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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
0.000464 0.000464 1 1.0 0.5211 0.4996 15
0.002814 0.005164 2 2.0 0.5353 0.4634 15
0.007663 0.012511 4 4.0 0.5854 0.4407 15
0.028496 0.049329 8 8.0 0.5575 0.4417 15
0.032121 0.035746 16 16.0 0.5878 0.4856 15
0.033784 0.035448 32 32.0 0.6038 0.4587 15
0.024142 0.014499 64 64.0 0.5683 0.5709 15
0.023094 0.022045 128 128.0 0.5351 0.5345 15
0.019620 0.016147 256 256.0 0.5385 0.4893 15
0.015332 0.011044 512 512.0 0.5053 0.4533 15
0.014235 0.013139 1024 1024.0 0.5750 0.5082 15
0.012660 0.011085 2048 2048.0 0.5204 0.5024 15
0.010422 0.008184 4096 4096.0 0.5042 0.4605 15
0.009502 0.008583 8192 8192.0 0.4967 0.4662 15
0.008239 0.006975 16384 16384.0 0.5011 0.5380 15
0.009165 0.010091 32768 32768.0 0.3915 0.5794 15
0.011114 0.013062 65536 65536.0 0.5043 0.4496 15
finished run
number of examples per pass = 74746
passes used = 1
weighted example sum = 69521
weighted label sum = 35113.3
average loss = 0.0106413
best constant = 0.505067
best constant's loss = 0.249974
total feature number = 1119986
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