 | Run 7923 |
| Creator | internal |
| Program | boostexter |
| Dataset | tic-tac-toe |
| Task type | MulticlassClassification |
| Created | 31d15h ago |
| Download | Login required! |
Done! 
| 1s | |
| 32M | |
| MulticlassClassification | |
| 1s | |
| 0.015 | |
| 0s | |
| 0.021 | |
| 0s |
Log file
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0.999530 th-err= 0.094850 test= nan train= 0.0149031 rnd 833: wh-err= 0.999429 th-err= 0.094796 test= nan train= 0.0149031 rnd 834: wh-err= 0.999532 th-err= 0.094752 test= nan train= 0.0149031 rnd 835: wh-err= 0.999454 th-err= 0.094700 test= nan train= 0.0149031 rnd 836: wh-err= 0.999436 th-err= 0.094647 test= nan train= 0.0149031 rnd 837: wh-err= 0.999456 th-err= 0.094595 test= nan train= 0.0149031 rnd 838: wh-err= 0.999347 th-err= 0.094534 test= nan train= 0.0149031 rnd 839: wh-err= 0.999381 th-err= 0.094475 test= nan train= 0.0149031 rnd 840: wh-err= 0.999443 th-err= 0.094423 test= nan train= 0.0149031 rnd 841: wh-err= 0.999590 th-err= 0.094384 test= nan train= 0.0149031 rnd 842: wh-err= 0.999440 th-err= 0.094331 test= nan train= 0.0149031 rnd 843: wh-err= 0.999564 th-err= 0.094290 test= nan train= 0.0149031 rnd 844: wh-err= 0.999481 th-err= 0.094241 test= nan train= 0.0149031 rnd 845: wh-err= 0.999447 th-err= 0.094189 test= nan train= 0.0149031 rnd 846: wh-err= 0.999478 th-err= 0.094140 test= nan train= 0.0149031 rnd 847: wh-err= 0.999326 th-err= 0.094076 test= nan train= 0.0149031 rnd 848: wh-err= 0.999527 th-err= 0.094032 test= nan train= 0.0149031 rnd 849: wh-err= 0.999472 th-err= 0.093982 test= nan train= 0.0149031 rnd 850: wh-err= 0.999553 th-err= 0.093940 test= nan train= 0.0149031 rnd 851: wh-err= 0.999453 th-err= 0.093889 test= nan train= 0.0149031 rnd 852: wh-err= 0.999551 th-err= 0.093847 test= nan train= 0.0149031 rnd 853: wh-err= 0.999476 th-err= 0.093798 test= nan train= 0.0149031 rnd 854: wh-err= 0.999459 th-err= 0.093747 test= nan train= 0.0149031 rnd 855: wh-err= 0.999478 th-err= 0.093698 test= nan train= 0.0149031 rnd 856: wh-err= 0.999368 th-err= 0.093639 test= nan train= 0.0149031 rnd 857: wh-err= 0.999406 th-err= 0.093583 test= nan train= 0.0149031 rnd 858: wh-err= 0.999467 th-err= 0.093533 test= nan train= 0.0149031 rnd 859: wh-err= 0.999610 th-err= 0.093497 test= nan train= 0.0149031 rnd 860: wh-err= 0.999463 th-err= 0.093446 test= nan train= 0.0149031 rnd 861: wh-err= 0.999582 th-err= 0.093407 test= nan train= 0.0149031 rnd 862: wh-err= 0.999502 th-err= 0.093361 test= nan train= 0.0149031 rnd 863: wh-err= 0.999470 th-err= 0.093311 test= nan train= 0.0149031 rnd 864: wh-err= 0.999499 th-err= 0.093265 test= nan train= 0.0149031 rnd 865: wh-err= 0.999349 th-err= 0.093204 test= nan train= 0.0149031 rnd 866: wh-err= 0.999546 th-err= 0.093162 test= nan train= 0.0149031 rnd 867: wh-err= 0.999492 th-err= 0.093114 test= nan train= 0.0149031 rnd 868: wh-err= 0.999575 th-err= 0.093075 test= nan train= 0.0149031 rnd 869: wh-err= 0.999476 th-err= 0.093026 test= nan train= 0.0149031 rnd 870: wh-err= 0.999570 th-err= 0.092986 test= nan train= 0.0149031 rnd 871: wh-err= 0.999497 th-err= 0.092939 test= nan train= 