 | Run 14408 |
| Creator | internal |
| Program | boostexter |
| Dataset | Light Curves v1 DataSet |
| Task type | MulticlassClassification |
| Created | 28d17h ago |
| Download | Login required! |
Done! 
| 44s | |
| 29M | |
| MulticlassClassification | |
| 43s | |
| 0 | |
| 1s | |
| 0.248 | |
| 0s |
Log file
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0.0000000 rnd 899: wh-err= 0.977767 th-err= 0.000000 test= -nan train= 0.0000000 rnd 900: wh-err= 0.977657 th-err= 0.000000 test= -nan train= 0.0000000 rnd 901: wh-err= 0.979310 th-err= 0.000000 test= -nan train= 0.0000000 rnd 902: wh-err= 0.980099 th-err= 0.000000 test= -nan train= 0.0000000 rnd 903: wh-err= 0.983130 th-err= 0.000000 test= -nan train= 0.0000000 rnd 904: wh-err= 0.980250 th-err= 0.000000 test= -nan train= 0.0000000 rnd 905: wh-err= 0.980775 th-err= 0.000000 test= -nan train= 0.0000000 rnd 906: wh-err= 0.978734 th-err= 0.000000 test= -nan train= 0.0000000 rnd 907: wh-err= 0.977928 th-err= 0.000000 test= -nan train= 0.0000000 rnd 908: wh-err= 0.980018 th-err= 0.000000 test= -nan train= 0.0000000 rnd 909: wh-err= 0.979113 th-err= 0.000000 test= -nan train= 0.0000000 rnd 910: wh-err= 0.980867 th-err= 0.000000 test= -nan train= 0.0000000 rnd 911: wh-err= 0.982326 th-err= 0.000000 test= -nan train= 0.0000000 rnd 912: wh-err= 0.980580 th-err= 0.000000 test= -nan train= 0.0000000 rnd 913: wh-err= 0.979055 th-err= 0.000000 test= -nan train= 0.0000000 rnd 914: wh-err= 0.980676 th-err= 0.000000 test= -nan train= 0.0000000 rnd 915: wh-err= 0.978799 th-err= 0.000000 test= -nan train= 0.0000000 rnd 916: wh-err= 0.980910 th-err= 0.000000 test= -nan train= 0.0000000 rnd 917: wh-err= 0.980651 th-err= 0.000000 test= -nan train= 0.0000000 rnd 918: wh-err= 0.982859 th-err= 0.000000 test= -nan train= 0.0000000 rnd 919: wh-err= 0.981993 th-err= 0.000000 test= -nan train= 0.0000000 rnd 920: wh-err= 0.978999 th-err= 0.000000 test= -nan train= 0.0000000 rnd 921: wh-err= 0.982310 th-err= 0.000000 test= -nan train= 0.0000000 rnd 922: wh-err= 0.980818 th-err= 0.000000 test= -nan train= 0.0000000 rnd 923: wh-err= 0.981735 th-err= 0.000000 test= -nan train= 0.0000000 rnd 924: wh-err= 0.979131 th-err= 0.000000 test= -nan train= 0.0000000 rnd 925: wh-err= 0.982124 th-err= 0.000000 test= -nan train= 0.0000000 rnd 926: wh-err= 0.982263 th-err= 0.000000 test= -nan train= 0.0000000 rnd 927: wh-err= 0.983351 th-err= 0.000000 test= -nan train= 0.0000000 rnd 928: wh-err= 0.980070 th-err= 0.000000 test= -nan train= 0.0000000 rnd 929: wh-err= 0.980531 th-err= 0.000000 test= -nan train= 0.0000000 rnd 930: wh-err= 0.980917 th-err= 0.000000 test= -nan train= 0.0000000 rnd 931: wh-err= 0.980476 th-err= 0.000000 test= -nan train= 0.0000000 rnd 932: wh-err= 0.982004 th-err= 0.000000 test= -nan train= 0.0000000 rnd 933: wh-err= 0.979544 th-err= 0.000000 test= -nan train= 0.0000000 rnd 934: wh-err= 0.981251 th-err= 0.000000 test= -nan train= 0.0000000 rnd 935: wh-err= 0.982979 th-err= 0.000000 test= -nan train= 0.0000000 rnd 936: wh-err= 0.983977 th-err= 0.000000 test= -nan train= 0.0000000 rnd 937: wh-err= 0.984104 th-err= 0.000000 test= -nan train= 0.0000000 rnd 938: wh-err= 0.983355 th-err= 0.000000 test= -nan train= 0.0000000 rnd 939: wh-err= 0.985019 th-err= 0.000000 test= -nan train= 0.0000000 rnd 940: wh-err= 0.976736 th-err= 0.000000 test= -nan train= 0.0000000 rnd 941: wh-err= 0.979691 th-err= 0.000000 test= -nan train= 0.0000000 rnd 942: wh-err= 0.980909 th-err= 0.000000 test= -nan train= 0.0000000 rnd 943: wh-err= 0.980956 th-err= 0.000000 test= -nan train= 0.0000000 rnd 944: wh-err= 0.979653 th-err= 0.000000 test= -nan train= 0.0000000 rnd 945: wh-err= 0.980624 th-err= 0.000000 test= -nan train= 0.0000000 rnd 946: wh-err= 0.978544 th-err= 0.000000 test= -nan train= 0.0000000 rnd 947: wh-err= 0.981430 th-err= 0.000000 test= -nan train= 0.0000000 rnd 948: wh-err= 0.982238 th-err= 0.000000 test= -nan train= 0.0000000 rnd 949: wh-err= 0.977243 th-err= 0.000000 test= -nan train= 0.0000000 rnd 950: wh-err= 0.980423 th-err= 0.000000 test= -nan train= 0.0000000 rnd 951: wh-err= 0.980228 th-err= 0.000000 test= -nan train= 0.0000000 rnd 952: wh-err= 