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Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
final_regressor = models/0002.model
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
0.271591 0.271591 1 1.0 0.5211 0.0000 15
0.147424 0.023257 2 2.0 0.5353 0.3827 15
0.087392 0.027360 4 4.0 0.5854 0.7212 15
0.065205 0.043018 8 8.0 0.5575 0.6282 15
0.052171 0.039137 16 16.0 0.5878 0.5432 15
0.028349 0.004528 32 32.0 0.6038 0.6164 15
0.015826 0.003302 64 64.0 0.5683 0.5103 15
0.010695 0.005565 128 128.0 0.5351 0.5203 15
0.007506 0.004318 256 256.0 0.5385 0.5448 15
0.005298 0.003090 512 512.0 0.5053 0.5509 15
0.003397 0.001496 1024 1024.0 0.5750 0.6291 15
0.002153 0.000908 2048 2048.0 0.5204 0.4690 15
0.001347 0.000542 4096 4096.0 0.5042 0.4932 15
0.001101 0.000854 8192 8192.0 0.4967 0.5019 15
0.000951 0.000800 16384 16384.0 0.5011 0.4993 15
0.000952 0.000953 32768 32768.0 0.3915 0.3980 15
0.001235 0.001517 65536 65536.0 0.5043 0.5583 15
finished run
number of examples per pass = 74746
passes used = 1
weighted example sum = 69521
weighted label sum = 35113.3
average loss = 0.00124849
best constant = 0.505067
best constant's loss = 0.249974
total feature number = 1119986
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