#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("AugmentedExpectedImprovement", "Noisy InfillCriterion, Extension of the Expected Improvement as described in\n Global optimization of stochastic black-box systems via sequential kriging meta-models.\r\nHuang, D., Allen, T., Notz, W., Zeng, N.")]
  public class AugmentedExpectedImprovement : ExpectedImprovementBase {

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

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

    #region Properties
    public double Alpha => AlphaParameter.Value.Value;
    [Storable]
    private double Tau;
    #endregion

    #region Constructors, Serialization and Cloning 
    [StorableConstructor]
    protected AugmentedExpectedImprovement(bool deserializing) : base(deserializing) { }
    protected AugmentedExpectedImprovement(AugmentedExpectedImprovement original, Cloner cloner) : base(original, cloner) {
      Tau = original.Tau;
    }
    public AugmentedExpectedImprovement() {
      Parameters.Add(new ValueParameter<DoubleValue>(AlphaParameterName, "The Alpha value specifiying the robustness of the \"effective best solution\". Recommended value is 1", new DoubleValue(1.0)));
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new AugmentedExpectedImprovement(this, cloner);
    }
    #endregion

    public override double Evaluate(RealVector vector) {
      var model = RegressionSolution.Model as IConfidenceRegressionModel;
      return base.Evaluate(vector) * (1 - Tau / Math.Sqrt(model.GetVariance(vector) + Tau * Tau));
    }

    protected override double Evaluate(RealVector vector, double estimatedFitness, double estimatedStandardDeviation) {
      var d = GetEstimatedImprovement(BestFitness, estimatedFitness, estimatedStandardDeviation, ExploitationWeight, ExpensiveMaximization);
      return d * (1 - Tau / Math.Sqrt(estimatedStandardDeviation * estimatedStandardDeviation + Tau * Tau));
    }

    protected override double FindBestFitness(IConfidenceRegressionSolution solution) {
      Tau = RegressionSolution.EstimatedTrainingValues.Zip(RegressionSolution.ProblemData.TargetVariableTrainingValues, (d, d1) => Math.Abs(d - d1)).Average();
      var bestSolution = new RealVector(Encoding.Length);
      var xssIndex = solution.EstimatedTrainingValues.Zip(solution.EstimatedTrainingValues, (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++) bestSolution[j] = solution.ProblemData.Dataset.GetDoubleValue(i, j);
      return RegressionSolution.Model.GetEstimation(bestSolution);
    }
  }
}