#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using System;
using System.Collections.Generic;
using System.Linq;

namespace HeuristicLab.OptimizationExpertSystem.Common {
  [Item("Overall Best Recommender", "")]
  [StorableClass]
  public class OverallBestRecommender : ParameterizedNamedItem, IAlgorithmInstanceRecommender {

    private IFixedValueParameter<DoubleValue> NeighborhoodFactorParameter {
      get { return (IFixedValueParameter<DoubleValue>)Parameters["NeighborhoodFactor"]; }
    }
    
    [StorableConstructor]
    private OverallBestRecommender(bool deserializing) : base(deserializing) { }
    private OverallBestRecommender(OverallBestRecommender original, Cloner cloner) : base(original, cloner) { }
    public OverallBestRecommender() { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new OverallBestRecommender(this, cloner);
    }

    public IRecommendationModel TrainModel(IRun[] problemInstances, KnowledgeCenter kc, string[] characteristics) {
      var instances = new List<KeyValuePair<IAlgorithm, double>>();
      foreach (var relevantRuns in kc.GetKnowledgeBaseByAlgorithm()) {
        var algorithm = relevantRuns.Key;
        var pis = relevantRuns.Value.Select(x => ((StringValue)x.Parameters["Problem Name"]).Value).Distinct()
                              .Select(x => Tuple.Create(x, problemInstances.SingleOrDefault(y => ((StringValue)y.Parameters["Problem Name"]).Value == x)))
                              .Where(x => x.Item2 != null)
                              .Select(x => Tuple.Create(x.Item1, ((DoubleValue)x.Item2.Parameters["BestKnownQuality"]).Value))
                              .ToDictionary(x => x.Item1, x => x.Item2);
        var avgERT = 0.0;
        var count = 0;
        foreach (var problemRuns in relevantRuns.Value.GroupBy(x => ((StringValue)x.Parameters["Problem Name"]).Value)) {
          double bkq;
          if (!pis.TryGetValue(problemRuns.Key, out bkq)) continue;
          var ert = ExpectedRuntimeHelper.CalculateErt(problemRuns.ToList(), "QualityPerEvaluations", kc.GetTarget(bkq, kc.MinimumTarget.Value, kc.Maximization), kc.Maximization).ExpectedRuntime;
          if (double.IsInfinity(ert)) ert = int.MaxValue;
          avgERT += ert;
          count++;
        }
        avgERT /= count;
        instances.Add(new KeyValuePair<IAlgorithm, double>(algorithm, avgERT));
      }

      return new FixedRankModel(instances.OrderBy(x => x.Value));
    }
  }
}