 | Run 38748 |
| Creator | chuertas |
| Program | boostexter no-awk |
| Dataset | Virus 183,855x9 00-FF w/Chi2 (Small Train) |
| Task type | MulticlassClassification |
| Created | 3y64d ago |
Done! 
| 55m52s | |
| 269M | |
| MulticlassClassification | |
| 53m37s | |
| 0 | |
| 32s | |
| 0.010 | |
| 1m14s |
Log file
... (lines omitted) ... rnd 633: wh-err= 0.941990 th-err= 0.000000 test= -nan train= 0.0000000 rnd 634: wh-err= 0.937911 th-err= 0.000000 test= -nan train= 0.0000000 rnd 635: wh-err= 0.935664 th-err= 0.000000 test= -nan train= 0.0000000 rnd 636: wh-err= 0.940804 th-err= 0.000000 test= -nan train= 0.0000000 rnd 637: wh-err= 0.927419 th-err= 0.000000 test= -nan train= 0.0000000 rnd 638: wh-err= 0.941528 th-err= 0.000000 test= -nan train= 0.0000000 rnd 639: wh-err= 0.925402 th-err= 0.000000 test= -nan train= 0.0000000 rnd 640: wh-err= 0.937480 th-err= 0.000000 test= -nan train= 0.0000000 rnd 641: wh-err= 0.936276 th-err= 0.000000 test= -nan train= 0.0000000 rnd 642: wh-err= 0.926782 th-err= 0.000000 test= -nan train= 0.0000000 rnd 643: wh-err= 0.928410 th-err= 0.000000 test= -nan train= 0.0000000 rnd 644: wh-err= 0.939606 th-err= 0.000000 test= -nan train= 0.0000000 rnd 645: wh-err= 0.933075 th-err= 0.000000 test= -nan train= 0.0000000 rnd 646: wh-err= 0.929432 th-err= 0.000000 test= -nan train= 0.0000000 rnd 647: wh-err= 0.920108 th-err= 0.000000 test= -nan train= 0.0000000 rnd 648: wh-err= 0.911535 th-err= 0.000000 test= -nan train= 0.0000000 rnd 649: wh-err= 0.920616 th-err= 0.000000 test= -nan train= 0.0000000 rnd 650: wh-err= 0.935131 th-err= 0.000000 test= -nan train= 0.0000000 rnd 651: wh-err= 0.933615 th-err= 0.000000 test= -nan train= 0.0000000 rnd 652: wh-err= 0.935262 th-err= 0.000000 test= -nan train= 0.0000000 rnd 653: wh-err= 0.925228 th-err= 0.000000 test= -nan train= 0.0000000 rnd 654: wh-err= 0.925369 th-err= 0.000000 test= -nan train= 0.0000000 rnd 655: wh-err= 0.939673 th-err= 0.000000 test= -nan train= 0.0000000 rnd 656: wh-err= 0.933883 th-err= 0.000000 test= -nan train= 0.0000000 rnd 657: wh-err= 0.944674 th-err= 0.000000 test= -nan train= 0.0000000 rnd 658: wh-err= 0.925188 th-err= 0.000000 test= -nan train= 0.0000000 rnd 659: wh-err= 0.923857 th-err= 0.000000 test= -nan train= 0.0000000 rnd 660: wh-err= 0.927554 th-err= 0.000000 test= -nan train= 0.0000000 rnd 661: wh-err= 0.941668 th-err= 0.000000 test= -nan train= 0.0000000 rnd 662: wh-err= 0.932459 th-err= 0.000000 test= -nan train= 0.0000000 rnd 663: wh-err= 0.934432 th-err= 0.000000 test= -nan train= 0.0000000 rnd 664: wh-err= 0.934076 th-err= 0.000000 test= -nan train= 0.0000000 rnd 665: wh-err= 0.923037 th-err= 0.000000 test= -nan train= 0.0000000 rnd 666: wh-err= 0.938621 th-err= 0.000000 test= -nan train= 0.0000000 rnd 667: wh-err= 0.939757 th-err= 0.000000 test= -nan train= 0.0000000 rnd 668: wh-err= 0.941449 th-err= 0.000000 test= -nan train= 0.0000000 rnd 669: wh-err= 0.936269 th-err= 0.000000 test= -nan train= 0.0000000 rnd 670: wh-err= 0.943574 th-err= 0.000000 test= -nan train= 0.0000000 rnd 671: wh-err= 0.943296 th-err= 0.000000 test= -nan train= 0.0000000 rnd 672: wh-err= 0.942312 th-err= 0.000000 test= -nan train= 0.0000000 rnd 673: wh-err= 0.943884 th-err= 0.000000 test= -nan train= 0.0000000 rnd 674: wh-err= 0.935670 th-err= 0.000000 test= -nan train= 0.0000000 rnd 675: wh-err= 0.940649 th-err= 0.000000 test= -nan train= 0.0000000 rnd 676: wh-err= 