source: branches/NCA/HeuristicLab.Algorithms.NCA/3.3/NCA.cs @ 8425

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Linq;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.NCA {
  /// <summary>
  /// Neighborhood Components Analysis
  /// </summary>
  [Item("NCA", "Neihborhood Components Analysis is described in J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov. 2005. Neighbourhood Component Analysis. Advances in Neural Information Processing Systems, 17. pp. 513-520.")]
  [Creatable("Data Analysis")]
  [StorableClass]
  public sealed class NCA : FixedDataAnalysisAlgorithm<IClassificationProblem> {
    #region Parameter Properties
    public IValueLookupParameter<IntValue> KParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["k"]; }
    }
    public IValueLookupParameter<IntValue> ReduceDimensionsParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["ReduceDimensions"]; }
    }
    private IConstrainedValueParameter<INCAInitializer> InitializationParameter {
      get { return (IConstrainedValueParameter<INCAInitializer>)Parameters["Initialization"]; }
    }
    #endregion

    #region Properties
    public IntValue K {
      get { return KParameter.Value; }
    }
    public IntValue ReduceDimensions {
      get { return ReduceDimensionsParameter.Value; }
    }
    #endregion

    [StorableConstructor]
    private NCA(bool deserializing) : base(deserializing) { }
    private NCA(NCA original, Cloner cloner) : base(original, cloner) { }
    public NCA()
      : base() {
      Parameters.Add(new ValueLookupParameter<IntValue>("k", "The k for the nearest neighbor.", new IntValue(1)));
      Parameters.Add(new ValueLookupParameter<IntValue>("ReduceDimensions", "The number of dimensions that NCA should reduce the data to.", new IntValue(2)));
      Parameters.Add(new ConstrainedValueParameter<INCAInitializer>("Initialization", "Which method should be used to initialize the matrix. Typically LDA (linear discriminant analysis) should provide a good estimate."));

      INCAInitializer defaultInitializer = null;
      foreach (var initializer in ApplicationManager.Manager.GetInstances<INCAInitializer>().OrderBy(x => x.ItemName)) {
        if (initializer is LDAInitializer) defaultInitializer = initializer;
        InitializationParameter.ValidValues.Add(initializer);
      }
      if (defaultInitializer != null) InitializationParameter.Value = defaultInitializer;

      Problem = new ClassificationProblem();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new NCA(this, cloner);
    }

    public override void Prepare() {
      if (Problem != null) base.Prepare();
    }

    protected override void Run() {
      var classification = NeighborhoodComponentsAnalysis.CreateNCASolution(Problem.ProblemData, K.Value, ReduceDimensions.Value, InitializationParameter.Value);
      Results.Add(new Result("ClassificationSolution", "The classification solution.", classification));
    }
  }
}