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only testing
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = 0002c.predict
using no cache
Reading datafile = train-sets/0002.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.001106 0.001106 1 1.0 0.5211 0.4879 15
0.000916 0.000727 2 2.0 0.5353 0.5622 15
0.000972 0.001028 4 4.0 0.5854 0.5459 15
0.001390 0.001808 8 8.0 0.5575 0.4953 15
0.001267 0.001143 16 16.0 0.5878 0.6107 15
0.001210 0.001154 32 32.0 0.6038 0.6036 15
0.001174 0.001138 64 64.0 0.5683 0.5469 15
0.001136 0.001097 128 128.0 0.5351 0.5104 15
0.001235 0.001335 256 256.0 0.5385 0.4930 15
0.004733 0.008230 512 512.0 0.5053 0.6079 15
finished run
number of examples per pass = 1000
passes used = 1
weighted example sum = 1000
weighted label sum = 526.518
average loss = 0.00326562
best constant = 0.526518
total feature number = 14996
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