0.939755 th-err= 0.000000 test= -nan train= 0.0000000 rnd 677: wh-err= 0.937205 th-err= 0.000000 test= -nan train= 0.0000000 rnd 678: wh-err= 0.933960 th-err= 0.000000 test= -nan train= 0.0000000 rnd 679: wh-err= 0.930607 th-err= 0.000000 test= -nan train= 0.0000000 rnd 680: wh-err= 0.933826 th-err= 0.000000 test= -nan train= 0.0000000 rnd 681: wh-err= 0.938765 th-err= 0.000000 test= -nan train= 0.0000000 rnd 682: wh-err= 0.936570 th-err= 0.000000 test= -nan train= 0.0000000 rnd 683: wh-err= 0.932092 th-err= 0.000000 test= -nan train= 0.0000000 rnd 684: wh-err= 0.940176 th-err= 0.000000 test= -nan train= 0.0000000 rnd 685: wh-err= 0.944018 th-err= 0.000000 test= -nan train= 0.0000000 rnd 686: wh-err= 0.934891 th-err= 0.000000 test= -nan train= 0.0000000 rnd 687: wh-err= 0.931201 th-err= 0.000000 test= -nan train= 0.0000000 rnd 688: wh-err= 0.939890 th-err= 0.000000 test= -nan train= 0.0000000 rnd 689: wh-err= 0.934934 th-err= 0.000000 test= -nan train= 0.0000000 rnd 690: wh-err= 0.935991 th-err= 0.000000 test= -nan train= 0.0000000 rnd 691: wh-err= 0.931720 th-err= 0.000000 test= -nan train= 0.0000000 rnd 692: wh-err= 0.916769 th-err= 0.000000 test= -nan train= 0.0000000 rnd 693: wh-err= 0.941350 th-err= 0.000000 test= -nan train= 0.0000000 rnd 694: wh-err= 0.941960 th-err= 0.000000 test= -nan train= 0.0000000 rnd 695: wh-err= 0.941202 th-err= 0.000000 test= -nan train= 0.0000000 rnd 696: wh-err= 0.937207 th-err= 0.000000 test= -nan train= 0.0000000 rnd 697: wh-err= 0.940518 th-err= 0.000000 test= -nan train= 0.0000000 rnd 698: wh-err= 0.922195 th-err= 0.000000 test= -nan train= 0.0000000 rnd 699: wh-err= 0.932613 th-err= 0.000000 test= -nan train= 0.0000000 rnd 700: wh-err= 0.934213 th-err= 0.000000 test= -nan train= 0.0000000 rnd 701: wh-err= 0.929413 th-err= 0.000000 test= -nan train= 0.0000000 rnd 702: wh-err= 0.936695 th-err= 0.000000 test= -nan train= 0.0000000 rnd 703: wh-err= 0.930899 th-err= 0.000000 test= -nan train= 0.0000000 rnd 704: wh-err= 0.907055 th-err= 0.000000 test= -nan train= 0.0000000 rnd 705: wh-err= 0.932424 th-err= 0.000000 test= -nan train= 0.0000000 rnd 706: wh-err= 0.937121 th-err= 0.000000 test= -nan train= 0.0000000 rnd 707: wh-err= 0.932738 th-err= 0.000000 test= -nan train= 0.0000000 rnd 708: wh-err= 0.924202 th-err= 0.000000 test= -nan train= 0.0000000 rnd 709: wh-err= 0.936446 th-err= 0.000000 test= -nan train= 0.0000000 rnd 710: wh-err= 0.939642 th-err= 0.000000 test= -nan train= 0.0000000 rnd 711: wh-err= 0.938611 th-err= 0.000000 test= -nan train= 0.0000000 rnd 712: wh-err= 0.940137 th-err= 0.000000 test= -nan train= 0.0000000 rnd 713: wh-err= 0.936705 th-err= 0.000000 test= -nan train= 0.0000000 rnd 714: wh-err= 0.939179 th-err= 0.000000 test= -nan train= 0.0000000 rnd 715: wh-err= 0.936537 th-err= 0.000000 test= -nan train= 0.0000000 rnd 716: wh-err= 0.939043 th-err= 0.000000 test= -nan train= 0.0000000 rnd 717: wh-err= 0.934579 th-err= 0.000000 test= -nan train= 0.0000000 rnd 718: wh-err= 0.941319 th-err= 0.000000 test= -nan train= 0.0000000 rnd 719: wh-err= 0.935460 th-err= 0.000000 test= -nan train= 0.0000000 rnd 720: wh-err= 0.932952 th-err= 0.000000 test= -nan train= 0.0000000 rnd 721: wh-err= 0.924712 th-err= 0.000000 test= -nan train= 0.0000000 rnd 722: wh-err= 0.926197 th-err= 0.000000 test= -nan train= 0.0000000 rnd 723: wh-err= 0.933713 th-err= 0.000000 test= -nan train= 0.0000000 rnd 724: wh-err= 