0.0149031 rnd 872: wh-err= 0.999481 th-err= 0.092891 test= nan train= 0.0149031 rnd 873: wh-err= 0.999499 th-err= 0.092844 test= nan train= 0.0149031 rnd 874: wh-err= 0.999388 th-err= 0.092788 test= nan train= 0.0149031 rnd 875: wh-err= 0.999428 th-err= 0.092734 test= nan train= 0.0149031 rnd 876: wh-err= 0.999490 th-err= 0.092687 test= nan train= 0.0149031 rnd 877: wh-err= 0.999629 th-err= 0.092653 test= nan train= 0.0149031 rnd 878: wh-err= 0.999484 th-err= 0.092605 test= nan train= 0.0149031 rnd 879: wh-err= 0.999599 th-err= 0.092568 test= nan train= 0.0149031 rnd 880: wh-err= 0.999522 th-err= 0.092524 test= nan train= 0.0149031 rnd 881: wh-err= 0.999491 th-err= 0.092477 test= nan train= 0.0149031 rnd 882: wh-err= 0.999519 th-err= 0.092432 test= nan train= 0.0149031 rnd 883: wh-err= 0.999371 th-err= 0.092374 test= nan train= 0.0149031 rnd 884: wh-err= 0.999565 th-err= 0.092334 test= nan train= 0.0149031 rnd 885: wh-err= 0.999511 th-err= 0.092289 test= nan train= 0.0149031 rnd 886: wh-err= 0.999596 th-err= 0.092251 test= nan train= 0.0149031 rnd 887: wh-err= 0.999497 th-err= 0.092205 test= nan train= 0.0149031 rnd 888: wh-err= 0.999587 th-err= 0.092167 test= nan train= 0.0149031 rnd 889: wh-err= 0.999516 th-err= 0.092122 test= nan train= 0.0149031 rnd 890: wh-err= 0.999501 th-err= 0.092076 test= nan train= 0.0149031 rnd 891: wh-err= 0.999518 th-err= 0.092032 test= nan train= 0.0149031 rnd 892: wh-err= 0.999408 th-err= 0.091977 test= nan train= 0.0149031 rnd 893: wh-err= 0.999450 th-err= 0.091927 test= nan train= 0.0149031 rnd 894: wh-err= 0.999511 th-err= 0.091882 test= nan train= 0.0149031 rnd 895: wh-err= 0.999646 th-err= 0.091849 test= nan train= 0.0149031 rnd 896: wh-err= 0.999504 th-err= 0.091804 test= nan train= 0.0149031 rnd 897: wh-err= 0.999615 th-err= 0.091768 test= nan train= 0.0149031 rnd 898: wh-err= 0.999541 th-err= 0.091726 test= nan train= 0.0149031 rnd 899: wh-err= 0.999511 th-err= 0.091681 test= nan train= 0.0149031 rnd 900: wh-err= 0.999537 th-err= 0.091639 test= nan train= 0.0149031 rnd 901: wh-err= 0.999391 th-err= 0.091583 test= nan train= 0.0149031 rnd 902: wh-err= 0.999583 th-err= 0.091545 test= nan train= 0.0149031 rnd 903: wh-err= 0.999529 th-err= 0.091502 test= nan train= 0.0149031 rnd 904: wh-err= 0.999615 th-err= 0.091467 test= nan train= 0.0149031 rnd 905: wh-err= 0.999517 th-err= 0.091422 test= nan train= 0.0149031 rnd 906: wh-err= 0.999641 th-err= 0.091390 test= nan train= 0.0149031 rnd 907: wh-err= 0.999576 th-err= 0.091351 test= nan train= 0.0149031 rnd 908: wh-err= 0.999477 th-err= 0.091303 test= nan train= 0.0149031 rnd 909: wh-err= 0.999534 th-err= 0.091261 test= nan train= 0.0149031 rnd 910: wh-err= 0.999434 th-err= 0.091209 test= nan train= 0.0149031 rnd 911: wh-err= 0.999460 th-err= 0.091160 test= nan train= 0.0149031 rnd 912: wh-err= 0.999541 th-err= 0.091118 test= nan train= 0.0149031 rnd 913: wh-err= 0.999664 th-err= 0.091087 test= nan train= 0.0149031 rnd 914: wh-err= 0.999518 th-err= 0.091043 test= nan train= 0.0149031 rnd 915: wh-err= 0.999603 th-err= 0.091007 test= nan train= 0.0149031 rnd 916: wh-err= 0.999507 th-err= 0.090962 