Changeset 2138
- Timestamp:
- Dec 24, 2009, 9:23:49 PM (13 years ago)
- Location:
- trunk
- Files:
-
- 6 edited
Legend:
- Unmodified
- Added
- Removed
-
trunk/NEWS
r2129 r2138 21 21 A complete list of closed tickets can be found here [[br]] 22 22 http://dev.thep.lu.se/yat/query?status=closed&milestone=yat+0.6 23 24 Version 0.5.6 (released 24 December 2009) 25 26 - Fixed bugs in EnsembleBuilder::predict (bugs #567 and #579) 27 - Corrected dependency libs in libyat.la (bug #573) 28 29 A complete list of closed tickets can be found here [[br]] 30 http://dev.thep.lu.se/yat/query?status=closed&milestone=yat+0.5.6 23 31 24 32 Version 0.5.5 (released 5 November 2009) -
trunk/m4/gsl.m4
r2129 r2138 42 42 GSL_LIBS=`$GSL_CONFIG --libs` 43 43 ac_save_LIBS="$LIBS" 44 CXXFLAGS="$CXXFLAGS $GSL_CFLAGS"45 44 LIBS="$GSL_LIBS $LIBS" 46 45 AC_LINK_IFELSE([AC_LANG_PROGRAM(, [return 0])],,[no_gsl=yes]) -
trunk/m4/version.m4
r2119 r2138 59 59 # yat-0.5.4 2:3:0 60 60 # yat-0.5.5 2:4:0 61 # yat-0.5.6 2:5:0 61 62 # 62 63 # *Accidently, the libtool number was not updated for yat 0.5 -
trunk/test/ensemble_test.cc
r2119 r2138 5 5 Copyright (C) 2007 Jari Häkkinen, Peter Johansson 6 6 Copyright (C) 2008 Jari Häkkinen, Peter Johansson, Markus Ringnér 7 Copyright (C) 2009 Peter Johansson 7 8 8 9 This file is part of the yat library, http://dev.thep.lu.se/yat … … 114 115 suite.err() << "build ensemble" << std::endl; 115 116 ensemble.build(); 117 utility::Vector out(target.size(),0); 118 for (size_t i = 0; i<out.size(); ++i) { 119 out(i)=ensemble.validate()[0][i].mean(); 120 } 121 statistics::AUC roc; 122 suite.err() << roc.score(target,out) << std::endl; 123 116 124 std::vector<std::vector<statistics::Averager> > result; 117 125 ensemble.predict(kernel_lookup, result); 118 119 utility::Vector out(target.size(),0); 120 for (size_t i = 0; i<out.size(); ++i) 121 out(i)=ensemble.validate()[0][i].mean(); 122 statistics::AUC roc; 123 suite.err() << roc.score(target,out) << std::endl; 126 for (size_t i = 0; i<result.size(); ++i) { 127 for (size_t j=0; j<result[0].size(); ++j) { 128 if (!suite.add(result[i][j].variance() > 0)) { 129 suite.err() << "error: element " << i << " " << j << "\n"; 130 suite.err() << "expected finite prediction varince\n"; 131 suite.err() << "found: " << result[i][j].variance() << "\n"; 132 } 133 } 134 } 135 136 { 137 suite.err() << "test ensemble of SVMs with weighted kernel" << std::endl; 138 classifier::MatrixLookupWeighted wdata(data_core); 139 classifier::Kernel_SEV kernel(wdata, *kf); 140 classifier::KernelLookup wkl(kernel); 141 classifier::EnsembleBuilder<classifier::SVM, classifier::KernelLookup> 142 ensemble(svm, wkl, sampler); 143 suite.err() << "build ensemble" << std::endl; 144 ensemble.build(); 145 ensemble.validate(); 146 std::vector<std::vector<statistics::Averager> > result; 147 ensemble.predict(wkl, result); 148 } 124 149 125 150 { -
trunk/yat/Makefile.am
r2121 r2138 6 6 # Copyright (C) 2004 Jari Häkkinen, Peter Johansson, Cecilia Ritz 7 7 # Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, Peter Johansson 8 # Copyright (C) 2009 Peter Johansson 8 9 # Copyright (C) 2009 Peter Johansson 9 10 # … … 32 33 nodist_EXTRA_libyat_la_SOURCES = dummy.cc 33 34 34 libyat_la_LDFLAGS = -version-info $(YAT_LT_VERSION) 35 libyat_la_LDFLAGS = -version-info $(YAT_LT_VERSION) $(AM_LDFLAGS) 35 36 36 37 libyat_la_LIBADD = \ -
trunk/yat/classifier/EnsembleBuilder.h
r2121 r2138 193 193 Data sub_data = test_data(data, k); 194 194 classifier(k).predict(sub_data,prediction); 195 } 196 197 for(size_t i=0; i<prediction.rows();i++) 198 for(size_t j=0; j<prediction.columns();j++) 199 result[i][j].add(prediction(i,j)); 195 for(size_t i=0; i<prediction.rows();i++) 196 for(size_t j=0; j<prediction.columns();j++) 197 result[i][j].add(prediction(i,j)); 198 } 200 199 } 201 200 … … 233 232 // weighted case 234 233 if (kernel.weighted()){ 235 YAT_ASSERT(false);236 234 // no feature selection 237 235 if (kernel.data_weighted().rows()==subset_->training_features(k).size()) … … 248 246 249 247 // feature selection 250 return subset_->training_data(k).test_kernel(test_data(kernel.data(),k)); 248 MatrixLookup ml = test_data(kernel.data(),k); 249 return subset_->training_data(k).test_kernel(ml); 251 250 } 252 251
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