Changeset 1013 for trunk/test
 Timestamp:
 Feb 1, 2008, 4:34:30 PM (15 years ago)
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 1 edited
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trunk/test/ncc_test.cc
r1000 r1013 31 31 #include "yat/classifier/Target.h" 32 32 #include "yat/utility/matrix.h" 33 #include "yat/statistics/euclidean_vector_distance.h" 33 34 #include "yat/statistics/pearson_vector_distance.h" 34 35 #include "yat/utility/utility.h" … … 45 46 using namespace theplu::yat; 46 47 48 double deviation(const utility::matrix& a, const utility::matrix& b) { 49 double sl=0; 50 for (size_t i=0; i<a.rows(); i++){ 51 for (size_t j=0; j<a.columns(); j++){ 52 sl += fabs(a(i,j)b(i,j)); 53 } 54 } 55 sl /= (a.columns()*a.rows()); 56 return sl; 57 } 58 47 59 int main(const int argc,const char* argv[]) 48 60 { … … 59 71 bool ok = true; 60 72 73 ///////////////////////////////////////////// 74 // First test of constructor and training 75 ///////////////////////////////////////////// 61 76 classifier::MatrixLookup ml(4,4); 62 77 std::vector<std::string> vec(4, "pos"); … … 66 81 *error << "training...\n"; 67 82 ncctmp.train(); 83 *error << "done\n"; 84 85 ///////////////////////////////////////////// 86 // A test of predictions using unweighted data 87 ///////////////////////////////////////////// 88 utility::matrix data1(3,4); 89 for(size_t i=0;i<3;i++) { 90 data1(i,0)=3i; 91 data1(i,1)=5i; 92 data1(i,2)=i+1; 93 data1(i,3)=i+3; 94 } 95 std::vector<std::string> vec1(4, "pos"); 96 vec1[0]="neg"; 97 vec1[1]="neg"; 98 99 classifier::MatrixLookup ml1(data1); 100 classifier::Target target1(vec1); 101 102 classifier::NCC<statistics::euclidean_vector_distance_tag> ncc1(ml1,target1); 103 ncc1.train(); 104 utility::matrix prediction1; 105 ncc1.predict(ml1,prediction1); 106 double slack_bound=2e7; 107 utility::matrix result1(2,4); 108 result1(0,0)=result1(0,1)=result1(1,2)=result1(1,3)=sqrt(3.0); 109 result1(0,2)=result1(0,3)=result1(1,0)=result1(1,1)=sqrt(11.0); 110 double slack = deviation(prediction1,result1); 111 if (slack > slack_bound  std::isnan(slack)){ 112 *error << "Difference to expected prediction too large\n"; 113 *error << "slack: " << slack << std::endl; 114 *error << "expected less than " << slack_bound << std::endl; 115 ok = false; 116 } 117 118 ////////////////////////////////////////////////////////////////////////// 119 // A test of predictions using unweighted training and weighted test data 120 ////////////////////////////////////////////////////////////////////////// 121 utility::matrix weights1(3,4,1.0); 122 weights1(0,0)=weights1(1,1)=weights1(2,2)=weights1(1,3)=0.0; 123 classifier::MatrixLookupWeighted mlw1(data1,weights1); 124 ncc1.predict(mlw1,prediction1); 125 result1(0,2)=result1(0,3)=result1(1,0)=result1(1,1)=sqrt(15.0); 126 slack = deviation(prediction1,result1); 127 if (slack > slack_bound  std::isnan(slack)){ 128 *error << "Difference to expected prediction too large\n"; 129 *error << "slack: " << slack << std::endl; 130 *error << "expected less than " << slack_bound << std::endl; 131 ok = false; 132 } 133 68 134 135 136 ////////////////////////////////////////////////////////////////////////// 137 // A test of predictions using Sorlie data 138 ////////////////////////////////////////////////////////////////////////// 69 139 std::ifstream is("data/sorlie_centroid_data.txt"); 70 140 utility::matrix data(is,'\t'); … … 98 168 } 99 169 100 double slack = 0; 101 for (size_t i=0; i<centroids.rows(); i++){ 102 for (size_t j=0; j<centroids.columns(); j++){ 103 slack += fabs(centroids(i,j)ncc.centroids()(i,j)); 104 } 105 } 106 slack /= (centroids.columns()*centroids.rows()); 107 double slack_bound=2e7; 170 slack = deviation(centroids,ncc.centroids()); 108 171 if (slack > slack_bound  std::isnan(slack)){ 109 172 *error << "Difference to stored centroids too large\n"; … … 113 176 } 114 177 115 *error << " prediction...\n";178 *error << "...predicting...\n"; 116 179 utility::matrix prediction; 117 180 ncc.predict(dataviewweighted,prediction); … … 122 185 is.close(); 123 186 124 slack = 0; 125 for (size_t i=0; i<result.rows(); i++){ 126 for (size_t j=0; j<result.columns(); j++){ 127 slack += fabs(result(i,j)prediction(i,j)); 128 } 129 } 130 slack /= (result.columns()*result.rows()); 187 slack = deviation(result,prediction); 131 188 if (slack > slack_bound  std::isnan(slack)){ 132 189 *error << "Difference to stored prediction too large\n"; … … 135 192 ok = false; 136 193 } 137 138 // testing rejection of KernelLookups 194 *error << "done\n"; 195 196 ////////////////////////////////////////////////////////////////////////// 197 // Testing rejection of KernelLookups 198 ////////////////////////////////////////////////////////////////////////// 139 199 classifier::PolynomialKernelFunction kf; 140 200 classifier::Kernel_MEV kernel(ml,kf); … … 161 221 *error << "OK" << std::endl; 162 222 163 164 223 if (error!=&std::cerr) 165 224 delete error;
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