using System;
using System.Collections.Generic;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Threading.Tasks;
using HeuristicLab.Analysis.FitnessLandscape;
using HeuristicLab.Problems.Instances.QAPLIB;
using HeuristicLab.Problems.QuadraticAssignment;
using HeuristicLab.Random;

namespace ProblemInstanceIdentifier {
  class Program {
    static void Main(string[] args) {

      /*var classes = instances.Select(InstanceDescriptor.FromProblemOnly).GroupBy(x => x.Cls);
      foreach (var cls in classes.OrderBy(x => x.Key)) {
        Console.WriteLine("{0};{1}", cls.Key, cls.Count());
      }*/

      var dwFeatureSets = new Dictionary<string, HashSet<string>>() {
        { "SBF", new HashSet<string>() { "Sharpness", "Bumpiness", "Flatness" } },
        { "FRQ", new HashSet<string>() { "DownQ2", "NeutQ2", "UpQ2", "DownQ1", "NeutQ1", "UpQ1", "DownQ3", "NeutQ3", "UpQ3" } },
        { "SBF+FRQ", new HashSet<string>() { "Sharpness", "Bumpiness", "Flatness", "DownQ2", "NeutQ2", "UpQ2", "DownQ1", "NeutQ1", "UpQ1", "DownQ3", "NeutQ3", "UpQ3" } },
        { "RUG", new HashSet<string>() { "AutoCorrelation1", "CorrelationLength" } },
        { "IAL", new HashSet<string>() { "InformationContent", "DensityBasinInformation", "PartialInformationContent", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" } },
        { "RUG+IAL", new HashSet<string>() { "AutoCorrelation1", "CorrelationLength", "InformationContent", "DensityBasinInformation", "PartialInformationContent", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" } },
        { "SBF+IAL", new HashSet<string>() { "Sharpness", "Bumpiness", "Flatness", "InformationContent", "DensityBasinInformation", "PartialInformationContent", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" } },
        { "FRQ+IAL", new HashSet<string>() { "DownQ2", "NeutQ2", "UpQ2", "DownQ1", "NeutQ1", "UpQ1", "DownQ3", "NeutQ3", "UpQ3", "InformationContent", "DensityBasinInformation", "PartialInformationContent", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" } },
        { "SBF+FRQ+IAL", new HashSet<string>() { "Sharpness", "Bumpiness", "Flatness", "DownQ2", "NeutQ2", "UpQ2", "DownQ1", "NeutQ1", "UpQ1", "DownQ3", "NeutQ3", "UpQ3", "InformationContent", "DensityBasinInformation", "PartialInformationContent", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" } },
        { "SBF+FRQ+RUG+IAL", new HashSet<string>() { "Sharpness", "Bumpiness", "Flatness", "DownQ2", "NeutQ2", "UpQ2", "DownQ1", "NeutQ1", "UpQ1", "DownQ3", "NeutQ3", "UpQ3", "AutoCorrelation1", "CorrelationLength", "InformationContent", "DensityBasinInformation", "PartialInformationContent", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" } },
      };

      var dwTypes = new Dictionary<string, QAPDirectedWalk.WalkType> {
        { "(rr)-dw", QAPDirectedWalk.WalkType.RandomRandom },
        { "(rl)-dw", QAPDirectedWalk.WalkType.RandomLocal },
        { "(ll)-dw", QAPDirectedWalk.WalkType.LocalLocal },
        { "(li)-dw", QAPDirectedWalk.WalkType.LocalInverse }
      };

      var dwPaths = new List<int> { 1, 2, 5, 10, 20, 50, 100, 200, 500 };

      var random = new Random();
      using (var writer = File.CreateText("results.csv")) {
        writer.AutoFlush = true;
        var header = string.Format("{0}\t{1}\t{2}\t{3}\t{4}\t{5}\t{6}\t{7}\t{8}\t{9}\t{10}\t{11}\t{12}",
          "Rep", "Dimension", "FSet", "Type", "TrainEff", "TestEff", "ExCnt", "ExRnk", "ClsCnt", "ClsRnk", "TotCnt", "TrainEff2", "TestEff2");
        writer.WriteLine(header);
        Console.WriteLine(header);
        foreach (var dim in new[] { 20, 30, 40, 50, 100 }) {
          foreach (var feat in dwFeatureSets) {
            foreach (var type in dwTypes) {
              for (var rep = 0; rep < 10; rep++) {
                var instances = GetSomeRandomInstances(dimension: dim, totalInstances: 20, seed: random.Next());

                var explorers = from p in dwPaths
                                select new PathRelinkingExplorer() { Features = feat.Value, Paths = p, Type = type.Value, BestImprovement = false };
                ExploreMatching(writer, instances, explorers.ToArray(), explorers.ToArray(), string.Format("{0}\t{1}\t{2}\t{3}", rep, dim, feat.Key, type.Key));
              }
            }
          }
        }
      }