0.931319 th-err= 0.000000 test= -nan train= 0.0000000 rnd 725: wh-err= 0.939401 th-err= 0.000000 test= -nan train= 0.0000000 rnd 726: wh-err= 0.940629 th-err= 0.000000 test= -nan train= 0.0000000 rnd 727: wh-err= 0.938887 th-err= 0.000000 test= -nan train= 0.0000000 rnd 728: wh-err= 0.929258 th-err= 0.000000 test= -nan train= 0.0000000 rnd 729: wh-err= 0.914333 th-err= 0.000000 test= -nan train= 0.0000000 rnd 730: wh-err= 0.938967 th-err= 0.000000 test= -nan train= 0.0000000 rnd 731: wh-err= 0.936886 th-err= 0.000000 test= -nan train= 0.0000000 rnd 732: wh-err= 0.934756 th-err= 0.000000 test= -nan train= 0.0000000 rnd 733: wh-err= 0.937437 th-err= 0.000000 test= -nan train= 0.0000000 rnd 734: wh-err= 0.933604 th-err= 0.000000 test= -nan train= 0.0000000 rnd 735: wh-err= 0.937160 th-err= 0.000000 test= -nan train= 0.0000000 rnd 736: wh-err= 0.939180 th-err= 0.000000 test= -nan train= 0.0000000 rnd 737: wh-err= 0.940550 th-err= 0.000000 test= -nan train= 0.0000000 rnd 738: wh-err= 0.936927 th-err= 0.000000 test= -nan train= 0.0000000 rnd 739: wh-err= 0.947679 th-err= 0.000000 test= -nan train= 0.0000000 rnd 740: wh-err= 0.941108 th-err= 0.000000 test= -nan train= 0.0000000 rnd 741: wh-err= 0.940595 th-err= 0.000000 test= -nan train= 0.0000000 rnd 742: wh-err= 0.938400 th-err= 0.000000 test= -nan train= 0.0000000 rnd 743: wh-err= 0.940367 th-err= 0.000000 test= -nan train= 0.0000000 rnd 744: wh-err= 0.943264 th-err= 0.000000 test= -nan train= 0.0000000 rnd 745: wh-err= 0.939388 th-err= 0.000000 test= -nan train= 0.0000000 rnd 746: wh-err= 0.941072 th-err= 0.000000 test= -nan train= 0.0000000 rnd 747: wh-err= 0.937106 th-err= 0.000000 test= -nan train= 0.0000000 rnd 748: wh-err= 0.939942 th-err= 0.000000 test= -nan train= 0.0000000 rnd 749: wh-err= 0.945575 th-err= 0.000000 test= -nan train= 0.0000000 rnd 750: wh-err= 0.939747 th-err= 0.000000 test= -nan train= 0.0000000 rnd 751: wh-err= 0.931828 th-err= 0.000000 test= -nan train= 0.0000000 rnd 752: wh-err= 0.939205 th-err= 0.000000 test= -nan train= 0.0000000 rnd 753: wh-err= 0.932074 th-err= 0.000000 test= -nan train= 0.0000000 rnd 754: wh-err= 0.932126 th-err= 0.000000 test= -nan train= 0.0000000 rnd 755: wh-err= 0.939502 th-err= 0.000000 test= -nan train= 0.0000000 rnd 756: wh-err= 0.910534 th-err= 0.000000 test= -nan train= 0.0000000 rnd 757: wh-err= 0.921316 th-err= 0.000000 test= -nan train= 0.0000000 rnd 758: wh-err= 0.917882 th-err= 0.000000 test= -nan train= 0.0000000 rnd 759: wh-err= 0.916834 th-err= 0.000000 test= -nan train= 0.0000000 rnd 760: wh-err= 0.905381 th-err= 0.000000 test= -nan train= 0.0000000 rnd 761: wh-err= 0.929281 th-err= 0.000000 test= -nan train= 0.0000000 rnd 762: wh-err= 0.932669 th-err= 0.000000 test= -nan train= 0.0000000 rnd 763: wh-err= 0.929482 th-err= 0.000000 test= -nan train= 0.0000000 rnd 764: wh-err= 0.930182 th-err= 0.000000 test= -nan train= 0.0000000 rnd 765: wh-err= 0.935948 th-err= 0.000000 test= -nan train= 0.0000000 rnd 766: wh-err= 0.928336 th-err= 0.000000 test= -nan train= 0.0000000 rnd 767: wh-err= 0.938391 th-err= 0.000000 test= -nan train= 0.0000000 rnd 768: wh-err= 0.932322 th-err= 0.000000 test= -nan train= 0.0000000 rnd 769: wh-err= 0.927845 th-err= 0.000000 test= -nan train= 0.0000000 rnd 770: wh-err= 0.927354 th-err= 0.000000 test= -nan train= 0.0000000 rnd 771: wh-err= 0.926692 th-err= 0.000000 test= -nan train= 0.0000000 rnd 772: wh-err= 