source: branches/ParameterBinding/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Parameter.cs @ 12051

/*
 * SVM.NET Library
 * Copyright (C) 2008 Matthew Johnson
 * 
 * This program 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.
 * 
 * This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 */


using System;
using System.Collections.Generic;

namespace SVM {
  /// <summary>
  /// Contains all of the types of SVM this library can model.
  /// </summary>
  public enum SvmType {
    /// <summary>
    /// C-SVC.
    /// </summary>
    C_SVC,
    /// <summary>
    /// nu-SVC.
    /// </summary>
    NU_SVC,
    /// <summary>
    /// one-class SVM
    /// </summary>
    ONE_CLASS,
    /// <summary>
    /// epsilon-SVR
    /// </summary>
    EPSILON_SVR,
    /// <summary>
    /// nu-SVR
    /// </summary>
    NU_SVR
  };
  /// <summary>
  /// Contains the various kernel types this library can use.
  /// </summary>
  public enum KernelType {
    /// <summary>
    /// Linear: u'*v
    /// </summary>
    LINEAR,
    /// <summary>
    /// Polynomial: (gamma*u'*v + coef0)^degree
    /// </summary>
    POLY,
    /// <summary>
    /// Radial basis function: exp(-gamma*|u-v|^2)
    /// </summary>
    RBF,
    /// <summary>
    /// Sigmoid: tanh(gamma*u'*v + coef0)
    /// </summary>
    SIGMOID,
    /// <summary>
    /// Precomputed kernel
    /// </summary>
    PRECOMPUTED,
  };

  /// <summary>
  /// This class contains the various parameters which can affect the way in which an SVM
  /// is learned.  Unless you know what you are doing, chances are you are best off using
  /// the default values.
  /// </summary>
  [Serializable]
  public class Parameter : ICloneable {
    private SvmType _svmType;
    private KernelType _kernelType;
    private int _degree;
    private double _gamma;
    private double _coef0;

    private double _cacheSize;
    private double _C;
    private double _eps;

    private Dictionary<int, double> _weights;
    private double _nu;
    private double _p;
    private bool _shrinking;
    private bool _probability;

    /// <summary>
    /// Default Constructor.  Gives good default values to all parameters.
    /// </summary>
    public Parameter() {
      _svmType = SvmType.C_SVC;
      _kernelType = KernelType.RBF;
      _degree = 3;
      _gamma = 0; // 1/k
      _coef0 = 0;
      _nu = 0.5;
      _cacheSize = 40;
      _C = 1;
      _eps = 1e-3;
      _p = 0.1;
      _shrinking = true;
      _probability = false;
      _weights = new Dictionary<int, double>();
    }

    /// <summary>
    /// Type of SVM (default C-SVC)
    /// </summary>
    public SvmType SvmType {
      get {
        return _svmType;
      }
      set {
        _svmType = value;
      }
    }
    /// <summary>
    /// Type of kernel function (default Polynomial)
    /// </summary>
    public KernelType KernelType {
      get {
        return _kernelType;
      }
      set {
        _kernelType = value;
      }
    }
    /// <summary>
    /// Degree in kernel function (default 3).
    /// </summary>
    public int Degree {
      get {
        return _degree;
      }
      set {
        _degree = value;
      }
    }
    /// <summary>
    /// Gamma in kernel function (default 1/k)
    /// </summary>
    public double Gamma {
      get {
        return _gamma;
      }
      set {
        _gamma = value;
      }
    }
    /// <summary>
    /// Zeroeth coefficient in kernel function (default 0)
    /// </summary>
    public double Coefficient0 {
      get {
        return _coef0;
      }
      set {
        _coef0 = value;
      }
    }

    /// <summary>
    /// Cache memory size in MB (default 100)
    /// </summary>
    public double CacheSize {
      get {
        return _cacheSize;
      }
      set {
        _cacheSize = value;
      }
    }
    /// <summary>
    /// Tolerance of termination criterion (default 0.001)
    /// </summary>
    public double EPS {
      get {
        return _eps;
      }
      set {
        _eps = value;
      }
    }
    /// <summary>
    /// The parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
    /// </summary>
    public double C {
      get {
        return _C;
      }
      set {
        _C = value;
      }
    }

    /// <summary>
    /// Contains custom weights for class labels.  Default weight value is 1.
    /// </summary>
    public Dictionary<int, double> Weights {
      get {
        return _weights;
      }
    }

    /// <summary>
    /// The parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
    /// </summary>
    public double Nu {
      get {
        return _nu;
      }
      set {
        _nu = value;
      }
    }
    /// <summary>
    /// The epsilon in loss function of epsilon-SVR (default 0.1)
    /// </summary>
    public double P {
      get {
        return _p;
      }
      set {
        _p = value;
      }
    }
    /// <summary>
    /// Whether to use the shrinking heuristics, (default True)
    /// </summary>
    public bool Shrinking {
      get {
        return _shrinking;
      }
      set {
        _shrinking = value;
      }
    }
    /// <summary>
    /// Whether to train an SVC or SVR model for probability estimates, (default False)
    /// </summary>
    public bool Probability {
      get {
        return _probability;
      }
      set {
        _probability = value;
      }
    }


    #region ICloneable Members
    /// <summary>
    /// Creates a memberwise clone of this parameters object.
    /// </summary>
    /// <returns>The clone (as type Parameter)</returns>
    public object Clone() {
      return base.MemberwiseClone();
    }

    #endregion
  }
}