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only testing
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
predictions = bs.prreg.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
0.000331 0.000331 1 1.0 0.5211 0.5030 15
0.002435 0.004538 2 2.0 0.5353 0.4679 15
0.006879 0.011324 4 4.0 0.5854 0.4496 15
0.027055 0.047230 8 8.0 0.5575 0.4540 15
0.029899 0.032743 16 16.0 0.5878 0.4943 15
0.031465 0.033032 32 32.0 0.6038 0.4657 15
0.022291 0.013117 64 64.0 0.5683 0.5623 15
0.021452 0.020612 128 128.0 0.5351 0.5293 15
0.018406 0.015360 256 256.0 0.5385 0.4922 15
0.014747 0.011088 512 512.0 0.5053 0.4511 15
0.013612 0.012477 1024 1024.0 0.5750 0.5087 15
0.012210 0.010808 2048 2048.0 0.5204 0.5062 15
0.010295 0.008380 4096 4096.0 0.5042 0.4586 15
0.009424 0.008553 8192 8192.0 0.4967 0.4608 15
0.008262 0.007101 16384 16384.0 0.5011 0.5461 15
0.009269 0.010275 32768 32768.0 0.3915 0.5825 15
0.011352 0.013435 65536 65536.0 0.5043 0.4461 15
finished run
number of examples per pass = 74746
passes used = 1
weighted example sum = 69521
weighted label sum = 0
average loss = 0.010892
best constant = -1.43843e-05
total feature number = 1119986
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