminate() {
|
---|
| 693 | return MaximumEvaluations.HasValue && Context.EvaluatedSolutions >= MaximumEvaluations.Value
|
---|
| 694 | || MaximumExecutionTime.HasValue && ExecutionTime >= MaximumExecutionTime.Value
|
---|
[14450] | 695 | || TargetQuality.HasValue && (Problem.Maximization && Context.BestQuality >= TargetQuality.Value
|
---|
| 696 | || !Problem.Maximization && Context.BestQuality <= TargetQuality.Value);
|
---|
[14420] | 697 | }
|
---|
| 698 |
|
---|
| 699 | public event PropertyChangedEventHandler PropertyChanged;
|
---|
| 700 | protected void OnPropertyChanged(string property) {
|
---|
| 701 | var handler = PropertyChanged;
|
---|
| 702 | if (handler != null) handler(this, new PropertyChangedEventArgs(property));
|
---|
| 703 | }
|
---|
| 704 |
|
---|
| 705 | #region Engine Helper
|
---|
| 706 | protected void RunOperator(IOperator op, IScope scope, CancellationToken cancellationToken) {
|
---|
| 707 | var stack = new Stack<IOperation>();
|
---|
| 708 | stack.Push(Context.CreateChildOperation(op, scope));
|
---|
| 709 |
|
---|
| 710 | while (stack.Count > 0) {
|
---|
| 711 | cancellationToken.ThrowIfCancellationRequested();
|
---|
| 712 |
|
---|
| 713 | var next = stack.Pop();
|
---|
| 714 | if (next is OperationCollection) {
|
---|
| 715 | var coll = (OperationCollection)next;
|
---|
| 716 | for (int i = coll.Count - 1; i >= 0; i--)
|
---|
| 717 | if (coll[i] != null) stack.Push(coll[i]);
|
---|
| 718 | } else if (next is IAtomicOperation) {
|
---|
| 719 | var operation = (IAtomicOperation)next;
|
---|
| 720 | try {
|
---|
| 721 | next = operation.Operator.Execute((IExecutionContext)operation, cancellationToken);
|
---|
| 722 | } catch (Exception ex) {
|
---|
| 723 | stack.Push(operation);
|
---|
| 724 | if (ex is OperationCanceledException) throw ex;
|
---|
| 725 | else throw new OperatorExecutionException(operation.Operator, ex);
|
---|
| 726 | }
|
---|
| 727 | if (next != null) stack.Push(next);
|
---|
| 728 | }
|
---|
| 729 | }
|
---|
| 730 | }
|
---|
| 731 | #endregion
|
---|
| 732 |
|
---|
| 733 | #region Math Helper
|
---|
| 734 | // normal distribution CDF (left of x) for N(0;1) standard normal distribution
|
---|
| 735 | // from http://www.johndcook.com/blog/csharp_phi/
|
---|
| 736 | // license: "This code is in the public domain. Do whatever you want with it, no strings attached."
|
---|
| 737 | // added: 2016-11-19 21:46 CET
|
---|
| 738 | protected static double Phi(double x) {
|
---|
| 739 | // constants
|
---|
| 740 | double a1 = 0.254829592;
|
---|
| 741 | double a2 = -0.284496736;
|
---|
| 742 | double a3 = 1.421413741;
|
---|
| 743 | double a4 = -1.453152027;
|
---|
| 744 | double a5 = 1.061405429;
|
---|
| 745 | double p = 0.3275911;
|
---|
| 746 |
|
---|
| 747 | // Save the sign of x
|
---|
| 748 | int sign = 1;
|
---|
| 749 | if (x < 0)
|
---|
| 750 | sign = -1;
|
---|
| 751 | x = Math.Abs(x) / Math.Sqrt(2.0);
|
---|
| 752 |
|
---|
| 753 | // A&S formula 7.1.26
|
---|
| 754 | double t = 1.0 / (1.0 + p * x);
|
---|
| 755 | double y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.Exp(-x * x);
|
---|
| 756 |
|
---|
| 757 | return 0.5 * (1.0 + sign * y);
|
---|
| 758 | }
|
---|
| 759 | #endregion
|
---|
| 760 | }
|
---|
| 761 | }
|
---|