ServerRun 38958
Creatorchuertas
Programboostexter 500r no-awk
DatasetEye Plain v0
Task typeMulticlassClassification
Created2y148d ago
Done! Flag_green
46m56s
204M
MulticlassClassification
45m58s
0.263
35s
0.268
15s

Log file

... (lines omitted) ...
rnd  133: wh-err= 0.999775  th-err= 0.505898  test=      -nan  train= 0.2650073 
rnd  134: wh-err= 0.999819  th-err= 0.505806  test=      -nan  train= 0.2650073 
rnd  135: wh-err= 0.999755  th-err= 0.505682  test=      -nan  train= 0.2650073 
rnd  136: wh-err= 0.999759  th-err= 0.505560  test=      -nan  train= 0.2649667 
rnd  137: wh-err= 0.999762  th-err= 0.505440  test=      -nan  train= 0.2648853 
rnd  138: wh-err= 0.999748  th-err= 0.505313  test=      -nan  train= 0.2649260 
rnd  139: wh-err= 0.999756  th-err= 0.505190  test=      -nan  train= 0.2649260 
rnd  140: wh-err= 0.999698  th-err= 0.505038  test=      -nan  train= 0.2649260 
rnd  141: wh-err= 0.999762  th-err= 0.504917  test=      -nan  train= 0.2648853 
rnd  142: wh-err= 0.999756  th-err= 0.504794  test=      -nan  train= 0.2648853 
rnd  143: wh-err= 0.999753  th-err= 0.504669  test=      -nan  train= 0.2649260 
rnd  144: wh-err= 0.999754  th-err= 0.504545  test=      -nan  train= 0.2649260 
rnd  145: wh-err= 0.999738  th-err= 0.504413  test=      -nan  train= 0.2649260 
rnd  146: wh-err= 0.999727  th-err= 0.504275  test=      -nan  train= 0.2649667 
rnd  147: wh-err= 0.999768  th-err= 0.504158  test=      -nan  train= 0.2649667 
rnd  148: wh-err= 0.999737  th-err= 0.504025  test=      -nan  train= 0.2649260 
rnd  149: wh-err= 0.999741  th-err= 0.503894  test=      -nan  train= 0.2649667 
rnd  150: wh-err= 0.999757  th-err= 0.503772  test=      -nan  train= 0.2648853 
rnd  151: wh-err= 0.999742  th-err= 0.503642  test=      -nan  train= 0.2647633 
rnd  152: wh-err= 0.999756  th-err= 0.503519  test=      -nan  train= 0.2648040 
rnd  153: wh-err= 0.999772  th-err= 0.503404  test=      -nan  train= 0.2648040 
rnd  154: wh-err= 0.999751  th-err= 0.503278  test=      -nan  train= 0.2648446 
rnd  155: wh-err= 0.999723  th-err= 0.503139  test=      -nan  train= 0.2648040 
rnd  156: wh-err= 0.999759  th-err= 0.503018  test=      -nan  train= 0.2648446 
rnd  157: wh-err= 0.999778  th-err= 0.502907  test=      -nan  train= 0.2648446 
rnd  158: wh-err= 0.999758  th-err= 0.502785  test=      -nan  train= 0.2648446 
rnd  159: wh-err= 0.999761  th-err= 0.502665  test=      -nan  train= 0.2648040 
rnd  160: wh-err= 0.999780  th-err= 0.502554  test=      -nan  train= 0.2648446 
rnd  161: wh-err= 0.999761  th-err= 0.502434  test=      -nan  train= 0.2648853 
rnd  162: wh-err= 0.999757  th-err= 0.502312  test=      -nan  train= 0.2648040 
rnd  163: wh-err= 0.999760  th-err= 0.502192  test=      -nan  train= 0.2648446 
rnd  164: wh-err= 0.999808  th-err= 0.502095  test=      -nan  train= 0.2648446 
rnd  165: wh-err= 0.999789  th-err= 0.501989  test=      -nan  train= 0.2648446 
rnd  166: wh-err= 0.999773  th-err= 0.501875  test=      -nan  train= 0.2649260 
rnd  167: wh-err= 0.999769  th-err= 0.501759  test=      -nan  train= 0.2649260 
rnd  168: wh-err= 0.999831  th-err= 0.501674  test=      -nan  train= 0.2649260 
rnd  169: wh-err= 0.999773  th-err= 0.501560  test=      -nan  train= 0.2649260 
rnd  170: wh-err= 0.999771  th-err= 0.501446  test=      -nan  train= 0.2648446 
rnd  171: wh-err= 0.999773  th-err= 0.501332  test=      -nan  train= 0.2649667 
rnd  172: wh-err= 0.999773  th-err= 0.501218  test=      -nan  train= 0.2648853 
rnd  173: wh-err= 0.999735  th-err= 0.501085  test=      -nan  train= 0.2648853 
rnd  174: wh-err= 0.999684  th-err= 0.500927  test=      -nan  train= 0.2649260 
rnd  175: wh-err= 0.999719  th-err= 0.500786  test=      -nan  train= 0.2649260 
rnd  176: wh-err= 0.999748  th-err= 0.500660  test=      -nan  train= 0.2648446 