test= nan train= 0.0149031 rnd 917: wh-err= 0.999565 th-err= 0.090923 test= nan train= 0.0149031 rnd 918: wh-err= 0.999568 th-err= 0.090884 test= nan train= 0.0149031 rnd 919: wh-err= 0.999419 th-err= 0.090831 test= nan train= 0.0149031 rnd 920: wh-err= 0.999613 th-err= 0.090796 test= nan train= 0.0149031 rnd 921: wh-err= 0.999541 th-err= 0.090754 test= nan train= 0.0149031 rnd 922: wh-err= 0.999635 th-err= 0.090721 test= nan train= 0.0149031 rnd 923: wh-err= 0.999523 th-err= 0.090678 test= nan train= 0.0149031 rnd 924: wh-err= 0.999608 th-err= 0.090642 test= nan train= 0.0149031 rnd 925: wh-err= 0.999558 th-err= 0.090602 test= nan train= 0.0149031 rnd 926: wh-err= 0.999549 th-err= 0.090561 test= nan train= 0.0149031 rnd 927: wh-err= 0.999564 th-err= 0.090522 test= nan train= 0.0149031 rnd 928: wh-err= 0.999441 th-err= 0.090471 test= nan train= 0.0149031 rnd 929: wh-err= 0.999489 th-err= 0.090425 test= nan train= 0.0149031 rnd 930: wh-err= 0.999548 th-err= 0.090384 test= nan train= 0.0149031 rnd 931: wh-err= 0.999677 th-err= 0.090355 test= nan train= 0.0149031 rnd 932: wh-err= 0.999538 th-err= 0.090313 test= nan train= 0.0149031 rnd 933: wh-err= 0.999644 th-err= 0.090281 test= nan train= 0.0149031 rnd 934: wh-err= 0.999581 th-err= 0.090243 test= nan train= 0.0149031 rnd 935: wh-err= 0.999550 th-err= 0.090202 test= nan train= 0.0149031 rnd 936: wh-err= 0.999571 th-err= 0.090164 test= nan train= 0.0149031 rnd 937: wh-err= 0.999429 th-err= 0.090112 test= nan train= 0.0149031 rnd 938: wh-err= 0.999614 th-err= 0.090077 test= nan train= 0.0149031 rnd 939: wh-err= 0.999562 th-err= 0.090038 test= nan train= 0.0149031 rnd 940: wh-err= 0.999648 th-err= 0.090006 test= nan train= 0.0149031 rnd 941: wh-err= 0.999544 th-err= 0.089965 test= nan train= 0.0149031 rnd 942: wh-err= 0.999667 th-err= 0.089935 test= nan train= 0.0149031 rnd 943: wh-err= 0.999608 th-err= 0.089900 test= nan train= 0.0149031 rnd 944: wh-err= 0.999513 th-err= 0.089856 test= nan train= 0.0149031 rnd 945: wh-err= 0.999581 th-err= 0.089819 test= nan train= 0.0149031 rnd 946: wh-err= 0.999469 th-err= 0.089771 test= nan train= 0.0149031 rnd 947: wh-err= 0.999499 th-err= 0.089726 test= nan train= 0.0149031 rnd 948: wh-err= 0.999577 th-err= 0.089688 test= nan train= 0.0149031 rnd 949: wh-err= 0.999693 th-err= 0.089661 test= nan train= 0.0149031 rnd 950: wh-err= 0.999545 th-err= 0.089620 test= nan train= 0.0149031 rnd 951: wh-err= 0.999636 th-err= 0.089587 test= nan train= 0.0149031 rnd 952: wh-err= 0.999542 th-err= 0.089546 test= nan train= 0.0149031 rnd 953: wh-err= 0.999597 th-err= 0.089510 test= nan train= 0.0149031 rnd 954: wh-err= 0.999605 th-err= 0.089475 test= nan train= 0.0149031 rnd 955: wh-err= 0.999457 th-err= 0.089426 test= nan train= 0.0149031 rnd 956: wh-err= 0.999643 th-err= 0.089394 test= nan train= 0.0149031 rnd 957: wh-err= 0.999573 th-err= 0.089356 test= nan train= 0.0149031 rnd 958: wh-err= 0.999668 th-err= 0.089326 test= nan train= 0.0149031 rnd 959: wh-err= 0.999557 th-err= 0.089287 test= nan train= 0.0149031 rnd 960: wh-err= 0.999636 th-err= 0.089254 test= nan train= 0.0149031 rnd 961: wh-err= 