0.931163 th-err= 0.000000 test= -nan train= 0.0000000 rnd 773: wh-err= 0.917184 th-err= 0.000000 test= -nan train= 0.0000000 rnd 774: wh-err= 0.929305 th-err= 0.000000 test= -nan train= 0.0000000 rnd 775: wh-err= 0.933243 th-err= 0.000000 test= -nan train= 0.0000000 rnd 776: wh-err= 0.931434 th-err= 0.000000 test= -nan train= 0.0000000 rnd 777: wh-err= 0.936226 th-err= 0.000000 test= -nan train= 0.0000000 rnd 778: wh-err= 0.936190 th-err= 0.000000 test= -nan train= 0.0000000 rnd 779: wh-err= 0.940776 th-err= 0.000000 test= -nan train= 0.0000000 rnd 780: wh-err= 0.932291 th-err= 0.000000 test= -nan train= 0.0000000 rnd 781: wh-err= 0.935370 th-err= 0.000000 test= -nan train= 0.0000000 rnd 782: wh-err= 0.931555 th-err= 0.000000 test= -nan train= 0.0000000 rnd 783: wh-err= 0.924905 th-err= 0.000000 test= -nan train= 0.0000000 rnd 784: wh-err= 0.926237 th-err= 0.000000 test= -nan train= 0.0000000 rnd 785: wh-err= 0.936462 th-err= 0.000000 test= -nan train= 0.0000000 rnd 786: wh-err= 0.943790 th-err= 0.000000 test= -nan train= 0.0000000 rnd 787: wh-err= 0.939672 th-err= 0.000000 test= -nan train= 0.0000000 rnd 788: wh-err= 0.939680 th-err= 0.000000 test= -nan train= 0.0000000 rnd 789: wh-err= 0.939900 th-err= 0.000000 test= -nan train= 0.0000000 rnd 790: wh-err= 0.945333 th-err= 0.000000 test= -nan train= 0.0000000 rnd 791: wh-err= 0.938901 th-err= 0.000000 test= -nan train= 0.0000000 rnd 792: wh-err= 0.931935 th-err= 0.000000 test= -nan train= 0.0000000 rnd 793: wh-err= 0.914805 th-err= 0.000000 test= -nan train= 0.0000000 rnd 794: wh-err= 0.930158 th-err= 0.000000 test= -nan train= 0.0000000 rnd 795: wh-err= 0.927575 th-err= 0.000000 test= -nan train= 0.0000000 rnd 796: wh-err= 0.913524 th-err= 0.000000 test= -nan train= 0.0000000 rnd 797: wh-err= 0.932237 th-err= 0.000000 test= -nan train= 0.0000000 rnd 798: wh-err= 0.936980 th-err= 0.000000 test= -nan train= 0.0000000 rnd 799: wh-err= 0.931977 th-err= 0.000000 test= -nan train= 0.0000000 rnd 800: wh-err= 0.934750 th-err= 0.000000 test= -nan train= 0.0000000 rnd 801: wh-err= 0.933348 th-err= 0.000000 test= -nan train= 0.0000000 rnd 802: wh-err= 0.930599 th-err= 0.000000 test= -nan train= 0.0000000 rnd 803: wh-err= 0.928374 th-err= 0.000000 test= -nan train= 0.0000000 rnd 804: wh-err= 0.931592 th-err= 0.000000 test= -nan train= 0.0000000 rnd 805: wh-err= 0.927121 th-err= 0.000000 test= -nan train= 0.0000000 rnd 806: wh-err= 0.936891 th-err= 0.000000 test= -nan train= 0.0000000 rnd 807: wh-err= 0.925560 th-err= 0.000000 test= -nan train= 0.0000000 rnd 808: wh-err= 0.928792 th-err= 0.000000 test= -nan train= 0.0000000 rnd 809: wh-err= 0.937685 th-err= 0.000000 test= -nan train= 0.0000000 rnd 810: wh-err= 0.934923 th-err= 0.000000 test= -nan train= 0.0000000 rnd 811: wh-err= 0.930832 th-err= 0.000000 test= -nan train= 0.0000000 rnd 812: wh-err= 0.924194 th-err= 0.000000 test= -nan train= 0.0000000 rnd 813: wh-err= 0.929925 th-err= 0.000000 test= -nan train= 0.0000000 rnd 814: wh-err= 0.929973 th-err= 0.000000 test= -nan train= 0.0000000 rnd 815: wh-err= 0.931111 th-err= 0.000000 test= -nan train= 0.0000000 rnd 816: wh-err= 0.936435 th-err= 0.000000 test= -nan train= 0.0000000 rnd 817: wh-err= 0.940421 th-err= 0.000000 test= -nan train= 0.0000000 rnd 818: wh-err= 0.939981 th-err= 0.000000 test= -nan train= 0.0000000 rnd 819: wh-err= 0.940006 th-err= 0.000000 test= -nan train= 0.0000000 rnd 820: wh-err= 0.926297 