source: branches/ParameterBinding/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Training.cs @ 6409

/*
 * 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;

namespace SVM {
  /// <summary>
  /// Class containing the routines to train SVM models.
  /// </summary>
  public static class Training {
    /// <summary>
    /// Whether the system will output information to the console during the training process.
    /// </summary>
    public static bool IsVerbose {
      get {
        return Procedures.IsVerbose;
      }
      set {
        Procedures.IsVerbose = value;
      }
    }

    private static double doCrossValidation(Problem problem, Parameter parameters, int nr_fold, bool shuffleTraining) {
      int i;
      double[] target = new double[problem.Count];
      Procedures.svm_cross_validation(problem, parameters, nr_fold, target, shuffleTraining);
      int total_correct = 0;
      double total_error = 0;
      //double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
      if (parameters.SvmType == SvmType.EPSILON_SVR || parameters.SvmType == SvmType.NU_SVR) {
        for (i = 0; i < problem.Count; i++) {
          double y = problem.Y[i];
          double v = target[i];
          total_error += (v - y) * (v - y);
          //sumv += v;
          //sumy += y;
          //sumvv += v * v;
          //sumyy += y * y;
          //sumvy += v * y;
        }
        return total_error / problem.Count; // return MSE
        // (problem.Count * sumvy - sumv * sumy) / (Math.Sqrt(problem.Count * sumvv - sumv * sumv) * Math.Sqrt(problem.Count * sumyy - sumy * sumy));
      } else
        for (i = 0; i < problem.Count; i++)
          if (target[i] == problem.Y[i])
            ++total_correct;
      return (double)total_correct / problem.Count;
    }
    /// <summary>
    /// Legacy.  Allows use as if this was svm_train.  See libsvm documentation for details on which arguments to pass.
    /// </summary>
    /// <param name="args"></param>
    [Obsolete("Provided only for legacy compatibility, use the other Train() methods")]
    public static void Train(params string[] args) {
      Parameter parameters;
      Problem problem;
      bool crossValidation;
      int nrfold;
      string modelFilename;
      parseCommandLine(args, out parameters, out problem, out crossValidation, out nrfold, out modelFilename);
      if (crossValidation)
        PerformCrossValidation(problem, parameters, nrfold, true);
      else Model.Write(modelFilename, Train(problem, parameters));
    }

    /// <summary>
    /// Performs cross validation.
    /// </summary>
    /// <param name="problem">The training data</param>
    /// <param name="parameters">The parameters to test</param>
    /// <param name="nrfold">The number of cross validations to use</param>
    /// <returns>The cross validation score</returns>
    public static double PerformCrossValidation(Problem problem, Parameter parameters, int nrfold, bool shuffleTraining) {
      string error = Procedures.svm_check_parameter(problem, parameters);
      if (error == null)
        return doCrossValidation(problem, parameters, nrfold, shuffleTraining);
      else throw new Exception(error);
    }

    /// <summary>
    /// Trains a model using the provided training data and parameters.
    /// </summary>
    /// <param name="problem">The training data</param>
    /// <param name="parameters">The parameters to use</param>
    /// <returns>A trained SVM Model</returns>
    public static Model Train(Problem problem, Parameter parameters) {
      string error = Procedures.svm_check_parameter(problem, parameters);

      if (error == null)
        return Procedures.svm_train(problem, parameters);
      else throw new Exception(error);
    }

    private static void parseCommandLine(string[] args, out Parameter parameters, out Problem problem, out bool crossValidation, out int nrfold, out string modelFilename) {
      int i;

      parameters = new Parameter();
      // default values

      crossValidation = false;
      nrfold = 0;

      // parse options
      for (i = 0; i < args.Length; i++) {
        if (args[i][0] != '-')
          break;
        ++i;
        switch (args[i - 1][1]) {

          case 's':
            parameters.SvmType = (SvmType)int.Parse(args[i]);
            break;

          case 't':
            parameters.KernelType = (KernelType)int.Parse(args[i]);
            break;

          case 'd':
            parameters.Degree = int.Parse(args[i]);
            break;

          case 'g':
            parameters.Gamma = double.Parse(args[i]);
            break;

          case 'r':
            parameters.Coefficient0 = double.Parse(args[i]);
            break;

          case 'n':
            parameters.Nu = double.Parse(args[i]);
            break;

          case 'm':
            parameters.CacheSize = double.Parse(args[i]);
            break;

          case 'c':
            parameters.C = double.Parse(args[i]);
            break;

          case 'e':
            parameters.EPS = double.Parse(args[i]);
            break;

          case 'p':
            parameters.P = double.Parse(args[i]);
            break;

          case 'h':
            parameters.Shrinking = int.Parse(args[i]) == 1;
            break;

          case 'b':
            parameters.Probability = int.Parse(args[i]) == 1;
            break;

          case 'v':
            crossValidation = true;
            nrfold = int.Parse(args[i]);
            if (nrfold < 2) {
              throw new ArgumentException("n-fold cross validation: n must >= 2");
            }
            break;

          case 'w':
            parameters.Weights[int.Parse(args[i - 1].Substring(2))] = double.Parse(args[1]);
            break;

          default:
            throw new ArgumentException("Unknown Parameter");
        }
      }

      // determine filenames

      if (i >= args.Length)
        throw new ArgumentException("No input file specified");

      problem = Problem.Read(args[i]);

      if (parameters.Gamma == 0)
        parameters.Gamma = 1.0 / problem.MaxIndex;

      if (i < args.Length - 1)
        modelFilename = args[i + 1];
      else {
        int p = args[i].LastIndexOf('/') + 1;
        modelFilename = args[i].Substring(p) + ".model";
      }
    }
  }
}