#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 . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Algorithms.DataAnalysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; // ReSharper disable once CheckNamespace namespace HeuristicLab.Algorithms.EGO { [StorableClass] [Item("NeighbourDistance", "Exploration by maximizing the distance to the nearest neighbour")] public class NeighbourDistance : InfillCriterionBase { private VantagePointTree> Points; #region Constructors, Serialization and Cloning [StorableConstructor] protected NeighbourDistance(bool deserializing) : base(deserializing) { } protected NeighbourDistance(NeighbourDistance original, Cloner cloner) : base(original, cloner) { } public NeighbourDistance() { } public override IDeepCloneable Clone(Cloner cloner) { return new NeighbourDistance(this, cloner); } #endregion public override double Evaluate(RealVector vector) { if (Points == null) Points = CreateTree(); IList> neighbours; IList distances; Points.Search(vector, 1, out neighbours, out distances); return distances[0]; } public override void Initialize() { Points = CreateTree(); } private VantagePointTree> CreateTree() { var data = RegressionSolution.ProblemData.Dataset; var rows = RegressionSolution.ProblemData.AllIndices; var cols = RegressionSolution.ProblemData.AllowedInputVariables; return new VantagePointTree>(new EuclideanDistance(), rows.Select(r => cols.Select(c => data.GetDoubleValue(c, r)).ToArray())); } } }