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Generating 2-grams for all namespaces.
Generating 4-skips for all namespaces.
Num weight bits = 24
learning rate = 0.125
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train-sets/rcv1_raw_cb_small.vw
num sources = 1
average since example example current current current
loss last counter weight label predict features
*estimate* *estimate* avglossreg last pred last correct
2.000000 2.000000 1 1.0 known 1 280 1.000000 0.000000 1.000000
1.000000 0.000000 2 2.0 known 2 598 0.500397 0.028172 0.000000
0.500000 0.000000 4 4.0 known 2 784 0.250681 0.023444 0.000000
0.250000 0.000000 8 8.0 known 2 850 0.247231 0.064843 0.000000
0.620405 0.990810 16 16.0 known 2 118 0.473622 0.062276 1.000000
0.320962 0.021519 32 32.0 known 1 166 0.391822 0.037413 0.000000
0.334178 0.347393 64 64.0 known 1 340 0.400066 0.213940 1.000000
0.288107 0.242037 128 128.0 known 1 610 0.326892 0.213630 1.000000
0.234279 0.180451 256 256.0 known 2 712 0.306077 0.517904 0.000000
0.204043 0.173807 512 512.0 known 1 424 0.275817 0.297387 1.000000
0.148564 0.093086 1024 1024.0 known 1 574 0.231517 0.755462 1.000000
0.120964 0.093363 2048 2048.0 known 2 166 0.190603 0.217041 0.000000
0.111878 0.102792 4096 4096.0 known 1 664 0.156218 0.363329 0.000000
0.096249 0.080620 8192 8192.0 known 2 598 0.125803 0.838514 0.000000
finished run
number of examples per pass = 10000
passes used = 1
weighted example sum = 10000
weighted label sum = 0
average loss = 0.0934776
best constant = 0
total feature number = 4476364
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