#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 System;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

// ReSharper disable once CheckNamespace
namespace HeuristicLab.Algorithms.EGO {

  [StorableClass]
  [Item("RobustImprovementMeassure", "Adding or Subtracting the variance * factor to the model estimation")]
  public class RobustImprovement : InfillCriterionBase {

    #region ParameterNames
    private const string ConfidenceWeightParameterName = "ConfidenceWeight";
    #endregion

    #region ParameterProperties
    public IFixedValueParameter<DoubleValue> ConfidenceWeightParameter
    {
      get { return Parameters[ConfidenceWeightParameterName] as IFixedValueParameter<DoubleValue>; }
    }
    #endregion

    #region Properties
    private double ConfidenceWeight
    {
      get { return ConfidenceWeightParameter.Value.Value; }
    }
    #endregion

    #region HL-Constructors, Serialization and Cloning 
    [StorableConstructor]
    private RobustImprovement(bool deserializing) : base(deserializing) { }
    private RobustImprovement(RobustImprovement original, Cloner cloner) : base(original, cloner) { }
    public RobustImprovement() {
      Parameters.Add(new FixedValueParameter<DoubleValue>(ConfidenceWeightParameterName, "A value between 0 and 1 indicating the focus on exploration (0) or exploitation (1)", new DoubleValue(0.5)));
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new RobustImprovement(this, cloner);
    }
    #endregion

    public override double Evaluate(IRegressionSolution solution, RealVector vector, bool maximization) {
      var model = solution.Model as IConfidenceRegressionModel;
      if (model == null) throw new ArgumentException("can not calculate EI without confidence measure");
      var yhat = model.GetEstimation(vector);
      var s = Math.Sqrt(model.GetVariance(vector)) * ConfidenceWeight;
      return maximization ? yhat + s : yhat - s;
    }

  }
}