th-err= 0.000000 test= -nan train= 0.0000000 rnd 821: wh-err= 0.929018 th-err= 0.000000 test= -nan train= 0.0000000 rnd 822: wh-err= 0.935325 th-err= 0.000000 test= -nan train= 0.0000000 rnd 823: wh-err= 0.931006 th-err= 0.000000 test= -nan train= 0.0000000 rnd 824: wh-err= 0.931358 th-err= 0.000000 test= -nan train= 0.0000000 rnd 825: wh-err= 0.930471 th-err= 0.000000 test= -nan train= 0.0000000 rnd 826: wh-err= 0.926333 th-err= 0.000000 test= -nan train= 0.0000000 rnd 827: wh-err= 0.943061 th-err= 0.000000 test= -nan train= 0.0000000 rnd 828: wh-err= 0.936234 th-err= 0.000000 test= -nan train= 0.0000000 rnd 829: wh-err= 0.936844 th-err= 0.000000 test= -nan train= 0.0000000 rnd 830: wh-err= 0.940032 th-err= 0.000000 test= -nan train= 0.0000000 rnd 831: wh-err= 0.923266 th-err= 0.000000 test= -nan train= 0.0000000 rnd 832: wh-err= 0.917382 th-err= 0.000000 test= -nan train= 0.0000000 rnd 833: wh-err= 0.937009 th-err= 0.000000 test= -nan train= 0.0000000 rnd 834: wh-err= 0.932483 th-err= 0.000000 test= -nan train= 0.0000000 rnd 835: wh-err= 0.924527 th-err= 0.000000 test= -nan train= 0.0000000 rnd 836: wh-err= 0.926863 th-err= 0.000000 test= -nan train= 0.0000000 rnd 837: wh-err= 0.919921 th-err= 0.000000 test= -nan train= 0.0000000 rnd 838: wh-err= 0.932369 th-err= 0.000000 test= -nan train= 0.0000000 rnd 839: wh-err= 0.933371 th-err= 0.000000 test= -nan train= 0.0000000 rnd 840: wh-err= 0.917221 th-err= 0.000000 test= -nan train= 0.0000000 rnd 841: wh-err= 0.941110 th-err= 0.000000 test= -nan train= 0.0000000 rnd 842: wh-err= 0.930799 th-err= 0.000000 test= -nan train= 0.0000000 rnd 843: wh-err= 0.926719 th-err= 0.000000 test= -nan train= 0.0000000 rnd 844: wh-err= 0.939488 th-err= 0.000000 test= -nan train= 0.0000000 rnd 845: wh-err= 0.941986 th-err= 0.000000 test= -nan train= 0.0000000 rnd 846: wh-err= 0.934170 th-err= 0.000000 test= -nan train= 0.0000000 rnd 847: wh-err= 0.931412 th-err= 0.000000 test= -nan train= 0.0000000 rnd 848: wh-err= 0.932178 th-err= 0.000000 test= -nan train= 0.0000000 rnd 849: wh-err= 0.914764 th-err= 0.000000 test= -nan train= 0.0000000 rnd 850: wh-err= 0.945046 th-err= 0.000000 test= -nan train= 0.0000000 rnd 851: wh-err= 0.940493 th-err= 0.000000 test= -nan train= 0.0000000 rnd 852: wh-err= 0.929939 th-err= 0.000000 test= -nan train= 0.0000000 rnd 853: wh-err= 0.942402 th-err= 0.000000 test= -nan train= 0.0000000 rnd 854: wh-err= 0.937219 th-err= 0.000000 test= -nan train= 0.0000000 rnd 855: wh-err= 0.918369 th-err= 0.000000 test= -nan train= 0.0000000 rnd 856: wh-err= 0.936099 th-err= 0.000000 test= -nan train= 0.0000000 rnd 857: wh-err= 0.933790 th-err= 0.000000 test= -nan train= 0.0000000 rnd 858: wh-err= 0.936582 th-err= 0.000000 test= -nan train= 0.0000000 rnd 859: wh-err= 0.942918 th-err= 0.000000 test= -nan train= 0.0000000 rnd 860: wh-err= 0.942200 th-err= 0.000000 test= -nan train= 0.0000000 rnd 861: wh-err= 0.942159 th-err= 0.000000 test= -nan train= 0.0000000 rnd 862: wh-err= 0.947230 th-err= 0.000000 test= -nan train= 0.0000000 rnd 863: wh-err= 0.940915 th-err= 0.000000 test= -nan train= 0.0000000 rnd 864: wh-err= 0.941609 th-err= 0.000000 test= -nan train= 0.0000000 rnd 865: wh-err= 0.942063 th-err= 0.000000 test= -nan train= 0.0000000 rnd 866: wh-err= 0.941758 th-err= 0.000000 test= -nan train= 0.0000000 rnd 867: wh-err= 0.936986 th-err= 0.000000 test= -nan train= 0.0000000 rnd 868: wh-err= 0.935826 