Changeset 14544 for branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/LinearLinkage/SolutionModel

Timestamp:

01/05/17 00:32:43 (8 years ago)

Author:

abeham

Message:

#2701:

• LLE: Added equality comparer
• MemPR:
  • Added GPR to learn about heuristic performance
  • Changed Breeding to do more exhaustive search on crossover
  • Added Delinking separately to Relinking
  • Rewrote d/relinking for LLE
  • Reduce usage of local search
  • Renamed TabuWalk to AdaptiveWalk
  • Rewrote adaptive walk for binary problems
  • Renamed LLE namespace to Grouping to avoid namespace clashes

Location:

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/LinearLinkage/SolutionModel/Univariate

Files:

• StaticAPI/Trainer.cs (modified) (1 diff)
• UnbiasedModelTrainer.cs (modified) (1 diff)
• UnivariateSolutionModel.cs (modified) (3 diffs)

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/LinearLinkage/SolutionModel/Univariate/StaticAPI/Trainer.cs

@@ -23 +23 @@
 using HeuristicLab.Algorithms.MemPR.Interfaces;
 using HeuristicLab.Core;
+using HeuristicLab.Encodings.LinearLinkageEncoding;
 
-namespace HeuristicLab.Algorithms.MemPR.LinearLinkage.SolutionModel.Univariate {
+namespace HeuristicLab.Algorithms.MemPR.Grouping.SolutionModel.Univariate {
   public static class Trainer {
 
-    public static ISolutionModel<Encodings.LinearLinkageEncoding.LinearLinkage> Train(IRandom random,
-        IEnumerable<Encodings.LinearLinkageEncoding.LinearLinkage> pop) {
+    public static ISolutionModel<LinearLinkage> Train(IRandom random,
+        IEnumerable<LinearLinkage> pop) {
       return UnivariateModel.Create(random, pop);
     }

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/LinearLinkage/SolutionModel/Univariate/UnbiasedModelTrainer.cs

@@ -24 +24 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
+using HeuristicLab.Encodings.LinearLinkageEncoding;
 using HeuristicLab.Optimization;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.MemPR.LinearLinkage.SolutionModel.Univariate {
+namespace HeuristicLab.Algorithms.MemPR.Grouping.SolutionModel.Univariate {
   [Item("Unbiased Univariate Model Trainer (linear linkage)", "", ExcludeGenericTypeInfo = true)]
   [StorableClass]
   public class UniasedModelTrainer<TContext> : NamedItem, ISolutionModelTrainer<TContext>
-    where TContext : IPopulationBasedHeuristicAlgorithmContext<SingleObjectiveBasicProblem<Encodings.LinearLinkageEncoding.LinearLinkageEncoding>, Encodings.LinearLinkageEncoding.LinearLinkage>, ISolutionModelContext<Encodings.LinearLinkageEncoding.LinearLinkage> {
+    where TContext : IPopulationBasedHeuristicAlgorithmContext<SingleObjectiveBasicProblem<LinearLinkageEncoding>, LinearLinkage>, ISolutionModelContext<LinearLinkage> {
     
     [StorableConstructor]

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/LinearLinkage/SolutionModel/Univariate/UnivariateSolutionModel.cs

@@ -26 +26 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Encodings.LinearLinkageEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Algorithms.MemPR.LinearLinkage.SolutionModel.Univariate {
+namespace HeuristicLab.Algorithms.MemPR.Grouping.SolutionModel.Univariate {
   [Item("Univariate solution model (linear linkage)", "")]
   [StorableClass]
-  public sealed class UnivariateModel : Item, ISolutionModel<Encodings.LinearLinkageEncoding.LinearLinkage> {
+  public sealed class UnivariateModel : Item, ISolutionModel<LinearLinkage> {
     [Storable]
     public IntMatrix Frequencies { get; set; }
@@ -61 +62 @@
     }
 
-    public Encodings.LinearLinkageEncoding.LinearLinkage Sample() {
+    public LinearLinkage Sample() {
       var N = Frequencies.Rows;
-      var centroid = Encodings.LinearLinkageEncoding.LinearLinkage.SingleElementGroups(N);
+      var centroid = LinearLinkage.SingleElementGroups(N);
       var dict = new Dictionary<int, int>();
       for (var i = N - 1; i >= 0; i--) {
@@ -87 +88 @@
     }
 
-    public static ISolutionModel<Encodings.LinearLinkageEncoding.LinearLinkage> Create(IRandom random, IEnumerable<Encodings.LinearLinkageEncoding.LinearLinkage> population) {
+    public static ISolutionModel<LinearLinkage> Create(IRandom random, IEnumerable<LinearLinkage> population) {
       var iter = population.GetEnumerator();
       if (!iter.MoveNext()) throw new ArgumentException("Cannot create solution model from empty population.");