      //var training = GenerateData(instances, rrDwExplorer, parallel: true);
      //var standardizer = InstancesStandardizer.CreateAndApply(training);
      //var test = GenerateData(instances, rrDwExplorer, parallel: true);
      //standardizer.Apply(test);
      //PrintMatchResult(Compare(training, test));

      //Console.WriteLine("=== Path Relinking Walk ===");
      //ExploreMatching(instances,
      //new[] { 1, 5, 10, 20, 50, 100, 200 }.Select(x => new PathRelinkingExplorer() { LocalOptima = false, Paths = x }).ToArray(),
      //new[] { 1, 5, 10, 20, 50, 100, 200 }.Select(x => new PathRelinkingExplorer() { LocalOptima = false, Paths = x }).ToArray(),
      //parallel: true);
      //Console.WriteLine("=== Random Walk ===");
      //ExploreMatching(instances,
      //new[] { 300, 1500, 3000, 6000, 15000, 30000, 60000 }.Select(x => new RandomWalkExplorer() { Iterations = x }).ToArray(),
      //new[] { 300, 1500, 3000, 6000, 15000, 30000, 60000 }.Select(x => new RandomWalkExplorer() { Iterations = x }).ToArray(),
      //parallel: true);
      //Console.WriteLine("=== Adaptive Walk ===");
      //ExploreMatching(instances,
      //new[] { 300, 1500, 3000, 6000, 15000, 30000, 60000 }.Select(x => new AdaptiveWalkExplorer() { Iterations = x / 10, SampleSize = 10 }).ToArray(),
      //new[] { 300, 1500, 3000, 6000, 15000, 30000, 60000 }.Select(x => new AdaptiveWalkExplorer() { Iterations = x / 10, SampleSize = 10 }).ToArray(),
      //parallel: true);
      //Console.WriteLine("=== Up/Down Walk ===");
      //ExploreMatching(instances,
      //new[] { 300, 1500, 3000, 6000, 15000, 30000, 60000 }.Select(x => new UpDownWalkExplorer() { Iterations = x / 10, SampleSize = 10 }).ToArray(),
      //new[] { 300, 1500, 3000, 6000, 15000, 30000, 60000 }.Select(x => new UpDownWalkExplorer() { Iterations = x / 10, SampleSize = 10 }).ToArray(),
      //parallel: true);
    }

    private static List<QuadraticAssignmentProblem> GetSomeRandomInstances(int dimension, int totalInstances, int seed) {
      var sync = new object();
      var provider = new OneSizeInstanceProvider(dimension);
      var instances = new List<QuadraticAssignmentProblem>();
      var random = new FastRandom(seed);
      Parallel.ForEach(provider.GetDataDescriptors().Shuffle(random), desc => {
        var qapData = provider.LoadData(desc);
        if (instances.Count >= totalInstances) return;
        var qap = new QuadraticAssignmentProblem();
        qap.Load(qapData);
        lock (sync) {
          if (instances.Count >= totalInstances) return;
          instances.Add(qap);
        }
      });
      return instances;
    }

    private static List<InstanceDescriptor> GenerateData(List<QuadraticAssignmentProblem> instances, InstanceExplorer explorer, bool parallel = false) {
      var sync = new object();
      var data = new List<InstanceDescriptor>();
      Action<QuadraticAssignmentProblem> body = (qap) => {
        var instance = explorer.Explore(qap);
        if (instance == null) return;
        lock (sync) {
          data.Add(instance);
        }
      };
      if (parallel) {
        Parallel.ForEach(instances.Select(x => (QuadraticAssignmentProblem)x.Clone()), body);
      } else {
        foreach (var qap in instances) body(qap);
      }
      return data;
    }

    private static MatchResult Compare(List<InstanceDescriptor> training, List<InstanceDescriptor> test) {
      int exactCount = 0, clsCount = 0, totalCount = 0;
      int exactRank = 0, clsRank = 0;
      foreach (var e in test) {
        var ordered = training.OrderBy(x => x.CalculateSimilarity(e)).ToList();
        var bestMatch = ordered.First();
        if (bestMatch.Cls == e.Cls) clsCount++;
        if (bestMatch.Name == e.Name) exactCount++;
        var r = ordered.FindIndex((id) => id.Name == e.Name);
        if (r < 0) continue;
        totalCount++;
        exactRank += r + 1;
        clsRank += ordered.FindIndex((id) => id.Cls == e.Cls) + 1;
      }

