 | Run 38748 |
| Creator | chuertas |
| Program | boostexter no-awk |
| Dataset | Virus 183,855x9 00-FF w/Chi2 (Small Train) |
| Task type | MulticlassClassification |
| Created | 2y163d ago |
Done! 
| 55m52s | |
| 269M | |
| MulticlassClassification | |
| 53m37s | |
| 0 | |
| 32s | |
| 0.010 | |
| 1m14s |
Log file
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0.0000000 rnd 899: wh-err= 0.929050 th-err= 0.000000 test= -nan train= 0.0000000 rnd 900: wh-err= 0.941209 th-err= 0.000000 test= -nan train= 0.0000000 rnd 901: wh-err= 0.940390 th-err= 0.000000 test= -nan train= 0.0000000 rnd 902: wh-err= 0.923766 th-err= 0.000000 test= -nan train= 0.0000000 rnd 903: wh-err= 0.928570 th-err= 0.000000 test= -nan train= 0.0000000 rnd 904: wh-err= 0.927701 th-err= 0.000000 test= -nan train= 0.0000000 rnd 905: wh-err= 0.930604 th-err= 0.000000 test= -nan train= 0.0000000 rnd 906: wh-err= 0.930701 th-err= 0.000000 test= -nan train= 0.0000000 rnd 907: wh-err= 0.941466 th-err= 0.000000 test= -nan train= 0.0000000 rnd 908: wh-err= 0.937772 th-err= 0.000000 test= -nan train= 0.0000000 rnd 909: wh-err= 0.927313 th-err= 0.000000 test= -nan train= 0.0000000 rnd 910: wh-err= 0.917904 th-err= 0.000000 test= -nan train= 0.0000000 rnd 911: wh-err= 0.920937 th-err= 0.000000 test= -nan train= 0.0000000 rnd 912: wh-err= 0.933194 th-err= 0.000000 test= -nan train= 0.0000000 rnd 913: wh-err= 0.935770 th-err= 0.000000 test= -nan train= 0.0000000 rnd 914: wh-err= 0.935078 th-err= 0.000000 test= -nan train= 0.0000000 rnd 915: wh-err= 0.932415 th-err= 0.000000 test= -nan train= 0.0000000 rnd 916: wh-err= 0.932496 th-err= 0.000000 test= -nan train= 0.0000000 rnd 917: wh-err= 0.935827 th-err= 0.000000 test= -nan train= 0.0000000 rnd 918: wh-err= 0.926637 th-err= 0.000000 test= -nan train= 0.0000000 rnd 919: wh-err= 0.931837 th-err= 0.000000 test= -nan train= 0.0000000 rnd 920: wh-err= 0.931298 th-err= 0.000000 test= -nan train= 0.0000000 rnd 921: wh-err= 0.933893 th-err= 0.000000 test= -nan train= 0.0000000 rnd 922: wh-err= 0.934483 th-err= 0.000000 test= -nan train= 0.0000000 rnd 923: wh-err= 0.935254 th-err= 0.000000 test= -nan train= 0.0000000 rnd 924: wh-err= 0.935695 th-err= 0.000000 test= -nan train= 0.0000000 rnd 925: wh-err= 0.936380 th-err= 0.000000 test= -nan train= 0.0000000 rnd 926: wh-err= 0.931696 th-err= 0.000000 test= -nan train= 0.0000000 rnd 927: wh-err= 0.941721 th-err= 0.000000 test= -nan train= 0.0000000 rnd 928: wh-err= 0.939776 th-err= 0.000000 test= -nan train= 0.0000000 rnd 929: wh-err= 0.941799 th-err= 0.000000 test= -nan train= 0.0000000 rnd 930: wh-err= 0.943337 th-err= 0.000000 test= -nan train= 0.0000000 rnd 931: wh-err= 0.936976 th-err= 0.000000 test= -nan train= 0.0000000 rnd 932: wh-err= 0.940701 th-err= 0.000000 test= -nan train= 0.0000000 rnd 933: wh-err= 0.943102 th-err= 0.000000 test= -nan train= 0.0000000 rnd 934: wh-err= 0.933098 th-err= 0.000000 test= -nan train= 0.0000000 rnd 935: wh-err= 0.934555 th-err= 0.000000 test= -nan train= 0.0000000 rnd 936: wh-err= 0.928003 th-err= 0.000000 test= -nan train= 0.0000000 rnd 937: wh-err= 0.925750 th-err= 0.000000 test= -nan train= 0.0000000 rnd 938: wh-err= 0.934433 th-err= 0.000000 test= -nan train= 0.0000000 rnd 939: wh-err= 0.933025 th-err= 0.000000 test= -nan train= 0.0000000 rnd 940: wh-err= 0.935050 th-err= 0.000000 test= -nan train= 0.0000000 rnd 941: wh-err= 0.932181 th-err= 0.000000 test= -nan train= 0.0000000 rnd 942: wh-err= 0.934466 th-err= 0.000000 test= -nan train= 0.0000000 rnd 943: wh-err= 0.924869 th-err= 0.000000 test= -nan train= 0.0000000 rnd 944: wh-err= 0.931388 th-err= 0.000000 test= -nan train= 0.0000000 rnd 945: wh-err= 0.933258 th-err= 0.000000 test= -nan train= 0.0000000 rnd 946: wh-err= 0.916966 th-err= 0.000000 test= -nan train= 0.0000000 rnd 947: wh-err= 0.919277 th-err= 0.000000 test= -nan train= 0.0000000 rnd 948: wh-err= 0.916459 th-err= 0.000000 test= -nan train= 0.0000000 rnd 949: wh-err= 0.921323 th-err= 0.000000 test= -nan train= 0.0000000 rnd 950: wh-err= 0.927240 th-err= 0.000000 test= -nan train= 0.0000000 rnd 951: wh-err= 0.927192 th-err= 0.000000 test= -nan train= 0.0000000 rnd 952: wh-err= 0.937402 th-err= 0.000000 test= -nan train= 0.0000000 rnd 953: wh-err= 0.942252 th-err= 0.000000 test= -nan train= 0.0000000 rnd 954: wh-err= 0.940286 th-err= 0.000000 test= -nan