Changeset 6197 for branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

Timestamp:

05/14/11 18:49:46 (13 years ago)

Author:

cneumuel

Message:

#1215

• some fixes

File:

• branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs (modified) (9 diffs)

branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

@@ -48 +48 @@
     //private static double mutationProbability = 0.10;
 
-    private static int metaAlgorithmPopulationSize = 10;
-    private static int metaAlgorithmMaxGenerations = 20;
+    private static int metaAlgorithmPopulationSize = 4;
+    private static int metaAlgorithmMaxGenerations = 10;
     private static double metaAlgorithmMutationProbability = 0.10;
-    private static int metaProblemRepetitions = 2;
-    private static int baseAlgorithmMaxGenerations = 10;
+    private static int metaProblemRepetitions = 3;
+    private static int baseAlgorithmMaxGenerations = 20;
     private static int baseAlgorithmPopulationSize = 10;
-    
+
     public override void Run() {
       ContentManager.Initialize(new PersistenceContentManager());
@@ -60 +60 @@
       //TestTableBuilder();
       //TestShorten();
+
+
+      var x = typeof(float).IsPrimitive;
 
       //TestSimilarities(); return;
@@ -79 +82 @@
 
       //TestSymbolicDataAnalysisGrammar(); return;
+      //TestObjectGraphObjectsTraversal(); return;
+      //TestParameterizedItem(); return;
 
       //MetaOptimizationProblem metaOptimizationProblem = LoadOptimizationProblem("Meta Optimization Problem (Genetic Programming - Symbolic Regression 3.4 scaled).hl");
@@ -92 +97 @@
       //EvolutionStrategy metaLevelAlgorithm = GetMetaES(metaOptimizationProblem);
 
-      //Console.WriteLine("Press enter to start");
-      //Console.ReadLine();
+      //Console.WriteLine("Press enter to start"); Console.ReadLine();
       //TestConfiguration(algorithmVc, typeof(GeneticAlgorithm), metaOptimizationProblem.Problems.First());
 
-      //Console.WriteLine("Press enter to start");
-      //Console.ReadLine();
+      Console.WriteLine("Press enter to start"); Console.ReadLine();
       TestOptimization(metaLevelAlgorithm);
 
@@ -103 +106 @@
 
       Console.ReadLine();
+    }
+
+    private void TestParameterizedItem() {
+      var value = new MyParameterizedItem();
+      Console.WriteLine("P1=1;; " + value.ToString());
+
+      var vc = new ParameterizedValueConfiguration(value, typeof(MyParameterizedItem), true);
+      vc.Optimize = true;
+      ((IntValue)vc.ParameterConfigurations.Single(x => x.Name == "P1").ActualValue.Value).Value = 22;
+      Console.WriteLine("P1=22;; " + value.ToString());
+      vc.ParameterConfigurations.Single(x => x.Name == "P1").ActualValue.Value = new IntValue(33);
+      Console.WriteLine("P1=33;; " + value.ToString());
+      vc.Parameterize(value);
+      Console.WriteLine("P1=33;; " + value.ToString());
+            
+      Console.ReadLine();
+    }
+
+    private void TestObjectGraphObjectsTraversal() {
+      //var obj = new GeneticAlgorithm();
+      var obj = ContentManager.Load("Genetic Programming - Symbolic Regression 3.4_scaled_paused.hl");
+      Console.WriteLine("loaded");
+
+      for (int i = 0; i < 10; i++) {
+        var sw = new Stopwatch();
+        sw.Start();
+        var objects = obj.GetObjectGraphObjects().ToArray();
+        sw.Stop();
+
+        var typeCount = GetTypeCount(objects).ToArray();
+        Console.WriteLine("objects: {0}", objects.Count());
+        Console.WriteLine(sw.Elapsed);
+      }
+    }
+
+    private IOrderedEnumerable<KeyValuePair<Type, long>> GetTypeCount(object[] objects) {
+      var dict = new Dictionary<Type, long>();
+      foreach (var item in objects) {
+        var t = item.GetType();
+        if (!dict.ContainsKey(t))
+          dict.Add(t, 0);
+        dict[t]++;
+      }
+      return dict.OrderByDescending(x => x.Value);
     }
 