rnd  177: wh-err= 0.999766  th-err= 0.500542  test=      -nan  train= 0.2648446 
rnd  178: wh-err= 0.999753  th-err= 0.500419  test=      -nan  train= 0.2648446 
rnd  179: wh-err= 0.999753  th-err= 0.500295  test=      -nan  train= 0.2648446 
rnd  180: wh-err= 0.999742  th-err= 0.500166  test=      -nan  train= 0.2648446 
rnd  181: wh-err= 0.999790  th-err= 0.500062  test=      -nan  train= 0.2648446 
rnd  182: wh-err= 0.999772  th-err= 0.499948  test=      -nan  train= 0.2648446 
rnd  183: wh-err= 0.999791  th-err= 0.499843  test=      -nan  train= 0.2648446 
rnd  184: wh-err= 0.999784  th-err= 0.499735  test=      -nan  train= 0.2648040 
rnd  185: wh-err= 0.999776  th-err= 0.499623  test=      -nan  train= 0.2648853 
rnd  186: wh-err= 0.999788  th-err= 0.499517  test=      -nan  train= 0.2647633 
rnd  187: wh-err= 0.999772  th-err= 0.499403  test=      -nan  train= 0.2647226 
rnd  188: wh-err= 0.999790  th-err= 0.499298  test=      -nan  train= 0.2647226 
rnd  189: wh-err= 0.999782  th-err= 0.499189  test=      -nan  train= 0.2646006 
rnd  190: wh-err= 0.999749  th-err= 0.499064  test=      -nan  train= 0.2646006 
rnd  191: wh-err= 0.999781  th-err= 0.498955  test=      -nan  train= 0.2645599 
rnd  192: wh-err= 0.999776  th-err= 0.498843  test=      -nan  train= 0.2645599 
rnd  193: wh-err= 0.999777  th-err= 0.498732  test=      -nan  train= 0.2646413 
rnd  194: wh-err= 0.999771  th-err= 0.498618  test=      -nan  train= 0.2646413 
rnd  195: wh-err= 0.999733  th-err= 0.498485  test=      -nan  train= 0.2646820 
rnd  196: wh-err= 0.999751  th-err= 0.498361  test=      -nan  train= 0.2646006 
rnd  197: wh-err= 0.999753  th-err= 0.498238  test=      -nan  train= 0.2646006 
rnd  198: wh-err= 0.999807  th-err= 0.498142  test=      -nan  train= 0.2645599 
rnd  199: wh-err= 0.999781  th-err= 0.498033  test=      -nan  train= 0.2646006 
rnd  200: wh-err= 0.999757  th-err= 0.497911  test=      -nan  train= 0.2646820 
rnd  201: wh-err= 0.999689  th-err= 0.497757  test=      -nan  train= 0.2646006 
rnd  202: wh-err= 0.999723  th-err= 0.497619  test=      -nan  train= 0.2646006 
rnd  203: wh-err= 0.999766  th-err= 0.497502  test=      -nan  train= 0.2645599 
rnd  204: wh-err= 0.999774  th-err= 0.497390  test=      -nan  train= 0.2645599 
rnd  205: wh-err= 0.999747  th-err= 0.497264  test=      -nan  train= 0.2645599 
rnd  206: wh-err= 0.999770  th-err= 0.497150  test=      -nan  train= 0.2645599 
rnd  207: wh-err= 0.999756  th-err= 0.497029  test=      -nan  train= 0.2645599 
rnd  208: wh-err= 0.999754  th-err= 0.496906  test=      -nan  train= 0.2646006 
rnd  209: wh-err= 0.999775  th-err= 0.496794  test=      -nan  train= 0.2645193 
rnd  210: wh-err= 0.999760  th-err= 0.496675  test=      -nan  train= 0.2646413 
rnd  211: wh-err= 0.999778  th-err= 0.496565  test=      -nan  train= 0.2646006 
rnd  212: wh-err= 0.999809  th-err= 0.496470  test=      -nan  train= 0.2646006 
rnd  213: wh-err= 0.999799  th-err= 0.496370  test=      -nan  train= 0.2646006 
rnd  214: wh-err= 0.999742  th-err= 0.496242  test=      -nan  train= 0.2645599 
rnd  215: wh-err= 0.999814  th-err= 0.496150  test=      -nan  train= 0.2645599 
rnd  216: wh-err= 0.999837  th-err= 0.496069  test=      -nan  train= 0.2646006 
rnd  217: wh-err= 0.999793  th-err= 0.495966  test=      -nan  train= 0.2645599 
rnd  218: wh-err= 0.999793  th-err= 0.495864  test=      -nan  train= 0.2645599 
rnd  219: wh-err= 0.999771  th-err= 0.495750  test=      -nan  train= 0.2645599 
rnd  220: wh-err= 0.999799  th-err= 0.495651  test=      -nan  train= 0.2646006 
rnd  221: wh-err= 0.999776  th-err= 0.495539  test=      -nan  train= 0.2646006 
rnd  222: wh-err= 0.999767  th-err= 0.495424  test=      -nan  train= 0.2645599 
rnd  223: wh-err= 0.999781  th-err= 0.495316  test=      -nan  train= 0.2646006 
rnd  224: wh-err= 0.999795  th-err= 0.495214  test=      -nan  train= 0.2645599 