      return new MatchResult() {
        ExactCount = exactCount,
        ClsCount = clsCount,
        TotalCount = totalCount,
        ExactAverageRank = exactRank / (double)totalCount,
        ClsAverageRank = clsRank / (double)totalCount,
        TrainingDescriptionEffort = training.Average(x => x.DescriptionEffort),
        TestDescriptionEffort = test.Average(x => x.DescriptionEffort)
      };
    }

    private static void PrintData(List<InstanceDescriptor> instances) {
      using (var iter = instances.GetEnumerator()) {
        if (!iter.MoveNext()) return;
        Console.WriteLine(string.Join(";", new[] {"Name", "Cls", "Dimension", "Effort"}
          .Concat(iter.Current != null ? iter.Current.FeatureNames : new [] { "(null)" })));
        do {
          PrintInstanceLine(iter.Current);
        } while (iter.MoveNext());
      }
    }

    private static void PrintInstanceLine(InstanceDescriptor instance) {
      Console.WriteLine(string.Join(";",
        new[] {instance.Name, instance.Cls, instance.Dimension.ToString(CultureInfo.CurrentCulture), instance.DescriptionEffort.ToString(CultureInfo.CurrentCulture)}
          .Concat(instance.FeatureValues.Select(x => x.ToString(CultureInfo.CurrentCulture)))));
    }

    private static void ExploreMatching(StreamWriter writer, List<QuadraticAssignmentProblem> instances, InstanceExplorer[] trainingExplorers, InstanceExplorer[] testExporers, string info, bool parallel = false) {
      var sync = new object();

      var knowledgeBase = new Dictionary<InstanceExplorer, Tuple<InstancesStandardizer, List<InstanceDescriptor>>>();
      Action<InstanceExplorer> trainingBody = (kbExplorer) => {
        var trainingData = GenerateData(instances, kbExplorer, parallel);
        var standardizer = InstancesStandardizer.Create(trainingData);
        standardizer.Apply(trainingData);
        lock (sync) {
          knowledgeBase.Add(kbExplorer, Tuple.Create(standardizer, trainingData));
        }
      };
      if (parallel) {
        Parallel.ForEach(trainingExplorers, trainingBody);
      } else {
        foreach (var kbExplorer in trainingExplorers) trainingBody(kbExplorer);
      }

      var experimentBase = new Dictionary<InstanceExplorer, List<InstanceDescriptor>>();
      Action<InstanceExplorer> testBody = (expExplorer) => {
        var testData = GenerateData(instances, expExplorer, parallel);
        lock (sync) {
          experimentBase.Add(expExplorer, testData);
        }
      };
      if (parallel) {
        Parallel.ForEach(testExporers, testBody);
      } else {
        foreach (var expExplorer in testExporers) testBody(expExplorer);
      }

      var data = from kb in knowledgeBase
        from exp in experimentBase
        select new { Training = kb, Test = exp };

      if (parallel) {
        Parallel.ForEach(data, (point) => {
          var normalizedTest = point.Test.Value.Select(x => new InstanceDescriptor(x)).ToList();
          point.Training.Value.Item1.Apply(normalizedTest);
          var result = Compare(point.Training.Value.Item2, normalizedTest);
          lock (sync) {
            string output = string.Format("{0}\t{1}\t{2}\t{3:F2}\t{4}\t{5:F2}\t{6}\t{7}\t{8}\t{9}",
              info, point.Training.Key.Effort, point.Test.Key.Effort, result.ExactCount,
              result.ExactAverageRank, result.ClsCount, result.ClsAverageRank, result.TotalCount,
              result.TrainingDescriptionEffort, result.TestDescriptionEffort);
            writer.WriteLine(output);
            Console.WriteLine(output);
          }
        });
      } else {
        foreach (var point in data) {
          var normalizedTest = point.Test.Value.Select(x => new InstanceDescriptor(x)).ToList();
          point.Training.Value.Item1.Apply(normalizedTest);
          var result = Compare(point.Training.Value.Item2, normalizedTest);
          string output = string.Format("{0}\t{1}\t{2}\t{3:F2}\t{4}\t{5:F2}\t{6}\t{7}\t{8}\t{9}",
            info, point.Training.Key.Effort, point.Test.Key.Effort, result.ExactCount,
            result.ExactAverageRank, result.ClsCount, result.ClsAverageRank, result.TotalCount,
            result.TrainingDescriptionEffort, result.TestDescriptionEffort);
          writer.WriteLine(output);
          Console.WriteLine(output);
        }
      }
    }

    private class MatchResult {
      public int ExactCount { get; set; }
      public int ClsCount { get; set; }
      public int TotalCount { get; set; }
      public double ExactAverageRank { get; set; }
      public double ClsAverageRank { get; set; }

      public double TrainingDescriptionEffort { get; set; }
      public double TestDescriptionEffort { get; set; }
    }
  }
}