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Changeset 12503 for branches


Ignore:
Timestamp:
06/23/15 20:00:14 (9 years ago)
Author:
aballeit
Message:

#2283 added GUI and charts; fixed MCTS

Location:
branches/HeuristicLab.Problems.GrammaticalOptimization
Files:
473 added
8 edited

Legend:

Unmodified
Added
Removed

  • branches/HeuristicLab.Problems.GrammaticalOptimization/GrammaticalOptimization.sln

    r12099 r12503  
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     71EndProject
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    120159  EndGlobalSection
    121160  GlobalSection(SolutionProperties) = preSolution

  • branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.Bandits/Policies/UCTPolicy.cs

    r11981 r12503  
    3030          q = double.PositiveInfinity;
    3131        } else {
    32           q = aInfo.SumReward / aInfo.Tries + 2.0 * c * Math.Sqrt(Math.Log(totalTries) / aInfo.Tries);
     32            q = aInfo.SumReward / aInfo.Tries + 2.0 * c * Math.Sqrt(Math.Log(totalTries) / aInfo.Tries);
    3333        }
    3434        if (q > bestQ) {

  • branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.GeneticProgramming/OffspringSelectionGP.cs

    r12023 r12503  
    4646        // osga.Engine = new ParallelEngine.ParallelEngine();
    4747        osga.Engine = new SequentialEngine.SequentialEngine();
    48         osga.ExceptionOccurred += (sender, args) => { Console.WriteLine(args.Value.Message); wh.Set(); };
    49         osga.Stopped += (sender, args) => { wh.Set(); };
     48        //osga.ExceptionOccurred += (sender, args) => { Console.WriteLine(args.Value.Message); wh.Set(); };
     49        //osga.Stopped += (sender, args) => { wh.Set(); };
    5050
    5151        int numEvals = 0;

  • branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.MonteCarloTreeSearch/Base/TreeNode.cs

    r12098 r12503  
    1717        public IBanditPolicyActionInfo actionInfo;
    1818
    19         public TreeNode(TreeNode parent, string phrase)
     19        public TreeNode(TreeNode parent, string phrase, IBanditPolicyActionInfo actionInfo)
    2020        {
    2121            this.parent = parent;
    2222            this.phrase = phrase;
    23             actionInfo = new DefaultPolicyActionInfo();
     23            this.actionInfo = actionInfo;
    2424        }
    2525        public bool IsLeaf()

  • branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.MonteCarloTreeSearch/MonteCarloTreeSearch.cs

    r12098 r12503  
    5353                string phrase = currentNode.phrase;
    5454
    55                 if (!grammar.IsTerminal(phrase))
     55                if (!grammar.IsTerminal(phrase) && phrase.Length <= maxLen)
    5656                {
    5757                    ExpandTreeNode(currentNode);
    … …  
    6060                        currentNode.children[behaviourPolicy.SelectAction(random, currentNode.GetChildActionInfos())];
    6161                }
    62                 double quality = simulation.Simulate(currentNode);
    63                 OnSolutionEvaluated(phrase, quality);
     62                if (currentNode.phrase.Length <= maxLen)
     63                {
     64                    double quality = simulation.Simulate(currentNode);
     65                    OnSolutionEvaluated(phrase, quality);
    6466
    65                 Propagate(currentNode, quality);
     67                    Propagate(currentNode, quality);
     68                }
    6669            }
    6770        }
    … …  
    8891                            if (newSequence.Length <= maxLen)
    8992                            {
    90                                 TreeNode childNode = new TreeNode(treeNode, newSequence.ToString());
     93                                TreeNode childNode = new TreeNode(treeNode, newSequence.ToString(), behaviourPolicy.CreateActionInfo());
    9194                                treeNode.children.Add(childNode);
    9295                            }
    … …  
    101104            StopRequested = false;
    102105            bestQuality = 0.0;
    103             rootNode = new TreeNode(null, grammar.SentenceSymbol.ToString());
     106            rootNode = new TreeNode(null, grammar.SentenceSymbol.ToString(), behaviourPolicy.CreateActionInfo());
    104107        }
    105108

  • branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Problems.GrammaticalOptimization/Problems/SantaFeAntProblem.cs

    r12099 r12503  
    5555    }
    5656
    57     private static void Run(Ant ant, string sentence, ref int p, bool stopAfterFirst = false) {
     57      // changed stopAfterFirst to true..
     58    private static void Run(Ant ant, string sentence, ref int p, bool stopAfterFirst = true) {
    5859      while (!ant.Done()) {
    5960        if (p >= sentence.Length) {

  • branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Problems.GrammaticalOptimization/Sequence.cs

    r11793 r12503  
    1818  // - terminal symbols can be almost all other characters
    1919  public class Sequence : IEnumerable<char> {
    20     private const int maxIdx = 200;
     20      // changed maxIdx to 10000...
     21    private const int maxIdx = 10000;
    2122
    2223    private int len;

  • branches/HeuristicLab.Problems.GrammaticalOptimization/Main/Program.cs

    r12098 r12503  
    3434        private static void RunDemo()
    3535        {
    36 
    37 
    3836            int maxIterations = 100000;
    3937            int iterations = 0;
    … …  
    4644            var problem = new RoyalPairProblem();
    4745            //var problem = new EvenParityProblem();
    48             //var alg = new SequentialSearch(problem, 23, random, 0,
    49             // new HeuristicLab.Algorithms.Bandits.GrammarPolicies.GenericGrammarPolicy(problem, new UCB1TunedPolicy()));
    50             var alg = new MonteCarloTreeSearch(problem, 23, random, new UCB1Policy(), new RandomSimulation(problem, random, 23));
     46            var alg = new SequentialSearch(problem, 23, random, 0,
     47            new HeuristicLab.Algorithms.Bandits.GrammarPolicies.GenericGrammarPolicy(problem, new UCB1TunedPolicy()));
     48            //var alg = new MonteCarloTreeSearch(problem, 23, random, new UCB1Policy(), new RandomSimulation(problem, random, 23));
    5149
    5250
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