#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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.Encodings.ParameterConfigurationEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.MetaOptimization {
  /// <summary>
  /// TODO
  /// </summary>
  [Item("PMOEvaluator", "TODO")]
  [StorableClass]
  public class PMOEvaluator : AlgorithmOperator, IParameterConfigurationEvaluator {
    #region Parameter properties
    public ILookupParameter<IRandom> RandomParameter {
      get { return (LookupParameter<IRandom>)Parameters["Random"]; }
    }
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public ILookupParameter<TypeValue> AlgorithmTypeParameter {
      get { return (ILookupParameter<TypeValue>)Parameters[MetaOptimizationProblem.AlgorithmTypeParameterName]; }
    }
    public ILookupParameter<IItemList<IProblem>> ProblemsParameter {
      get { return (ILookupParameter<IItemList<IProblem>>)Parameters[MetaOptimizationProblem.ProblemsParameterName]; }
    }
    public ILookupParameter<ParameterConfigurationTree> ParameterConfigurationParameter {
      get { return (ILookupParameter<ParameterConfigurationTree>)Parameters["ParameterConfigurationTree"]; }
    }
    public LookupParameter<IntValue> RepetitionsParameter {
      get { return (LookupParameter<IntValue>)Parameters[MetaOptimizationProblem.RepetitionsParameterName]; }
    }
    public LookupParameter<IntValue> GenerationsParameter {
      get { return (LookupParameter<IntValue>)Parameters["Generations"]; }
    }
    public LookupParameter<ResultCollection> ResultsParameter {
      get { return (LookupParameter<ResultCollection>)Parameters["Results"]; }
    }
    private ScopeParameter CurrentScopeParameter {
      get { return (ScopeParameter)Parameters["CurrentScope"]; }
    }
    public IScope CurrentScope {
      get { return CurrentScopeParameter.ActualValue; }
    }
    #endregion

    #region Constructors and Cloning
    public PMOEvaluator()
      : base() {
      Initialize();
    }

    [StorableConstructor]
    protected PMOEvaluator(bool deserializing) : base(deserializing) { }
    protected PMOEvaluator(PMOEvaluator original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new PMOEvaluator(this, cloner);
    }

    private void Initialize() {
      #region Create parameters
      Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used to initialize the new random permutation."));
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The evaluated quality of the ParameterVector."));
      Parameters.Add(new LookupParameter<TypeValue>(MetaOptimizationProblem.AlgorithmTypeParameterName, ""));
      Parameters.Add(new LookupParameter<IItemList<IProblem>>(MetaOptimizationProblem.ProblemsParameterName, ""));
      Parameters.Add(new LookupParameter<ParameterConfigurationTree>("ParameterConfigurationTree", ""));
      Parameters.Add(new LookupParameter<IntValue>(MetaOptimizationProblem.RepetitionsParameterName, "Number of evaluations on one problem."));
      Parameters.Add(new LookupParameter<IntValue>("Generations", ""));
      Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied."));
      #endregion

      var algorithmSubScopesCreator = new AlgorithmSubScopesCreator();
      var uniformSubScopesProcessor = new UniformSubScopesProcessor();
      var algorithmEvaluator = new AlgorithmEvaluator();
      var algorithmRunsAnalyzer = new AlgorithmRunsAnalyzer();

      uniformSubScopesProcessor.Parallel.Value = true;

      this.OperatorGraph.InitialOperator = algorithmSubScopesCreator;
      algorithmSubScopesCreator.Successor = uniformSubScopesProcessor;
      uniformSubScopesProcessor.Operator = algorithmEvaluator;
      uniformSubScopesProcessor.Successor = algorithmRunsAnalyzer;
      algorithmRunsAnalyzer.Successor = null;
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      ///// TODO: only for debug reasons - remove later (set this in stored algs)
      ((UniformSubScopesProcessor)((AlgorithmSubScopesCreator)this.OperatorGraph.InitialOperator).Successor).Parallel.Value = true;
    }
    #endregion
  }
}