#region License Information /* HeuristicLab * Copyright (C) 2002-2018 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.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis { [Item("Clustering Transformation Model", "A clustering model that was transformed back to match the original variables after the training was performed on transformed variables.")] [StorableClass] public class ClusteringTransformationModel : DataAnalysisTransformationModel, IClusteringTransformationModel { public new IClusteringModel OriginalModel { get { return (IClusteringModel)base.OriginalModel; } } #region Constructor, Cloning & Persistence public ClusteringTransformationModel(IClusteringModel originalModel, IEnumerable transformations) : base(originalModel, transformations) { } protected ClusteringTransformationModel(ClusteringTransformationModel original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new ClusteringTransformationModel(this, cloner); } [StorableConstructor] protected ClusteringTransformationModel(bool deserializing) : base(deserializing) { } #endregion public IEnumerable GetClusterValues(IDataset dataset, IEnumerable rows) { var transformedInputs = DataAnalysisTransformation.Transform(dataset, InputTransformations); return OriginalModel.GetClusterValues(transformedInputs, rows); } } }