0.980743 th-err= 0.000000 test= -nan train= 0.0000000 rnd 953: wh-err= 0.981953 th-err= 0.000000 test= -nan train= 0.0000000 rnd 954: wh-err= 0.983001 th-err= 0.000000 test= -nan train= 0.0000000 rnd 955: wh-err= 0.983338 th-err= 0.000000 test= -nan train= 0.0000000 rnd 956: wh-err= 0.983535 th-err= 0.000000 test= -nan train= 0.0000000 rnd 957: wh-err= 0.984573 th-err= 0.000000 test= -nan train= 0.0000000 rnd 958: wh-err= 0.983660 th-err= 0.000000 test= -nan train= 0.0000000 rnd 959: wh-err= 0.980618 th-err= 0.000000 test= -nan train= 0.0000000 rnd 960: wh-err= 0.984612 th-err= 0.000000 test= -nan train= 0.0000000 rnd 961: wh-err= 0.982629 th-err= 0.000000 test= -nan train= 0.0000000 rnd 962: wh-err= 0.984367 th-err= 0.000000 test= -nan train= 0.0000000 rnd 963: wh-err= 0.983390 th-err= 0.000000 test= -nan train= 0.0000000 rnd 964: wh-err= 0.984239 th-err= 0.000000 test= -nan train= 0.0000000 rnd 965: wh-err= 0.984480 th-err= 0.000000 test= -nan train= 0.0000000 rnd 966: wh-err= 0.981508 th-err= 0.000000 test= -nan train= 0.0000000 rnd 967: wh-err= 0.984883 th-err= 0.000000 test= -nan train= 0.0000000 rnd 968: wh-err= 0.984281 th-err= 0.000000 test= -nan train= 0.0000000 rnd 969: wh-err= 0.981292 th-err= 0.000000 test= -nan train= 0.0000000 rnd 970: wh-err= 0.978592 th-err= 0.000000 test= -nan train= 0.0000000 rnd 971: wh-err= 0.982261 th-err= 0.000000 test= -nan train= 0.0000000 rnd 972: wh-err= 0.983288 th-err= 0.000000 test= -nan train= 0.0000000 rnd 973: wh-err= 0.982020 th-err= 0.000000 test= -nan train= 0.0000000 rnd 974: wh-err= 0.981370 th-err= 0.000000 test= -nan train= 0.0000000 rnd 975: wh-err= 0.983531 th-err= 0.000000 test= -nan train= 0.0000000 rnd 976: wh-err= 0.982689 th-err= 0.000000 test= -nan train= 0.0000000 rnd 977: wh-err= 0.981547 th-err= 0.000000 test= -nan train= 0.0000000 rnd 978: wh-err= 0.984359 th-err= 0.000000 test= -nan train= 0.0000000 rnd 979: wh-err= 0.983456 th-err= 0.000000 test= -nan train= 0.0000000 rnd 980: wh-err= 0.983422 th-err= 0.000000 test= -nan train= 0.0000000 rnd 981: wh-err= 0.982583 th-err= 0.000000 test= -nan train= 0.0000000 rnd 982: wh-err= 0.977321 th-err= 0.000000 test= -nan train= 0.0000000 rnd 983: wh-err= 0.982364 th-err= 0.000000 test= -nan train= 0.0000000 rnd 984: wh-err= 0.983823 th-err= 0.000000 test= -nan train= 0.0000000 rnd 985: wh-err= 0.982765 th-err= 0.000000 test= -nan train= 0.0000000 rnd 986: wh-err= 0.978884 th-err= 0.000000 test= -nan train= 0.0000000 rnd 987: wh-err= 0.983765 th-err= 0.000000 test= -nan train= 0.0000000 rnd 988: wh-err= 0.981179 th-err= 0.000000 test= -nan train= 0.0000000 rnd 989: wh-err= 0.981550 th-err= 0.000000 test= -nan train= 0.0000000 rnd 990: wh-err= 0.983137 th-err= 0.000000 test= -nan train= 0.0000000 rnd 991: wh-err= 0.983722 th-err= 0.000000 test= -nan train= 0.0000000 rnd 992: wh-err= 0.983772 th-err= 0.000000 test= -nan train= 0.0000000 rnd 993: wh-err= 0.985162 th-err= 0.000000 test= -nan train= 0.0000000 rnd 994: wh-err= 0.981793 th-err= 0.000000 test= -nan train= 0.0000000 rnd 995: wh-err= 0.982688 th-err= 0.000000 test= -nan train= 0.0000000 rnd 996: wh-err= 0.981770 th-err= 0.000000 test= -nan train= 0.0000000 rnd 997: wh-err= 0.984339 th-err= 0.000000 test= -nan train= 0.0000000 rnd 998: wh-err= 0.983710 th-err= 0.000000 test= -nan train= 0.0000000 rnd 999: wh-err= 0.985479 th-err= 0.000000 test= -nan train= 0.0000000 rnd 1000: wh-err= 0.983401 th-err= 0.000000 test= -nan train= 0.0000000 === END program1: ./run learn ../dataset2/train --- OK [43s] ===== 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 [0s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out Copyright 2001 AT&T. All rights reserved. Test error = 601.000000 / 601 = 1.000000 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s] ===== 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 [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out Copyright 2001 AT&T. All rights reserved. Test error = 258.000000 / 258 = 1.000000 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 0m45.500s user 0m29.470s sys 0m0.480s
Run specification 
Results
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