diff options
Diffstat (limited to 'test/train-sets/ref/progress-10.stderr')
| -rw-r--r-- | test/train-sets/ref/progress-10.stderr | 42 |
1 files changed, 21 insertions, 21 deletions
diff --git a/test/train-sets/ref/progress-10.stderr b/test/train-sets/ref/progress-10.stderr index 5ccb5b65..52657a6b 100644 --- a/test/train-sets/ref/progress-10.stderr +++ b/test/train-sets/ref/progress-10.stderr @@ -7,33 +7,33 @@ Reading datafile = train-sets/0001.dat num sources = 1 average since example example current current current loss last counter weight label predict features -0.265372 0.265372 10 10.0 1.0000 0.1663 34 -0.240389 0.215406 20 20.0 0.0000 0.2162 104 -0.243411 0.249456 30 30.0 0.0000 0.3470 82 -0.233421 0.203449 40 40.0 1.0000 0.4872 42 -0.223978 0.186208 50 50.0 0.0000 0.2378 60 -0.231688 0.270239 60 60.0 0.0000 0.3376 147 -0.230530 0.223581 70 70.0 1.0000 0.4861 134 -0.226160 0.195571 80 80.0 0.0000 0.2174 136 -0.218144 0.154011 90 90.0 0.0000 0.2778 139 -0.216603 0.202736 100 100.0 1.0000 0.3323 56 -0.217835 0.230161 110 110.0 1.0000 0.5978 97 -0.223529 0.286161 120 120.0 0.0000 0.4216 120 -0.221081 0.191702 130 130.0 1.0000 0.4029 54 -0.216509 0.157070 140 140.0 0.0000 0.4171 82 -0.211236 0.137422 150 150.0 1.0000 0.3988 148 -0.210661 0.202034 160 160.0 0.0000 0.6096 63 -0.205254 0.118743 170 170.0 1.0000 0.7744 69 -0.201144 0.131274 180 180.0 1.0000 0.7463 42 -0.198207 0.145334 190 190.0 1.0000 0.7428 34 -0.195090 0.135866 200 200.0 1.0000 0.5891 56 +0.251938 0.251938 10 10.0 1.0000 0.2253 34 +0.235272 0.218607 20 20.0 0.0000 0.2122 104 +0.240930 0.252246 30 30.0 0.0000 0.3390 82 +0.229258 0.194240 40 40.0 1.0000 0.5489 42 +0.225418 0.210062 50 50.0 0.0000 0.2070 60 +0.232323 0.266844 60 60.0 0.0000 0.3381 147 +0.229975 0.215893 70 70.0 1.0000 0.4733 134 +0.226344 0.200927 80 80.0 0.0000 0.2216 136 +0.217409 0.145923 90 90.0 0.0000 0.2602 139 +0.216703 0.210351 100 100.0 1.0000 0.3171 56 +0.218356 0.234887 110 110.0 1.0000 0.5796 97 +0.226824 0.319970 120 120.0 0.0000 0.4137 120 +0.223091 0.178299 130 130.0 1.0000 0.4105 54 +0.218306 0.156103 140 140.0 0.0000 0.3486 82 +0.212760 0.135116 150 150.0 1.0000 0.4022 148 +0.211695 0.195720 160 160.0 0.0000 0.6071 63 +0.206603 0.125119 170 170.0 1.0000 0.7091 69 +0.202214 0.127603 180 180.0 1.0000 0.7582 42 +0.198753 0.136460 190 190.0 1.0000 0.7647 34 +0.195760 0.138892 200 200.0 1.0000 0.5244 56 finished run number of examples per pass = 200 passes used = 1 weighted example sum = 200 weighted label sum = 91 -average loss = 0.19509 +average loss = 0.19576 best constant = 0.455 best constant's loss = 0.247975 total feature number = 15482 |