train= 0.0000000 rnd 955: wh-err= 0.934809 th-err= 0.000000 test= -nan train= 0.0000000 rnd 956: wh-err= 0.929609 th-err= 0.000000 test= -nan train= 0.0000000 rnd 957: wh-err= 0.933360 th-err= 0.000000 test= -nan train= 0.0000000 rnd 958: wh-err= 0.934965 th-err= 0.000000 test= -nan train= 0.0000000 rnd 959: wh-err= 0.932463 th-err= 0.000000 test= -nan train= 0.0000000 rnd 960: wh-err= 0.929227 th-err= 0.000000 test= -nan train= 0.0000000 rnd 961: wh-err= 0.936096 th-err= 0.000000 test= -nan train= 0.0000000 rnd 962: wh-err= 0.933819 th-err= 0.000000 test= -nan train= 0.0000000 rnd 963: wh-err= 0.925315 th-err= 0.000000 test= -nan train= 0.0000000 rnd 964: wh-err= 0.940690 th-err= 0.000000 test= -nan train= 0.0000000 rnd 965: wh-err= 0.945688 th-err= 0.000000 test= -nan train= 0.0000000 rnd 966: wh-err= 0.941277 th-err= 0.000000 test= -nan train= 0.0000000 rnd 967: wh-err= 0.939853 th-err= 0.000000 test= -nan train= 0.0000000 rnd 968: wh-err= 0.928578 th-err= 0.000000 test= -nan train= 0.0000000 rnd 969: wh-err= 0.933984 th-err= 0.000000 test= -nan train= 0.0000000 rnd 970: wh-err= 0.942366 th-err= 0.000000 test= -nan train= 0.0000000 rnd 971: wh-err= 0.933345 th-err= 0.000000 test= -nan train= 0.0000000 rnd 972: wh-err= 0.935332 th-err= 0.000000 test= -nan train= 0.0000000 rnd 973: wh-err= 0.931326 th-err= 0.000000 test= -nan train= 0.0000000 rnd 974: wh-err= 0.928939 th-err= 0.000000 test= -nan train= 0.0000000 rnd 975: wh-err= 0.928354 th-err= 0.000000 test= -nan train= 0.0000000 rnd 976: wh-err= 0.926973 th-err= 0.000000 test= -nan train= 0.0000000 rnd 977: wh-err= 0.923546 th-err= 0.000000 test= -nan train= 0.0000000 rnd 978: wh-err= 0.928798 th-err= 0.000000 test= -nan train= 0.0000000 rnd 979: wh-err= 0.936770 th-err= 0.000000 test= -nan train= 0.0000000 rnd 980: wh-err= 0.939196 th-err= 0.000000 test= -nan train= 0.0000000 rnd 981: wh-err= 0.934383 th-err= 0.000000 test= -nan train= 0.0000000 rnd 982: wh-err= 0.926717 th-err= 0.000000 test= -nan train= 0.0000000 rnd 983: wh-err= 0.927012 th-err= 0.000000 test= -nan train= 0.0000000 rnd 984: wh-err= 0.935121 th-err= 0.000000 test= -nan train= 0.0000000 rnd 985: wh-err= 0.927554 th-err= 0.000000 test= -nan train= 0.0000000 rnd 986: wh-err= 0.917326 th-err= 0.000000 test= -nan train= 0.0000000 rnd 987: wh-err= 0.925328 th-err= 0.000000 test= -nan train= 0.0000000 rnd 988: wh-err= 0.929990 th-err= 0.000000 test= -nan train= 0.0000000 rnd 989: wh-err= 0.934714 th-err= 0.000000 test= -nan train= 0.0000000 rnd 990: wh-err= 0.939694 th-err= 0.000000 test= -nan train= 0.0000000 rnd 991: wh-err= 0.935607 th-err= 0.000000 test= -nan train= 0.0000000 rnd 992: wh-err= 0.921865 th-err= 0.000000 test= -nan train= 0.0000000 rnd 993: wh-err= 0.935719 th-err= 0.000000 test= -nan train= 0.0000000 rnd 994: wh-err= 0.934412 th-err= 0.000000 test= -nan train= 0.0000000 rnd 995: wh-err= 0.934809 th-err= 0.000000 test= -nan train= 0.0000000 rnd 996: wh-err= 0.935131 th-err= 0.000000 test= -nan train= 0.0000000 rnd 997: wh-err= 0.938021 th-err= 0.000000 test= -nan train= 0.0000000 rnd 998: wh-err= 0.941892 th-err= 0.000000 test= -nan train= 0.0000000 rnd 999: wh-err= 0.937003 th-err= 0.000000 test= -nan train= 0.0000000 rnd 1000: wh-err= 0.930311 th-err= 0.000000 test= -nan train= 0.0000000 === END program1: ./run learn ../dataset2/train --- OK [3217s] ===== MAIN: predict/evaluate on train data ===== === START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in === END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [3s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out Copyright 2001 AT&T. All rights reserved. Test error = 2800.000000 / 3260 = 0.858896 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [32s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [6s] ===== MAIN: predict/evaluate on test data ===== === START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in === END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [6s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out Copyright 2001 AT&T. All rights reserved. Test error = 6495.000000 / 7608 = 0.853707 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [74s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [14s] real 55m52.315s user 54m41.629s sys 0m19.693s
Run specification 
Results
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