1 | // $Id: ncc_test.cc 916 2007-09-30 00:50:10Z peter $ |
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2 | |
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3 | /* |
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4 | Copyright (C) 2006 Jari Häkkinen, Markus Ringnér |
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5 | |
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6 | This file is part of the yat library, http://trac.thep.lu.se/trac/yat |
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7 | |
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8 | The yat library is free software; you can redistribute it and/or |
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9 | modify it under the terms of the GNU General Public License as |
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10 | published by the Free Software Foundation; either version 2 of the |
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11 | License, or (at your option) any later version. |
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12 | |
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13 | The yat library is distributed in the hope that it will be useful, |
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14 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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16 | General Public License for more details. |
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17 | |
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18 | You should have received a copy of the GNU General Public License |
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19 | along with this program; if not, write to the Free Software |
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20 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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21 | 02111-1307, USA. |
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22 | */ |
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23 | |
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24 | #include "yat/classifier/IGP.h" |
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25 | #include "yat/classifier/MatrixLookup.h" |
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26 | #include "yat/classifier/MatrixLookupWeighted.h" |
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27 | #include "yat/classifier/NCC.h" |
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28 | #include "yat/classifier/Target.h" |
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29 | #include "yat/utility/matrix.h" |
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30 | #include "yat/statistics/vector_distance_ptr.h" |
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31 | #include "yat/statistics/pearson_vector_distance.h" |
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32 | #include "yat/utility/utility.h" |
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33 | |
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34 | #include <cassert> |
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35 | #include <fstream> |
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36 | #include <iostream> |
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37 | #include <sstream> |
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38 | #include <string> |
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39 | #include <limits> |
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40 | #include <cmath> |
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41 | |
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42 | using namespace theplu::yat; |
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43 | |
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44 | int main(const int argc,const char* argv[]) |
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45 | { |
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46 | |
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47 | std::ostream* error; |
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48 | if (argc>1 && argv[1]==std::string("-v")) |
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49 | error = &std::cerr; |
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50 | else { |
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51 | error = new std::ofstream("/dev/null"); |
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52 | if (argc>1) |
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53 | std::cout << "ncc_test -v : for printing extra information\n"; |
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54 | } |
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55 | *error << "testing ncc" << std::endl; |
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56 | bool ok = true; |
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57 | |
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58 | classifier::MatrixLookupWeighted ml(4,4); |
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59 | std::vector<std::string> vec(4, "pos"); |
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60 | vec[3]="bjds"; |
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61 | classifier::Target target(vec); |
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62 | statistics::vector_distance_lookup_weighted_ptr dist= |
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63 | statistics::vector_distance<statistics::pearson_vector_distance_tag>; |
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64 | classifier::NCC ncctmp(ml,target,dist); |
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65 | *error << "training...\n"; |
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66 | ncctmp.train(); |
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67 | |
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68 | std::ifstream is("data/sorlie_centroid_data.txt"); |
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69 | utility::matrix data(is,'\t'); |
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70 | is.close(); |
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71 | |
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72 | is.open("data/sorlie_centroid_classes.txt"); |
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73 | classifier::Target targets(is); |
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74 | is.close(); |
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75 | |
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76 | // Generate weight matrix with 0 for missing values and 1 for others. |
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77 | utility::matrix weights(data.rows(),data.columns(),0.0); |
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78 | utility::nan(data,weights); |
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79 | |
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80 | classifier::MatrixLookupWeighted dataviewweighted(data,weights); |
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81 | statistics::vector_distance_lookup_weighted_ptr distance= |
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82 | statistics::vector_distance<statistics::pearson_vector_distance_tag>; |
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83 | classifier::NCC ncc(dataviewweighted,targets,distance); |
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84 | *error << "training...\n"; |
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85 | ncc.train(); |
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86 | |
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87 | // Comparing the centroids to stored result |
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88 | is.open("data/sorlie_centroids.txt"); |
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89 | utility::matrix centroids(is); |
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90 | is.close(); |
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91 | |
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92 | if(centroids.rows() != ncc.centroids().rows() || |
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93 | centroids.columns() != ncc.centroids().columns()) { |
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94 | *error << "Error in the dimensionality of centroids\n"; |
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95 | *error << "Nof rows: " << centroids.rows() << " expected: " |
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96 | << ncc.centroids().rows() << std::endl; |
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97 | *error << "Nof columns: " << centroids.columns() << " expected: " |
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98 | << ncc.centroids().columns() << std::endl; |
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99 | } |
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100 | |
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101 | double slack = 0; |
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102 | for (size_t i=0; i<centroids.rows(); i++){ |
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103 | for (size_t j=0; j<centroids.columns(); j++){ |
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104 | slack += fabs(centroids(i,j)-ncc.centroids()(i,j)); |
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105 | } |
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106 | } |
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107 | slack /= (centroids.columns()*centroids.rows()); |
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108 | double slack_bound=2e-7; |
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109 | if (slack > slack_bound || std::isnan(slack)){ |
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110 | *error << "Difference to stored centroids too large\n"; |
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111 | *error << "slack: " << slack << std::endl; |
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112 | *error << "expected less than " << slack_bound << std::endl; |
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113 | ok = false; |
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114 | } |
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115 | |
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116 | *error << "prediction...\n"; |
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117 | utility::matrix prediction; |
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118 | ncc.predict(dataviewweighted,prediction); |
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119 | |
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120 | // Comparing the prediction to stored result |
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121 | is.open("data/sorlie_centroid_predictions.txt"); |
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122 | utility::matrix result(is,'\t'); |
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123 | is.close(); |
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124 | |
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125 | slack = 0; |
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126 | for (size_t i=0; i<result.rows(); i++){ |
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127 | for (size_t j=0; j<result.columns(); j++){ |
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128 | slack += fabs(result(i,j)-prediction(i,j)); |
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129 | } |
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130 | } |
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131 | slack /= (result.columns()*result.rows()); |
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132 | if (slack > slack_bound || std::isnan(slack)){ |
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133 | *error << "Difference to stored prediction too large\n"; |
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134 | *error << "slack: " << slack << std::endl; |
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135 | *error << "expected less than " << slack_bound << std::endl; |
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136 | ok = false; |
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137 | } |
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138 | |
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139 | |
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140 | if(ok) |
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141 | *error << "OK" << std::endl; |
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142 | |
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143 | |
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144 | if (error!=&std::cerr) |
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145 | delete error; |
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146 | |
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147 | if(ok) |
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148 | return 0; |
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149 | return -1; |
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150 | |
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151 | } |
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