#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 System.Linq;
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("ExpectedQuantileImprovement", "Noisy InfillCriterion, Extension of the Expected Improvement as described in \n Noisy expectedimprovement and on - line computation time allocation for the optimization of simulators with tunable fidelitys\r\nPicheny, V., Ginsbourger, D., Richet, Y")]
  public class ExpectedQuantileImprovement : ExpectedImprovement {

    #region Parameternames
    public const string AlphaParameterName = "Alpha";
    public const string MaxEvaluationsParameterName = "MaxEvaluations";
    #endregion

    #region Parameters
    public IFixedValueParameter<DoubleValue> AlphaParameter => Parameters[AlphaParameterName] as IFixedValueParameter<DoubleValue>;
    public IValueParameter<IntValue> MaxEvaluationsParameter => Parameters[MaxEvaluationsParameterName] as IValueParameter<IntValue>;
    #endregion

    #region Properties

    public int MaxEvaluations => MaxEvaluationsParameter.Value.Value;
    public double Alpha => AlphaParameter.Value.Value;
    [Storable]
    private double Tau;

    #endregion

    #region HL-Constructors, Serialization and Cloning 
    [StorableConstructor]
    private ExpectedQuantileImprovement(bool deserializing) : base(deserializing) { }

    private ExpectedQuantileImprovement(ExpectedQuantileImprovement original, Cloner cloner) : base(original, cloner) {
      Tau = original.Tau;
    }

    public ExpectedQuantileImprovement() {
      Parameters.Add(new FixedValueParameter<DoubleValue>(AlphaParameterName, "The Alpha value specifiying the robustness of the \"effective best solution\". Recommended value is 1.0", new DoubleValue(1.0)));
      Parameters.Add(new ValueParameter<IntValue>(MaxEvaluationsParameterName, "The maximum number of evaluations allowed for EGO", new IntValue(100)));
      MaxEvaluationsParameter.Hidden = true;
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new ExpectedQuantileImprovement(this, cloner);
    }
    #endregion

    public override double Evaluate(RealVector vector) {
      var model = RegressionSolution.Model as IConfidenceRegressionModel;
      var s2 = model.GetVariance(vector);

      var yhat = model.GetEstimation(vector) + Alpha * Math.Sqrt(Tau * s2 / (Tau + s2));
      var s = Math.Sqrt(s2 * s2 / (Tau + s2));

      return GetEstimatedImprovement(YMin, yhat, s, ExploitationWeight);
    }

    protected override void Initialize() {
      if (ExpensiveMaximization) throw new NotImplementedException("AugmentedExpectedImprovement for maximization not yet implemented");
      var solution = RegressionSolution as IConfidenceRegressionSolution;
      if (solution == null) throw new ArgumentException("can not calculate Augmented EI without a regression solution providing confidence values");

      Tau = RegressionSolution.EstimatedTrainingValues.Zip(RegressionSolution.ProblemData.TargetVariableTrainingValues, (d, d1) => Math.Abs(d - d1)).Average();
      Tau = Tau * Tau / (MaxEvaluations - RegressionSolution.ProblemData.Dataset.Rows + 1);

      var xss = new RealVector(Encoding.Length);
      var xssIndex = solution.EstimatedTrainingVariances.Zip(solution.EstimatedTrainingVariances, (m, s2) => m + Alpha * Math.Sqrt(s2)).ArgMin(x => x);
      var i = solution.ProblemData.TrainingIndices.ToArray()[xssIndex];
      for (var j = 0; j < Encoding.Length; j++) xss[j] = solution.ProblemData.Dataset.GetDoubleValue(i, j);

      YMin = RegressionSolution.Model.GetEstimation(xss);
    }

  }
}