ServerRun 38741
Creatorchuertas
Programboostexter 500r no-awk
Datasetmulticlass-sample
Task typeMulticlassClassification
Created2y130d ago
Done! Flag_green
1s
30M
MulticlassClassification
0s
0
0s
0.500
0s

Log file

... (lines omitted) ...
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rnd  355: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  356: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  357: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  358: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  359: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  360: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  361: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  362: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  363: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  364: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  365: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  366: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  367: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  368: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  369: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  370: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  371: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  372: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  373: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  374: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  375: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  376: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  377: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  378: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  379: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  380: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  381: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  382: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  383: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  384: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  385: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  386: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  387: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  388: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  389: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  390: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  391: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  392: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  393: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  394: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  395: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  396: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  397: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  398: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  399: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  400: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  401: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  402: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  403: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  404: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  405: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  406: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  407: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  408: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  409: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  410: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  411: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  412: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  413: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  414: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  415: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  416: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  417: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  418: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  419: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  420: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  421: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  422: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  423: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  424: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  425: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  426: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  427: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  428: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  429: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  430: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  431: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  432: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  433: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  434: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  435: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  436: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  437: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  438: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  439: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  440: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  441: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  442: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  443: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  444: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  445: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  446: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  447: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  448: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  449: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  450: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  451: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  452: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  453: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  454: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  455: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  456: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  457: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  458: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  459: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  460: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  461: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  462: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  463: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  464: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  465: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  466: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  467: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  468: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  469: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  470: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  471: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  472: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  473: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  474: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  475: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  476: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  477: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  478: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  479: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  480: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  481: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  482: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  483: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  484: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  485: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  486: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  487: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  488: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  489: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  490: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  491: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  492: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  493: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  494: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  495: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  496: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  497: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  498: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  499: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  500: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
=== END program1: ./run learn ../dataset2/train --- OK [0s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Copyright 2001 AT&T.  All rights reserved.



Test error = 3.000000 / 4 = 0.750000
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== 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 = 1.000000 / 2 = 0.500000
=== 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	0m1.797s
user	0m0.916s
sys	0m0.524s

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