#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.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { [Item("NCA Model", "")] [StorableClass] public class NcaModel : ClassificationModel, INcaModel { public override IEnumerable VariablesUsedForPrediction { get { return allowedInputVariables; } } [Storable] private double[,] transformationMatrix; public double[,] TransformationMatrix { get { return (double[,])transformationMatrix.Clone(); } } [Storable] private string[] allowedInputVariables; [Storable] private INearestNeighbourModel nnModel; [Storable] private double[] classValues; [StorableConstructor] protected NcaModel(bool deserializing) : base(deserializing) { } protected NcaModel(NcaModel original, Cloner cloner) : base(original, cloner) { this.transformationMatrix = (double[,])original.transformationMatrix.Clone(); this.allowedInputVariables = (string[])original.allowedInputVariables.Clone(); this.nnModel = cloner.Clone(original.nnModel); this.classValues = (double[])original.classValues.Clone(); } public NcaModel(int k, double[,] transformationMatrix, IDataset dataset, IEnumerable rows, string targetVariable, IEnumerable allowedInputVariables, double[] classValues) : base(targetVariable) { Name = ItemName; Description = ItemDescription; this.transformationMatrix = (double[,])transformationMatrix.Clone(); this.allowedInputVariables = allowedInputVariables.ToArray(); this.classValues = (double[])classValues.Clone(); var ds = ReduceDataset(dataset, rows); nnModel = new NearestNeighbourModel(ds, Enumerable.Range(0, ds.Rows), k, ds.VariableNames.Last(), ds.VariableNames.Take(transformationMatrix.GetLength(1)), classValues); } public override IDeepCloneable Clone(Cloner cloner) { return new NcaModel(this, cloner); } public override IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows) { var ds = ReduceDataset(dataset, rows); return nnModel.GetEstimatedClassValues(ds, Enumerable.Range(0, ds.Rows)); } public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) { return new NcaClassificationSolution(this, new ClassificationProblemData(problemData)); } INcaClassificationSolution INcaModel.CreateClassificationSolution(IClassificationProblemData problemData) { return new NcaClassificationSolution(this, new ClassificationProblemData(problemData)); } public double[,] Reduce(IDataset dataset, IEnumerable rows) { var data = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows); var targets = dataset.GetDoubleValues(TargetVariable, rows).ToArray(); var result = new double[data.GetLength(0), transformationMatrix.GetLength(1) + 1]; for (int i = 0; i < data.GetLength(0); i++) for (int j = 0; j < data.GetLength(1); j++) { for (int x = 0; x < transformationMatrix.GetLength(1); x++) { result[i, x] += data[i, j] * transformationMatrix[j, x]; } result[i, transformationMatrix.GetLength(1)] = targets[i]; } return result; } public Dataset ReduceDataset(IDataset dataset, IEnumerable rows) { return new Dataset(Enumerable .Range(0, transformationMatrix.GetLength(1)) .Select(x => "X" + x.ToString()) .Concat(TargetVariable.ToEnumerable()), Reduce(dataset, rows)); } } }