source: branches/HeuristicLab.Problems.GrammaticalOptimization/Main/Program.cs @ 11728

using System;
using System.Collections.Generic;
using System.Data;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using HeuristicLab.Algorithms.Bandits;
using HeuristicLab.Algorithms.GrammaticalOptimization;
using HeuristicLab.Problems.GrammaticalOptimization;

namespace Main {
  class Program {
    static void Main(string[] args) {
      // RunDemo();
      RunGridTest();
    }

    private static void RunGridTest() {
      int maxIterations = 150000;
      var globalRandom = new Random(31415);
      var reps = 10;
      Parallel.ForEach(new int[] { 1, 5, 10, 100, 500, 1000 }, (randomTries) => {
        Random localRand;
        lock (globalRandom) {
          localRand = new Random(globalRandom.Next());
        }
        var policyFactories = new Func<int, IPolicy>[]
        {
          (numActions) => new RandomPolicy(localRand, numActions), 
          (numActions) => new UCB1Policy(numActions), 
          (numActions) => new UCB1TunedPolicy(numActions), 
          (numActions) => new UCBNormalPolicy(numActions), 
          (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.01), 
          (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.05), 
          (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.1), 
          (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.2), 
          (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.5), 
          (numActions) => new GaussianThompsonSamplingPolicy(localRand, numActions), 
          (numActions) => new BernoulliThompsonSamplingPolicy(localRand, numActions)
        };

        foreach (var policyFactory in policyFactories)
          for (int i = 0; i < reps; i++) {
            int iterations = 0;
            var sw = new Stopwatch();
            var globalStatistics = new SentenceSetStatistics();

            // var problem = new SymbolicRegressionPoly10Problem();
            var problem = new SantaFeAntProblem();
            //var problem = new PalindromeProblem();
            //var problem = new HardPalindromeProblem();
            //var problem = new RoyalPairProblem();
            //var problem = new EvenParityProblem();
            var alg = new MctsSampler(problem, 17, localRand, randomTries, policyFactory);
            //var alg = new ExhaustiveBreadthFirstSearch(problem, 25);
            //var alg = new AlternativesContextSampler(problem, 25);

            alg.SolutionEvaluated += (sentence, quality) => {
              iterations++;
              globalStatistics.AddSentence(sentence, quality);
              if (iterations % 10000 == 0) {
                Console.WriteLine("{0} {1} {2}", randomTries, policyFactory(1), globalStatistics);
              }
            };

            sw.Start();

            alg.Run(maxIterations);

            sw.Stop();
          }
      });
    }

    private static void RunDemo() {
      // TODO: implement threshold ascent
      // TODO: implement inspection for MCTS

      int maxIterations = 10000000;
      int iterations = 0;
      var sw = new Stopwatch();
      double bestQuality = 0;
      string bestSentence = "";
      var globalStatistics = new SentenceSetStatistics();
      var random = new Random(31415);

      // var problem = new SymbolicRegressionPoly10Problem();
      var problem = new SantaFeAntProblem();
      //var problem = new PalindromeProblem();
      //var problem = new HardPalindromeProblem();
      //var problem = new RoyalPairProblem();
      //var problem = new EvenParityProblem();
      var alg = new MctsSampler(problem, 17, random);
      //var alg = new ExhaustiveBreadthFirstSearch(problem, 25);
      //var alg = new AlternativesContextSampler(problem, 25);

      alg.FoundNewBestSolution += (sentence, quality) => {
        bestQuality = quality;
        bestSentence = sentence;
        Console.WriteLine("{0,10} {1,10:F5} {2,10:F5} {3}", iterations, bestQuality, quality, sentence);
      };
      alg.SolutionEvaluated += (sentence, quality) => {
        iterations++;
        globalStatistics.AddSentence(sentence, quality);
        if (iterations % 10000 == 0) {
          //Console.WriteLine("{0,10} {1,10:F5} {2,10:F5} {3}", iterations, bestQuality, quality, sentence);
          Console.WriteLine(globalStatistics.ToString());
        }
      };


      sw.Start();

      alg.Run(maxIterations);

      sw.Stop();

      Console.WriteLine("{0,10} Best soultion: {1,10:F5} {2}", iterations, bestQuality, bestSentence);
      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);
    }
  }
}