er C is not specified. C=1 would be applied this time. model_name is specified but the way to save is not specified. the model will be saved at the end model_name is data/streamsvm_model Trainer id 0 Iteration 1 time 279.346566 obj 9848.694509 gap 6.150838 Cache access 13237417 epsilon 1.274152 active size 9675 access ratio 47.254550 access speed(M/sec) 0.047387 Trainer id 0 Iteration 2 time 297.081339 obj 10376.918432 gap 4.585239 Cache access 13909444 epsilon 0.100929 active size 8720 access ratio 49.653532 access speed(M/sec) 0.046820 Trainer id 0 Iteration 3 time 318.703953 obj 10380.234806 gap 0.400062 Cache access 13655784 epsilon 0.000269 active size 1606 access ratio 48.748024 access speed(M/sec) 0.042848 Trainer id 0 Iteration 4 time 302.305856 obj 10380.244612 gap 0.095953 Cache access 12151680 epsilon 0.000071 active size 1560 access ratio 43.378717 access speed(M/sec) 0.040197 Trainer id 0 Iteration 5 time 272.086716 obj 10380.244735 gap 0.000240 Cache access 11496425 epsilon 0.000064 active size 1553 access ratio 41.039607 access speed(M/sec) 0.042253 Total cache access 64450750 obj 10380.244735 Total time 1469.524430 real 25m9.155s user 46m34.383s sys 1m27.857s

One interesting thing about StreamSVM is that it uses two cores. It has the --num_learners command line parameter, but according to the author it doesn’t work at the moment. As far as hyperparams go, in the paper the best value for C for this dataset is 1, which is also the default.

$ time ./StreamSVMPredict -f