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creating quadratic features for pairs: Tf ff
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
final_regressor = models/0002a.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 197
0.158403 0.045214 2 2.0 0.5353 0.3226 197
0.129123 0.099844 4 4.0 0.5854 0.8820 197
0.073886 0.018648 8 8.0 0.5575 0.5422 197
0.049608 0.025331 16 16.0 0.5878 0.5784 197
0.040011 0.030414 32 32.0 0.6038 0.5690 197
0.028903 0.017794 64 64.0 0.5683 0.4790 197
0.017959 0.007014 128 128.0 0.5351 0.5342 197
0.013025 0.008091 256 256.0 0.5385 0.5708 197
0.008556 0.004087 512 512.0 0.5053 0.5591 197
0.005115 0.001674 1024 1024.0 0.5750 0.6564 197
0.003015 0.000914 2048 2048.0 0.5204 0.5034 197
0.001725 0.000436 4096 4096.0 0.5042 0.4956 197
0.001152 0.000578 8192 8192.0 0.4967 0.5119 197
0.000896 0.000640 16384 16384.0 0.5011 0.5310 197
0.000768 0.000640 32768 32768.0 0.3915 0.3951 197
0.000837 0.000906 65536 65536.0 0.5043 0.5774 197
finished run
number of examples per pass = 74746
passes used = 1
weighted example sum = 69521
weighted label sum = 35113.3
average loss = 0.000841171
best constant = 0.505067
best constant's loss = 0.249974
total feature number = 14692454
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