rnd  225: wh-err= 0.999817  th-err= 0.495124  test=      -nan  train= 0.2645193 
rnd  226: wh-err= 0.999823  th-err= 0.495036  test=      -nan  train= 0.2644379 
rnd  227: wh-err= 0.999847  th-err= 0.494960  test=      -nan  train= 0.2643973 
rnd  228: wh-err= 0.999796  th-err= 0.494859  test=      -nan  train= 0.2643973 
rnd  229: wh-err= 0.999785  th-err= 0.494753  test=      -nan  train= 0.2643973 
rnd  230: wh-err= 0.999791  th-err= 0.494649  test=      -nan  train= 0.2643973 
rnd  231: wh-err= 0.999767  th-err= 0.494534  test=      -nan  train= 0.2643973 
rnd  232: wh-err= 0.999822  th-err= 0.494446  test=      -nan  train= 0.2643973 
rnd  233: wh-err= 0.999822  th-err= 0.494358  test=      -nan  train= 0.2645193 
rnd  234: wh-err= 0.999824  th-err= 0.494271  test=      -nan  train= 0.2645599 
rnd  235: wh-err= 0.999784  th-err= 0.494164  test=      -nan  train= 0.2645599 
rnd  236: wh-err= 0.999807  th-err= 0.494069  test=      -nan  train= 0.2644786 
rnd  237: wh-err= 0.999787  th-err= 0.493963  test=      -nan  train= 0.2645193 
rnd  238: wh-err= 0.999818  th-err= 0.493873  test=      -nan  train= 0.2645193 
rnd  239: wh-err= 0.999803  th-err= 0.493776  test=      -nan  train= 0.2645193 
rnd  240: wh-err= 0.999801  th-err= 0.493678  test=      -nan  train= 0.2645193 
rnd  241: wh-err= 0.999803  th-err= 0.493581  test=      -nan  train= 0.2645193 
rnd  242: wh-err= 0.999807  th-err= 0.493486  test=      -nan  train= 0.2645193 
rnd  243: wh-err= 0.999805  th-err= 0.493389  test=      -nan  train= 0.2646006 
rnd  244: wh-err= 0.999810  th-err= 0.493296  test=      -nan  train= 0.2646006 
rnd  245: wh-err= 0.999835  th-err= 0.493215  test=      -nan  train= 0.2646006 
rnd  246: wh-err= 0.999811  th-err= 0.493121  test=      -nan  train= 0.2646006 
rnd  247: wh-err= 0.999834  th-err= 0.493040  test=      -nan  train= 0.2646006 
rnd  248: wh-err= 0.999804  th-err= 0.492943  test=      -nan  train= 0.2646413 
rnd  249: wh-err= 0.999761  th-err= 0.492825  test=      -nan  train= 0.2646413 
rnd  250: wh-err= 0.999786  th-err= 0.492719  test=      -nan  train= 0.2646413 
rnd  251: wh-err= 0.999805  th-err= 0.492623  test=      -nan  train= 0.2646413 
rnd  252: wh-err= 0.999763  th-err= 0.492507  test=      -nan  train= 0.2646413 
rnd  253: wh-err= 0.999706  th-err= 0.492362  test=      -nan  train= 0.2646413 
rnd  254: wh-err= 0.999760  th-err= 0.492244  test=      -nan  train= 0.2646413 
rnd  255: wh-err= 0.999802  th-err= 0.492146  test=      -nan  train= 0.2646820 
rnd  256: wh-err= 0.999803  th-err= 0.492049  test=      -nan  train= 0.2646820 
rnd  257: wh-err= 0.999785  th-err= 0.491943  test=      -nan  train= 0.2647633 
rnd  258: wh-err= 0.999810  th-err= 0.491850  test=      -nan  train= 0.2647633 
rnd  259: wh-err= 0.999714  th-err= 0.491709  test=      -nan  train= 0.2647226 
rnd  260: wh-err= 0.999761  th-err= 0.491591  test=      -nan  train= 0.2647226 
rnd  261: wh-err= 0.999757  th-err= 0.491472  test=      -nan  train= 0.2647226 
rnd  262: wh-err= 0.999783  th-err= 0.491365  test=      -nan  train= 0.2648040 
rnd  263: wh-err= 0.999788  th-err= 0.491261  test=      -nan  train= 0.2647633 
rnd  264: wh-err= 0.999765  th-err= 0.491146  test=      -nan  train= 0.2647633 
rnd  265: wh-err= 0.999790  th-err= 0.491043  test=      -nan  train= 0.2647226 
rnd  266: wh-err= 0.999799  th-err= 0.490944  test=      -nan  train= 0.2647633 
rnd  267: wh-err= 0.999801  th-err= 0.490846  test=      -nan  train= 0.2647633 
rnd  268: wh-err= 0.999807  th-err= 0.490752  test=      -nan  train= 0.2646006 
rnd  269: wh-err= 0.999804  th-err= 0.490655  test=      -nan  train= 0.2645599 
rnd  270: wh-err= 0.999731  th-err= 0.490523  test=      -nan  train= 0.2645193 
rnd  271: wh-err= 0.999750  th-err= 0.490401  test=      -nan  train= 0.2644379 
