Custom Query (4 matches)

when compiling latest version of Multiboost with typedef float AlphaReal

/scratch/fradav/MultiBoost/src/Utils/Utils.cpp: In function ‘double nor_utils::getROC(std::vector<std::pair<int, float>, std::allocator<std::pair<int, float> > >&)’:
/scratch/fradav/MultiBoost/src/Utils/Utils.cpp:359: error: new declaration ‘double nor_utils::getROC(std::vector<std::pair<int, float>, std::allocator<std::pair<int, float> > >&)’
/scratch/fradav/MultiBoost/src/Utils/Utils.h:401: error: ambiguates old declaration ‘AlphaReal nor_utils::getROC(std::vector<std::pair<int, float>, std::allocator<std::pair<int, float> > >&)’

{{{multiboost --fileformat arff --csample num 100 --iisize 15x15 --seed 71 --traintest ./uspsHaarTrain.arff ./uspsHaarTest.arff 5 --outputinfo ./OK- uspsHaar-TreeLearner-HaarSingleStumpLearner-traintest.dta --shypname ./OK- uspsHaar-TreeLearner-HaarSingleStumpLearner-traintest.xml --learnertype TreeLearner --baselearnertype HaarSingleStumpLearner 5 --verbose 6 --> Using learner: TreeLearner Warning: No strong learner is given. Set to default (AdaBoost). The strong learner is AdaBoostMH Segmentation fault }}}

with gdb :

Program received signal EXC_BAD_ACCESS, Could not access memory.
Reason: KERN_INVALID_ADDRESS at address: 0x0000000000000000
0x00000001000e5cf7 in MultiBoost::TreeLearner::initLearningOptions
(this=0x100b00fe0, args=@0x7fff5fbfe600) at
src/WeakLearners/TreeLearner.cpp:90
90                      pWeakHypothesisSource->initLearningOptions(args);

(gdb) l
85                              cerr << "The weak hypothesis must be a
ScalarLearner!!!" << endl;
86                              exit(-1);
87                      }
88
89
90                      pWeakHypothesisSource->initLearningOptions(args);
91
92                      for( int ib = 0; ib < _numBaseLearners; ++ib ) {
93
_baseLearners.push_back(dynamic_cast<ScalarLearner*>(pWeakHypothesisSource->create()));
94
dynamic_cast<BaseLearner*>(_baseLearners[ib])->initLearningOptions(args);

with the example sparse dataset and

multiboost --fileformat svmlight --constant --weightpolicy proportional --traintest ./train.txt ./test.txt 1000 --outputinfo ./OK/OK-sparse--SingleSparseStumpLearner-traintest.dta --shypname ./OK/OK-sparse--SingleSparseStumpLearner-traintest.xml --learnertype SingleSparseStumpLearner --verbose 0 --seed 71

multiboost --fileformat svmlight --constant --weightpolicy proportional --test ./test.txt ./OK/OK-sparse--SingleSparseStumpLearner-traintest.xml 1000 ./OK/OK-sparse--SingleSparseStumpLearner-test-predictions.dta --outputinfo ./OK/OK-sparse--SingleSparseStumpLearner-test.dta --learnertype SingleSparseStumpLearner --verbose 0 --seed 71

Results from outputinfo in the test are not consistent with the ErrTrn of the traintest.