Changeset 12290 for branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/Test

Timestamp:

04/07/15 14:31:06 (10 years ago)

Author:

gkronber

Message:

#2283 created a new branch to separate development from aballeit

Location:

branches/HeuristicLab.Problems.GrammaticalOptimization-gkr

Files:

• . (copied) (copied from branches/HeuristicLab.Problems.GrammaticalOptimization)
• Test/RunDemo.cs (modified) (1 diff)
• Test/TestTunedSettings.cs (modified) (4 diffs)

branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/Test/RunDemo.cs

@@ -26 +26 @@
          () => new RandomPolicy(), 
           () => new ActiveLearningPolicy(),  
-         () => new EpsGreedyPolicy(0.01, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.05, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.1, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.2, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.01, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.05, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.1, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.2, (aInfo)=> aInfo.MaxReward, "max"), 
          //() => new GaussianThompsonSamplingPolicy(), 
          () => new GaussianThompsonSamplingPolicy(true), 

branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/Test/TestTunedSettings.cs

@@ -42 +42 @@
          () => new RandomPolicy(), 
          () => new ActiveLearningPolicy(),  
-         () => new EpsGreedyPolicy(0.01, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.05, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.1, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.2, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.01, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.05, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.1, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.2, (aInfo)=> aInfo.MaxReward, "max"), 
          //() => new GaussianThompsonSamplingPolicy(), 
          () => new GaussianThompsonSamplingPolicy(true), 
@@ -152 +152 @@
          () => new RandomPolicy(), 
          () => new ActiveLearningPolicy(),  
-         () => new EpsGreedyPolicy(0.01, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.05, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.1, (aInfo)=> aInfo.MaxReward, "max"), 
-         () => new EpsGreedyPolicy(0.2, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.01, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.05, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.1, (aInfo)=> aInfo.MaxReward, "max"), 
+         // () => new EpsGreedyPolicy(0.2, (aInfo)=> aInfo.MaxReward, "max"), 
          //() => new GaussianThompsonSamplingPolicy(), 
          () => new GaussianThompsonSamplingPolicy(true), 
@@ -255 +255 @@
       var instanceFactories = new Func<int, ISymbolicExpressionTreeProblem>[]
       {
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Nguyen F7", true), 
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Keijzer 6", true), 
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Vladislavleva-4", true), 
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Spatial", true), 
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Friedman - II", true), 
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Tower", true), 
+        //(randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Nguyen F7", true),   very easy?!
+        //(randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Keijzer 6", true),  very easy?!
+        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Vladislavleva-4", 1.0, true),  // kommenda - const opt
+        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Spatial", 1.0, true), 
+        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Friedman - II", 1.0, true), 
+        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Tower", 1.0, true), 
       };
 
@@ -348 +348 @@
       var instanceFactories = new Func<int, ISymbolicExpressionTreeProblem>[]
       {
-        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Poly-10", true ), 
+        (randSeed) => (ISymbolicExpressionTreeProblem)new SymbolicRegressionProblem(new Random(randSeed), "Poly-10", 1.0, true ), 
       };