rnd  272: wh-err= 0.999798  th-err= 0.490302  test=      -nan  train= 0.2642753 
rnd  273: wh-err= 0.999786  th-err= 0.490197  test=      -nan  train= 0.2642753 
rnd  274: wh-err= 0.999820  th-err= 0.490108  test=      -nan  train= 0.2642753 
rnd  275: wh-err= 0.999809  th-err= 0.490015  test=      -nan  train= 0.2642753 
rnd  276: wh-err= 0.999807  th-err= 0.489920  test=      -nan  train= 0.2642753 
rnd  277: wh-err= 0.999775  th-err= 0.489810  test=      -nan  train= 0.2642346 
rnd  278: wh-err= 0.999836  th-err= 0.489729  test=      -nan  train= 0.2642346 
rnd  279: wh-err= 0.999813  th-err= 0.489638  test=      -nan  train= 0.2642753 
rnd  280: wh-err= 0.999760  th-err= 0.489520  test=      -nan  train= 0.2641939 
rnd  281: wh-err= 0.999853  th-err= 0.489448  test=      -nan  train= 0.2641532 
rnd  282: wh-err= 0.999819  th-err= 0.489360  test=      -nan  train= 0.2641532 
rnd  283: wh-err= 0.999753  th-err= 0.489239  test=      -nan  train= 0.2641532 
rnd  284: wh-err= 0.999814  th-err= 0.489148  test=      -nan  train= 0.2641532 
rnd  285: wh-err= 0.999809  th-err= 0.489055  test=      -nan  train= 0.2641126 
rnd  286: wh-err= 0.999809  th-err= 0.488961  test=      -nan  train= 0.2641126 
rnd  287: wh-err= 0.999795  th-err= 0.488861  test=      -nan  train= 0.2641939 
rnd  288: wh-err= 0.999807  th-err= 0.488767  test=      -nan  train= 0.2642753 
rnd  289: wh-err= 0.999787  th-err= 0.488663  test=      -nan  train= 0.2641532 
rnd  290: wh-err= 0.999809  th-err= 0.488569  test=      -nan  train= 0.2641939 
rnd  291: wh-err= 0.999833  th-err= 0.488488  test=      -nan  train= 0.2642753 
rnd  292: wh-err= 0.999800  th-err= 0.488390  test=      -nan  train= 0.2642346 
rnd  293: wh-err= 0.999802  th-err= 0.488294  test=      -nan  train= 0.2642753 
rnd  294: wh-err= 0.999809  th-err= 0.488200  test=      -nan  train= 0.2642346 
rnd  295: wh-err= 0.999816  th-err= 0.488110  test=      -nan  train= 0.2642346 
rnd  296: wh-err= 0.999818  th-err= 0.488021  test=      -nan  train= 0.2643159 
rnd  297: wh-err= 0.999818  th-err= 0.487933  test=      -nan  train= 0.2643159 
rnd  298: wh-err= 0.999774  th-err= 0.487823  test=      -nan  train= 0.2642346 
rnd  299: wh-err= 0.999806  th-err= 0.487728  test=      -nan  train= 0.2642753 
rnd  300: wh-err= 0.999829  th-err= 0.487645  test=      -nan  train= 0.2642346 
rnd  301: wh-err= 0.999819  th-err= 0.487556  test=      -nan  train= 0.2642753 
rnd  302: wh-err= 0.999800  th-err= 0.487459  test=      -nan  train= 0.2643159 
rnd  303: wh-err= 0.999811  th-err= 0.487367  test=      -nan  train= 0.2643159 
rnd  304: wh-err= 0.999781  th-err= 0.487260  test=      -nan  train= 0.2642346 
rnd  305: wh-err= 0.999810  th-err= 0.487168  test=      -nan  train= 0.2643159 
rnd  306: wh-err= 0.999788  th-err= 0.487064  test=      -nan  train= 0.2642346 
rnd  307: wh-err= 0.999828  th-err= 0.486981  test=      -nan  train= 0.2642346 
rnd  308: wh-err= 0.999810  th-err= 0.486888  test=      -nan  train= 0.2641126 
rnd  309: wh-err= 0.999815  th-err= 0.486798  test=      -nan  train= 0.2641126 
rnd  310: wh-err= 0.999815  th-err= 0.486708  test=      -nan  train= 0.2642346 
rnd  311: wh-err= 0.999814  th-err= 0.486617  test=      -nan  train= 0.2641532 
rnd  312: wh-err= 0.999789  th-err= 0.486514  test=      -nan  train= 0.2641126 
rnd  313: wh-err= 0.999785  th-err= 0.486410  test=      -nan  train= 0.2641939 
rnd  314: wh-err= 0.999779  th-err= 0.486302  test=      -nan  train= 0.2642753 
rnd  315: wh-err= 0.999842  th-err= 0.486225  test=      -nan  train= 0.2643159 
rnd  316: wh-err= 0.999823  th-err= 0.486139  test=      -nan  train= 0.2643566 
rnd  317: wh-err= 0.999839  th-err= 0.486061  test=      -nan  train= 0.2643159 
rnd  318: wh-err= 0.999844  th-err= 0.485985  test=      -nan  train= 0.2643159 
rnd  319: wh-err= 0.999855  th-err= 0.485915  test=      -nan  train= 0.2643566 
