source: branches/Trunk/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Prediction.cs @ 6833

/*
 * 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.Diagnostics;
using System.IO;

namespace SVM {
  /// <summary>
  /// Class containing the routines to perform class membership prediction using a trained SVM.
  /// </summary>
  public static class Prediction {
    /// <summary>
    /// Predicts the class memberships of all the vectors in the problem.
    /// </summary>
    /// <param name="problem">The SVM Problem to solve</param>
    /// <param name="outputFile">File for result output</param>
    /// <param name="model">The Model to use</param>
    /// <param name="predict_probability">Whether to output a distribution over the classes</param>
    /// <returns>Percentage correctly labelled</returns>
    public static double Predict(
        Problem problem,
        string outputFile,
        Model model,
        bool predict_probability) {
      int correct = 0;
      int total = 0;
      double error = 0;
      double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
      StreamWriter output = outputFile != null ? new StreamWriter(outputFile) : null;

      SvmType svm_type = Procedures.svm_get_svm_type(model);
      int nr_class = Procedures.svm_get_nr_class(model);
      int[] labels = new int[nr_class];
      double[] prob_estimates = null;

      if (predict_probability) {
        if (svm_type == SvmType.EPSILON_SVR || svm_type == SvmType.NU_SVR) {
          Console.WriteLine("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + Procedures.svm_get_svr_probability(model));
        } else {
          Procedures.svm_get_labels(model, labels);
          prob_estimates = new double[nr_class];
          if (output != null) {
            output.Write("labels");
            for (int j = 0; j < nr_class; j++) {
              output.Write(" " + labels[j]);
            }
            output.Write("\n");
          }
        }
      }
      for (int i = 0; i < problem.Count; i++) {
        double target = problem.Y[i];
        Node[] x = problem.X[i];

        double v;
        if (predict_probability && (svm_type == SvmType.C_SVC || svm_type == SvmType.NU_SVC)) {
          v = Procedures.svm_predict_probability(model, x, prob_estimates);
          if (output != null) {
            output.Write(v + " ");
            for (int j = 0; j < nr_class; j++) {
              output.Write(prob_estimates[j] + " ");
            }
            output.Write("\n");
          }
        } else {
          v = Procedures.svm_predict(model, x);
          if (output != null)
            output.Write(v + "\n");
        }

        if (v == target)
          ++correct;
        error += (v - target) * (v - target);
        sumv += v;
        sumy += target;
        sumvv += v * v;
        sumyy += target * target;
        sumvy += v * target;
        ++total;
      }
      if (output != null)
        output.Close();
      return (double)correct / total;
    }

    /// <summary>
    /// Predict the class for a single input vector.
    /// </summary>
    /// <param name="model">The Model to use for prediction</param>
    /// <param name="x">The vector for which to predict class</param>
    /// <returns>The result</returns>
    public static double Predict(Model model, Node[] x) {
      return Procedures.svm_predict(model, x);
    }

    /// <summary>
    /// Predicts a class distribution for the single input vector.
    /// </summary>
    /// <param name="model">Model to use for prediction</param>
    /// <param name="x">The vector for which to predict the class distribution</param>
    /// <returns>A probability distribtion over classes</returns>
    public static double[] PredictProbability(Model model, Node[] x) {
      SvmType svm_type = Procedures.svm_get_svm_type(model);
      if (svm_type != SvmType.C_SVC && svm_type != SvmType.NU_SVC)
        throw new Exception("Model type " + svm_type + " unable to predict probabilities.");
      int nr_class = Procedures.svm_get_nr_class(model);
      double[] probEstimates = new double[nr_class];
      Procedures.svm_predict_probability(model, x, probEstimates);
      return probEstimates;
    }

    private static void exit_with_help() {
      Debug.Write("usage: svm_predict [options] test_file model_file output_file\n" + "options:\n" + "-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n");
      Environment.Exit(1);
    }

    /// <summary>
    /// Legacy method, provided to allow usage as though this were the command line version of libsvm.
    /// </summary>
    /// <param name="args">Standard arguments passed to the svm_predict exectutable.  See libsvm documentation for details.</param>
    [Obsolete("Use the other version of Predict() instead")]
    public static void Predict(params string[] args) {
      int i = 0;
      bool predictProbability = false;

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

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

          default:
            throw new ArgumentException("Unknown option");

        }
      }
      if (i >= args.Length)
        throw new ArgumentException("No input, model and output files provided");

      Problem problem = Problem.Read(args[i]);
      Model model = Model.Read(args[i + 1]);
      Predict(problem, args[i + 2], model, predictProbability);
    }
  }
}