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

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)
• Main/Main.csproj (modified) (1 diff)
• Main/Program.cs (modified) (2 diffs)

branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/Main/Main.csproj

@@ -56 +56 @@
       <Name>HeuristicLab.Algorithms.MonteCarloTreeSearch</Name>
     </ProjectReference>
+    <ProjectReference Include="..\HeuristicLab.Problems.GrammaticalOptimization.SymbReg\HeuristicLab.Problems.GrammaticalOptimization.SymbReg.csproj">
+      <Project>{17A7A380-86CE-482D-8D22-CBD70CC97F0D}</Project>
+      <Name>HeuristicLab.Problems.GrammaticalOptimization.SymbReg</Name>
+    </ProjectReference>
     <ProjectReference Include="..\HeuristicLab.Problems.GrammaticalOptimization\HeuristicLab.Problems.GrammaticalOptimization.csproj">
       <Project>{cb9dccf6-667e-4a13-b82d-dbd6b45a045e}</Project>

branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/Main/Program.cs

@@ -3 +3 @@
 using System.Globalization;
 using HeuristicLab.Algorithms.Bandits.BanditPolicies;
+using HeuristicLab.Algorithms.Bandits.GrammarPolicies;
 using HeuristicLab.Algorithms.GrammaticalOptimization;
 using HeuristicLab.Algorithms.MonteCarloTreeSearch;
@@ -18 +19 @@
 // TODO: reward discounting (fÌr verÀnderliche reward distributions Ìber zeit). speziellen unit-test dafÌr erstellen
 // TODO: constant optimization 
+using HeuristicLab.Problems.GrammaticalOptimization.SymbReg;
 
 
-namespace Main
-{
-    class Program
-    {
-        static void Main(string[] args)
-        {
-            CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;
+namespace Main {
+  class Program {
+    static void Main(string[] args) {
+      CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;
 
-            RunDemo();
-        }
+      RunDemo();
+    }
 
 
-        private static void RunDemo()
-        {
+    private static void RunDemo() {
+
+      for (int i = 0; i < 100; i++) {
+        int maxIterations = 2000000;
+        int iterations = 0;
+
+        var globalStatistics = new SentenceSetStatistics();
+        var random = new Random();
+
+        var problem = new SymbolicRegressionPoly10Problem();
+        //var problem = new SantaFeAntProblem();              
+        //var problem = new RoyalPairProblem(25);
+        //var problem = new FindPhrasesProblem(random, 10, 5, 3, 5, 5, 1.0, 0.9, true);
+        //var problem = new PrimePolynomialProblem();
+        //var problem = new SymbolicRegressionProblem(random,
+        //  //@"C:\reps\HeuristicLab\branches\HeuristicLab.Problems.GrammaticalOptimization\HeuristicLab.Problems.GrammaticalOptimization.SymbReg\nht-train.csv",
+        //  @"C:\reps\fhooe-new\research\Datasets\Benchmark\kommenda-1.csv",
+        //  1.0,
+        //  true);
+        // //var problem = new PrimePolynomialProblem();
+        // var alg = new SequentialSearch(problem, 25, random, 0,
+        //   new HeuristicLab.Algorithms.Bandits.GrammarPolicies.GenericGrammarPolicy(problem, new UCB1TunedPolicy()));
+        var alg = new SequentialSearch(problem, 25, random, 0,
+          new GenericPolicy(problem, new HeuristicLab.Algorithms.Bandits.BanditPolicies.EpsGreedyPolicy(0.1)));
+        //var alg = new MonteCarloTreeSearch(problem, 23, random, new UCB1Policy(), new RandomSimulation(problem, random, 30));
 
 
-            int maxIterations = 100000;
-            int iterations = 0;
+        alg.FoundNewBestSolution += (sentence, quality) => {
+          //Console.WriteLine("{0}", globalStatistics);
+        };
 
-            var globalStatistics = new SentenceSetStatistics();
-            var random = new Random();
+        alg.SolutionEvaluated += (sentence, quality) => {
+          iterations++;
+          globalStatistics.AddSentence(sentence, quality);
 
-            //var problem = new SymbolicRegressionPoly10Problem();
-            //var problem = new SantaFeAntProblem();              
-            var problem = new RoyalPairProblem();
-            //var problem = new EvenParityProblem();
-            //var alg = new SequentialSearch(problem, 23, random, 0,
-            // new HeuristicLab.Algorithms.Bandits.GrammarPolicies.GenericGrammarPolicy(problem, new UCB1TunedPolicy()));
-            var alg = new MonteCarloTreeSearch(problem, 23, random, new UCB1Policy(), new RandomSimulation(problem, random, 23));
+          // comment this if you don't want to see solver statistics
+          if (iterations % 100 == 0) {
+            if (iterations % 1000 == 0) Console.Clear();
+            Console.SetCursorPosition(0, 0);
+            alg.PrintStats();
+          }
 
+          // uncomment this if you want to collect statistics of the generated sentences
+          //if (iterations % 100 == 0) {
+          //  Console.WriteLine("{0}", globalStatistics);
+          //}
+        };
 
-            alg.FoundNewBestSolution += (sentence, quality) =>
-            {
-                //Console.WriteLine("{0}", globalStatistics);
-            };
+        var sw = new Stopwatch();
+        sw.Start();
+        alg.Run(maxIterations);
+        sw.Stop();
 
-            alg.SolutionEvaluated += (sentence, quality) =>
-            {
-                iterations++;
-                globalStatistics.AddSentence(sentence, quality);
+        Console.WriteLine(globalStatistics);
 
-                // comment this if you don't want to see solver statistics
-                if (iterations % 100 == 0)
-                {
-                    if (iterations % 10000 == 0) Console.Clear();
-                    Console.SetCursorPosition(0, 0);
-                    alg.PrintStats();
-                }
-
-                // uncomment this if you want to collect statistics of the generated sentences
-                // if (iterations % 1000 == 0) {
-                //   Console.WriteLine("{0}", globalStatistics);
-                // }
-            };
-
-            var sw = new Stopwatch();
-            sw.Start();
-            alg.Run(maxIterations);
-            sw.Stop();
-
-            Console.Clear();
-            alg.PrintStats();
-            Console.WriteLine(globalStatistics);
-            Console.WriteLine("{0:F2} sec {1,10:F1} sols/sec {2,10:F1} ns/sol",
-              sw.Elapsed.TotalSeconds,
-              maxIterations / (double)sw.Elapsed.TotalSeconds,
-              (double)sw.ElapsedMilliseconds * 1000 / maxIterations);
-        }
+        Console.WriteLine("{0:F2} sec {1,10:F1} sols/sec {2,10:F1} ns/sol",
+          sw.Elapsed.TotalSeconds,
+          maxIterations / (double)sw.Elapsed.TotalSeconds,
+          (double)sw.ElapsedMilliseconds * 1000 / maxIterations);
+      }
     }
+  }
 }