ServerRun 38811
Creatorchuertas
Programboostexter 150r no-awk
Datasetmulticlass-sample
Task typeMulticlassClassification
Created2y221d ago
Done! Flag_green
1s
29M
MulticlassClassification
1s
0
0s
0.500
0s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Copyright 2001 AT&T.  All rights reserved.

rnd    1: wh-err= 0.427811  th-err= 0.427811  test=      -nan  train= 0.0000000 
rnd    2: wh-err= 0.439393  th-err= 0.187977  test=      -nan  train= 0.0000000 
rnd    3: wh-err= 0.444271  th-err= 0.083513  test=      -nan  train= 0.0000000 
rnd    4: wh-err= 0.446137  th-err= 0.037258  test=      -nan  train= 0.0000000 
rnd    5: wh-err= 0.446824  th-err= 0.016648  test=      -nan  train= 0.0000000 
rnd    6: wh-err= 0.447073  th-err= 0.007443  test=      -nan  train= 0.0000000 
rnd    7: wh-err= 0.447163  th-err= 0.003328  test=      -nan  train= 0.0000000 
rnd    8: wh-err= 0.447195  th-err= 0.001488  test=      -nan  train= 0.0000000 
rnd    9: wh-err= 0.447207  th-err= 0.000666  test=      -nan  train= 0.0000000 
rnd   10: wh-err= 0.447211  th-err= 0.000298  test=      -nan  train= 0.0000000 
rnd   11: wh-err= 0.447213  th-err= 0.000133  test=      -nan  train= 0.0000000 
rnd   12: wh-err= 0.447213  th-err= 0.000060  test=      -nan  train= 0.0000000 
rnd   13: wh-err= 0.447213  th-err= 0.000027  test=      -nan  train= 0.0000000 
rnd   14: wh-err= 0.447214  th-err= 0.000012  test=      -nan  train= 0.0000000 
rnd   15: wh-err= 0.447214  th-err= 0.000005  test=      -nan  train= 0.0000000 
rnd   16: wh-err= 0.447214  th-err= 0.000002  test=      -nan  train= 0.0000000 
rnd   17: wh-err= 0.447214  th-err= 0.000001  test=      -nan  train= 0.0000000 
rnd   18: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   19: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   20: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   21: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   22: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   23: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   24: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   25: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   26: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   27: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   28: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   29: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   30: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   31: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   32: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   33: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   34: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   35: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   36: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   37: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   38: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   39: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   40: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   41: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   42: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   43: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   44: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   45: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   46: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   47: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   48: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   49: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   50: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   51: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   52: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   53: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   54: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   55: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   56: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   57: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   58: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   59: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   60: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   61: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   62: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   63: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   64: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   65: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   66: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   67: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   68: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   69: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   70: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   71: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   72: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   73: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   74: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   75: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   76: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   77: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   78: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   79: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   80: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   81: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   82: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   83: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   84: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   85: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   86: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   87: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   88: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   89: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   90: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   91: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   92: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   93: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   94: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   95: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   96: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   97: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   98: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd   99: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  100: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  101: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  102: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  103: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  104: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  105: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  106: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  107: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  108: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  109: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  110: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  111: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  112: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  113: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  114: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  115: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  116: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  117: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  118: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  119: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  120: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  121: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  122: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  123: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  124: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  125: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  126: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  127: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  128: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  129: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  130: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  131: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  132: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  133: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  134: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  135: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  136: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  137: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  138: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  139: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  140: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  141: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  142: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  143: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  144: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  145: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  146: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  147: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  148: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  149: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
rnd  150: wh-err= 0.447214  th-err= 0.000000  test=      -nan  train= 0.0000000 
=== END program1: ./run learn ../dataset2/train --- OK [1s]

===== 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 = 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	0m1.410s
user	0m0.672s
sys	0m0.528s

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