/usr/share/doc/vowpal-wabbit-doc/examples/test/train-sets/ref/big-constant.stderr is in vowpal-wabbit-doc 8.1.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
creating cache_file = train-sets/big-constant.dat.cache
Reading datafile = train-sets/big-constant.dat
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.102639 0.102639 1 1.0 1000.3204 1000.0000 23
0.357116 0.611592 2 2.0 999.5383 1000.3204 23
3.682232 7.007348 4 4.0 1001.4026 998.1737 23
4.046600 4.410968 8 8.0 1000.9437 999.5551 23
3.395118 2.743637 16 16.0 1003.0878 1000.4111 23
2.562052 1.728985 32 32.0 1000.0400 999.6089 23
1.986633 1.411214 64 64.0 1001.1794 1000.0136 23
1.758230 1.529827 128 128.0 998.8786 999.7436 23
1.330741 0.903252 256 256.0 1000.6227 1000.4050 23
0.934195 0.537650 512 512.0 1001.3120 1000.6472 23
0.586290 0.238384 1024 1024.0 1001.0090 1000.9323 23
0.332232 0.078174 2048 2048.0 999.2150 999.4189 23
0.174341 0.016450 4096 4096.0 999.5161 999.5195 23
0.088085 0.001830 8192 8192.0 999.5844 999.5403 23
finished run
number of examples per pass = 100
passes used = 100
weighted example sum = 10000.000000
weighted label sum = 9998961.669922
average loss = 0.072191
best constant = 999.896179
total feature number = 230000
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