th-err= 0.000000 test= -nan train= 0.0000000 rnd 869: wh-err= 0.932403 th-err= 0.000000 test= -nan train= 0.0000000 rnd 870: wh-err= 0.932931 th-err= 0.000000 test= -nan train= 0.0000000 rnd 871: wh-err= 0.911746 th-err= 0.000000 test= -nan train= 0.0000000 rnd 872: wh-err= 0.927445 th-err= 0.000000 test= -nan train= 0.0000000 rnd 873: wh-err= 0.929987 th-err= 0.000000 test= -nan train= 0.0000000 rnd 874: wh-err= 0.923195 th-err= 0.000000 test= -nan train= 0.0000000 rnd 875: wh-err= 0.927436 th-err= 0.000000 test= -nan train= 0.0000000 rnd 876: wh-err= 0.935367 th-err= 0.000000 test= -nan train= 0.0000000 rnd 877: wh-err= 0.935422 th-err= 0.000000 test= -nan train= 0.0000000 rnd 878: wh-err= 0.925168 th-err= 0.000000 test= -nan train= 0.0000000 rnd 879: wh-err= 0.928810 th-err= 0.000000 test= -nan train= 0.0000000 rnd 880: wh-err= 0.921679 th-err= 0.000000 test= -nan train= 0.0000000 rnd 881: wh-err= 0.933061 th-err= 0.000000 test= -nan train= 0.0000000 rnd 882: wh-err= 0.935710 th-err= 0.000000 test= -nan train= 0.0000000 rnd 883: wh-err= 0.926208 th-err= 0.000000 test= -nan train= 0.0000000 rnd 884: wh-err= 0.921838 th-err= 0.000000 test= -nan train= 0.0000000 rnd 885: wh-err= 0.936822 th-err= 0.000000 test= -nan train= 0.0000000 rnd 886: wh-err= 0.939812 th-err= 0.000000 test= -nan train= 0.0000000 rnd 887: wh-err= 0.936039 th-err= 0.000000 test= -nan train= 0.0000000 rnd 888: wh-err= 0.935023 th-err= 0.000000 test= -nan train= 0.0000000 rnd 889: wh-err= 0.941359 th-err= 0.000000 test= -nan train= 0.0000000 rnd 890: wh-err= 0.935925 th-err= 0.000000 test= -nan train= 0.0000000 rnd 891: wh-err= 0.940024 th-err= 0.000000 test= -nan train= 0.0000000 rnd 892: wh-err= 0.944292 th-err= 0.000000 test= -nan train= 0.0000000 rnd 893: wh-err= 0.933163 th-err= 0.000000 test= -nan train= 0.0000000 rnd 894: wh-err= 0.935571 th-err= 0.000000 test= -nan train= 0.0000000 rnd 895: wh-err= 0.921875 th-err= 0.000000 test= -nan train= 0.0000000 rnd 896: wh-err= 0.933115 th-err= 0.000000 test= -nan train= 0.0000000 rnd 897: wh-err= 0.939325 th-err= 0.000000 test= -nan train= 0.0000000 rnd 898: wh-err= 0.939768 th-err= 0.000000 test= -nan train= 0.0000000 rnd 899: wh-err= 0.929050 th-err= 0.000000 test= -nan train= 0.0000000 rnd 900: wh-err= 0.941209 th-err= 0.000000 test= -nan train= 0.0000000 rnd 901: wh-err= 0.940390 th-err= 0.000000 test= -nan train= 0.0000000 rnd 902: wh-err= 0.923766 th-err= 0.000000 test= -nan train= 0.0000000 rnd 903: wh-err= 0.928570 th-err= 0.000000 test= -nan train= 0.0000000 rnd 904: wh-err= 0.927701 th-err= 0.000000 test= -nan train= 0.0000000 rnd 905: wh-err= 0.930604 th-err= 0.000000 test= -nan train= 0.0000000 rnd 906: wh-err= 0.930701 th-err= 0.000000 test= -nan train= 0.0000000 rnd 907: wh-err= 0.941466 th-err= 0.000000 test= -nan train= 0.0000000 rnd 908: wh-err= 0.937772 th-err= 0.000000 test= -nan train= 0.0000000 rnd 909: wh-err= 0.927313 th-err= 0.000000 test= -nan train= 0.0000000 rnd 910: wh-err= 0.917904 th-err= 0.000000 test= -nan train= 0.0000000 rnd 911: wh-err= 0.920937 th-err= 0.000000 test= -nan train= 0.0000000 rnd 912: wh-err= 0.933194 th-err= 0.000000 test= -nan train= 0.0000000 rnd 913: wh-err= 0.935770 th-err= 0.000000 test= -nan train= 0.0000000 rnd 914: wh-err= 0.935078 th-err= 0.000000 test= -nan train= 0.0000000 rnd 915: wh-err= 0.932415 th-err= 0.000000 test= -nan train= 0.0000000 rnd 916: wh-err= 0.932496 