rnd  320: wh-err= 0.999831  th-err= 0.485832  test=      -nan  train= 0.2643566 
rnd  321: wh-err= 0.999834  th-err= 0.485752  test=      -nan  train= 0.2643159 
rnd  322: wh-err= 0.999823  th-err= 0.485666  test=      -nan  train= 0.2642753 
rnd  323: wh-err= 0.999814  th-err= 0.485576  test=      -nan  train= 0.2642346 
rnd  324: wh-err= 0.999823  th-err= 0.485490  test=      -nan  train= 0.2642346 
rnd  325: wh-err= 0.999827  th-err= 0.485406  test=      -nan  train= 0.2642753 
rnd  326: wh-err= 0.999852  th-err= 0.485334  test=      -nan  train= 0.2642753 
rnd  327: wh-err= 0.999828  th-err= 0.485251  test=      -nan  train= 0.2642346 
rnd  328: wh-err= 0.999818  th-err= 0.485162  test=      -nan  train= 0.2641939 
rnd  329: wh-err= 0.999834  th-err= 0.485082  test=      -nan  train= 0.2641939 
rnd  330: wh-err= 0.999835  th-err= 0.485002  test=      -nan  train= 0.2642346 
rnd  331: wh-err= 0.999819  th-err= 0.484914  test=      -nan  train= 0.2641939 
rnd  332: wh-err= 0.999832  th-err= 0.484832  test=      -nan  train= 0.2642346 
rnd  333: wh-err= 0.999832  th-err= 0.484751  test=      -nan  train= 0.2643159 
rnd  334: wh-err= 0.999835  th-err= 0.484671  test=      -nan  train= 0.2643159 
rnd  335: wh-err= 0.999823  th-err= 0.484586  test=      -nan  train= 0.2643566 
rnd  336: wh-err= 0.999835  th-err= 0.484506  test=      -nan  train= 0.2643566 
rnd  337: wh-err= 0.999849  th-err= 0.484433  test=      -nan  train= 0.2643566 
rnd  338: wh-err= 0.999835  th-err= 0.484353  test=      -nan  train= 0.2643566 
rnd  339: wh-err= 0.999829  th-err= 0.484270  test=      -nan  train= 0.2643159 
rnd  340: wh-err= 0.999836  th-err= 0.484191  test=      -nan  train= 0.2643159 
rnd  341: wh-err= 0.999847  th-err= 0.484117  test=      -nan  train= 0.2642346 
rnd  342: wh-err= 0.999838  th-err= 0.484038  test=      -nan  train= 0.2642753 
rnd  343: wh-err= 0.999814  th-err= 0.483948  test=      -nan  train= 0.2643973 
rnd  344: wh-err= 0.999855  th-err= 0.483878  test=      -nan  train= 0.2643566 
rnd  345: wh-err= 0.999838  th-err= 0.483800  test=      -nan  train= 0.2643566 
rnd  346: wh-err= 0.999850  th-err= 0.483727  test=      -nan  train= 0.2643159 
rnd  347: wh-err= 0.999838  th-err= 0.483648  test=      -nan  train= 0.2643159 
rnd  348: wh-err= 0.999835  th-err= 0.483569  test=      -nan  train= 0.2643566 
rnd  349: wh-err= 0.999866  th-err= 0.483504  test=      -nan  train= 0.2643566 
rnd  350: wh-err= 0.999843  th-err= 0.483428  test=      -nan  train= 0.2641939 
rnd  351: wh-err= 0.999841  th-err= 0.483351  test=      -nan  train= 0.2642346 
rnd  352: wh-err= 0.999801  th-err= 0.483255  test=      -nan  train= 0.2641532 
rnd  353: wh-err= 0.999783  th-err= 0.483150  test=      -nan  train= 0.2642346 
rnd  354: wh-err= 0.999806  th-err= 0.483056  test=      -nan  train= 0.2641939 
rnd  355: wh-err= 0.999821  th-err= 0.482970  test=      -nan  train= 0.2641532 
rnd  356: wh-err= 0.999810  th-err= 0.482878  test=      -nan  train= 0.2641532 
rnd  357: wh-err= 0.999757  th-err= 0.482761  test=      -nan  train= 0.2641532 
rnd  358: wh-err= 0.999790  th-err= 0.482659  test=      -nan  train= 0.2641532 
rnd  359: wh-err= 0.999825  th-err= 0.482574  test=      -nan  train= 0.2640312 
rnd  360: wh-err= 0.999813  th-err= 0.482484  test=      -nan  train= 0.2641532 
rnd  361: wh-err= 0.999866  th-err= 0.482419  test=      -nan  train= 0.2641532 
rnd  362: wh-err= 0.999824  th-err= 0.482334  test=      -nan  train= 0.2640719 
rnd  363: wh-err= 0.999841  th-err= 0.482258  test=      -nan  train= 0.2639499 
rnd  364: wh-err= 0.999858  th-err= 0.482189  test=      -nan  train= 0.2639906 
rnd  365: wh-err= 0.999822  th-err= 0.482104  test=      -nan  train= 0.2639906 
rnd  366: wh-err= 0.999838  th-err= 0.482026  test=      -nan  train= 0.2639092 
