source: trunk/test/knn_test.cc @ 999

// $Id: knn_test.cc 999 2007-12-23 20:03:12Z jari $

/*
  Copyright (C) 2007 Peter Johansson, Markus Ringnér

  This file is part of the yat library, http://trac.thep.lu.se/yat

  The yat library 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 2 of the
  License, or (at your option) any later version.

  The yat library 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, write to the Free Software
  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
  02111-1307, USA.
*/

#include "yat/classifier/KNN.h"
#include "yat/classifier/MatrixLookupWeighted.h"
#include "yat/statistics/euclidean_vector_distance.h"
#include "yat/statistics/pearson_vector_distance.h"
#include "yat/utility/matrix.h"


#include <cassert>
#include <fstream>
#include <iostream>
#include <list>
#include <string>
#include <vector>


using namespace theplu::yat;

int main(const int argc,const char* argv[])

{  
  std::ostream* error;
  if (argc>1 && argv[1]==std::string("-v"))
    error = &std::cerr;
  else {
    error = new std::ofstream("/dev/null");
    if (argc>1)
      std::cout << "knn_test -v : for printing extra information\n";
  }
  *error << "testing knn" << std::endl;
  bool ok = true;
  
  std::ifstream is("data/sorlie_centroid_data.txt");
  utility::matrix data(is,'\t');
  is.close();
  
  is.open("data/sorlie_centroid_classes.txt");
  classifier::Target targets(is);
  is.close();

  // Generate weight matrix with 0 for missing values and 1 for others.
  utility::matrix weights(data.rows(),data.columns(),0.0);
  utility::nan(data,weights);

  classifier::MatrixLookupWeighted dataviewweighted(data,weights);
  classifier::KNN<statistics::pearson_vector_distance_tag> knn(dataviewweighted,targets);
  *error << "training KNN" << std::endl;
  knn.train();
  
  utility::matrix prediction;
  knn.predict(dataviewweighted,prediction);
  *error << prediction << std::endl;
  
  if(!ok) {
    *error << "knn_test failed" << std::endl;
  }
  else {
    *error << "OK" << std::endl;
  }
  if (error!=&std::cerr)
    delete error;
  if (ok=true)  
    return 0;
  return -1;
}