#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);
}
}
}