source: branches/2745_EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/InfillCriteria/ExpectedImprovement.cs @ 17915

#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 HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Problems.DataAnalysis;

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

    [StorableType("a9a7c704-6620-4129-9da3-74d590c8422a")]
    [Item("ExpectedImprovementMeassure", "Extension of the Expected Improvement to a weighted version by ANDRAS SÓBESTER , STEPHEN J. LEARY and ANDY J. KEANE   in \n On the Design of Optimization Strategies Based on Global Response Surface Approximation Models")]
  public sealed class ExpectedImprovement : ExpectedImprovementBase {
    #region Constructors, Serialization and Cloning 
    [StorableConstructor]
    private ExpectedImprovement(StorableConstructorFlag deserializing) : base(deserializing) { }
    private ExpectedImprovement(ExpectedImprovement original, Cloner cloner) : base(original, cloner) { }
    public ExpectedImprovement() { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new ExpectedImprovement(this, cloner);
    }
    #endregion

    public override double Evaluate(RealVector vector) {
      var model = RegressionSolution.Model as IConfidenceRegressionModel;
      var yhat = model.GetEstimation(vector);
      var s = Math.Sqrt(model.GetVariance(vector));
      return GetEstimatedImprovement(BestFitness, yhat, s, ExploitationWeight, ExpensiveMaximization);
    }

    protected override double Evaluate(RealVector vector, double estimatedFitness, double estimatedStandardDeviation) {
      return GetEstimatedImprovement(BestFitness, estimatedFitness, estimatedStandardDeviation, ExploitationWeight, ExpensiveMaximization);
    }

    protected override double FindBestFitness(IConfidenceRegressionSolution solution) {
      return ExpensiveMaximization ? solution.ProblemData.TargetVariableTrainingValues.Max() : solution.ProblemData.TargetVariableTrainingValues.Min();
    }
  }
}