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

namespace HeuristicLab.Encodings.ParameterConfigurationEncoding {
  /// <summary>
  /// An operator which crosses two sets of parameters.
  /// </summary>
  [Item("ParameterConfigurationCrossover", "An operator which crosses two sets of parameters.")]
  [StorableClass]
  public class ParameterConfigurationCrossover : SingleSuccessorOperator, IParameterConfigurationCrossover {
    public override bool CanChangeName {
      get { return false; }
    }

    public ILookupParameter<IRandom> RandomParameter {
      get { return (LookupParameter<IRandom>)Parameters["Random"]; }
    }
    public ILookupParameter<ItemArray<ParameterConfigurationTree>> ParentsParameter {
      get { return (ScopeTreeLookupParameter<ParameterConfigurationTree>)Parameters["Parents"]; }
    }
    public ILookupParameter<ParameterConfigurationTree> ChildParameter {
      get { return (ILookupParameter<ParameterConfigurationTree>)Parameters["Child"]; }
    }

    public IValueLookupParameter<IIntValueCrossover> IntValueCrossoverParameter {
      get { return (IValueLookupParameter<IIntValueCrossover>)Parameters["IntValueCrossover"]; }
    }
    public IValueLookupParameter<IDoubleValueCrossover> DoubleValueCrossoverParameter {
      get { return (IValueLookupParameter<IDoubleValueCrossover>)Parameters["DoubleValueCrossover"]; }
    }

    /// <summary>
    /// Whether the problem is a maximization or minimization problem.
    /// </summary>
    public ValueLookupParameter<BoolValue> MaximizationParameter {
      get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
    }

    /// <summary>
    /// The quality of the parents.
    /// </summary>
    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
    }

    #region Constructors and Cloning
    public ParameterConfigurationCrossover()
      : base() {
      Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used for stochastic crossover operators."));
      Parameters.Add(new ScopeTreeLookupParameter<ParameterConfigurationTree>("Parents", "The parent vectors which should be crossed."));
      Parameters.Add(new LookupParameter<ParameterConfigurationTree>("Child", "The child vector resulting from the crossover."));
      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "Whether the problem is a maximization problem or not."));
      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The quality values of the parents."));
      Parameters.Add(new ValueLookupParameter<IIntValueCrossover>("IntValueCrossover", ""));
      Parameters.Add(new ValueLookupParameter<IDoubleValueCrossover>("DoubleValueCrossover", ""));
    }

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

    public override IOperation Apply() {
      if (MaximizationParameter.ActualValue == null) throw new InvalidOperationException("ParameterConfigurationCrossover: Parameter " + MaximizationParameter.ActualName + " could not be found.");
      if (QualityParameter.ActualValue == null || QualityParameter.ActualValue.Length != 2) throw new InvalidOperationException("ParameterConfigurationCrossover: Parameter " + QualityParameter.ActualName + " could not be found, or not in the same quantity as there are parents.");
      ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
      bool maximization = MaximizationParameter.ActualValue.Value;

      ParameterConfigurationTree child1;
      ParameterConfigurationTree child2;

      if (maximization && qualities[0].Value >= qualities[1].Value || !maximization && qualities[0].Value <= qualities[1].Value) {
        child1 = (ParameterConfigurationTree)ParentsParameter.ActualValue[0].Clone();
        child2 = ParentsParameter.ActualValue[1];
      } else {
        child1 = (ParameterConfigurationTree)ParentsParameter.ActualValue[1].Clone();
        child2 = ParentsParameter.ActualValue[0];
      }

      Cross(RandomParameter.ActualValue, child1, child2, IntValueCrossoverParameter.ActualValue, DoubleValueCrossoverParameter.ActualValue);
      ChildParameter.ActualValue = child1;

      return base.Apply();
    }

    public static void Apply(IRandom random, IOptimizable configuartion, IOptimizable other, IIntValueCrossover intValueCrossover, IDoubleValueCrossover doubleValueCrossover) {
      Cross(random, configuartion, other, intValueCrossover, doubleValueCrossover);
    }

    public static void Cross(IRandom random, IOptimizable configuration, IOptimizable other, IIntValueCrossover intValueCrossover, IDoubleValueCrossover doubleValueCrossover) {
      if (configuration is ParameterizedValueConfiguration) {
        var configVc = (ParameterizedValueConfiguration)configuration;
        var otherVc = (ParameterizedValueConfiguration)other;
        for (int i = 0; i < configVc.ParameterConfigurations.Count; i++)
          Cross(random, configVc.ParameterConfigurations.ElementAt(i), otherVc.ParameterConfigurations.ElementAt(i), intValueCrossover, doubleValueCrossover);
      } else if (configuration is ParameterConfiguration) {
        var configPc = (IParameterConfiguration)configuration;
        if (configPc.Optimize) {
          var otherPc = (IParameterConfiguration)other;
          for (int i = 0; i < configPc.ValueConfigurations.Count; i++)
            if (configPc.ValueConfigurations.ItemChecked(configPc.ValueConfigurations[i]))
              if (configPc.ValueConfigurations[i].Optimize) Cross(random, configPc.ValueConfigurations[i], otherPc.ValueConfigurations[i], intValueCrossover, doubleValueCrossover);
          if (random.NextDouble() > 0.5) configPc.ActualValueConfigurationIndex = otherPc.ActualValueConfigurationIndex;
          configPc.ActualValue = configPc.ValueConfigurations[configPc.ActualValueConfigurationIndex].ActualValue;
        }
      } else if (configuration is RangeValueConfiguration) {
        var configVc = (RangeValueConfiguration)configuration;
        var value = configVc.ActualValue.Value;
        var range = configVc.RangeConstraint;
        if (value is IntValue)
          intValueCrossover.Apply(random, (IntValue)value, (IntValue)other.ActualValue.Value, (IntValueRange)range);
        else if (value is PercentValue)
          doubleValueCrossover.Apply(random, (PercentValue)value, (DoubleValue)other.ActualValue.Value, ((PercentValueRange)range).AsDoubleValueRange());
        else if (value is DoubleValue)
          doubleValueCrossover.Apply(random, (DoubleValue)value, (DoubleValue)other.ActualValue.Value, (DoubleValueRange)range);
      }
    }
  }
}