source: branches/DataAnalysis.PopulationDiversityAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/SupportVectorMachine/SupportVectorMachineModel.cs @ 7317

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 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;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using SVM;

namespace HeuristicLab.Problems.DataAnalysis.SupportVectorMachine {
  /// <summary>
  /// Represents a support vector machine model.
  /// </summary>
  [StorableClass]
  [Item("SupportVectorMachineModel", "Represents a support vector machine model.")]
  public sealed class SupportVectorMachineModel : NamedItem, IDataAnalysisModel {
    [StorableConstructor]
    private SupportVectorMachineModel(bool deserializing) : base(deserializing) { }
    private SupportVectorMachineModel(SupportVectorMachineModel original, Cloner cloner)
      : base(original, cloner) {
      // only using a shallow copy here! (gkronber)
      this.model = original.model;
      this.rangeTransform = original.rangeTransform;
    }
    public SupportVectorMachineModel() : base() { }

    private SVM.Model model;
    /// <summary>
    /// Gets or sets the SVM model.
    /// </summary>
    public SVM.Model Model {
      get { return model; }
      set {
        if (value != model) {
          if (value == null) throw new ArgumentNullException();
          model = value;
          OnChanged(EventArgs.Empty);
        }
      }
    }

    /// <summary>
    /// Gets or sets the range transformation for the model.
    /// </summary>
    private SVM.RangeTransform rangeTransform;
    public SVM.RangeTransform RangeTransform {
      get { return rangeTransform; }
      set {
        if (value != rangeTransform) {
          if (value == null) throw new ArgumentNullException();
          rangeTransform = value;
          OnChanged(EventArgs.Empty);
        }
      }
    }

    public IEnumerable<double> GetEstimatedValues(DataAnalysisProblemData problemData, int start, int end) {
      SVM.Problem problem = SupportVectorMachineUtil.CreateSvmProblem(problemData, Enumerable.Range(start, end - start));
      SVM.Problem scaledProblem = Scaling.Scale(RangeTransform, problem);

      return (from row in Enumerable.Range(0, scaledProblem.Count)
              select SVM.Prediction.Predict(Model, scaledProblem.X[row]))
              .ToList();
    }

    #region events
    public event EventHandler Changed;
    private void OnChanged(EventArgs e) {
      var handlers = Changed;
      if (handlers != null)
        handlers(this, e);
    }
    #endregion

    #region persistence
    [Storable]
    private int[] SupportVectorIndizes {
      get { return this.Model.SupportVectorIndizes; }
      set { this.Model.SupportVectorIndizes = value; }
    }

    [Storable]
    private string ModelAsString {
      get {
        using (MemoryStream stream = new MemoryStream()) {
          SVM.Model.Write(stream, Model);
          stream.Seek(0, System.IO.SeekOrigin.Begin);
          StreamReader reader = new StreamReader(stream);
          return reader.ReadToEnd();
        }
      }
      set {
        using (MemoryStream stream = new MemoryStream(Encoding.ASCII.GetBytes(value))) {
          model = SVM.Model.Read(stream);
        }
      }
    }
    [Storable]
    private string RangeTransformAsString {
      get {
        using (MemoryStream stream = new MemoryStream()) {
          SVM.RangeTransform.Write(stream, RangeTransform);
          stream.Seek(0, System.IO.SeekOrigin.Begin);
          StreamReader reader = new StreamReader(stream);
          return reader.ReadToEnd();
        }
      }
      set {
        using (MemoryStream stream = new MemoryStream(Encoding.ASCII.GetBytes(value))) {
          RangeTransform = SVM.RangeTransform.Read(stream);
        }
      }
    }
    #endregion

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

    /// <summary>
    ///  Exports the <paramref name="model"/> in string representation to stream <paramref name="s"/>
    /// </summary>
    /// <param name="model">The support vector regression model to export</param>
    /// <param name="s">The stream to export the model to</param>
    public static void Export(SupportVectorMachineModel model, Stream s) {
      StreamWriter writer = new StreamWriter(s);
      writer.WriteLine("RangeTransform:");
      writer.Flush();
      using (MemoryStream memStream = new MemoryStream()) {
        SVM.RangeTransform.Write(memStream, model.RangeTransform);
        memStream.Seek(0, SeekOrigin.Begin);
        memStream.WriteTo(s);
      }
      writer.WriteLine("Model:");
      writer.Flush();
      using (MemoryStream memStream = new MemoryStream()) {
        SVM.Model.Write(memStream, model.Model);
        memStream.Seek(0, SeekOrigin.Begin);
        memStream.WriteTo(s);
      }
      s.Flush();
    }

    /// <summary>
    /// Imports a support vector machine model given as string representation.
    /// </summary>
    /// <param name="reader">The reader to retrieve the string representation from</param>
    /// <returns>The imported support vector machine model.</returns>
    public static SupportVectorMachineModel Import(TextReader reader) {
      SupportVectorMachineModel model = new SupportVectorMachineModel();
      while (reader.ReadLine().Trim() != "RangeTransform:") ; // read until line "RangeTransform";
      model.RangeTransform = SVM.RangeTransform.Read(reader);
      // read until "Model:"
      while (reader.ReadLine().Trim() != "Model:") ;
      model.Model = SVM.Model.Read(reader);
      return model;
    }
  }
}