source: branches/HeuristicLab.Problems.GrammaticalOptimization/Main/Program.cs @ 12009

using System;
using System.Diagnostics;
using System.Globalization;
using HeuristicLab.Algorithms.Bandits.BanditPolicies;
using HeuristicLab.Algorithms.GrammaticalOptimization;
using HeuristicLab.Problems.GrammaticalOptimization;

// NOTES: gkronber
// TODO: feature extraction for full symbolic expressions and experiment for all benchmark problems
// TODO: why does GaussianThompsonSampling work so well with MCTS for the artificial ant problem?
// TODO: research thompson sampling for max bandit?
// TODO: verify TA implementation using example from the original paper      
// TODO: implement thompson sampling for gaussian mixture models
// TODO: gleichzeitige modellierung von transformierter zielvariable (y, 1/y, log(y), exp(y), sqrt(y), ...)
// TODO: vergleich bei complete-randomly möglichst kurze sÀtze generieren vs. einfach zufÀllig alternativen wÀhlen
// TODO: reward discounting (fÌr verÀnderliche reward distributions Ìber zeit). speziellen unit-test dafÌr erstellen
// TODO: constant optimization 


namespace Main {
  class Program {
    static void Main(string[] args) {
      CultureInfo.DefaultThreadCurrentCulture = CultureInfo.InvariantCulture;

      RunDemo();
    }


    private static void RunDemo() {


      int maxIterations = 100000;
      int iterations = 0;

      var globalStatistics = new SentenceSetStatistics();
      var random = new Random();

      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()));


      alg.FoundNewBestSolution += (sentence, quality) => {
        //Console.WriteLine("{0}", globalStatistics);
      };

      alg.SolutionEvaluated += (sentence, quality) => {
        iterations++;
        globalStatistics.AddSentence(sentence, quality);

        // 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);
    }
  }
}