NICE_GP_HIK_Exp/progs/testFPClassifier.cpp

/**
* @file testFPClassifier.cpp
* @brief main program for classifier evaluation
* @author Erik Rodner
* @date 2007-10-12
*/

#include <fstream>
#include <iostream>

#include <core/basics/numerictools.h>
#include <core/basics/Config.h>
#include <core/basics/StringTools.h>

//----------

#include <vislearning/baselib/Preprocess.h>

#include <vislearning/cbaselib/MultiDataset.h>
#include <vislearning/cbaselib/ClassificationResults.h>
#include <vislearning/cbaselib/MutualInformation.h>

#include <vislearning/classifier/classifierbase/FeaturePoolClassifier.h>
#include <vislearning/classifier/fpclassifier/gphik/FPCGPHIK.h>
#include <vislearning/classifier/fpclassifier/randomforest/FPCRandomForestTransfer.h>
#include <vislearning/classifier/classifierinterfaces/VCFeaturePool.h>

#include <vislearning/math/cluster/GMM.h>

//----------



#undef DEBUG

using namespace OBJREC;

using namespace NICE;
using namespace std;

int main ( int argc, char **argv )
{
  fprintf ( stderr, "testClassifier: init\n" );

  std::set_terminate ( __gnu_cxx::__verbose_terminate_handler );

  Config conf ( argc, argv );

  FPCGPHIK *classifier = new FPCGPHIK ( &conf, "ClassiferGPHIK" );

  string trainfn = conf.gS ( "data", "trainfile" );
  string testfn = conf.gS ( "data", "testfile" );

  Examples trainex;
  ifstream intrain ( trainfn.c_str() );

  int parts = 0;

  while ( intrain.good() )
  {
    string line;
    getline ( intrain, line );
    vector<string> split;
    StringTools::split ( line, ' ', split );

    if ( split.size() == 0 )
      break;

    if ( parts > 0 )
      assert ( parts == ( int ) split.size() );
    parts = split.size();

    int classno =  atoi ( split[0].c_str() );
    SparseVector *sv = new SparseVector();

    for ( uint i = 1; i < split.size();i++ )
    {
      vector<string> split2;
      StringTools::split ( split[i], ':', split2 );
      assert ( split2.size() == 2 );
      ( *sv ) [atoi ( split2[0].c_str() ) ] = atof ( split2[1].c_str() );
    }

    Example example;
    example.vec = NULL;
    example.svec = sv;

    trainex.push_back ( pair<int, Example> ( classno, example ) );

  }

  cout << "trainex.size(): " << trainex.size() << endl;

  Examples testex;
  ifstream intest ( testfn.c_str() );

  parts = 0;

  while ( intest.good() )
  {
    string line;
    getline ( intest, line );
    vector<string> split;
    StringTools::split ( line, ' ', split );

    if ( split.size() == 0 )
      break;

    if ( parts > 0 )
      assert ( parts == ( int ) split.size() );
    parts = split.size();

    int classno =  atoi ( split[0].c_str() );
    SparseVector *sv = new SparseVector();

    for ( uint i = 1; i < split.size();i++ )
    {
      vector<string> split2;
      StringTools::split ( split[i], ':', split2 );
      assert ( split2.size() == 2 );
      
      double val = atof (split2[1].c_str());
      
      if(val != 0.0)
        ( *sv ) [atoi ( split2[0].c_str() ) ] =  val;
    }

    Example example;
    example.vec = NULL;
    example.svec = sv;

    testex.push_back ( pair<int, Example> ( classno, example ) );

  }

  cout << "testex.size(): " << testex.size() << endl;

  FeaturePool fp;
  classifier->train ( fp, trainex );

  int counter = 0;
  
  for ( uint e = 0; e < testex.size(); e++ )
  {
    ClassificationResult r = classifier->classify ( testex[e].second );
    
    int bestclass = 0;
    double bestval = r.scores[0];
    
    for ( int j = 1 ; j < r.scores.size(); j++ )
    {
      if(r.scores[j] > bestval)
      {
        bestclass = j;
        bestval = r.scores[j];
      }
    }
    
    if(bestclass == testex[e].first)
      counter++;
  }

  cout << "avg: " << (double)counter/(double)testex.size() << endl;

  return 0;
}