#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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Operators; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { [Item("NcaModelCreator", "Creates an NCA model with a given matrix.")] [StorableClass] public class NcaModelCreator : SingleSuccessorOperator, INcaModelCreator { public ILookupParameter KParameter { get { return (ILookupParameter)Parameters["K"]; } } public ILookupParameter DimensionsParameter { get { return (ILookupParameter)Parameters["Dimensions"]; } } public ILookupParameter NcaMatrixParameter { get { return (ILookupParameter)Parameters["NcaMatrix"]; } } public ILookupParameter NcaMatrixGradientsParameter { get { return (ILookupParameter)Parameters["NcaMatrixGradients"]; } } public ILookupParameter ProblemDataParameter { get { return (ILookupParameter)Parameters["ProblemData"]; } } public ILookupParameter NcaModelParameter { get { return (ILookupParameter)Parameters["NcaModel"]; } } [StorableConstructor] protected NcaModelCreator(bool deserializing) : base(deserializing) { } protected NcaModelCreator(NcaModelCreator original, Cloner cloner) : base(original, cloner) { } public NcaModelCreator() { Parameters.Add(new LookupParameter("K", "How many neighbors should be considered for classification.")); Parameters.Add(new LookupParameter("Dimensions", "The dimensions to which the feature space should be reduced to.")); Parameters.Add(new LookupParameter("NcaMatrix", "The optimized matrix.")); Parameters.Add(new LookupParameter("NcaMatrixGradients", "The gradients from the matrix that is being optimized.")); Parameters.Add(new LookupParameter("ProblemData", "The classification problem data.")); Parameters.Add(new LookupParameter("NcaModel", "The NCA model that should be created.")); } public override IDeepCloneable Clone(Cloner cloner) { return new NcaModelCreator(this, cloner); } public override IOperation Apply() { var k = KParameter.ActualValue.Value; var dim = DimensionsParameter.ActualValue.Value; var vector = NcaMatrixParameter.ActualValue; var matrix = new double[vector.Length / dim, dim]; for (int i = 0; i < matrix.GetLength(0); i++) for (int j = 0; j < dim; j++) { matrix[i, j] = vector[i * dim + j]; } var problemData = ProblemDataParameter.ActualValue; NcaModelParameter.ActualValue = new NcaModel(k, matrix, problemData.Dataset, problemData.TrainingIndices, problemData.TargetVariable, problemData.AllowedInputVariables, problemData.ClassValues.ToArray()); return base.Apply(); } } }