source: trunk/test/ncc_test.cc @ 901

Last change on this file since 901 was 901, checked in by Markus Ringnér, 16 years ago

Added a template KNN classifier where the distance measure is the template. Refs #250 and #182

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 4.0 KB
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1// $Id: ncc_test.cc 901 2007-09-27 09:00:05Z markus $
2
3/*
4  Copyright (C) 2006 Jari Häkkinen, Markus Ringnér
5
6  This file is part of the yat library, http://trac.thep.lu.se/trac/yat
7
8  The yat library is free software; you can redistribute it and/or
9  modify it under the terms of the GNU General Public License as
10  published by the Free Software Foundation; either version 2 of the
11  License, or (at your option) any later version.
12
13  The yat library is distributed in the hope that it will be useful,
14  but WITHOUT ANY WARRANTY; without even the implied warranty of
15  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
16  General Public License for more details.
17
18  You should have received a copy of the GNU General Public License
19  along with this program; if not, write to the Free Software
20  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
21  02111-1307, USA.
22*/
23
24#include "yat/classifier/IGP.h"
25#include "yat/classifier/MatrixLookup.h"
26#include "yat/classifier/MatrixLookupWeighted.h"
27#include "yat/classifier/NCC.h"
28#include "yat/classifier/Target.h"
29#include "yat/utility/matrix.h"
30#include "yat/statistics/vector_distance_ptr.h"
31#include "yat/statistics/pearson_vector_distance.h"
32#include "yat/utility/utility.h"
33
34#include <cassert>
35#include <fstream>
36#include <iostream>
37#include <sstream>
38#include <string>
39#include <limits>
40#include <cmath>
41
42using namespace theplu::yat;
43
44int main(const int argc,const char* argv[])
45{ 
46
47  std::ostream* error;
48  if (argc>1 && argv[1]==std::string("-v"))
49    error = &std::cerr;
50  else {
51    error = new std::ofstream("/dev/null");
52    if (argc>1)
53      std::cout << "ncc_test -v : for printing extra information\n";
54  }
55  *error << "testing ncc" << std::endl;
56  bool ok = true;
57
58  std::ifstream is("data/sorlie_centroid_data.txt");
59  utility::matrix data(is,'\t');
60  is.close();
61
62  is.open("data/sorlie_centroid_classes.txt");
63  classifier::Target targets(is);
64  is.close();
65
66  // Generate weight matrix with 0 for missing values and 1 for others.
67  utility::matrix weights(data.rows(),data.columns(),0.0);
68  utility::nan(data,weights);
69     
70  classifier::MatrixLookupWeighted dataviewweighted(data,weights);
71  statistics::vector_distance_lookup_weighted_ptr distance=
72    statistics::vector_distance<statistics::pearson_vector_distance_tag>;
73  classifier::NCC ncc(dataviewweighted,targets,distance);
74  ncc.train();
75
76  // Comparing the centroids to stored result
77  is.open("data/sorlie_centroids.txt");
78  utility::matrix centroids(is);
79  is.close();
80
81  if(centroids.rows() != ncc.centroids().rows() ||
82     centroids.columns() != ncc.centroids().columns()) {
83    *error << "Error in the dimensionality of centroids\n";
84    *error << "Nof rows: " << centroids.rows() << " expected: " 
85           << ncc.centroids().rows() << std::endl;
86    *error << "Nof columns: " << centroids.columns() << " expected: " 
87           << ncc.centroids().columns() << std::endl;
88  }
89
90  double slack = 0;
91  for (size_t i=0; i<centroids.rows(); i++){
92    for (size_t j=0; j<centroids.columns(); j++){
93      slack += fabs(centroids(i,j)-ncc.centroids()(i,j));
94    }
95  }
96  slack /= (centroids.columns()*centroids.rows());
97  double slack_bound=2e-7;
98  if (slack > slack_bound || std::isnan(slack)){
99    *error << "Difference to stored centroids too large\n";
100    *error << "slack: " << slack << std::endl;
101    *error << "expected less than " << slack_bound << std::endl;
102    ok = false;
103  }
104
105  utility::matrix prediction;
106  ncc.predict(dataviewweighted,prediction);
107 
108  // Comparing the prediction to stored result
109  is.open("data/sorlie_centroid_predictions.txt");
110  utility::matrix result(is,'\t');
111  is.close();
112
113  slack = 0;
114  for (size_t i=0; i<result.rows(); i++){
115    for (size_t j=0; j<result.columns(); j++){
116        slack += fabs(result(i,j)-prediction(i,j));
117    }
118  }
119  slack /= (result.columns()*result.rows());
120  if (slack > slack_bound || std::isnan(slack)){
121    *error << "Difference to stored prediction too large\n";
122    *error << "slack: " << slack << std::endl;
123    *error << "expected less than " << slack_bound << std::endl;
124    ok = false;
125  }
126 
127
128  if(ok)
129    *error << "OK" << std::endl;
130
131
132  if (error!=&std::cerr)
133    delete error;
134
135  if(ok) 
136    return 0;
137  return -1;
138 
139}
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