#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.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using System.Linq;

namespace HeuristicLab.OptimizationExpertSystem.Common {
  [Item("K-Nearest Neighbor Recommender", "")]
  [StorableClass]
  public sealed class KNearestNeighborRecommender : ParameterizedNamedItem, IAlgorithmInstanceRecommender {
    
    private IFixedValueParameter<IntValue> KParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters["K"]; }
    }

    [StorableConstructor]
    private KNearestNeighborRecommender(bool deserializing) : base(deserializing) { }
    private KNearestNeighborRecommender(KNearestNeighborRecommender original, Cloner cloner) : base(original, cloner) { }
    public KNearestNeighborRecommender() {
      Parameters.Add(new FixedValueParameter<IntValue>("K", "The number of nearest neighbors to consider.", new IntValue(3)));
    }

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

    public IRecommendationModel TrainModel(IRun[] problemInstances, KnowledgeCenter kc, string[] characteristics) {
      var perfData = problemInstances.Select(pi => new { ProblemInstance = pi, Performance = kc.GetAlgorithmPerformance(pi) })
                                     .ToDictionary(x => x.ProblemInstance, x => x.Performance);
      return new KNearestNeighborModel(KParameter.Value.Value, perfData, characteristics);
    }
  }
}