rnd  367: wh-err= 0.999807  th-err= 0.481933  test=      -nan  train= 0.2639499 
rnd  368: wh-err= 0.999809  th-err= 0.481841  test=      -nan  train= 0.2639092 
rnd  369: wh-err= 0.999779  th-err= 0.481734  test=      -nan  train= 0.2638279 
rnd  370: wh-err= 0.999798  th-err= 0.481636  test=      -nan  train= 0.2639499 
rnd  371: wh-err= 0.999807  th-err= 0.481543  test=      -nan  train= 0.2639092 
rnd  372: wh-err= 0.999828  th-err= 0.481460  test=      -nan  train= 0.2639499 
rnd  373: wh-err= 0.999844  th-err= 0.481385  test=      -nan  train= 0.2638279 
rnd  374: wh-err= 0.999856  th-err= 0.481316  test=      -nan  train= 0.2637059 
rnd  375: wh-err= 0.999830  th-err= 0.481234  test=      -nan  train= 0.2637059 
rnd  376: wh-err= 0.999826  th-err= 0.481150  test=      -nan  train= 0.2636652 
rnd  377: wh-err= 0.999833  th-err= 0.481070  test=      -nan  train= 0.2636652 
rnd  378: wh-err= 0.999831  th-err= 0.480989  test=      -nan  train= 0.2636245 
rnd  379: wh-err= 0.999841  th-err= 0.480912  test=      -nan  train= 0.2635025 
rnd  380: wh-err= 0.999740  th-err= 0.480787  test=      -nan  train= 0.2637059 
rnd  381: wh-err= 0.999826  th-err= 0.480704  test=      -nan  train= 0.2637059 
rnd  382: wh-err= 0.999788  th-err= 0.480602  test=      -nan  train= 0.2637465 
rnd  383: wh-err= 0.999843  th-err= 0.480527  test=      -nan  train= 0.2636652 
rnd  384: wh-err= 0.999844  th-err= 0.480452  test=      -nan  train= 0.2637059 
rnd  385: wh-err= 0.999833  th-err= 0.480371  test=      -nan  train= 0.2637465 
rnd  386: wh-err= 0.999798  th-err= 0.480275  test=      -nan  train= 0.2637465 
rnd  387: wh-err= 0.999830  th-err= 0.480193  test=      -nan  train= 0.2637059 
rnd  388: wh-err= 0.999806  th-err= 0.480099  test=      -nan  train= 0.2636245 
rnd  389: wh-err= 0.999807  th-err= 0.480007  test=      -nan  train= 0.2636652 
rnd  390: wh-err= 0.999867  th-err= 0.479943  test=      -nan  train= 0.2637059 
rnd  391: wh-err= 0.999839  th-err= 0.479866  test=      -nan  train= 0.2637872 
rnd  392: wh-err= 0.999840  th-err= 0.479789  test=      -nan  train= 0.2635839 
rnd  393: wh-err= 0.999871  th-err= 0.479727  test=      -nan  train= 0.2637059 
rnd  394: wh-err= 0.999858  th-err= 0.479659  test=      -nan  train= 0.2635839 
rnd  395: wh-err= 0.999844  th-err= 0.479584  test=      -nan  train= 0.2636245 
rnd  396: wh-err= 0.999804  th-err= 0.479490  test=      -nan  train= 0.2636245 
rnd  397: wh-err= 0.999842  th-err= 0.479414  test=      -nan  train= 0.2636245 
rnd  398: wh-err= 0.999825  th-err= 0.479331  test=      -nan  train= 0.2636245 
rnd  399: wh-err= 0.999811  th-err= 0.479240  test=      -nan  train= 0.2636652 
rnd  400: wh-err= 0.999822  th-err= 0.479154  test=      -nan  train= 0.2636652 
rnd  401: wh-err= 0.999838  th-err= 0.479077  test=      -nan  train= 0.2635839 
rnd  402: wh-err= 0.999868  th-err= 0.479014  test=      -nan  train= 0.2636652 
rnd  403: wh-err= 0.999839  th-err= 0.478937  test=      -nan  train= 0.2637465 
rnd  404: wh-err= 0.999851  th-err= 0.478865  test=      -nan  train= 0.2637059 
rnd  405: wh-err= 0.999883  th-err= 0.478809  test=      -nan  train= 0.2637059 
rnd  406: wh-err= 0.999829  th-err= 0.478727  test=      -nan  train= 0.2637872 
rnd  407: wh-err= 0.999850  th-err= 0.478655  test=      -nan  train= 0.2637059 
rnd  408: wh-err= 0.999851  th-err= 0.478584  test=      -nan  train= 0.2636652 
rnd  409: wh-err= 0.999836  th-err= 0.478505  test=      -nan  train= 0.2636652 
rnd  410: wh-err= 0.999842  th-err= 0.478430  test=      -nan  train= 0.2636245 
rnd  411: wh-err= 0.999815  th-err= 0.478341  test=      -nan  train= 0.2636652 
rnd  412: wh-err= 0.999799  th-err= 0.478245  test=      -nan  train= 0.2635839 
rnd  413: wh-err= 0.999841  th-err= 0.478169  test=      -nan  train= 0.2635839 
