[11659] | 1 | using System;
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| 2 | using System.Collections.Generic;
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[11727] | 3 | using System.Data;
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[11659] | 4 | using System.Diagnostics;
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| 5 | using System.Linq;
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| 6 | using System.Text;
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[11727] | 7 | using System.Threading.Tasks;
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| 8 | using HeuristicLab.Algorithms.Bandits;
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[11659] | 9 | using HeuristicLab.Algorithms.GrammaticalOptimization;
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| 10 | using HeuristicLab.Problems.GrammaticalOptimization;
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| 11 |
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| 12 | namespace Main {
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| 13 | class Program {
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| 14 | static void Main(string[] args) {
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[11727] | 15 | // RunDemo();
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| 16 | RunGridTest();
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| 17 | }
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| 18 |
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| 19 | private static void RunGridTest() {
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| 20 | int maxIterations = 150000;
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| 21 | var globalRandom = new Random(31415);
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| 22 | var reps = 10;
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| 23 | Parallel.ForEach(new int[] { 1, 5, 10, 100, 500, 1000 }, (randomTries) => {
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| 24 | Random localRand;
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| 25 | lock (globalRandom) {
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| 26 | localRand = new Random(globalRandom.Next());
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| 27 | }
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| 28 | var policyFactories = new Func<int, IPolicy>[]
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| 29 | {
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| 30 | (numActions) => new RandomPolicy(localRand, numActions),
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| 31 | (numActions) => new UCB1Policy(numActions),
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| 32 | (numActions) => new UCB1TunedPolicy(numActions),
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| 33 | (numActions) => new UCBNormalPolicy(numActions),
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| 34 | (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.01),
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| 35 | (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.05),
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| 36 | (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.1),
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| 37 | (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.2),
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| 38 | (numActions) => new EpsGreedyPolicy(localRand, numActions, 0.5),
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| 39 | (numActions) => new GaussianThompsonSamplingPolicy(localRand, numActions),
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| 40 | (numActions) => new BernoulliThompsonSamplingPolicy(localRand, numActions)
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| 41 | };
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| 42 |
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| 43 | foreach (var policyFactory in policyFactories)
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| 44 | for (int i = 0; i < reps; i++) {
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| 45 | int iterations = 0;
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| 46 | var sw = new Stopwatch();
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| 47 | var globalStatistics = new SentenceSetStatistics();
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| 48 |
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| 49 | // var problem = new SymbolicRegressionPoly10Problem();
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| 50 | var problem = new SantaFeAntProblem();
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| 51 | //var problem = new PalindromeProblem();
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| 52 | //var problem = new HardPalindromeProblem();
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| 53 | //var problem = new RoyalPairProblem();
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| 54 | //var problem = new EvenParityProblem();
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| 55 | var alg = new MctsSampler(problem, 17, localRand, randomTries, policyFactory);
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| 56 | //var alg = new ExhaustiveBreadthFirstSearch(problem, 25);
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| 57 | //var alg = new AlternativesContextSampler(problem, 25);
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| 58 |
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| 59 | alg.SolutionEvaluated += (sentence, quality) => {
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| 60 | iterations++;
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| 61 | globalStatistics.AddSentence(sentence, quality);
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| 62 | if (iterations % 10000 == 0) {
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| 63 | Console.WriteLine("{0} {1} {2}", randomTries, policyFactory(1), globalStatistics);
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| 64 | }
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| 65 | };
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| 66 |
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| 67 | sw.Start();
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| 68 |
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| 69 | alg.Run(maxIterations);
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| 70 |
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| 71 | sw.Stop();
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| 72 | }
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| 73 | });
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| 74 | }
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| 75 |
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| 76 | private static void RunDemo() {
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| 77 | // TODO: implement threshold ascent
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| 78 | // TODO: implement inspection for MCTS
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| 79 |
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[11690] | 80 | int maxIterations = 10000000;
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[11659] | 81 | int iterations = 0;
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| 82 | var sw = new Stopwatch();
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| 83 | double bestQuality = 0;
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| 84 | string bestSentence = "";
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[11727] | 85 | var globalStatistics = new SentenceSetStatistics();
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| 86 | var random = new Random(31415);
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[11659] | 87 |
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[11727] | 88 | // var problem = new SymbolicRegressionPoly10Problem();
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| 89 | var problem = new SantaFeAntProblem();
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| 90 | //var problem = new PalindromeProblem();
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| 91 | //var problem = new HardPalindromeProblem();
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| 92 | //var problem = new RoyalPairProblem();
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| 93 | //var problem = new EvenParityProblem();
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| 94 | var alg = new MctsSampler(problem, 17, random);
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| 95 | //var alg = new ExhaustiveBreadthFirstSearch(problem, 25);
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| 96 | //var alg = new AlternativesContextSampler(problem, 25);
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[11659] | 97 |
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[11727] | 98 | alg.FoundNewBestSolution += (sentence, quality) => {
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[11659] | 99 | bestQuality = quality;
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| 100 | bestSentence = sentence;
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| 101 | Console.WriteLine("{0,10} {1,10:F5} {2,10:F5} {3}", iterations, bestQuality, quality, sentence);
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| 102 | };
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[11727] | 103 | alg.SolutionEvaluated += (sentence, quality) => {
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[11659] | 104 | iterations++;
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[11727] | 105 | globalStatistics.AddSentence(sentence, quality);
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[11690] | 106 | if (iterations % 10000 == 0) {
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[11727] | 107 | //Console.WriteLine("{0,10} {1,10:F5} {2,10:F5} {3}", iterations, bestQuality, quality, sentence);
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| 108 | Console.WriteLine(globalStatistics.ToString());
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[11659] | 109 | }
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| 110 | };
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| 111 |
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| 112 |
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| 113 | sw.Start();
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| 114 |
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[11727] | 115 | alg.Run(maxIterations);
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[11659] | 116 |
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| 117 | sw.Stop();
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| 118 |
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| 119 | Console.WriteLine("{0,10} Best soultion: {1,10:F5} {2}", iterations, bestQuality, bestSentence);
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| 120 | Console.WriteLine("{0:F2} sec {1,10:F1} sols/sec {2,10:F1} ns/sol",
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| 121 | sw.Elapsed.TotalSeconds,
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| 122 | maxIterations / (double)sw.Elapsed.TotalSeconds,
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| 123 | (double)sw.ElapsedMilliseconds * 1000 / maxIterations);
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| 124 | }
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| 125 | }
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| 126 | }
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