source: trunk/test/vector_distance_test.cc @ 1050

// $Id: vector_distance_test.cc 1050 2008-02-07 18:47:34Z peter $

/*
  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/DataLookupWeighted1D.h"
#include "yat/classifier/MatrixLookupWeighted.h"
#include "yat/statistics/euclidean_distance.h"
#include "yat/statistics/pearson_distance.h"
#include "yat/utility/matrix.h"
#include "yat/utility/vector.h"

#include <cassert>
#include <fstream>
#include <iostream>
#include <list>
#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 << "distance_test -v : for printing extra information\n";
  }
  *error << "testing distance" << std::endl;
  bool ok = true;
  
  utility::vector a(3,1);
  a(1) = 2;
  utility::vector b(3,0);
  b(2) = 1;
  
  double tolerance=1e-4;
  statistics::EuclideanDistance eucl_dist;
  double dist=eucl_dist(a.begin(),a.end(),b.begin());
  if(fabs(dist-2.23607)>tolerance) {
    *error << "Error in unweighted Euclidean distance " << std::endl;
    ok=false;
  }
  
  statistics::PearsonDistance pear_dist;
  dist=pear_dist(a.begin(),a.end(),b.begin());
  if(fabs(dist-1.5)>tolerance) {
    *error << "Error in unweighted Pearson distance " << std::endl;
    ok=false;
  }
  
  
  // Testing weighted versions
  utility::matrix m(2,3,1);
  m(0,1)=2;
  m(1,0)=0;
  m(1,1)=0;
  utility::matrix w(2,3,1);
  w(0,0)=0;
  classifier::MatrixLookupWeighted mw(m,w);
  classifier::DataLookupWeighted1D aw(mw,0,true);
  classifier::DataLookupWeighted1D bw(mw,1,true);
  
  dist=eucl_dist(aw.begin(),aw.end(),bw.begin());
  
  if(fabs(dist-sqrt(6))>tolerance) {
    *error << "Error in weighted Euclidean distance " << std::endl;
    ok=false;
  }
  
  dist=pear_dist(aw.begin(),aw.end(),bw.begin());
  
  if(fabs(dist-2)>tolerance) {
    *error << "Error in weighted Pearson distance " << std::endl;
    ok=false;
  }
  
    
  // Test with std::vectors
  std::vector<double> sa(3,1);
  sa[1] = 2;
  std::vector<double> sb(3,0);
  sb[2] = 1;
  
  dist=eucl_dist(sa.begin(),sa.end(),sb.begin()); 
  if(fabs(dist-2.23607)>tolerance) {
    *error << "Error in distance for std::vector " << std::endl;
    ok=false;
  }
  
  // Test for a std::list and a std::vector
  std::list<double> la;
  std::copy(sa.begin(),sa.end(),std::back_inserter<std::list<double> >(la));
  dist=eucl_dist(la.begin(),la.end(),sb.begin());
  if(fabs(dist-2.23607)>tolerance) {
    *error << "Error in distance for std::list " << std::endl;
    ok=false;
  }
  
  if(!ok) {
    *error << "distance_test failed" << std::endl;
  }
  if (error!=&std::cerr)
    delete error;
  if (ok)  
    return 0;
  return -1;
}