#region License Information /* HeuristicLab * Copyright (C) 2002-2014 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.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class FeatureSelectionRegressionProblemData : RegressionProblemData { private const string SelectedFeaturesParameterName = "SelectedFeatures"; private const string WeightsParameterName = "Weights"; private const string OptimalRSquaredParameterName = "R² (best solution)"; public IValueParameter SelectedFeaturesParameter { get { return (IValueParameter)Parameters[SelectedFeaturesParameterName]; } } public IValueParameter WeightsParameter { get { return (IValueParameter)Parameters[WeightsParameterName]; } } public IValueParameter OptimalRSquaredParameter { get { return (IValueParameter)Parameters[OptimalRSquaredParameterName]; } } [StorableConstructor] protected FeatureSelectionRegressionProblemData(bool deserializing) : base(deserializing) { } protected FeatureSelectionRegressionProblemData(FeatureSelectionRegressionProblemData original, Cloner cloner) : base(original, cloner) { } public FeatureSelectionRegressionProblemData(Dataset ds, IEnumerable allowedInputVariables, string targetVariable, string[] selectedFeatures, double[] weights, double optimalRSquared) : base(ds, allowedInputVariables, targetVariable) { if (selectedFeatures.Length != weights.Length) throw new ArgumentException("Length of selected features vector does not match the length of the weights vector"); if (optimalRSquared < 0 || optimalRSquared > 1) throw new ArgumentException("Optimal R² is not in range [0..1]"); Parameters.Add(new FixedValueParameter( SelectedFeaturesParameterName, "Array of features used to generate the target values.", new StringArray(selectedFeatures).AsReadOnly())); Parameters.Add(new FixedValueParameter( WeightsParameterName, "Array of weights used to generate the target values.", (DoubleArray)(new DoubleArray(weights).AsReadOnly()))); Parameters.Add(new FixedValueParameter( OptimalRSquaredParameterName, "R² of the optimal solution.", (DoubleValue)(new DoubleValue(optimalRSquared).AsReadOnly()))); } public override IDeepCloneable Clone(Cloner cloner) { return new FeatureSelectionRegressionProblemData(this, cloner); } } }