#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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { /// /// Nearest neighbour regression data analysis algorithm. /// [Item("Nearest Neighbour Regression (kNN)", "Nearest neighbour regression data analysis algorithm (wrapper for ALGLIB).")] [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 150)] [StorableClass] public sealed class NearestNeighbourRegression : FixedDataAnalysisAlgorithm { private const string KParameterName = "K"; private const string NearestNeighbourRegressionModelResultName = "Nearest neighbour regression solution"; #region parameter properties public IFixedValueParameter KParameter { get { return (IFixedValueParameter)Parameters[KParameterName]; } } #endregion #region properties public int K { get { return KParameter.Value.Value; } set { if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K"); else KParameter.Value.Value = value; } } #endregion [StorableConstructor] private NearestNeighbourRegression(bool deserializing) : base(deserializing) { } private NearestNeighbourRegression(NearestNeighbourRegression original, Cloner cloner) : base(original, cloner) { } public NearestNeighbourRegression() : base() { Parameters.Add(new FixedValueParameter(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3))); Problem = new RegressionProblem(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { } public override IDeepCloneable Clone(Cloner cloner) { return new NearestNeighbourRegression(this, cloner); } #region nearest neighbour protected override void Run() { var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K); Results.Add(new Result(NearestNeighbourRegressionModelResultName, "The nearest neighbour regression solution.", solution)); } public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k) { var clonedProblemData = (IRegressionProblemData)problemData.Clone(); return new NearestNeighbourRegressionSolution(Train(problemData, k), clonedProblemData); } public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k) { return new NearestNeighbourModel(problemData.Dataset, problemData.TrainingIndices, k, problemData.TargetVariable, problemData.AllowedInputVariables); } #endregion } }