0.999590 th-err= 0.089218 test= nan train= 0.0149031 rnd 962: wh-err= 0.999583 th-err= 0.089181 test= nan train= 0.0149031 rnd 963: wh-err= 0.999597 th-err= 0.089145 test= nan train= 0.0149031 rnd 964: wh-err= 0.999474 th-err= 0.089098 test= nan train= 0.0149031 rnd 965: wh-err= 0.999526 th-err= 0.089056 test= nan train= 0.0149031 rnd 966: wh-err= 0.999583 th-err= 0.089018 test= nan train= 0.0149031 rnd 967: wh-err= 0.999704 th-err= 0.088992 test= nan train= 0.0149031 rnd 968: wh-err= 0.999569 th-err= 0.088954 test= nan train= 0.0149031 rnd 969: wh-err= 0.999698 th-err= 0.088927 test= nan train= 0.0149031 rnd 970: wh-err= 0.999651 th-err= 0.088896 test= nan train= 0.0149031 rnd 971: wh-err= 0.999544 th-err= 0.088855 test= nan train= 0.0149031 rnd 972: wh-err= 0.999609 th-err= 0.088820 test= nan train= 0.0149031 rnd 973: wh-err= 0.999468 th-err= 0.088773 test= nan train= 0.0149031 rnd 974: wh-err= 0.999636 th-err= 0.088741 test= nan train= 0.0149031 rnd 975: wh-err= 0.999602 th-err= 0.088706 test= nan train= 0.0149031 rnd 976: wh-err= 0.999681 th-err= 0.088677 test= nan train= 0.0149031 rnd 977: wh-err= 0.999565 th-err= 0.088639 test= nan train= 0.0149031 rnd 978: wh-err= 0.999643 th-err= 0.088607 test= nan train= 0.0149031 rnd 979: wh-err= 0.999548 th-err= 0.088567 test= nan train= 0.0149031 rnd 980: wh-err= 0.999617 th-err= 0.088533 test= nan train= 0.0149031 rnd 981: wh-err= 0.999626 th-err= 0.088500 test= nan train= 0.0149031 rnd 982: wh-err= 0.999499 th-err= 0.088456 test= nan train= 0.0149031 rnd 983: wh-err= 0.999560 th-err= 0.088417 test= nan train= 0.0149031 rnd 984: wh-err= 0.999597 th-err= 0.088381 test= nan train= 0.0149031 rnd 985: wh-err= 0.999721 th-err= 0.088356 test= nan train= 0.0149031 rnd 986: wh-err= 0.999573 th-err= 0.088319 test= nan train= 0.0149031 rnd 987: wh-err= 0.999673 th-err= 0.088290 test= nan train= 0.0149031 rnd 988: wh-err= 0.999627 th-err= 0.088257 test= nan train= 0.0149031 rnd 989: wh-err= 0.999608 th-err= 0.088222 test= nan train= 0.0149031 rnd 990: wh-err= 0.999627 th-err= 0.088190 test= nan train= 0.0149031 rnd 991: wh-err= 0.999480 th-err= 0.088144 test= nan train= 0.0149031 rnd 992: wh-err= 0.999657 th-err= 0.088113 test= nan train= 0.0149031 rnd 993: wh-err= 0.999604 th-err= 0.088079 test= nan train= 0.0149031 rnd 994: wh-err= 0.999693 th-err= 0.088051 test= nan train= 0.0149031 rnd 995: wh-err= 0.999583 th-err= 0.088015 test= nan train= 0.0149031 rnd 996: wh-err= 0.999699 th-err= 0.087988 test= nan train= 0.0149031 rnd 997: wh-err= 0.999655 th-err= 0.087958 test= nan train= 0.0149031 rnd 998: wh-err= 0.999565 th-err= 0.087920 test= nan train= 0.0149031 rnd 999: wh-err= 0.999637 th-err= 0.087888 test= nan train= 0.0149031 rnd 1000: wh-err= 0.999515 th-err= 0.087845 test= nan train= 0.0149031 === 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 = 671.000000 / 671 = 1.000000 === 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 = 287.000000 / 287 = 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 [0s] real 0m2.162s user 0m0.780s sys 0m0.128s
Run specification 
Results
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