rnd  414: wh-err= 0.999840  th-err= 0.478092  test=      -nan  train= 0.2635432 
rnd  415: wh-err= 0.999842  th-err= 0.478017  test=      -nan  train= 0.2635839 
rnd  416: wh-err= 0.999837  th-err= 0.477939  test=      -nan  train= 0.2635839 
rnd  417: wh-err= 0.999845  th-err= 0.477865  test=      -nan  train= 0.2635025 
rnd  418: wh-err= 0.999882  th-err= 0.477809  test=      -nan  train= 0.2635432 
rnd  419: wh-err= 0.999849  th-err= 0.477737  test=      -nan  train= 0.2636652 
rnd  420: wh-err= 0.999847  th-err= 0.477663  test=      -nan  train= 0.2636652 
rnd  421: wh-err= 0.999846  th-err= 0.477590  test=      -nan  train= 0.2637059 
rnd  422: wh-err= 0.999825  th-err= 0.477506  test=      -nan  train= 0.2637059 
rnd  423: wh-err= 0.999817  th-err= 0.477419  test=      -nan  train= 0.2636245 
rnd  424: wh-err= 0.999857  th-err= 0.477350  test=      -nan  train= 0.2633805 
rnd  425: wh-err= 0.999842  th-err= 0.477275  test=      -nan  train= 0.2634212 
rnd  426: wh-err= 0.999862  th-err= 0.477209  test=      -nan  train= 0.2632992 
rnd  427: wh-err= 0.999867  th-err= 0.477146  test=      -nan  train= 0.2632178 
rnd  428: wh-err= 0.999849  th-err= 0.477074  test=      -nan  train= 0.2631365 
rnd  429: wh-err= 0.999837  th-err= 0.476996  test=      -nan  train= 0.2631365 
rnd  430: wh-err= 0.999830  th-err= 0.476915  test=      -nan  train= 0.2631772 
rnd  431: wh-err= 0.999865  th-err= 0.476851  test=      -nan  train= 0.2633398 
rnd  432: wh-err= 0.999846  th-err= 0.476777  test=      -nan  train= 0.2632585 
rnd  433: wh-err= 0.999828  th-err= 0.476695  test=      -nan  train= 0.2631772 
rnd  434: wh-err= 0.999853  th-err= 0.476625  test=      -nan  train= 0.2631365 
rnd  435: wh-err= 0.999833  th-err= 0.476546  test=      -nan  train= 0.2631772 
rnd  436: wh-err= 0.999846  th-err= 0.476472  test=      -nan  train= 0.2631772 
rnd  437: wh-err= 0.999840  th-err= 0.476396  test=      -nan  train= 0.2630958 
rnd  438: wh-err= 0.999850  th-err= 0.476324  test=      -nan  train= 0.2630958 
rnd  439: wh-err= 0.999849  th-err= 0.476252  test=      -nan  train= 0.2631772 
rnd  440: wh-err= 0.999848  th-err= 0.476180  test=      -nan  train= 0.2632178 
rnd  441: wh-err= 0.999846  th-err= 0.476106  test=      -nan  train= 0.2632178 
rnd  442: wh-err= 0.999849  th-err= 0.476034  test=      -nan  train= 0.2631365 
rnd  443: wh-err= 0.999836  th-err= 0.475956  test=      -nan  train= 0.2632178 
rnd  444: wh-err= 0.999837  th-err= 0.475878  test=      -nan  train= 0.2632178 
rnd  445: wh-err= 0.999815  th-err= 0.475790  test=      -nan  train= 0.2631772 
rnd  446: wh-err= 0.999843  th-err= 0.475715  test=      -nan  train= 0.2632178 
rnd  447: wh-err= 0.999843  th-err= 0.475641  test=      -nan  train= 0.2630958 
rnd  448: wh-err= 0.999827  th-err= 0.475558  test=      -nan  train= 0.2630551 
rnd  449: wh-err= 0.999843  th-err= 0.475483  test=      -nan  train= 0.2630145 
rnd  450: wh-err= 0.999846  th-err= 0.475410  test=      -nan  train= 0.2629738 
rnd  451: wh-err= 0.999843  th-err= 0.475336  test=      -nan  train= 0.2630145 
rnd  452: wh-err= 0.999846  th-err= 0.475262  test=      -nan  train= 0.2630145 
rnd  453: wh-err= 0.999838  th-err= 0.475185  test=      -nan  train= 0.2630551 
rnd  454: wh-err= 0.999847  th-err= 0.475113  test=      -nan  train= 0.2628518 
rnd  455: wh-err= 0.999835  th-err= 0.475035  test=      -nan  train= 0.2628518 
rnd  456: wh-err= 0.999809  th-err= 0.474944  test=      -nan  train= 0.2628518 
rnd  457: wh-err= 0.999833  th-err= 0.474865  test=      -nan  train= 0.2628925 
rnd  458: wh-err= 0.999847  th-err= 0.474792  test=      -nan  train= 0.2628518 
rnd  459: wh-err= 0.999844  th-err= 0.474718  test=      -nan  train= 0.2629738 
rnd  460: wh-err= 0.999884  th-err= 0.474663  test=      -nan  train= 0.2629331 
rnd  461: wh-err= 0.999855  th-err= 0.474594  test=      -nan  train= 0.2629331 
