Status: Done!
Total Time
55m52s
Max Memory Usage
269M
Domain
MulticlassClassification
Learn time
53m37s
Train error
0
Predict train time
32s
Test error
0.010
Predict test time
1m14s
Log file
... (lines omitted) ...
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rnd 886: wh-err= 0.939812 th-err= 0.000000 test= -nan train= 0.0000000
rnd 887: wh-err= 0.936039 th-err= 0.000000 test= -nan train= 0.0000000
rnd 888: wh-err= 0.935023 th-err= 0.000000 test= -nan train= 0.0000000
rnd 889: wh-err= 0.941359 th-err= 0.000000 test= -nan train= 0.0000000
rnd 890: wh-err= 0.935925 th-err= 0.000000 test= -nan train= 0.0000000
rnd 891: wh-err= 0.940024 th-err= 0.000000 test= -nan train= 0.0000000
rnd 892: wh-err= 0.944292 th-err= 0.000000 test= -nan train= 0.0000000
rnd 893: wh-err= 0.933163 th-err= 0.000000 test= -nan train= 0.0000000
rnd 894: wh-err= 0.935571 th-err= 0.000000 test= -nan train= 0.0000000
rnd 895: wh-err= 0.921875 th-err= 0.000000 test= -nan train= 0.0000000
rnd 896: wh-err= 0.933115 th-err= 0.000000 test= -nan train= 0.0000000
rnd 897: wh-err= 0.939325 th-err= 0.000000 test= -nan train= 0.0000000
rnd 898: wh-err= 0.939768 th-err= 0.000000 test= -nan train= 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
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) boostexter no-awk : Adaboost on single level decision trees. Removed awk-dependency.
(dataset:Dataset) Virus 183,855x9 00-FF w/Chi2 (Small Train) :
(stripper:Program[Strip]) multiclass-utils : Validates and inspects a dataset in MulticlassClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
doTest:
evaluate:
errorRate: 0.00959516298633018
numErrors: 73
numExamples: 7608
success: true
time: 14
predict:
strip:
doTrain:
evaluate:
errorRate: 0.0
numErrors: 0
numExamples: 3260
success: true
time: 6
predict:
strip:
exitCode: 0
learn:
success: true
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