th-err= 0.000000 test= -nan train= 0.0000000 rnd 917: wh-err= 0.935827 th-err= 0.000000 test= -nan train= 0.0000000 rnd 918: wh-err= 0.926637 th-err= 0.000000 test= -nan train= 0.0000000 rnd 919: wh-err= 0.931837 th-err= 0.000000 test= -nan train= 0.0000000 rnd 920: wh-err= 0.931298 th-err= 0.000000 test= -nan train= 0.0000000 rnd 921: wh-err= 0.933893 th-err= 0.000000 test= -nan train= 0.0000000 rnd 922: wh-err= 0.934483 th-err= 0.000000 test= -nan train= 0.0000000 rnd 923: wh-err= 0.935254 th-err= 0.000000 test= -nan train= 0.0000000 rnd 924: wh-err= 0.935695 th-err= 0.000000 test= -nan train= 0.0000000 rnd 925: wh-err= 0.936380 th-err= 0.000000 test= -nan train= 0.0000000 rnd 926: wh-err= 0.931696 th-err= 0.000000 test= -nan train= 0.0000000 rnd 927: wh-err= 0.941721 th-err= 0.000000 test= -nan train= 0.0000000 rnd 928: wh-err= 0.939776 th-err= 0.000000 test= -nan train= 0.0000000 rnd 929: wh-err= 0.941799 th-err= 0.000000 test= -nan train= 0.0000000 rnd 930: wh-err= 0.943337 th-err= 0.000000 test= -nan train= 0.0000000 rnd 931: wh-err= 0.936976 th-err= 0.000000 test= -nan train= 0.0000000 rnd 932: wh-err= 0.940701 th-err= 0.000000 test= -nan train= 0.0000000 rnd 933: wh-err= 0.943102 th-err= 0.000000 test= -nan train= 0.0000000 rnd 934: wh-err= 0.933098 th-err= 0.000000 test= -nan train= 0.0000000 rnd 935: wh-err= 0.934555 th-err= 0.000000 test= -nan train= 0.0000000 rnd 936: wh-err= 0.928003 th-err= 0.000000 test= -nan train= 0.0000000 rnd 937: wh-err= 0.925750 th-err= 0.000000 test= -nan train= 0.0000000 rnd 938: wh-err= 0.934433 th-err= 0.000000 test= -nan train= 0.0000000 rnd 939: wh-err= 0.933025 th-err= 0.000000 test= -nan train= 0.0000000 rnd 940: wh-err= 0.935050 th-err= 0.000000 test= -nan train= 0.0000000 rnd 941: wh-err= 0.932181 th-err= 0.000000 test= -nan train= 0.0000000 rnd 942: wh-err= 0.934466 th-err= 0.000000 test= -nan train= 0.0000000 rnd 943: wh-err= 0.924869 th-err= 0.000000 test= -nan train= 0.0000000 rnd 944: wh-err= 0.931388 th-err= 0.000000 test= -nan train= 0.0000000 rnd 945: wh-err= 0.933258 th-err= 0.000000 test= -nan train= 0.0000000 rnd 946: wh-err= 0.916966 th-err= 0.000000 test= -nan train= 0.0000000 rnd 947: wh-err= 0.919277 th-err= 0.000000 test= -nan train= 0.0000000 rnd 948: wh-err= 0.916459 th-err= 0.000000 test= -nan train= 0.0000000 rnd 949: wh-err= 0.921323 th-err= 0.000000 test= -nan train= 0.0000000 rnd 950: wh-err= 0.927240 th-err= 0.000000 test= -nan train= 0.0000000 rnd 951: wh-err= 0.927192 th-err= 0.000000 test= -nan train= 0.0000000 rnd 952: wh-err= 0.937402 th-err= 0.000000 test= -nan train= 0.0000000 rnd 953: wh-err= 0.942252 th-err= 0.000000 test= -nan train= 0.0000000 rnd 954: wh-err= 0.940286 th-err= 0.000000 test= -nan train= 0.0000000 rnd 955: wh-err= 0.934809 th-err= 0.000000 test= -nan train= 0.0000000 rnd 956: wh-err= 0.929609 th-err= 0.000000 test= -nan train= 0.0000000 rnd 957: wh-err= 0.933360 th-err= 0.000000 test= -nan train= 0.0000000 rnd 958: wh-err= 0.934965 th-err= 0.000000 test= -nan train= 0.0000000 rnd 959: wh-err= 0.932463 th-err= 0.000000 test= -nan train= 0.0000000 rnd 960: wh-err= 0.929227 th-err= 0.000000 test= -nan train= 0.0000000 rnd 961: wh-err= 0.936096 th-err= 0.000000 test= -nan train= 0.0000000 rnd 962: wh-err= 0.933819 th-err= 0.000000 test= -nan train= 0.0000000 rnd 963: wh-err= 0.925315 th-err= 0.000000 test= -nan train= 0.0000000 rnd 964: wh-err= 0.940690 