@@ -733 +780 @@
     private static ParameterConfigurationTree SetupGAAlgorithm(Type baseLevelAlgorithmType, MetaOptimizationProblem metaOptimizationProblem) {
       metaOptimizationProblem.AlgorithmType.Value = baseLevelAlgorithmType;
-      
-      //metaOptimizationProblem.ProblemType.Value = typeof(SingleObjectiveTestFunctionProblem);
+
+      metaOptimizationProblem.ProblemType.Value = typeof(SingleObjectiveTestFunctionProblem);
       //metaOptimizationProblem.Problems.Clear();
       //metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
@@ -753 +800 @@
 
       // tower problem
-      metaOptimizationProblem.ImportAlgorithm((IAlgorithm)ContentManager.Load("Genetic Programming - Symbolic Regression 3.4_scaled.hl"));
-      metaOptimizationProblem.Maximization.Value = true;
+      //metaOptimizationProblem.ImportAlgorithm((IAlgorithm)ContentManager.Load("Genetic Programming - Symbolic Regression 3.4_scaled.hl"));
+      //metaOptimizationProblem.Maximization.Value = true;
 
       ParameterConfigurationTree algorithmVc = metaOptimizationProblem.ParameterConfigurationTree;
       ((IntValue)algorithmVc.AlgorithmConfiguration.ParameterConfigurations.Single(x => x.Name == "MaximumGenerations").ActualValue.Value).Value = baseAlgorithmMaxGenerations;
       ((IntValue)algorithmVc.AlgorithmConfiguration.ParameterConfigurations.Single(x => x.Name == "PopulationSize").ActualValue.Value).Value = baseAlgorithmPopulationSize;
-      
+
       //ConfigurePopulationSize(algorithmVc, 10, 100, 1);
-      //ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
-      //ConfigureMutationOperator(algorithmVc);
+      ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
+      ConfigureMutationOperator(algorithmVc);
       //ConfigureElites(algorithmVc, 0, 10, 1);
       //ConfigureSelectionOperator(algorithmVc, true);
 
-      ConfigureSymbolicExpressionGrammar(algorithmVc);
+      //ConfigureSymbolicExpressionGrammar(algorithmVc);
 
       return algorithmVc;
@@ -992 +1039 @@
 
       ContentManager.Save((IStorableContent)metaLevelAlgorithm, resultPath + "-before.hl", true);
-      
+
       using (var sw = new StreamWriter(outputPath)) {
         sw.AutoFlush = true;
@@ -1262 +1309 @@
     }
   }
+
+  class MyParameterizedItem : ParameterizedNamedItem {
+    public MyParameterizedItem() {
+      this.Parameters.Add(new ValueParameter<IntValue>("P1", new IntValue(1)));
+      this.Parameters.Add(new ValueParameter<IntValue>("P2", new IntValue(2)));
+      this.Parameters.Add(new ValueParameter<IntValue>("P3", new IntValue(3)));
+      this.Parameters.Add(new ValueParameter<MyOtherParameterizedItem>("P4", new MyOtherParameterizedItem()));
+    }
+
+    protected MyParameterizedItem(MyParameterizedItem original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new MyParameterizedItem(this, cloner);
+    }
+
+    public override string ToString() {
+      return string.Format("P1: {0}, P2: {1}, P3: {2}, P4: {3}", Parameters["P1"].ActualValue, Parameters["P2"].ActualValue, Parameters["P3"].ActualValue, Parameters["P4"].ActualValue);
+    }
+  }
+
+  class MyOtherParameterizedItem : ParameterizedNamedItem {
+    public MyOtherParameterizedItem() {
+      this.Parameters.Add(new ValueParameter<IntValue>("PP1", new IntValue(1)));
+      this.Parameters.Add(new ValueParameter<IntValue>("PP2", new IntValue(2)));
+      this.Parameters.Add(new ValueParameter<IntValue>("PP3", new IntValue(3)));
+    }
+
+    protected MyOtherParameterizedItem(MyOtherParameterizedItem original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new MyOtherParameterizedItem(this, cloner);
+    }
+
+    public override string ToString() {
+      return string.Format("PP1: {0}, PP2: {1}, PP3: {2}", Parameters["PP1"].ActualValue, Parameters["PP2"].ActualValue, Parameters["PP3"].ActualValue);
+    }
+  }
 }