Changeset 14680 for branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3

Timestamp:

02/17/17 13:01:00 (7 years ago)

Author:

abeham

Message:

#2701: disabled learning

• updated HeuristicLab.Data to trunk

Location:

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3

Files:

• . (modified) (1 prop)
• MemPRAlgorithm.cs (modified) (1 diff)
• MemPRContext.cs (modified) (9 diffs)

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3

@@ -9 +9 @@
 GeneratedArtifacts
 _Pvt_Extensions
+*.DotSettings

@@ -9 +9 @@
 GeneratedArtifacts
 _Pvt_Extensions
+*.DotSettings

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/MemPRAlgorithm.cs

@@ -248 +248 @@
           Context.AddToPopulation(child);
           Context.BestQuality = child.Fitness;
-          Analyze(token);
+          Analyze(CancellationToken.None);
           token.ThrowIfCancellationRequested();
           if (Terminate()) return;

branches/MemPRAlgorithm/HeuristicLab.Algorithms.MemPR/3.3/MemPRContext.cs

@@ -346 +346 @@
     #region Breeding Performance
     public void AddBreedingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, double parentDist, ISingleObjectiveSolutionScope<TSolution> child) {
+      return;
       if (IsBetter(a, b))
         breedingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, child.Fitness));
@@ -352 +353 @@
     }
     public void RelearnBreedingPerformanceModel() {
+      return;
       breedingPerformanceModel = RunRegression(PrepareRegression(ToListRow(breedingStat)), breedingPerformanceModel).Model;
     }
     public bool BreedingSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, double dist) {
+      return true;
       if (breedingPerformanceModel == null) return true;
       double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
@@ -372 +375 @@
     #region Relinking Performance
     public void AddRelinkingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, double parentDist, ISingleObjectiveSolutionScope<TSolution> child) {
+      return;
       if (IsBetter(a, b))
         relinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, Maximization ? child.Fitness - a.Fitness : a.Fitness - child.Fitness));
@@ -378 +382 @@
     }
     public void RelearnRelinkingPerformanceModel() {
+      return;
       relinkingPerformanceModel = RunRegression(PrepareRegression(ToListRow(relinkingStat)), relinkingPerformanceModel).Model;
     }
     public bool RelinkSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, double dist) {
+      return true;
       if (relinkingPerformanceModel == null) return true;
       double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
@@ -401 +407 @@
     #region Delinking Performance
     public void AddDelinkingResult(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, double parentDist, ISingleObjectiveSolutionScope<TSolution> child) {
+      return;
       if (IsBetter(a, b))
         delinkingStat.Add(Tuple.Create(a.Fitness, b.Fitness, parentDist, Maximization ? child.Fitness - a.Fitness : a.Fitness - child.Fitness));
@@ -407 +414 @@
     }
     public void RelearnDelinkingPerformanceModel() {
+      return;
       delinkingPerformanceModel = RunRegression(PrepareRegression(ToListRow(delinkingStat)), delinkingPerformanceModel).Model;
     }
     public bool DelinkSuited(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, double dist) {
+      return true;
       if (delinkingPerformanceModel == null) return true;
       double minI1 = double.MaxValue, minI2 = double.MaxValue, maxI1 = double.MinValue, maxI2 = double.MinValue;
@@ -429 +438 @@
     #region Sampling Performance
     public void AddSamplingResult(ISingleObjectiveSolutionScope<TSolution> sample, double avgDist) {
+      return;
       samplingStat.Add(Tuple.Create(avgDist, sample.Fitness));
       if (samplingStat.Count % 10 == 0) RelearnSamplingPerformanceModel();
     }
     public void RelearnSamplingPerformanceModel() {
+      return;
       samplingPerformanceModel = RunRegression(PrepareRegression(ToListRow(samplingStat)), samplingPerformanceModel).Model;
     }
     public bool SamplingSuited(double avgDist) {
+      return true;
       if (samplingPerformanceModel == null) return true;
       if (avgDist < samplingStat.Min(x => x.Item1) || avgDist > samplingStat.Max(x => x.Item1)) return true;
@@ -444 +456 @@
     #region Hillclimbing Performance
     public void AddHillclimbingResult(ISingleObjectiveSolutionScope<TSolution> input, ISingleObjectiveSolutionScope<TSolution> outcome) {
+      return;
       hillclimbingStat.Add(Tuple.Create(input.Fitness, Maximization ? outcome.Fitness - input.Fitness : input.Fitness - outcome.Fitness));
       if (hillclimbingStat.Count % 10 == 0) RelearnHillclimbingPerformanceModel();
     }
     public void RelearnHillclimbingPerformanceModel() {
+      return;
       hillclimbingPerformanceModel = RunRegression(PrepareRegression(ToListRow(hillclimbingStat)), hillclimbingPerformanceModel).Model;
     }
     public bool HillclimbingSuited(double startingFitness) {
+      return true;
       if (hillclimbingPerformanceModel == null) return true;
       if (startingFitness < HillclimbingStat.Min(x => x.Item1) || startingFitness > HillclimbingStat.Max(x => x.Item1))
@@ -460 +475 @@
     #region Adaptivewalking Performance
     public void AddAdaptivewalkingResult(ISingleObjectiveSolutionScope<TSolution> input, ISingleObjectiveSolutionScope<TSolution> outcome) {
+      return;
       adaptivewalkingStat.Add(Tuple.Create(input.Fitness, Maximization ? outcome.Fitness - input.Fitness : input.Fitness - outcome.Fitness));
       if (adaptivewalkingStat.Count % 10 == 0) RelearnAdaptiveWalkPerformanceModel();
     }
     public void RelearnAdaptiveWalkPerformanceModel() {
+      return;
       adaptiveWalkPerformanceModel = RunRegression(PrepareRegression(ToListRow(adaptivewalkingStat)), adaptiveWalkPerformanceModel).Model;
     }
     public bool AdaptivewalkingSuited(double startingFitness) {
+      return true;
       if (adaptiveWalkPerformanceModel == null) return true;
       if (startingFitness < AdaptivewalkingStat.Min(x => x.Item1) || startingFitness > AdaptivewalkingStat.Max(x => x.Item1))

@@ -9 +9 @@
 GeneratedArtifacts
 _Pvt_Extensions
+*.DotSettings