th-err= 0.000000 test= -nan train= 0.0000000 rnd 965: wh-err= 0.945688 th-err= 0.000000 test= -nan train= 0.0000000 rnd 966: wh-err= 0.941277 th-err= 0.000000 test= -nan train= 0.0000000 rnd 967: wh-err= 0.939853 th-err= 0.000000 test= -nan train= 0.0000000 rnd 968: wh-err= 0.928578 th-err= 0.000000 test= -nan train= 0.0000000 rnd 969: wh-err= 0.933984 th-err= 0.000000 test= -nan train= 0.0000000 rnd 970: wh-err= 0.942366 th-err= 0.000000 test= -nan train= 0.0000000 rnd 971: wh-err= 0.933345 th-err= 0.000000 test= -nan train= 0.0000000 rnd 972: wh-err= 0.935332 th-err= 0.000000 test= -nan train= 0.0000000 rnd 973: wh-err= 0.931326 th-err= 0.000000 test= -nan train= 0.0000000 rnd 974: wh-err= 0.928939 th-err= 0.000000 test= -nan train= 0.0000000 rnd 975: wh-err= 0.928354 th-err= 0.000000 test= -nan train= 0.0000000 rnd 976: wh-err= 0.926973 th-err= 0.000000 test= -nan train= 0.0000000 rnd 977: wh-err= 0.923546 th-err= 0.000000 test= -nan train= 0.0000000 rnd 978: wh-err= 0.928798 th-err= 0.000000 test= -nan train= 0.0000000 rnd 979: wh-err= 0.936770 th-err= 0.000000 test= -nan train= 0.0000000 rnd 980: wh-err= 0.939196 th-err= 0.000000 test= -nan train= 0.0000000 rnd 981: wh-err= 0.934383 th-err= 0.000000 test= -nan train= 0.0000000 rnd 982: wh-err= 0.926717 th-err= 0.000000 test= -nan train= 0.0000000 rnd 983: wh-err= 0.927012 th-err= 0.000000 test= -nan train= 0.0000000 rnd 984: wh-err= 0.935121 th-err= 0.000000 test= -nan train= 0.0000000 rnd 985: wh-err= 0.927554 th-err= 0.000000 test= -nan train= 0.0000000 rnd 986: wh-err= 0.917326 th-err= 0.000000 test= -nan train= 0.0000000 rnd 987: wh-err= 0.925328 th-err= 0.000000 test= -nan train= 0.0000000 rnd 988: wh-err= 0.929990 th-err= 0.000000 test= -nan train= 0.0000000 rnd 989: wh-err= 0.934714 th-err= 0.000000 test= -nan train= 0.0000000 rnd 990: wh-err= 0.939694 th-err= 0.000000 test= -nan train= 0.0000000 rnd 991: wh-err= 0.935607 th-err= 0.000000 test= -nan train= 0.0000000 rnd 992: wh-err= 0.921865 th-err= 0.000000 test= -nan train= 0.0000000 rnd 993: wh-err= 0.935719 th-err= 0.000000 test= -nan train= 0.0000000 rnd 994: wh-err= 0.934412 th-err= 0.000000 test= -nan train= 0.0000000 rnd 995: wh-err= 0.934809 th-err= 0.000000 test= -nan train= 0.0000000 rnd 996: wh-err= 0.935131 th-err= 0.000000 test= -nan train= 0.0000000 rnd 997: wh-err= 0.938021 th-err= 0.000000 test= -nan train= 0.0000000 rnd 998: wh-err= 0.941892 th-err= 0.000000 test= -nan train= 0.0000000 rnd 999: wh-err= 0.937003 th-err= 0.000000 test= -nan train= 0.0000000 rnd 1000: wh-err= 0.930311 th-err= 0.000000 test= -nan train= 0.0000000 === END program1: ./run learn ../dataset2/train --- OK [3217s] ===== 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 [3s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out Copyright 2001 AT&T. All rights reserved. Test error = 2800.000000 / 3260 = 0.858896 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [32s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [6s] ===== 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 [6s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out Copyright 2001 AT&T. All rights reserved. Test error = 6495.000000 / 7608 = 0.853707 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [74s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [14s] real 55m52.315s user 54m41.629s sys 0m19.693s
Run specification 
Results
Must be logged in to post comments.