rnd  462: wh-err= 0.999823  th-err= 0.474510  test=      -nan  train= 0.2629331 
rnd  463: wh-err= 0.999834  th-err= 0.474431  test=      -nan  train= 0.2628925 
rnd  464: wh-err= 0.999840  th-err= 0.474355  test=      -nan  train= 0.2628925 
rnd  465: wh-err= 0.999858  th-err= 0.474288  test=      -nan  train= 0.2628518 
rnd  466: wh-err= 0.999855  th-err= 0.474219  test=      -nan  train= 0.2629331 
rnd  467: wh-err= 0.999795  th-err= 0.474122  test=      -nan  train= 0.2629738 
rnd  468: wh-err= 0.999833  th-err= 0.474043  test=      -nan  train= 0.2630145 
rnd  469: wh-err= 0.999817  th-err= 0.473956  test=      -nan  train= 0.2629738 
rnd  470: wh-err= 0.999855  th-err= 0.473888  test=      -nan  train= 0.2629738 
rnd  471: wh-err= 0.999832  th-err= 0.473808  test=      -nan  train= 0.2629331 
rnd  472: wh-err= 0.999836  th-err= 0.473730  test=      -nan  train= 0.2630145 
rnd  473: wh-err= 0.999873  th-err= 0.473670  test=      -nan  train= 0.2630551 
rnd  474: wh-err= 0.999855  th-err= 0.473601  test=      -nan  train= 0.2630551 
rnd  475: wh-err= 0.999840  th-err= 0.473525  test=      -nan  train= 0.2630551 
rnd  476: wh-err= 0.999809  th-err= 0.473435  test=      -nan  train= 0.2630551 
rnd  477: wh-err= 0.999846  th-err= 0.473362  test=      -nan  train= 0.2630145 
rnd  478: wh-err= 0.999843  th-err= 0.473288  test=      -nan  train= 0.2630145 
rnd  479: wh-err= 0.999821  th-err= 0.473203  test=      -nan  train= 0.2630551 
rnd  480: wh-err= 0.999842  th-err= 0.473128  test=      -nan  train= 0.2630551 
rnd  481: wh-err= 0.999849  th-err= 0.473057  test=      -nan  train= 0.2630551 
rnd  482: wh-err= 0.999832  th-err= 0.472977  test=      -nan  train= 0.2631772 
rnd  483: wh-err= 0.999830  th-err= 0.472897  test=      -nan  train= 0.2630958 
rnd  484: wh-err= 0.999838  th-err= 0.472820  test=      -nan  train= 0.2630958 
rnd  485: wh-err= 0.999842  th-err= 0.472746  test=      -nan  train= 0.2629738 
rnd  486: wh-err= 0.999825  th-err= 0.472663  test=      -nan  train= 0.2630145 
rnd  487: wh-err= 0.999815  th-err= 0.472575  test=      -nan  train= 0.2630551 
rnd  488: wh-err= 0.999840  th-err= 0.472500  test=      -nan  train= 0.2630145 
rnd  489: wh-err= 0.999846  th-err= 0.472427  test=      -nan  train= 0.2630145 
rnd  490: wh-err= 0.999854  th-err= 0.472358  test=      -nan  train= 0.2630551 
rnd  491: wh-err= 0.999852  th-err= 0.472288  test=      -nan  train= 0.2629738 
rnd  492: wh-err= 0.999822  th-err= 0.472204  test=      -nan  train= 0.2629738 
rnd  493: wh-err= 0.999854  th-err= 0.472135  test=      -nan  train= 0.2630145 
rnd  494: wh-err= 0.999825  th-err= 0.472053  test=      -nan  train= 0.2627705 
rnd  495: wh-err= 0.999849  th-err= 0.471982  test=      -nan  train= 0.2628111 
rnd  496: wh-err= 0.999840  th-err= 0.471906  test=      -nan  train= 0.2628111 
rnd  497: wh-err= 0.999890  th-err= 0.471854  test=      -nan  train= 0.2627705 
rnd  498: wh-err= 0.999857  th-err= 0.471787  test=      -nan  train= 0.2628111 
rnd  499: wh-err= 0.999830  th-err= 0.471706  test=      -nan  train= 0.2628518 
rnd  500: wh-err= 0.999839  th-err= 0.471630  test=      -nan  train= 0.2628518 
=== END program1: ./run learn ../dataset2/train --- OK [2758s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Copyright 2001 AT&T.  All rights reserved.



Test error = 125.000000 / 24588 = 0.005084
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [35s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [5s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Copyright 2001 AT&T.  All rights reserved.



Test error = 56.000000 / 10538 = 0.005314
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [15s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s]


real	46m59.406s
user	46m2.701s
sys	0m10.597s

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